Home / Journals / CMC / Vol.73, No.1, 2022
Table of Content
  • Open Access

    ARTICLE

    Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles

    Jaeyong Kang1, Chul-Su Kim2, Jeong Won Kang3, Jeonghwan Gwak1,4,5,6,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1-14, 2022, DOI:10.32604/cmc.2022.023641
    Abstract The anomaly detection of the brake operating unit (BOU) in the brake systems on metro vehicle is critical for the safety and reliability of the trains. On the other hand, current periodic inspection and maintenance are unable to detect anomalies in an early stage. Also, building an accurate and stable system for detecting anomalies is extremely difficult. Therefore, we present an efficient model that use an ensemble of recurrent autoencoders to accurately detect the BOU abnormalities of metro trains. This is the first proposal to employ an ensemble deep learning technique to detect BOU abnormalities in metro train braking systems.… More >

  • Open Access

    ARTICLE

    K-Banhatti Sombor Invariants of Certain Computer Networks

    Khalid Hamid1, Muhammad Waseem Iqbal2,*, Abaid Ur Rehman Virk3, Muhammad Usman Ashraf4, Ahmed Mohammed Alghamdi5, Adel A. Bahaddad6, Khalid Ali Almarhabi7
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 15-31, 2022, DOI:10.32604/cmc.2022.028406
    Abstract Any number that can be uniquely determined by a graph is called a graph invariant. During the last twenty years’ countless mathematical graph invariants have been characterized and utilized for correlation analysis. However, no reliable examination has been embraced to decide, how much these invariants are related with a network graph or molecular graph. In this paper, it will discuss three different variants of bridge networks with good potential of prediction in the field of computer science, mathematics, chemistry, pharmacy, informatics and biology in context with physical and chemical structures and networks, because k-banhatti sombor invariants are freshly presented and… More >

  • Open Access

    ARTICLE

    Robust and High Accuracy Algorithm for Detection of Pupil Images

    Waleed El Nahal1, Hatim G. Zaini2, Raghad H. Zaini3, Sherif S. M. Ghoneim4,*, Ashraf Mohamed Ali Hassan5
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 33-50, 2022, DOI:10.32604/cmc.2022.028190
    Abstract Recently, many researchers have tried to develop a robust, fast, and accurate algorithm. This algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking, gaze-based human-computer interaction, medical applications (such as deaf and diabetes patients), and attention analysis. Many real-world conditions challenge the eye appearance, such as illumination, reflections, and occasions. On the other hand, individual differences in eye physiology and other sources of noise, such as contact lenses or make-up. The present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics applications. The proposed… More >

  • Open Access

    ARTICLE

    A TMA-Seq2seq Network for Multi-Factor Time Series Sea Surface Temperature Prediction

    Qi He1, Wenlong Li1, Zengzhou Hao2, Guohua Liu3, Dongmei Huang1, Wei Song1,*, Huifang Xu4, Fayez Alqahtani5, Jeong-Uk Kim6
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 51-67, 2022, DOI:10.32604/cmc.2022.026771
    Abstract Sea surface temperature (SST) is closely related to global climate change, ocean ecosystem, and ocean disaster. Accurate prediction of SST is an urgent and challenging task. With a vast amount of ocean monitoring data are continually collected, data-driven methods for SST time-series prediction show promising results. However, they are limited by neglecting complex interactions between SST and other ocean environmental factors, such as air temperature and wind speed. This paper uses multi-factor time series SST data to propose a sequence-to-sequence network with two-module attention (TMA-Seq2seq) for long-term time series SST prediction. Specifically, TMA-Seq2seq is an LSTM-based encoder-decoder architecture facilitated by… More >

  • Open Access

    ARTICLE

    Chosen-Ciphertext Attack Secure Public-Key Encryption with Keyword Search

    Hyun Sook Rhee*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 69-85, 2022, DOI:10.32604/cmc.2022.026751
    Abstract As the use of cloud storage for various services increases, the amount of private personal information along with data stored in the cloud storage is also increasing. To remotely use the data stored on the cloud storage, the data to be stored needs to be encrypted for this reason. Since “searchable encryption” is enable to search on the encrypted data without any decryption, it is one of convenient solutions for secure data management. A public key encryption with keyword search (for short, PEKS) is one of searchable encryptions. Abdalla et al. firstly defined IND-CCA security for PEKS to enhance it’s… More >

  • Open Access

    ARTICLE

    Evolutionary Intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

    Bassam A. Y. Alqaralleh1,*, Fahad Aldhaban1, Anas Abukaraki2, Esam A. AlQaralleh3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 87-101, 2022, DOI:10.32604/cmc.2022.026729
    Abstract Diabetic Retinopathy (DR) has become a widespread illness among diabetics across the globe. Retinal fundus images are generally used by physicians to detect and classify the stages of DR. Since manual examination of DR images is a time-consuming process with the risks of biased results, automated tools using Artificial Intelligence (AI) to diagnose the disease have become essential. In this view, the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification (ODL-FDRDC) technique. The intention of the proposed ODL-FDRDC technique is to identify DR and categorize its different grades using retinal fundus images. In addition, ODL-FDRDC… More >

  • Open Access

    ARTICLE

    Optimization of Channel Estimation Using ELMx-based in Massive MIMO

    Apinya Innok1, Chittapon Keawin2, Peerapong Uthansakul2,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 103-118, 2022, DOI:10.32604/cmc.2022.027106
    Abstract In communication channel estimation, the Least Square (LS) technique has long been a widely accepted and commonly used principle. This is because the simple calculation method is compared with other channel estimation methods. The Minimum Mean Squares Error (MMSE), which is developed later, is devised as the next step because the goal is to reduce the error rate in the communication system from the conventional LS technique which still has a higher error rate. These channel estimations are very important to modern communication systems, especially massive MIMO. Evaluating the massive MIMO channel is one of the most researched and debated… More >

  • Open Access

    ARTICLE

    Quantum Remote State Preparation Based on Quantum Network Coding

    Zhen-Zhen Li1, Zi-Chen Li1, Yi-Ru Sun2,3,*, Haseeb Ahmad4, Gang Xu5,6, Xiu-Bo Chen3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 119-132, 2022, DOI:10.32604/cmc.2022.027437
    Abstract As an innovative theory and technology, quantum network coding has become the research hotspot in quantum network communications. In this paper, a quantum remote state preparation scheme based on quantum network coding is proposed. Comparing with the general quantum remote state preparation schemes, our proposed scheme brings an arbitrary unknown quantum state finally prepared remotely through the quantum network, by designing the appropriate encoding and decoding steps for quantum network coding. What is worth mentioning, from the network model, this scheme is built on the quantum k-pair network which is the expansion of the typical bottleneck network—butterfly network. Accordingly, it… More >

  • Open Access

    ARTICLE

    Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

    Adel Hamdan Mohammad1,*, Tariq Alwada’n2, Omar Almomani3, Sami Smadi3, Nidhal ElOmari4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 133-150, 2022, DOI:10.32604/cmc.2022.027475
    Abstract Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bio-inspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value will be assigned for each… More >

  • Open Access

    ARTICLE

    Mode of Operation for Modification, Insertion, and Deletion of Encrypted Data

    Taek-Young Youn1, Nam-Su Jho2,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 151-164, 2022, DOI:10.32604/cmc.2022.026653
    Abstract

    Due to the development of 5G communication, many aspects of information technology (IT) services are changing. With the development of communication technologies such as 5G, it has become possible to provide IT services that were difficult to provide in the past. One of the services made possible through this change is cloud-based collaboration. In order to support secure collaboration over cloud, encryption technology to securely manage dynamic data is essential. However, since the existing encryption technology is not suitable for encryption of dynamic data, a new technology that can provide encryption for dynamic data is required for secure cloud-based collaboration.… More >

  • Open Access

    ARTICLE

    Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

    Wenbin Bi1, Fang Yu2, Ning Cao3,*, Russell Higgs4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 165-180, 2022, DOI:10.32604/cmc.2022.027776
    Abstract Load-time series data in mobile cloud computing of Internet of Vehicles (IoV) usually have linear and nonlinear composite characteristics. In order to accurately describe the dynamic change trend of such loads, this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV. Firstly, a chaotic analysis algorithm is implemented to process the load-time series, while some learning samples of load prediction are constructed. Secondly, a support vector machine (SVM) is used to establish a load prediction model, and an improved artificial bee colony (IABC) function is designed to enhance the learning ability… More >

  • Open Access

    ARTICLE

    Dual Band Switched Beam Textile Antenna for 5G Wireless Communications

    Pichaya Chaipanya1,*, Supachai Kaewuam1, Jiraphan Hirunruang1, Wichaya Suntara1, Nuchanart Santalunai2, Samran Santalunai3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 181-198, 2022, DOI:10.32604/cmc.2022.028616
    Abstract This paper presents the single element dual band switched beam textile antenna. The antenna can operate at frequencies of 0.7 and 2.6 GHz using for 5G wireless communication applications. Textile fabric is considered to be used for substrate layer at the parts of a microstrip antenna for wireless body area network. The beam pattern of antenna can be switched into two directions by changing the position of shorted-circuit points at each edge of antenna. The main beam direction is 45°/225° when corner A is shorted while it steers at 135°/315° when corner B is shorted circuit. The advantage of the… More >

  • Open Access

    ARTICLE

    Novel Architecture of Security Orchestration, Automation and Response in Internet of Blended Environment

    Minkyung Lee1, Julian Jang-Jaccard2, Jin Kwak3,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 199-223, 2022, DOI:10.32604/cmc.2022.028495
    Abstract New technologies that take advantage of the emergence of massive Internet of Things (IoT) and a hyper-connected network environment have rapidly increased in recent years. These technologies are used in diverse environments, such as smart factories, digital healthcare, and smart grids, with increased security concerns. We intend to operate Security Orchestration, Automation and Response (SOAR) in various environments through new concept definitions as the need to detect and respond automatically to rapidly increasing security incidents without the intervention of security personnel has emerged. To facilitate the understanding of the security concern involved in this newly emerging area, we offer the… More >

  • Open Access

    ARTICLE

    Improved Multi-party Quantum Key Agreement with Four-qubit Cluster States

    Hussein Abulkasim1,*, Eatedal Alabdulkreem2, Safwat Hamad3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 225-232, 2022, DOI:10.32604/cmc.2022.025727
    Abstract Quantum key agreement is a promising key establishing protocol that can play a significant role in securing 5G/6G communication networks. Recently, Liu et al. (Quantum Information Processing 18(8):1-10, 2019) proposed a multi-party quantum key agreement protocol based on four-qubit cluster states was proposed. The aim of their protocol is to agree on a shared secret key among multiple remote participants. Liu et al. employed four-qubit cluster states to be the quantum resources and the X operation to securely share a secret key. In addition, Liu et al.'s protocol guarantees that each participant makes an equal contribution to the final key.… More >

  • Open Access

    ARTICLE

    Feature Extraction and Classification of Plant Leaf Diseases Using Deep Learning Techniques

    K. Anitha1, S. Srinivasan2,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 233-247, 2022, DOI:10.32604/cmc.2022.026542
    Abstract In India’s economy, agriculture has been the most significant contributor. Despite the fact that agriculture’s contribution is decreasing as the world’s population grows, it continues to be the most important source of employment with a little margin of difference. As a result, there is a pressing need to pick up the pace in order to achieve competitive, productive, diverse, and long-term agriculture. Plant disease misinterpretations can result in the incorrect application of pesticides, causing crop harm. As a result, early detection of infections is critical as well as cost-effective for farmers. To diagnose the disease at an earlier stage, appropriate… More >

  • Open Access

    ARTICLE

    Novel Computing for the Delay Differential Two-Prey and One-Predator System

    Prem Junsawang1, Zulqurnain Sabir2, Muhammad Asif Zahoor Raja3, Soheil Salahshour4, Thongchai Botmart5,*, Wajaree Weera3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 249-263, 2022, DOI:10.32604/cmc.2022.028513
    Abstract The aim of these investigations is to find the numerical performances of the delay differential two-prey and one-predator system. The delay differential models are very significant and always difficult to solve the dynamical kind of ecological nonlinear two-prey and one-predator system. Therefore, a stochastic numerical paradigm based artificial neural network (ANN) along with the Levenberg-Marquardt backpropagation (L-MB) neural networks (NNs), i.e., L-MBNNs is proposed to solve the dynamical two-prey and one-predator model. Three different cases based on the dynamical two-prey and one-predator system have been discussed to check the correctness of the L-MBNNs. The statistic measures of these outcomes of… More >

  • Open Access

    ARTICLE

    Classification of Arrhythmia Based on Convolutional Neural Networks and Encoder-Decoder Model

    Jian Liu1,*, Xiaodong Xia1, Chunyang Han2, Jiao Hui3, Jim Feng4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 265-278, 2022, DOI:10.32604/cmc.2022.029227
    Abstract As a common and high-risk type of disease, heart disease seriously threatens people’s health. At the same time, in the era of the Internet of Thing (IoT), smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases. Therefore, the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases. In this paper, we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network (CNN) and Encoder-Decoder model. The model uses Long Short-Term Memory (LSTM) to consider the… More >

  • Open Access

    ARTICLE

    A Secure and Lightweight Chaos Based Image Encryption Scheme

    Fadia Ali Khan1, Jameel Ahmed1, Fehaid Alqahtani2, Suliman A. Alsuhibany3, Fawad Ahmed4, Jawad Ahmad5,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 279-294, 2022, DOI:10.32604/cmc.2022.028789
    Abstract In this paper, we present an image encryption scheme based on the multi-stage chaos-based image encryption algorithm. The method works on the principle of confusion and diffusion. The proposed scheme containing both confusion and diffusion modules are highly secure and effective as compared to the existing schemes. Initially, an image (red, green, and blue components) is partitioned into blocks with an equal number of pixels. Each block is then processed with Tinkerbell Chaotic Map (TBCM) to get shuffled pixels and shuffled blocks. Composite Fractal Function (CFF) change the value of pixels of each color component (layer) to obtain a random… More >

  • Open Access

    ARTICLE

    A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

    Mohammed Gollapalli1, Atta-ur-Rahman2,*, Dhiaa Musleh2, Nehad Ibrahim2, Muhammad Adnan Khan3, Sagheer Abbas4, Ayesha Atta5, Muhammad Aftab Khan6, Mehwash Farooqui6, Tahir Iqbal7, Mohammed Salih Ahmed6, Mohammed Imran B. Ahmed6, Dakheel Almoqbil8, Majd Nabeel2, Abdullah Omer2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 295-310, 2022, DOI:10.32604/cmc.2022.027925
    Abstract The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems. Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide. Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road. To address this overwhelming problem, in this article, a cloud-based intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach. The aim of the study is to reduce the delay… More >

  • Open Access

    ARTICLE

    Pulmonary Diseases Decision Support System Using Deep Learning Approach

    Yazan Al-Issa1, Ali Mohammad Alqudah2,*, Hiam Alquran3,2, Ahmed Al Issa4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 311-326, 2022, DOI:10.32604/cmc.2022.025750
    Abstract Pulmonary diseases are common throughout the world, especially in developing countries. These diseases include chronic obstructive pulmonary diseases, pneumonia, asthma, tuberculosis, fibrosis, and recently COVID-19. In general, pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists. In recent years, many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases. In this paper, the performance of four popular pretrained models (namely VGG16, DenseNet201, DarkNet19, and XceptionNet) in distinguishing between different pulmonary diseases was analyzed. To the best of our knowledge, this is the… More >

  • Open Access

    ARTICLE

    Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

    Hanan Abdullah Mengash1, Radwa Marzouk1, Siwar Ben Haj Hassine2, Anwer Mustafa Hilal3,*, Ishfaq Yaseen3, Abdelwahed Motwakel3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 327-342, 2022, DOI:10.32604/cmc.2022.026464
    Abstract Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem in wireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises a massive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is… More >

  • Open Access

    ARTICLE

    A Novel Convolutional Neural Networks Based Spinach Classification and Recognition System

    Sankar Sennan1, Digvijay Pandey2,*, Youseef Alotaibi3, Saleh Alghamdi4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 343-361, 2022, DOI:10.32604/cmc.2022.028334
    Abstract In the present scenario, Deep Learning (DL) is one of the most popular research algorithms to increase the accuracy of data analysis. Due to intra-class differences and inter-class variation, image classification is one of the most difficult jobs in image processing. Plant or spinach recognition or classification is one of the deep learning applications through its leaf. Spinach is more critical for human skin, bone, and hair, etc. It provides vitamins, iron, minerals, and protein. It is beneficial for diet and is readily available in people's surroundings. Many researchers have proposed various machine learning and deep learning algorithms to classify… More >

  • Open Access

    ARTICLE

    Characteristics of Climate Change in the Lake Basin Area of Gangcha County

    Wenzheng Yu1, Aodi Fu1, Li Shao2, Haitao Liu2, Xin Yao1,*, Tianliang Chen2, Hanxiaoya Zhang3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 363-379, 2022, DOI:10.32604/cmc.2022.027009
    Abstract This paper mainly was based on the average temperature, precipitation, humidity, and wind direction of Gangcha county from 1960 to 2013. By using wavelet analysis and Mann-Kendall (M-K) mutation analysis, specifically analyzed the climate change characteristics in the lake basin area of Gangcha county. The result showed that the climatic change in the lake basin area of Gangcha county is noticeable. The average temperature, average minimum temperature, average maximum temperature, and evaporation showed an increasing trend. But the evaporation in the study area was higher than precipitation. The average relative humidity showed a decreasing trend. And the sunshine and the… More >

  • Open Access

    ARTICLE

    Arithmetic Optimization with Deep Learning Enabled Anomaly Detection in Smart City

    Mahmoud Ragab1,2,3,*, Maha Farouk S. Sabir4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 381-395, 2022, DOI:10.32604/cmc.2022.027327
    Abstract In recent years, Smart City Infrastructures (SCI) have become familiar whereas intelligent models have been designed to improve the quality of living in smart cities. Simultaneously, anomaly detection in SCI has become a hot research topic and is widely explored to enhance the safety of pedestrians. The increasing popularity of video surveillance system and drastic increase in the amount of collected videos make the conventional physical investigation method to identify abnormal actions, a laborious process. In this background, Deep Learning (DL) models can be used in the detection of anomalies found through video surveillance systems. The current research paper develops… More >

  • Open Access

    ARTICLE

    A Compact Quad-Band Sickle-Shaped Monopole Antenna for GSM 900/WiMax/WLAN Applications

    Sujit Goswami1,*, Sujit Kumar Mandal1, Soumen Banerjee2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 397-410, 2022, DOI:10.32604/cmc.2022.025657
    Abstract In this paper a novel, compact, microstrip-fed, quad-band monopole antenna is presented for the application of Global System for Mobile communication (GSM 900), Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). The proposed antenna comprises of a sickle-shaped structure with four circular arc strips, and a modified rectangular ground plane. The four strips of the antenna are independently responsible for the four different resonant frequencies of the operating bands and can be tuned separately to control the radiation performance. The proposed quad-band antenna is designed to resonate at 940 MHz for GSM 900, 2.5 and 3.5 GHz for… More >

  • Open Access

    ARTICLE

    Design and Analysis of Antipodal Vivaldi Antennas for Breast Cancer Detection

    Shalermchon Tangwachirapan, Wanwisa Thaiwirot*, Prayoot Akkaraekthalin
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 411-431, 2022, DOI:10.32604/cmc.2022.028294
    Abstract This paper presents the design and analysis of antipodal Vivaldi antennas (AVAs) for breast cancer detection. In order to enhance the antenna gain, different techniques such as using the uniform and non-uniform corrugation, expanding the dielectric substrate and adding the parasitic patch are applied to original AVA. The design procedure of two developed AVA structures i.e., AVA with non-uniform corrugation and AVA with parasitic patch are presented. The proposed AVAs are designed on inexpensive FR4 substrate. The AVA with non-uniform corrugation has compact dimension of mm2 or , where is wavelength of the lowest operating frequency. The antenna can operate… More >

  • Open Access

    ARTICLE

    A Meshless Method for Retrieving Nonlinear Large External Forces on Euler-Bernoulli Beams

    Chih-Wen Chang*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 433-451, 2022, DOI:10.32604/cmc.2022.027021
    Abstract We retrieve unknown nonlinear large space-time dependent forces burdened with the vibrating nonlinear Euler-Bernoulli beams under varied boundary data, comprising two-end fixed, cantilevered, clamped-hinged, and simply supported conditions in this study. Even though some researchers used several schemes to overcome these forward problems of Euler-Bernoulli beams; however, an effective numerical algorithm to solve these inverse problems is still not available. We cope with the homogeneous boundary conditions, initial data, and final time datum for each type of nonlinear beam by employing a variety of boundary shape functions. The unknown nonlinear large external force can be recuperated via back-substitution of the… More >

  • Open Access

    ARTICLE

    Control of Linear Servo Carts with Integral-Based Disturbance Rejection

    Ibrahim M. Mehedi1,2,*, Abdulah Jeza Aljohani1,2, Ubaid M. Al-Saggaf1,2, Ahmed I. Iskanderani1, Thangam Palaniswamy1, Mohamed Mahmoud3, Mohammed J. Abdulaal1, Muhammad Bilal1,2, Waleed Alasmary4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 453-463, 2022, DOI:10.32604/cmc.2022.022921
    Abstract This paper describes a system designed for linear servo cart systems that employs an integral-based Linear Active Disturbance Rejection Control (ILADRC) scheme to detect and respond to disturbances. The upgrade in this control technique provides extensive immunity to uncertainties, attenuation, internal disturbances, and external sources of noise. The fundamental technology base of LADRC is Extended State Observer (ESO). LADRC, when combined with Integral action, becomes a hybrid control technique, namely ILADRC. Setpoint tracking is based on Bode’s Ideal Transfer Function (BITF) in this proposed ILADRC technique. This proves to be a very robust and appropriate pole placement scheme. The proposed… More >

  • Open Access

    ARTICLE

    Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

    Linshan Shen1, Ye Tian1,*, Liguo Zhang1,2, Guisheng Yin1, Tong Shuai3, Shuo Liang3, Zhuofei Wu4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 465-476, 2022, DOI:10.32604/cmc.2022.027488
    Abstract The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing. However, the existing semi-supervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution, and its performance is mainly due to the two being in the same distribution state. When there is out-of-class data in unlabeled data, its performance will be affected. In practical applications, it is difficult to ensure that unlabeled data does not contain out-of-category data,… More >

  • Open Access

    ARTICLE

    Enhanced Archimedes Optimization Algorithm for Clustered Wireless Sensor Networks

    E. Laxmi Lydia1, T. M. Nithya2, K. Vijayalakshmi3, Jeya Prakash Kadambaajan4, Gyanendra Prasad Joshi5, Sung Won Kim6,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 477-492, 2022, DOI:10.32604/cmc.2022.025939
    Abstract Wireless sensor networks (WSN) encompass a set of inexpensive and battery powered sensor nodes, commonly employed for data gathering and tracking applications. Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination. The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network. In this aspect, this paper presents an enhanced Archimedes optimization based cluster head selection (EAOA-CHS) approach for WSN. The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the… More >

  • Open Access

    ARTICLE

    An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

    Muhammad Zubair Rehman1, Muhammad Aamir2,*, Nazri Mohd. Nawi3, Abdullah Khan4, Saima Anwar Lashari5, Siyab Khan4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 493-511, 2022, DOI:10.32604/cmc.2022.021787
    Abstract

    Recently, many researchers have used nature inspired metaheuristic algorithms due to their ability to perform optimally on complex problems. To solve problems in a simple way, in the recent era bat algorithm has become famous due to its high tendency towards convergence to the global optimum most of the time. But, still the standard bat with random walk has a problem of getting stuck in local minima. In order to solve this problem, this research proposed bat algorithm with levy flight random walk. Then, the proposed Bat with Levy flight algorithm is further hybridized with three different variants of ANN.… More >

  • Open Access

    ARTICLE

    Game Theory Based Decision Coordination Strategy of Agricultural Logistics Service Information System

    Long Guo1, Dongsheng Sun1,*, Abdul Waheed2, Huijie Gao3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 513-532, 2022, DOI:10.32604/cmc.2022.028211
    Abstract Under the background of “Internet plus” rapid development, the agricultural logistics industry should apply information technology to every link of the agricultural product logistics industry chain. By making full use of the decision making module of the agricultural logistics information system, we can realize the full sharing of information and data resources, which makes the decision-making scheme of the agricultural logistics information system more optimized. In real economic society, the uncertainty and mismatch between the customer’s logistics service demand and the logistics service capability that the logistics service function provider can provide, that is, when the two information are asymmetric,… More >

  • Open Access

    ARTICLE

    Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

    Mahmoud Ragab1,2,3,*, Hesham A. Abdushkour4, Alaa F. Nahhas5, Wajdi H. Aljedaibi6
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 533-546, 2022, DOI:10.32604/cmc.2022.028856
    Abstract Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages. Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector. Computer aided diagnosis (CAD) models can be designed to effectually identify and classify the existence of lung cancer using medical images. The recently developed deep learning (DL) models find a way for accurate lung nodule classification process. Therefore, this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and… More >

  • Open Access

    ARTICLE

    Two-Stage High-Efficiency Encryption Key Update Scheme for LoRaWAN Based IoT Environment

    Kun-Lin Tsai1,2,*, Li-Woei Chen3, Fang-Yie Leu4,5, Chuan-Tian Wu1
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 547-562, 2022, DOI:10.32604/cmc.2022.026557
    Abstract Secure data communication is an essential requirement for an Internet of Things (IoT) system. Especially in Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) systems, when important data are hacked, it may induce property loss or life hazard. Even though many IoT-related communication protocols are equipped with secure policies, they still have some security weaknesses in their IoT systems. LoRaWAN is one of the low power wide-area network protocols, and it adopts Advanced Encryption Standard (AES) to provide message integrity and confidentiality. However, LoRaWAN's encryption key update scheme can be further improved. In this paper, a Two-stage… More >

  • Open Access

    ARTICLE

    Poisson-Gumbel Model for Wind Speed Threshold Estimation of Maximum Wind Speed

    Wenzheng Yu1, Yang Gao1, Zhengyu Yuan1, Xin Yao1,*, Mingxuan Zhu1, Hanxiaoya Zhang2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 563-576, 2022, DOI:10.32604/cmc.2022.027008
    Abstract

    Poisson-Gumbel joint distribution model uses maximum wind speed corresponding to multiple typhoons to construct sample sequence. Thresholds are usually used to filter sample sequences to make them more consistent with Poisson distribution. However, few studies have discussed the threshold setting and its impact on Poisson-Gumbel joint distribution model. In this study, a sample sequence based on the data of Qinzhou meteorological station from 2005 to 2018 were constructed. We set 0%, 5%, 10%, 20% and 30% gradient thresholds. Then, we analyzed the influence of threshold change on the calculation results of maximum wind speed in different return periods. The results… More >

  • Open Access

    ARTICLE

    Comprehensive DDoS Attack Classification Using Machine Learning Algorithms

    Olga Ussatova1,2, Aidana Zhumabekova1,*, Yenlik Begimbayeva2,3, Eric T. Matson4, Nikita Ussatov5
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 577-594, 2022, DOI:10.32604/cmc.2022.026552
    Abstract The fast development of Internet technologies ignited the growth of techniques for information security that protect data, networks, systems, and applications from various threats. There are many types of threats. The dedicated denial of service attack (DDoS) is one of the most serious and widespread attacks on Internet resources. This attack is intended to paralyze the victim's system and cause the service to fail. This work is devoted to the classification of DDoS attacks in the special network environment called Software-Defined Networking (SDN) using machine learning algorithms. The analyzed dataset included instances of two classes: benign and malicious. As the… More >

  • Open Access

    ARTICLE

    New Collective Signatures Based on the Elliptic Curve Discrete Logarithm Problem

    Tuan Nguyen Kim1,*, Duy Ho Ngoc2, Nikolay A. Moldovyan3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 595-610, 2022, DOI:10.32604/cmc.2022.023168
    Abstract There have been many digital signature schemes were developed based on the discrete logarithm problem on a finite field. In this study, we use the elliptic curve discrete logarithm problem to build new collective signature schemes. The cryptosystem on elliptic curve allows to generate digital signatures with the same level of security as other cryptosystems but with smaller keys. To extend practical applicability and enhance the security level of the group signature protocols, we propose two new types of collective digital signature schemes based on the discrete logarithm problem on the elliptic curve: i) the collective digital signature scheme shared… More >

  • Open Access

    ARTICLE

    Gaussian Process for a Single-channel EEG Decoder with Inconspicuous Stimuli and Eyeblinks

    Nur Syazreen Ahmad*, Jia Hui Teo, Patrick Goh
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 611-628, 2022, DOI:10.32604/cmc.2022.025823
    Abstract A single-channel electroencephalography (EEG) device, despite being widely accepted due to convenience, ease of deployment and suitability for use in complex environments, typically poses a great challenge for reactive brain-computer interface (BCI) applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles. In this study, a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control signal. The proposed decoder is… More >

  • Open Access

    ARTICLE

    Cross-Language Transfer Learning-based Lhasa-Tibetan Speech Recognition

    Zhijie Wang1, Yue Zhao1,*, Licheng Wu1, Xiaojun Bi1, Zhuoma Dawa2, Qiang Ji3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 629-639, 2022, DOI:10.32604/cmc.2022.027092
    Abstract As one of Chinese minority languages, Tibetan speech recognition technology was not researched upon as extensively as Chinese and English were until recently. This, along with the relatively small Tibetan corpus, has resulted in an unsatisfying performance of Tibetan speech recognition based on an end-to-end model. This paper aims to achieve an accurate Tibetan speech recognition using a small amount of Tibetan training data. We demonstrate effective methods of Tibetan end-to-end speech recognition via cross-language transfer learning from three aspects: modeling unit selection, transfer learning method, and source language selection. Experimental results show that the Chinese-Tibetan multi-language learning method using… More >

  • Open Access

    ARTICLE

    A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images

    Abdullah A. Asiri1, Amna Iqbal2, Javed Ferzund2, Tariq Ali2,*, Muhammad Aamir2, Khalaf A. Alshamrani1, Hassan A. Alshamrani1, Fawaz F. Alqahtani1, Muhammad Irfan3, Ali H. D. Alshehri1
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 641-655, 2022, DOI:10.32604/cmc.2022.029000
    Abstract Abnormal growth of brain tissues is the real cause of brain tumor. Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient. The manual segmentation of brain tumor magnetic resonance images (MRIs) takes time and results vary significantly in low-level features. To address this issue, we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network (CNN) for reliable images segmentation by considering the low-level features of MRI. In this model, we have extracted features through ResNet-50 architecture and fed these feature maps to… More >

  • Open Access

    ARTICLE

    A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos

    Muhammad Islam Kamran1, Muazzam A. Khan1, Suliman A. Alsuhibany2, Yazeed Yasin Ghadi3, Arshad4, Jameel Arif1, Jawad Ahmad5,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 657-672, 2022, DOI:10.32604/cmc.2022.028876
    Abstract The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation (4G) and 5th generation (5G) etc. Researchers have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure way. Instead of the classical random walk, this paper proposes the quantum walk to achieve high image security. Classical random walk exhibits randomness due to the stochastic transitions between states, on the other hand, the quantum walk is more random and achieve randomness due… More >

  • Open Access

    ARTICLE

    Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

    Shun Wang1, Lin Qiao2, Wei Fang3, Guodong Jing4, Victor S. Sheng5, Yong Zhang1,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 673-687, 2022, DOI:10.32604/cmc.2022.028411
    Abstract PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the temporal dependence of the long-term… More >

  • Open Access

    ARTICLE

    Construction of an Energy-Efficient Detour Non-Split Dominating Set in WSN

    G. Sheeba1,*, T. M. Selvarajan2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 689-700, 2022, DOI:10.32604/cmc.2022.021781
    Abstract Wireless sensor networks (WSNs) are one of the most important improvements due to their remarkable capacities and their continuous growth in various applications. However, the lifetime of WSNs is very confined because of the delimited energy limit of their sensor nodes. This is the reason why energy conservation is considered the main exploration worry for WSNs. For this energy-efficient routing is required to save energy and to subsequently drag out the lifetime of WSNs. In this report we use the Ant Colony Optimization (ACO) method and are evaluated using the Genetic Algorithm (GA), based on the Detour non-split dominant set… More >

  • Open Access

    ARTICLE

    Vertex Cover Optimization Using a Novel Graph Decomposition Approach

    Abdul Manan1, Shahida Bashir1, Abdul Majid2,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 701-717, 2022, DOI:10.32604/cmc.2022.027064
    Abstract The minimum vertex cover problem (MVCP) is a well-known combinatorial optimization problem of graph theory. The MVCP is an NP (nondeterministic polynomial) complete problem and it has an exponential growing complexity with respect to the size of a graph. No algorithm exits till date that can exactly solve the problem in a deterministic polynomial time scale. However, several algorithms are proposed that solve the problem approximately in a short polynomial time scale. Such algorithms are useful for large size graphs, for which exact solution of MVCP is impossible with current computational resources. The MVCP has a wide range of applications… More >

  • Open Access

    ARTICLE

    A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

    Jie Chen1, Chengsheng Yuan1,2,*, Chen Cui2, Zhihua Xia1, Xingming Sun1,3, Thangarajah Akilan4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 719-733, 2022, DOI:10.32604/cmc.2022.027984
    Abstract Fingerprint identification systems have been widely deployed in many occasions of our daily life. However, together with many advantages, they are still vulnerable to the presentation attack (PA) by some counterfeit fingerprints. To address challenges from PA, fingerprint liveness detection (FLD) technology has been proposed and gradually attracted people's attention. The vast majority of the FLD methods directly employ convolutional neural network (CNN), and rarely pay attention to the problem of over-parameterization and over-fitting of models, resulting in large calculation force of model deployment and poor model generalization. Aiming at filling this gap, this paper designs a lightweight multi-scale convolutional… More >

  • Open Access

    ARTICLE

    Managing Software Testing Technical Debt Using Evolutionary Algorithms

    Muhammad Abid Jamil*, Mohamed K. Nour
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 735-747, 2022, DOI:10.32604/cmc.2022.028386
    Abstract Technical debt (TD) happens when project teams carry out technical decisions in favor of a short-term goal(s) in their projects, whether deliberately or unknowingly. TD must be properly managed to guarantee that its negative implications do not outweigh its advantages. A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden. Test technical debt is the technical debt aspect of testing (or test debt). Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years. In this article, we assume that the… More >

  • Open Access

    ARTICLE

    Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

    Doaa Sami Khafaga1, Amel Ali Alhussan1,*, El-Sayed M. El-kenawy2,3, Ali E. Takieldeen3, Tarek M. Hassan4, Ehab A. Hegazy5, Elsayed Abdel Fattah Eid6, Abdelhameed Ibrahim7, Abdelaziz A. Abdelhamid8,9
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 749-765, 2022, DOI:10.32604/cmc.2022.029605
    Abstract One of the most common kinds of cancer is breast cancer. The early detection of it may help lower its overall rates of mortality. In this paper, we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images. The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest. In addition, to properly train the machine learning models, data augmentation is applied to increase the number of segmented regions using various scaling ratios. On the other hand, to extract the relevant features from the breast cancer cases, a set… More >

  • Open Access

    ARTICLE

    Wall Cracks Detection in Aerial Images Using Improved Mask R-CNN

    Wei Chen1, Caoyang Chen1,*, Mi Liu1, Xuhong Zhou2, Haozhi Tan3, Mingliang Zhang4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 767-782, 2022, DOI:10.32604/cmc.2022.028571
    Abstract The present paper proposes a detection method for building exterior wall cracks since manual detection methods have high risk and low efficiency. The proposed method is based on Unmanned Aerial Vehicle (UAV) and computer vision technology. First, a crack dataset of 1920 images was established using UAV to collect the images of a residential building exterior wall under different lighting conditions. Second, the average crack detection precisions of different methods including the Single Shot MultiBox Detector, You Only Look Once v3, You Only Look Once v4, Faster Regional Convolutional Neural Network (R-CNN) and Mask R-CNN methods were compared. Then, the… More >

  • Open Access

    ARTICLE

    New Representative Collective Signatures Based on the Discrete Logarithm Problem

    Tuan Nguyen Kim1,*, Duy Ho Ngoc2, Nikolay A. Moldovyan3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 783-799, 2022, DOI:10.32604/cmc.2022.024677
    Abstract The representative collective digital signature scheme allows the creation of a unique collective signature on document M that represents an entire signing community consisting of many individual signers and many different signing groups, each signing group is represented by a group leader. On document M, a collective signature can be created using the representative digital signature scheme that represents an entire community consisting of individual signers and signing groups, each of which is represented by a group leader. The characteristic of this type of letter is that it consists of three elements (U, E, S), one of which (U) is… More >

  • Open Access

    ARTICLE

    Optimal Fusion-Based Handcrafted with Deep Features for Brain Cancer Classification

    Mahmoud Ragab1,2,3,*, Sultanah M. Alshammari4, Amer H. Asseri2,5, Waleed K. Almutiry6
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 801-815, 2022, DOI:10.32604/cmc.2022.029140
    Abstract Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography (CT), or magnetic resonance imaging (MRI). An automated brain cancer classification using computer aided diagnosis (CAD) models can be designed to assist radiologists. With the recent advancement in computer vision (CV) and deep learning (DL) models, it is possible to automatically detect the tumor from images using a computer-aided design. This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features (HGSO-FHDF) technique for brain cancer classification. The proposed HGSO-FHDF technique aims for detecting and classifying different… More >

  • Open Access

    Vulnerability Analysis of MEGA Encryption Mechanism

    Qingbing Ji1,2,*, Zhihong Rao1,2, Lvlin Ni2, Wei Zhao2, Jing Fu3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 817-829, 2022, DOI:10.32604/cmc.2022.026949
    Abstract MEGA is an end-to-end encrypted cloud storage platform controlled by users. Moreover, the communication between MEGA client and server is carried out under the protection of Transport Layer Security (TLS) encryption, it is difficult to intercept the key data packets in the process of MEGA registration, login, file data upload, and download. These characteristics of MEGA have brought great difficulties to its forensics. This paper presents a method to attack MEGA to provide an effective method for MEGA’s forensics. By debugging the open-source code of MEGA and analyzing the security white paper published, this paper first clarifies the encryption mechanism… More >

  • Open Access

    ARTICLE

    Real-time Volume Preserving Constraints for Volumetric Model on GPU

    Hongly Va1, Min-Hyung Choi2, Min Hong3,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 831-848, 2022, DOI:10.32604/cmc.2022.029576
    Abstract This paper presents a parallel method for simulating real-time 3D deformable objects using the volume preservation mass-spring system method on tetrahedron meshes. In general, the conventional mass-spring system is manipulated as a force-driven method because it is fast, simple to implement, and the parameters can be controlled. However, the springs in traditional mass-spring system can be excessively elongated which cause severe stability and robustness issues that lead to shape restoring, simulation blow-up, and huge volume loss of the deformable object. In addition, traditional method that uses a serial process of the central processing unit (CPU) to solve the system in… More >

  • Open Access

    ARTICLE

    Computer Vision with Machine Learning Enabled Skin Lesion Classification Model

    Romany F. Mansour1,*, Sara A. Althubiti2, Fayadh Alenezi3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 849-864, 2022, DOI:10.32604/cmc.2022.029265
    Abstract Recently, computer vision (CV) based disease diagnosis models have been utilized in various areas of healthcare. At the same time, deep learning (DL) and machine learning (ML) models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools. This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification (CVOML-SLDC) model. The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images. Primarily, the CVOML-SLDC model derives a gaussian filtering (GF) approach to pre-process the input images and graph… More >

  • Open Access

    ARTICLE

    Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

    Doaa Sami Khafaga1, Amel Ali Alhussan1,*, El-Sayed M. El-kenawy2,3, Abdelhameed Ibrahim4, Said H. Abd Elkhalik3, Shady Y. El-Mashad5, Abdelaziz A. Abdelhamid6,7
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 865-881, 2022, DOI:10.32604/cmc.2022.028550
    Abstract The design of an antenna requires a careful selection of its parameters to retain the desired performance. However, this task is time-consuming when the traditional approaches are employed, which represents a significant challenge. On the other hand, machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended performance. In this paper, we propose a novel approach for accurately predicting the bandwidth of metamaterial antenna. The proposed approach is based on employing the recently emerged guided whale… More >

  • Open Access

    ARTICLE

    Sensitive Information Protection Model Based on Bayesian Game

    Yuzhen Liu1,2, Zhe Liu3, Xiaoliang Wang1,2,*, Qing Yang4, Guocai Zuo5, Frank Jiang6
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 883-898, 2022, DOI:10.32604/cmc.2022.029002
    Abstract

    A game measurement model considering the attacker's knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of service. We quantified the sensitive level of information according to the user's personalized sensitive information protection needs. Based on the probability distribution of sensitive level and attacker's knowledge background type, the strategy combination of service provider and attacker was analyzed, and a game-based sensitive information protection model was constructed. Through the combination of strategies under Bayesian equilibrium, the information entropy was used to measure the leakage of sensitive… More >

  • Open Access

    ARTICLE

    Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

    Ashit Kumar Dutta1,*, Yasser Albagory2, Abdul Rahaman Wahab Sait3, Ismail Mohamed Keshta1
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 899-915, 2022, DOI:10.32604/cmc.2022.026783
    Abstract Recently, autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare, agriculture, industrial automation, etc. Among the interesting applications of autonomous systems, their applicability in agricultural sector becomes significant. Autonomous unmanned aerial vehicles (UAVs) can be used for suitable site-specific weed management (SSWM) to improve crop productivity. In spite of substantial advancements in UAV based data collection systems, automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops. The recently developed deep learning (DL) models have exhibited effective performance in several… More >

  • Open Access

    ARTICLE

    Optimized Two-Level Ensemble Model for Predicting the Parameters of Metamaterial Antenna

    Abdelaziz A. Abdelhamid1,3,*, Sultan R. Alotaibi2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 917-933, 2022, DOI:10.32604/cmc.2022.027653
    Abstract Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation tools. In this paper, we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble model. The proposed ensemble model is composed of two levels of regression models. The first level consists of three strong models namely, random forest, support vector regression, and light gradient boosting machine. Whereas the second level is based on the ElasticNet regression model, which receives the prediction results from… More >

  • Open Access

    ARTICLE

    Reference Selection for Offline Hybrid Siamese Signature Verification Systems

    Tsung-Yu Lu1, Mu-En Wu2, Er-Hao Chen3, Yeong-Luh Ueng4,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 935-952, 2022, DOI:10.32604/cmc.2022.026717
    Abstract This paper presents an off-line handwritten signature verification system based on the Siamese network, where a hybrid architecture is used. The Residual neural Network (ResNet) is used to realize a powerful feature extraction model such that Writer Independent (WI) features can be effectively learned. A single-layer Siamese Neural Network (NN) is used to realize a Writer Dependent (WD) classifier such that the storage space can be minimized. For the purpose of reducing the impact of the high intraclass variability of the signature and ensuring that the Siamese network can learn more effectively, we propose a method of selecting a reference… More >

  • Open Access

    ARTICLE

    Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning

    Muhammad Umar Nasir1, Muhammad Adnan Khan1,2, Muhammad Zubair3, Taher M. Ghazal4,5, Raed A. Said6, Hussam Al Hamadi7,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 953-963, 2022, DOI:10.32604/cmc.2022.028958
    Abstract One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data. Furthermore, the complicated genetic disease has a very diverse genotype, making it challenging to find genetic markers. This is a challenging process since it must be completed effectively and efficiently. This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters. Using the patient’s medical history, we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder. To… More >

  • Open Access

    ARTICLE

    Deep Learning Enabled Disease Diagnosis for Secure Internet of Medical Things

    Sultan Ahmad1, Shakir Khan2, Mohamed Fahad AlAjmi3, Ashit Kumar Dutta4, L. Minh Dang5, Gyanendra Prasad Joshi6, Hyeonjoon Moon6,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 965-979, 2022, DOI:10.32604/cmc.2022.025760
    Abstract In recent times, Internet of Medical Things (IoMT) gained much attention in medical services and healthcare management domain. Since healthcare sector generates massive volumes of data like personal details, historical medical data, hospitalization records, and discharging records, IoMT devices too evolved with potentials to handle such high quantities of data. Privacy and security of the data, gathered by IoMT gadgets, are major issues while transmitting or saving it in cloud. The advancements made in Artificial Intelligence (AI) and encryption techniques find a way to handle massive quantities of medical data and achieve security. In this view, the current study presents… More >

  • Open Access

    ARTICLE

    Dynamics of Fractional Differential Model for Schistosomiasis Disease

    Thongchai Botmart1, Wajaree Weera1,*, Muhammad Asif Zahoor Raja2, Zulqurnain Sabir3, Qusain Hiader4, Gilder Cieza Altamirano5, Plinio Junior Muro Solano6, Alfonso Tesen Arroyo6
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 981-999, 2022, DOI:10.32604/cmc.2022.028921
    Abstract In the present study, a design of a fractional order mathematical model is presented based on the schistosomiasis disease. To observe more accurate performances of the results, the use of fractional order derivatives in the mathematical model is introduce based on the schistosomiasis disease is executed. The preliminary design of the fractional order mathematical model focused on schistosomiasis disease is classified as follows: uninfected with schistosomiasis, infected with schistosomiasis, recovered from infection, susceptible snail unafflicted with schistosomiasis disease and susceptible snail afflicted with this disease. The solutions to the proposed system of the fractional order mathematical model will be presented… More >

  • Open Access

    ARTICLE

    Early-Stage Segmentation and Characterization of Brain Tumor

    Syed Nauyan Rashid1, Muhammad Hanif2,*, Usman Habib2, Akhtar Khalil3, Omair Inam4, Hafeez Ur Rehman1
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1001-1017, 2022, DOI:10.32604/cmc.2022.023135
    Abstract Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain tissues. The life expectancy of patients diagnosed with gliomas decreases exponentially. Most gliomas are diagnosed in later stages, resulting in imminent death. On average, patients do not survive 14 months after diagnosis. The only way to minimize the impact of this inevitable disease is through early diagnosis. The Magnetic Resonance Imaging (MRI) scans, because of their better tissue contrast, are most frequently used to assess the brain tissues. The manual classification of MRI scans takes a reasonable amount of time to classify brain tumors. Besides this,… More >

  • Open Access

    ARTICLE

    Secure Communication Scheme based on A New Hyperchaotic System

    Khaled Benkouider1, Aceng Sambas2, Ibrahim Mohammed Sulaiman3, Mustafa Mamat4, Kottakkaran Sooppy Nisar5,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1019-1035, 2022, DOI:10.32604/cmc.2022.025836
    Abstract This study introduces a new continuous time differential system, which contains ten terms with three quadratic nonlinearities. The new system can demonstrate hyperchaotic, chaotic, quasi-periodic, and periodic behaviors for its different parameter values. All theoretical and numerical analysis are investigated to confirm the complex hyperchaotic behavior of our proposed model using many tools that include Kaplan-Yorke dimension, equilibrium points stability, bifurcation diagrams, and Lyapunov exponents. By means of Multisim software, the authors also designed an electronic circuit to confirm our proposed systems’ physical feasibility. MATLAB and Multisim simulation results excellently agree with each other, which validate the feasibility of our… More >

  • Open Access

    ARTICLE

    Improving the Ambient Intelligence Living Using Deep Learning Classifier

    Yazeed Yasin Ghadi1, Mouazma Batool2, Munkhjargal Gochoo3, Suliman A. Alsuhibany4, Tamara al Shloul5, Ahmad Jalal2, Jeongmin Park6,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1037-1053, 2022, DOI:10.32604/cmc.2022.027422
    Abstract Over the last decade, there is a surge of attention in establishing ambient assisted living (AAL) solutions to assist individuals live independently. With a social and economic perspective, the demographic shift toward an elderly population has brought new challenges to today’s society. AAL can offer a variety of solutions for increasing people’s quality of life, allowing them to live healthier and more independently for longer. In this paper, we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks (BiLSTM) and convolutional neural network (CNN) classifier. We first pre-processed the signal data, then used time-frequency… More >

  • Open Access

    ARTICLE

    Design of a Novel THz Modulator for B5G Communication

    Omar A. Saraereh*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1055-1066, 2022, DOI:10.32604/cmc.2022.030193
    Abstract Wireless data traffic has expanded at a rate that reminds us of Moore’s prediction for integrated circuits in recent years, necessitating ongoing attempts to supply wireless systems with ever-larger data rates in the near future, despite the under-deployment of 5G networks. Terahertz (THz) communication has been considered a viable response to communication blackout due to the rapid development of THz technology and sensors. THz communication has a high frequency, which allows for better penetration. It is a fast expanding and evolving industry, driven by an increase in wireless traffic volume and data transfer speeds. A THz modulator based on a… More >

  • Open Access

    ARTICLE

    Anomaly Detection Framework in Fog-to-Things Communication for Industrial Internet of Things

    Tahani Alatawi*, Ahamed Aljuhani
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1067-1086, 2022, DOI:10.32604/cmc.2022.029283
    Abstract The rapid development of the Internet of Things (IoT) in the industrial domain has led to the new term the Industrial Internet of Things (IIoT). The IIoT includes several devices, applications, and services that connect the physical and virtual space in order to provide smart, cost-effective, and scalable systems. Although the IIoT has been deployed and integrated into a wide range of industrial control systems, preserving security and privacy of such a technology remains a big challenge. An anomaly-based Intrusion Detection System (IDS) can be an effective security solution for maintaining the confidentiality, integrity, and availability of data transmitted in… More >

  • Open Access

    ARTICLE

    Rough Sets Hybridization with Mayfly Optimization for Dimensionality Reduction

    Ahmad Taher Azar1,2,*, Mustafa Samy Elgendy1, Mustafa Abdul Salam1,3, Khaled M. Fouad1,4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1087-1108, 2022, DOI:10.32604/cmc.2022.028184
    Abstract Big data is a vast amount of structured and unstructured data that must be dealt with on a regular basis. Dimensionality reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed easily. These tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning issues. To achieve dimensionality reduction for huge data sets, this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS. A novel hybrid strategy based on the Mayfly algorithm (MA) and the rough set (RS)… More >

  • Open Access

    ARTICLE

    An Enhanced Deep Learning Method for Skin Cancer Detection and Classification

    Mohamed W. Abo El-Soud1,2,*, Tarek Gaber2,3, Mohamed Tahoun2, Abdullah Alourani1
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1109-1123, 2022, DOI:10.32604/cmc.2022.028561
    Abstract The prevalence of melanoma skin cancer has increased in recent decades. The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins. Thus, the early diagnosis of melanoma is a key factor in improving the prognosis of the disease. Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images. Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases. This paper proposes a new method which can be used for… More >

  • Open Access

    ARTICLE

    An Optimal Framework for SDN Based on Deep Neural Network

    Abdallah Abdallah1, Mohamad Khairi Ishak2, Nor Samsiah Sani3, Imran Khan4, Fahad R. Albogamy5, Hirofumi Amano6, Samih M. Mostafa7,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1125-1140, 2022, DOI:10.32604/cmc.2022.025810
    Abstract Software-defined networking (SDN) is a new paradigm that promises to change by breaking vertical integration, decoupling network control logic from the underlying routers and switches, promoting (logical) network control centralization, and introducing network programming. However, the controller is similarly vulnerable to a “single point of failure”, an attacker can execute a distributed denial of service (DDoS) attack that invalidates the controller and compromises the network security in SDN. To address the problem of DDoS traffic detection in SDN, a novel detection approach based on information entropy and deep neural network (DNN) is proposed. This approach contains a DNN-based DDoS traffic… More >

  • Open Access

    ARTICLE

    Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

    M. M. Lotfy1, Hazem M. El-Bakry2, M. M. Elgayar3, Shaker El-Sappagh4,5, G. Abdallah M. I1, A. A. Soliman1, Kyung Sup Kwak6,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1141-1158, 2022, DOI:10.32604/cmc.2022.024193
    Abstract Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people, and its high number of deaths also by 7%. For that purpose, a proposed model of several stages was developed. The first stage is optimizing the images using dynamic adaptive histogram equalization, performing a semantic segmentation using DeepLabv3Plus, then augmenting the data by flipping it horizontally, rotating it, then flipping it vertically. The second stage builds a custom convolutional neural network model using several pre-trained ImageNet. Finally, the model compares the pre-trained data to the new output, while repeatedly… More >

  • Open Access

    ARTICLE

    Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications

    Mohammad Alamgeer1, Saud S. Alotaibi2, Shaha Al-Otaibi3, Nazik Alturki3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4, Ishfaq Yaseen4, Mohamed I. Eldesouki5
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1159-1175, 2022, DOI:10.32604/cmc.2022.028275
    Abstract Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies. Blockchain, data encryption, and deep learning (DL) models can be utilized to design efficient security solutions for IoT healthcare applications. In this aspect, this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning (BPPIE-ODL) technique for IoT healthcare applications. The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server. The proposed BPPIE-ODL technique encompasses the design of dragonfly… More >

  • Open Access

    ARTICLE

    Quaternion Integers Based Higher Length Cyclic Codes and Their Decoding Algorithm

    Muhammad Sajjad1, Tariq Shah1,*, Mohammad Mazyad Hazzazi2, Adel R. Alharbi3, Iqtadar Hussain4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1177-1194, 2022, DOI:10.32604/cmc.2022.025245
    Abstract The decoding algorithm for the correction of errors of arbitrary Mannheim weight has discussed for Lattice constellations and codes from quadratic number fields. Following these lines, the decoding algorithms for the correction of errors of length cyclic codes over quaternion integers of Quaternion Mannheim weight one up to two coordinates have considered. In continuation, the case of cyclic codes of lengths and has studied to improve the error correction efficiency. In this study, we present the decoding of cyclic codes of length and length 2 (where is prime integer and is Euler phi function) over Hamilton Quaternion integers of Quaternion… More >

  • Open Access

    ARTICLE

    Space Division Multiple Access for Cellular V2X Communications

    Doaa Sami Khafaga1, Mohammad Zubair Khan2, Muhammad Awais Javed3, Amel Ali Alhussan1,*, Wael Said4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1195-1206, 2022, DOI:10.32604/cmc.2022.028280
    Abstract Vehicular communication is the backbone of future Intelligent Transportation Systems (ITS). It offers a network-based solution for vehicle safety, cooperative awareness, and traffic management applications. For safety applications, Basic Safety Messages (BSM) containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic map. BSMs are shared using mode 4 of Cellular V2X (C-V2X) communications in which resources are allocated in an ad hoc manner. However, the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network, thus reducing the reliability of… More >

  • Open Access

    ARTICLE

    An Intelligent Framework for Recognizing Social Human-Object Interactions

    Mohammed Alarfaj1, Manahil Waheed2, Yazeed Yasin Ghadi3, Tamara al Shloul4, Suliman A. Alsuhibany5, Ahmad Jalal2, Jeongmin Park6,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1207-1223, 2022, DOI:10.32604/cmc.2022.025671
    Abstract Human object interaction (HOI) recognition plays an important role in the designing of surveillance and monitoring systems for healthcare, sports, education, and public areas. It involves localizing the human and object targets and then identifying the interactions between them. However, it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers. Hence, the proposed system offers an automated body-parts-based solution for HOI recognition. This system uses RGB (red, green, blue) images as input and segments the desired parts of the images through a segmentation… More >

  • Open Access

    ARTICLE

    Analysis of Eigenvalues for Molecular Structures

    Muhammad Haroon Aftab1, Kamel Jebreen2,*, Mohammad Issa Sowaity3, Muhammad Hussain4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1225-1236, 2022, DOI:10.32604/cmc.2022.029009
    Abstract In this article, we study different molecular structures such as Polythiophene network, for and , Orthosilicate (Nesosilicate) , Pyrosilicates (Sorosilicates) , Chain silicates (Pyroxenes), and Cyclic silicates (Ring Silicates) for their cardinalities, chromatic numbers, graph variations, eigenvalues obtained from the adjacency matrices which are square matrices in order and their corresponding characteristics polynomials. We convert the general structures of these chemical networks in to mathematical graphical structures. We transform the molecular structures of these chemical networks which are mentioned above, into a simple and undirected planar graph and sketch them with various techniques of mathematics. The matrices obtained from these… More >

  • Open Access

    ARTICLE

    An Efficient Path Planning Strategy in Mobile Sink Wireless Sensor Networks

    Najla Bagais*, Etimad Fadel, Amal Al-Mansour
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1237-1267, 2022, DOI:10.32604/cmc.2022.026070
    Abstract Wireless sensor networks (WSNs) are considered the backbone of the Internet of Things (IoT), which enables sensor nodes (SNs) to achieve applications similarly to human intelligence. However, integrating a WSN with the IoT is challenging and causes issues that require careful exploration. Prolonging the lifetime of a network through appropriately utilising energy consumption is among the essential challenges due to the limited resources of SNs. Thus, recent research has examined mobile sinks (MSs), which have been introduced to improve the overall efficiency of WSNs. MSs bear the burden of data collection instead of consuming energy at the routeing by SNs.… More >

  • Open Access

    ARTICLE

    Lower-Limb Motion-Based Ankle-Foot Movement Classification Using 2D-CNN

    Narathip Chaobankoh1, Tallit Jumphoo1, Monthippa Uthansakul1, Khomdet Phapatanaburi2, Bura Sindthupakorn3, Supakit Rooppakhun4, Peerapong Uthansakul1,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1269-1282, 2022, DOI:10.32604/cmc.2022.027474
    Abstract Recently, the Muscle-Computer Interface (MCI) has been extensively popular for employing Electromyography (EMG) signals to help the development of various assistive devices. However, few studies have focused on ankle foot movement classification considering EMG signals at limb position. This work proposes a new framework considering two EMG signals at a lower-limb position to classify the ankle movement characteristics based on normal walking cycles. For this purpose, we introduce a human ankle-foot movement classification method using a two-dimensional-convolutional neural network (2D-CNN) with low-cost EMG sensors based on lower-limb motion. The time-domain signals of EMG obtained from two sensors belonging to Dorsiflexion,… More >

  • Open Access

    ARTICLE

    An Efficient Ensemble Model for Various Scale Medical Data

    Heba A. Elzeheiry*, Sherief Barakat, Amira Rezk
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1283-1305, 2022, DOI:10.32604/cmc.2022.027345
    Abstract Electronic Health Records (EHRs) are the digital form of patients’ medical reports or records. EHRs facilitate advanced analytics and aid in better decision-making for clinical data. Medical data are very complicated and using one classification algorithm to reach good results is difficult. For this reason, we use a combination of classification techniques to reach an efficient and accurate classification model. This model combination is called the Ensemble model. We need to predict new medical data with a high accuracy value in a small processing time. We propose a new ensemble model MDRL which is efficient with different datasets. The MDRL… More >

  • Open Access

    ARTICLE

    A Mathematical Model for COVID-19 Image Enhancement based on Mittag-Leffler-Chebyshev Shift

    Ibtisam Aldawish1, Hamid A. Jalab2,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1307-1316, 2022, DOI:10.32604/cmc.2022.029445
    Abstract The lungs CT scan is used to visualize the spread of the disease across the lungs to obtain better knowledge of the state of the COVID-19 infection. Accurately diagnosing of COVID-19 disease is a complex challenge that medical system face during the pandemic time. To address this problem, this paper proposes a COVID-19 image enhancement based on Mittag-Leffler-Chebyshev polynomial as pre-processing step for COVID-19 detection and segmentation. The proposed approach comprises the Mittag-Leffler sum convoluted with Chebyshev polynomial. The idea for using the proposed image enhancement model is that it improves images with low gray-level changes by estimating the probability… More >

  • Open Access

    ARTICLE

    Design of Teaching System of Industrial Robots Using Mixed Reality Technology

    Guwei Li1, Yun Yang1, Zhou Li1,*, Jingchun Fan2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1317-1327, 2022, DOI:10.32604/cmc.2022.027652
    Abstract Traditional teaching and learning about industrial robots uses abstract instructions, which are difficult for students to understand. Meanwhile, there are safety issues associated with the use of practical training equipment. To address these problems, this paper developed an instructional system based on mixed-reality (MR) technology for teaching about industrial robots. The Siasun T6A-series robots were taken as a case study, and the Microsoft MR device HoloLens-2 was used as the instructional platform. First, the parameters of the robots were analyzed based on their structural drawings. Then, the robot modules were decomposed, and 1:1 three-dimensional (3D) digital reproductions were created in… More >

  • Open Access

    ARTICLE

    A Block Cipher Algorithm Based on Magic Square for Secure E-bank Systems

    Farah Tawfiq Abdul Hussien*, Abdul Monem S. Rahma, Hala Bahjat Abdul Wahab
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1329-1346, 2022, DOI:10.32604/cmc.2022.027582
    Abstract Nowadays the E-bank systems witnessed huge growth due to the huge developments in the internet and technologies. The transmitted information represents crucial information that is exposed to various kinds of attacks. This paper presents a new block cipher technique to provide security to the transmitted information between the customers and the e-bank systems. The proposed algorithm consists of 10 rounds, each round involves 5 operations. The operations involve Add round key, Sub bytes, Zigzag method, convert to vector, and Magic Square of order 11. The purpose of this algorithm is to make use of the complexity of the Magic Square… More >

  • Open Access

    ARTICLE

    Multiple Forgery Detection in Video Using Convolution Neural Network

    Vinay Kumar1,*, Vineet Kansal2, Manish Gaur2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1347-1364, 2022, DOI:10.32604/cmc.2022.023545
    Abstract With the growth of digital media data manipulation in today’s era due to the availability of readily handy tampering software, the authenticity of records is at high risk, especially in video. There is a dire need to detect such problem and do the necessary actions. In this work, we propose an approach to detect the interframe video forgery utilizing the deep features obtained from the parallel deep neural network model and thorough analytical computations. The proposed approach only uses the deep features extracted from the CNN model and then applies the conventional mathematical approach to these features to find the… More >

  • Open Access

    ARTICLE

    A Hybrid Neural Network-based Approach for Forecasting Water Demand

    Al-Batool Al-Ghamdi1,*, Souad Kamel2, Mashael Khayyat3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1365-1383, 2022, DOI:10.32604/cmc.2022.026246
    Abstract Water is a vital resource. It supports a multitude of industries, civilizations, and agriculture. However, climatic conditions impact water availability, particularly in desert areas where the temperature is high, and rain is scarce. Therefore, it is crucial to forecast water demand to provide it to sectors either on regular or emergency days. The study aims to develop an accurate model to forecast daily water demand under the impact of climatic conditions. This forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the future. Focusing on the… More >

  • Open Access

    ARTICLE

    PoEC: A Cross-Blockchain Consensus Mechanism for Governing Blockchain by Blockchain

    Jieren Cheng1,3, Yuan Zhang2,3,*, Yuming Yuan4, Hui Li4, Xiangyan Tang1,3, Victor S. Sheng5, Guangjing Hu1,3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1385-1402, 2022, DOI:10.32604/cmc.2022.026437
    Abstract The research on the governing blockchain by blockchain supervision system is an important development trend of blockchain technology. In this system there is a supervisory blockchain managing and governing the supervised blockchain based on blockchain technology, results in a uniquely cross-blockchain demand to consensus mechanism for solving the trust problem between supervisory blockchain and supervised blockchain. To solve this problem, this paper proposes a cross-blockchain consensus mechanism based on smart contract and a set of smart contracts endorse the cross-blockchain consensus. New consensus mechanism called Proof-of-Endorse-Contracts (PoEC) consensus, which firstly transfers the consensus reached in supervisory blockchain to supervised blockchain… More >

  • Open Access

    ARTICLE

    Dynamic Threshold-Based Approach to Detect Low-Rate DDoS Attacks on Software-Defined Networking Controller

    Mohammad Adnan Aladaileh, Mohammed Anbar*, Iznan H. Hasbullah, Abdullah Ahmed Bahashwan, Shadi Al-Sarawn
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1403-1416, 2022, DOI:10.32604/cmc.2022.029369
    Abstract The emergence of a new network architecture, known as Software Defined Networking (SDN), in the last two decades has overcome some drawbacks of traditional networks in terms of performance, scalability, reliability, security, and network management. However, the SDN is vulnerable to security threats that target its controller, such as low-rate Distributed Denial of Service (DDoS) attacks, The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component. Therefore, there is an urgent need to propose a detection approach for this type of attack… More >

  • Open Access

    ARTICLE

    Text-Independent Algorithm for Source Printer Identification Based on Ensemble Learning

    Naglaa F. El Abady1,*, Mohamed Taha1, Hala H. Zayed1,2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1417-1436, 2022, DOI:10.32604/cmc.2022.028044
    Abstract Because of the widespread availability of low-cost printers and scanners, document forgery has become extremely popular. Watermarks or signatures are used to protect important papers such as certificates, passports, and identification cards. Identifying the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s world. Source printer identification (SPI) has become increasingly popular for identifying frauds in printed documents. This paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes. A dataset of 1200 papers from 20 distinct (13)… More >

  • Open Access

    ARTICLE

    Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images

    Mohammad Alamgeer1, Hanan Abdullah Mengash2, Radwa Marzouk2, Mohamed K Nour3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4, Abu Sarwar Zamani4, Mohammed Rizwanullah4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1437-1448, 2022, DOI:10.32604/cmc.2022.027896
    Abstract Early detection of lung cancer can help for improving the survival rate of the patients. Biomedical imaging tools such as computed tomography (CT) image was utilized to the proper identification and positioning of lung cancer. The recently developed deep learning (DL) models can be employed for the effectual identification and classification of diseases. This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image, named DLCADLC-BCT technique. The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images. The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix (GLCM) model for feature… More >

  • Open Access

    ARTICLE

    Deep-sea Nodule Mineral Image Segmentation Algorithm Based on Pix2PixHD

    Wei Song1,2,3, Haolin Wang1, Xinping Zhang1, Jianxin Xia4,*, Tongmu Liu5, Yuxi Shi6
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1449-1462, 2022, DOI:10.32604/cmc.2022.027213
    Abstract Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and evaluation. The application of artificial intelligence technology to deep-sea mining projects can effectively improve the quality and efficiency of mining. The existing deep learning-based underwater image segmentation algorithms have problems such as the accuracy rate is not high enough and the running time is slightly longer. In order to improve the segmentation performance of underwater mineral images, this paper uses the Pix2PixHD (Pixel to Pixel High Definition) algorithm based on Conditional Generative Adversarial Network (CGAN) to segment deep-sea mineral images. The model uses… More >

  • Open Access

    ARTICLE

    Explainable Software Fault Localization Model: From Blackbox to Whitebox

    Abdulaziz Alhumam*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1463-1482, 2022, DOI:10.32604/cmc.2022.029473
    Abstract The most resource-intensive and laborious part of debugging is finding the exact location of the fault from the more significant number of code snippets. Plenty of machine intelligence models has offered the effective localization of defects. Some models can precisely locate the faulty with more than 95% accuracy, resulting in demand for trustworthy models in fault localization. Confidence and trustworthiness within machine intelligence-based software models can only be achieved via explainable artificial intelligence in Fault Localization (XFL). The current study presents a model for generating counterfactual interpretations for the fault localization model's decisions. Neural system approximations and disseminated presentation of… More >

  • Open Access

    ARTICLE

    Efficient Image Captioning Based on Vision Transformer Models

    Samar Elbedwehy1,*, T. Medhat2, Taher Hamza3, Mohammed F. Alrahmawy3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1483-1500, 2022, DOI:10.32604/cmc.2022.029313
    Abstract Image captioning is an emerging field in machine learning. It refers to the ability to automatically generate a syntactically and semantically meaningful sentence that describes the content of an image. Image captioning requires a complex machine learning process as it involves two sub models: a vision sub-model for extracting object features and a language sub-model that use the extracted features to generate meaningful captions. Attention-based vision transformers models have a great impact in vision field recently. In this paper, we studied the effect of using the vision transformers on the image captioning process by evaluating the use of four different… More >

  • Open Access

    ARTICLE

    Modified Anam-Net Based Lightweight Deep Learning Model for Retinal Vessel Segmentation

    Syed Irtaza Haider1, Khursheed Aurangzeb2,*, Musaed Alhussein2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1501-1526, 2022, DOI:10.32604/cmc.2022.025479
    Abstract The accurate segmentation of retinal vessels is a challenging task due to the presence of various pathologies as well as the low-contrast of thin vessels and non-uniform illumination. In recent years, encoder-decoder networks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we propose a lightweight convolutional neural network (CNN)-based encoder-decoder deep learning model for accurate retinal vessels segmentation. The proposed deep learning model consists of encoder-decoder architecture along with bottleneck layers that consist of depth-wise squeezing, followed by full-convolution, and finally depth-wise… More >

  • Open Access

    ARTICLE

    Fine-grained Ship Image Recognition Based on BCNN with Inception and AM-Softmax

    Zhilin Zhang1, Ting Zhang1, Zhaoying Liu1,*, Peijie Zhang1, Shanshan Tu1, Yujian Li2, Muhammad Waqas3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1527-1539, 2022, DOI:10.32604/cmc.2022.029297
    Abstract The fine-grained ship image recognition task aims to identify various classes of ships. However, small inter-class, large intra-class differences between ships, and lacking of training samples are the reasons that make the task difficult. Therefore, to enhance the accuracy of the fine-grained ship image recognition, we design a fine-grained ship image recognition network based on bilinear convolutional neural network (BCNN) with Inception and additive margin Softmax (AM-Softmax). This network improves the BCNN in two aspects. Firstly, by introducing Inception branches to the BCNN network, it is helpful to enhance the ability of extracting comprehensive features from ships. Secondly, by adding… More >

  • Open Access

    ARTICLE

    Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

    K. Pradeep Mohan Kumar1, S. Dhanasekaran2, I. S. Hephzi Punithavathi3, P. Duraipandy4, Ashit Kumar Dutta5, Irina V. Pustokhina6,*, Denis A. Pustokhin7
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1541-1555, 2022, DOI:10.32604/cmc.2022.028338
    Abstract Financial crisis prediction (FCP) models are used for predicting or forecasting the financial status of a company or financial firm. It is considered a challenging issue in the financial sector. Statistical and machine learning (ML) models can be employed for the design of accurate FCP models. Though numerous works have existed in the literature, it is needed to design effective FCP models adaptable to different datasets. This study designs a new bird swarm algorithm (BSA) with fuzzy min-max neural network (FMM-NN) model, named BSA-FMMNN for FCP. The major intention of the BSA-FMMNN model is to determine the financial status of… More >

  • Open Access

    ARTICLE

    Metaheuristics with Machine Learning Enabled Information Security on Cloud Environment

    Haya Mesfer Alshahrani1, Faisal S. Alsubaei2, Taiseer Abdalla Elfadil Eisa3, Mohamed K. Nour4, Manar Ahmed Hamza5,*, Abdelwahed Motwakel5, Abu Sarwar Zamani5, Ishfaq Yaseen5
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1557-1570, 2022, DOI:10.32604/cmc.2022.027135
    Abstract The increasing quantity of sensitive and personal data being gathered by data controllers has raised the security needs in the cloud environment. Cloud computing (CC) is used for storing as well as processing data. Therefore, security becomes important as the CC handles massive quantity of outsourced, and unprotected sensitive data for public access. This study introduces a novel chaotic chimp optimization with machine learning enabled information security (CCOML-IS) technique on cloud environment. The proposed CCOML-IS technique aims to accomplish maximum security in the CC environment by the identification of intrusions or anomalies in the network. The proposed CCOML-IS technique primarily… More >

  • Open Access

    ARTICLE

    Coverless Video Steganography Based on Frame Sequence Perceptual Distance Mapping

    Runze Li1, Jiaohua Qin1,*, Yun Tan1, Neal N. Xiong2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1571-1583, 2022, DOI:10.32604/cmc.2022.029378
    Abstract Most existing coverless video steganography algorithms use a particular video frame for information hiding. These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness. We propose a coverless video steganography method based on frame sequence perceptual distance mapping. In this method, we introduce Learned Perceptual Image Patch Similarity (LPIPS) to quantify the similarity between consecutive video frames to obtain the sequential features of the video. Then we establish the relationship map between features and the hash sequence for information hiding. In addition, the MongoDB database is used to store… More >

  • Open Access

    ARTICLE

    FPGA Implementation of 5G NR Primary and Secondary Synchronization

    Aytha Ramesh Kumar1,*, K. Lal Kishore2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1585-1600, 2022, DOI:10.32604/cmc.2022.021573
    Abstract The 5G communication systems are widely established for high-speed data processing to meet users demands. The 5G New Radio (NR) communications comprise a network of ultra-low latency, high processing speeds, high throughput and rapid synchronization with a time frame of 10 ms. Synchronization between User Equipment (UE) and 5G base station known as gNB is a fundamental procedure in a cellular system and it is performed by a synchronization signal. In 5G NR system, Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS) are used to detect the best serving base station with the help of a cell search procedure.… More >

  • Open Access

    ARTICLE

    Mutation Prediction for Coronaviruses Using Genome Sequence and Recurrent Neural Networks

    Pranav Pushkar1, Christo Ananth2, Preeti Nagrath1, Jehad F. Al-Amri5, Vividha1, Anand Nayyar3,4,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1601-1619, 2022, DOI:10.32604/cmc.2022.026205
    Abstract The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community. The recent ongoing SARS-Cov2 (Severe Acute Respiratory Syndrome) pandemic proved the unpreparedness for these situations. Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently. One major way to find out more information about such pathogens is by extracting the genetic data of such viruses. Though genetic data of viruses have been cultured and stored as… More >

  • Open Access

    ARTICLE

    Resource Scheduling Strategy for Performance Optimization Based on Heterogeneous CPU-GPU Platform

    Juan Fang1,*, Kuan Zhou1, Mengyuan Zhang1, Wei Xiang2,3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1621-1635, 2022, DOI:10.32604/cmc.2022.027147
    Abstract In recent years, with the development of processor architecture, heterogeneous processors including Center processing unit (CPU) and Graphics processing unit (GPU) have become the mainstream. However, due to the differences of heterogeneous core, the heterogeneous system is now facing many problems that need to be solved. In order to solve these problems, this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies. To improve the performance of the system, this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks. The combination strategy… More >

  • Open Access

    ARTICLE

    A Novel Peak-to-Average Power Ratio Reduction for 5G Advanced Waveforms

    Rajneesh Pareek1, Karthikeyan Rajagopal2, Himanshu Sharma1, Nidhi Gour1, Arun Kumar3, Sami Althahabi4, Haya Mesfer Alshahrani5, Mohamed Mousa6, Manar Ahmed Hamza7,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1637-1648, 2022, DOI:10.32604/cmc.2022.029563
    Abstract Multi and single carrier waveforms are utilized in cellular systems for high-speed data transmission. In The Fifth Generation (5G) system, several waveform techniques based on multi carrier waveforms are proposed. However, the Peak to Average Power Ratio (PAPR) is seen as one of the significant concerns in advanced waveforms as it degrades the efficiency of the framework. The proposed article documents the study, progress, and implementation of PAPR reduction algorithms for the 5G radio framework. We compare the PAPR algorithm performance for advanced and conventional waveforms. The simulation results reveal that the advanced Partial Transmission Sequence (PTS) and Selective Mapping… More >

  • Open Access

    ARTICLE

    Review of Nodule Mineral Image Segmentation Algorithms for Deep-Sea Mineral Resource Assessment

    Wei Song1,2,3, Lihui Dong1, Xiaobing Zhao1,3, Jianxin Xia4,*, Tongmu Liu5, Yuxi Shi6
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1649-1669, 2022, DOI:10.32604/cmc.2022.027214
    Abstract A large number of nodule minerals exist in the deep sea. Based on the factors of difficulty in shooting, high economic cost and high accuracy of resource assessment, large-scale planned commercial mining has not yet been conducted. Only experimental mining has been carried out in areas with high mineral density and obvious benefits after mineral resource assessment. As an efficient method for deep-sea mineral resource assessment, the deep towing system is equipped with a visual system for mineral resource analysis using collected images and videos, which has become a key component of resource assessment. Therefore, high accuracy in deep-sea mineral… More >

  • Open Access

    ARTICLE

    Analyzing and Enabling the Harmonious Coexistence of Heterogeneous Industrial Wireless Networks

    Bilal Khan1, Danish Shehzad1, Numan Shafi1, Ga-Young Kim2,*, Muhammad Umar Aftab1
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1671-1690, 2022, DOI:10.32604/cmc.2022.024918
    Abstract Nowadays multiple wireless communication systems operate in industrial environments side by side. In such an environment performance of one wireless network can be degraded by the collocated hostile wireless network having higher transmission power or higher carrier sensing threshold. Unlike the previous research works which considered IEEE 802.15.4 for the Industrial Wireless communication systems (iWCS) this paper examines the coexistence of IEEE 802.11 based iWCS used for delay-stringent communication in process automation and gWLAN (general-purpose WLAN) used for non-real time communication. In this paper, we present a Markov chain-based performance model that described the transmission failure of iWCS due to… More >

  • Open Access

    ARTICLE

    Multilevel Modelling for Surgical Tool Calibration Using LINEX Loss Function

    Mansour F. Yassen1,2,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1691-1706, 2022, DOI:10.32604/cmc.2022.029701
    Abstract Quantifying the tool–tissue interaction forces in surgery can be utilized in the training of inexperienced surgeons, assist them better use surgical tools and avoid applying excessive pressures. The voltages read from strain gauges are used to approximate the unknown values of implemented forces. To this objective, the force-voltage connection must be quantified in order to evaluate the interaction forces during surgery. The progress of appropriate statistical learning approaches to describe the link between the genuine force applied on the tissue and numerous outputs obtained from sensors installed on surgical equipment is a key problem. In this study, different probabilistic approaches… More >

  • Open Access

    ARTICLE

    On-line Recognition of Abnormal Patterns in Bivariate Autocorrelated Process Using Random Forest

    Miao Xu1, Bo Zhu1,*, Chunmei Chen1, Yuwei Wan2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1707-1722, 2022, DOI:10.32604/cmc.2022.027708
    Abstract It is not uncommon that two or more related process quality characteristics are needed to be monitored simultaneously in production process for most of time. Meanwhile, the observations obtained online are often serially autocorrelated due to high sampling frequency and process dynamics. This goes against the statistical I.I.D assumption in using the multivariate control charts, which may lead to the performance of multivariate control charts collapse soon. Meanwhile, the process control method based on pattern recognition as a non-statistical approach is not confined by this limitation, and further provide more useful information for quality practitioners to locate the assignable causes… More >

  • Open Access

    ARTICLE

    Secure Dengue Epidemic Prediction System: Healthcare Perspective

    Abdulaziz Aldaej*, Tariq Ahamed Ahanger, Mohammed Yousuf Uddin, Imdad Ullah
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1723-1745, 2022, DOI:10.32604/cmc.2022.027487
    Abstract Viral diseases transmitted by mosquitoes are emerging public health problems across the globe. Dengue is considered to be the most significant mosquito-oriented disease. Conspicuously, the present study provides an effective architecture for Dengue Virus Infection surveillance. The proposed system involves a 4-level architecture for the prediction and prevention of dengue infection outspread. The architectural levels including Dengue Information Acquisition level, Dengue Information Classification level, Dengue-Mining and Extraction level, and Dengue-Prediction and Decision Modeling level enable an individual to periodically monitor his/her probabilistic dengue fever measure. The prediction process is carried out so that proactive measures are taken beforehand. For predictive… More >

  • Open Access

    ARTICLE

    MagneFi: Multiuser, Multi-Building and Multi-Floor Geomagnetic Field Dataset for Indoor Positioning

    Imran Ashraf1, Muhammad Usman Ali2, Soojung Hur1, Gunzung Kim1, Yongwan Park1,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1747-1768, 2022, DOI:10.32604/cmc.2022.020610
    Abstract Indoor positioning and localization have emerged as a potential research area during the last few years owing to the wide proliferation of smartphones and the inception of location-attached services for the consumer industry. Due to the importance of precise location information, several positioning technologies are adopted such as Wi-Fi, ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, etc. Although Wi-Fi and magnetic field-based positioning are more attractive concerning the deployment of Wi-Fi access points and ubiquity of magnetic field data, the latter is preferred as it does not require any additional infrastructure as other approaches… More >

  • Open Access

    ARTICLE

    Unsupervised Graph-Based Tibetan Multi-Document Summarization

    Xiaodong Yan1,2, Yiqin Wang1,2, Wei Song1,2,*, Xiaobing Zhao1,2, A. Run3, Yang Yanxing4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1769-1781, 2022, DOI:10.32604/cmc.2022.027301
    Abstract Text summarization creates subset that represents the most important or relevant information in the original content, which effectively reduce information redundancy. Recently neural network method has achieved good results in the task of text summarization both in Chinese and English, but the research of text summarization in low-resource languages is still in the exploratory stage, especially in Tibetan. What’s more, there is no large-scale annotated corpus for text summarization. The lack of dataset severely limits the development of low-resource text summarization. In this case, unsupervised learning approaches are more appealing in low-resource languages as they do not require labeled data.… More >

  • Open Access

    ARTICLE

    Improved Harmony Search with Optimal Deep Learning Enabled Classification Model

    Mahmoud Ragab1,2,3,*, Adel A. Bahaddad4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1783-1797, 2022, DOI:10.32604/cmc.2022.028055
    Abstract Due to drastic increase in the generation of data, it is tedious to examine and derive high level knowledge from the data. The rising trends of high dimension data gathering and problem representation necessitates feature selection process in several machine learning processes. The feature selection procedure establishes a generally encountered issue of global combinatorial optimization. The FS process can lessen the number of features by the removal of unwanted and repetitive data. In this aspect, this article introduces an improved harmony search based global optimization for feature selection with optimal deep learning (IHSFS-ODL) enabled classification model. The proposed IHSFS-ODL technique… More >

  • Open Access

    ARTICLE

    Energy Price Forecasting Through Novel Fuzzy Type-1 Membership Functions

    Muhammad Hamza Azam1, Mohd Hilmi Hasan1,*, Azlinda A Malik2, Saima Hassan3, Said Jadid Abdulkadir1
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1799-1815, 2022, DOI:10.32604/cmc.2022.028292
    Abstract Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future prices. Electricity price forecasting have been a critical input to energy corporations’ strategic decision-making systems over the last 15 years. Many strategies have been utilized for price forecasting in the past, however Artificial Intelligence Techniques (Fuzzy Logic and ANN) have proven to be more efficient than traditional techniques (Regression and Time Series). Fuzzy logic is an approach that uses membership functions (MF) and fuzzy inference model to forecast future electricity prices. Fuzzy c-means (FCM) is one of the popular… More >

  • Open Access

    ARTICLE

    Speech Encryption with Fractional Watermark

    Yan Sun1,2,*, Cun Zhu1, Qi Cui3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1817-1825, 2022, DOI:10.32604/cmc.2022.029408
    Abstract Research on the feature of speech and image signals are carried out from two perspectives, the time domain and the frequency domain. The speech and image signals are a non-stationary signal, so FT is not used for the non-stationary characteristics of the signal. When short-term stable speech is obtained by windowing and framing the subsequent processing of the signal is completed by the Discrete Fourier Transform (DFT). The Fast Discrete Fourier Transform is a commonly used analysis method for speech and image signal processing in frequency domain. It has the problem of adjusting window size to a for desired resolution.… More >

  • Open Access

    ARTICLE

    Deep Learning Based Classification of Wrist Cracks from X-ray Imaging

    Jahangir Jabbar1, Muzammil Hussain2, Hassaan Malik2,*, Abdullah Gani3, Ali Haider Khan2, Muhammad Shiraz4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1827-1844, 2022, DOI:10.32604/cmc.2022.024965
    Abstract Wrist cracks are the most common sort of cracks with an excessive occurrence rate. For the routine detection of wrist cracks, conventional radiography (X-ray medical imaging) is used but periodically issues are presented by crack depiction. Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping. Indeed, many hospitals lack experienced clinicians to diagnose wrist cracks. Therefore, an automated system is required to reduce the burden on clinicians and identify cracks. In this study, we have designed a novel residual network-based convolutional neural network (CNN) for the crack detection of the wrist. For the… More >

  • Open Access

    ARTICLE

    Enhanced Robotic Vision System Based on Deep Learning and Image Fusion

    E. A. Alabdulkreem1, Ahmed Sedik2, Abeer D. Algarni3,*, Ghada M. El Banby4, Fathi E. Abd El-Samie3,5, Naglaa F. Soliman3,6
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1845-1861, 2022, DOI:10.32604/cmc.2022.023905
    Abstract Image fusion has become one of the interesting fields that attract researchers to integrate information from different image sources. It is involved in several applications. One of the recent applications is the robotic vision. This application necessitates image enhancement of both infrared (IR) and visible images. This paper presents a Robot Human Interaction System (RHIS) based on image fusion and deep learning. The basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured images. Then, an enhancement model is carried out on the fused image to increase its quality. Several image… More >

  • Open Access

    ARTICLE

    Optimal Beamforming for Secure Transmit in Practical Wireless Networks

    Qiuqin Yang1, Linfang Li1, Ming-Xing Luo1,*, Xiaojun Wang2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1863-1877, 2022, DOI:10.32604/cmc.2022.027120
    Abstract In real communication systems, secure and low-energy transmit scheme is very important. So far, most of schemes focus on secure transmit in special scenarios. In this paper, our goal is to propose a secure protocol in wireless networks involved various factors including artificial noise (AN), the imperfect receiver and imperfect channel state information (CSI) of eavesdropper, weight of beamforming (BF) vector, cooperative jammers (CJ), multiple receivers, and multiple eavesdroppers, and the analysis shows that the protocol can reduce the transmission power, and at the same time the safe reachability rate is greater than our pre-defined value, and the analysis results… More >

  • Open Access

    ARTICLE

    FPGA Implementation of Elliptic-Curve Diffie Hellman Protocol

    Sikandar Zulqarnain Khan1,*, Sajjad Shaukat Jamal2, Asher Sajid3, Muhammad Rashid4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1879-1894, 2022, DOI:10.32604/cmc.2022.028152
    Abstract This paper presents an efficient crypto processor architecture for key agreement using ECDH (Elliptic-curve Diffie Hellman) protocol over . The composition of our key-agreement architecture is expressed in consisting of the following: (i) Elliptic-curve Point Multiplication architecture for public key generation (DESIGN-I) and (ii) integration of DESIGN-I with two additional routing multiplexers and a controller for shared key generation (DESIGN-II). The arithmetic operators used in DESIGN-I and DESIGN-II contain an adder, squarer, a multiplier and inversion. A simple shift and add multiplication method is employed to retain lower hardware resources. Moreover, an essential inversion operation is operated using the Itoh-Tsujii… More >

  • Open Access

    ARTICLE

    Rice Disease Diagnosis System (RDDS)

    Sandhya Venu Vasantha1, Shirina Samreen2,*, Yelganamoni Lakshmi Aparna3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1895-1914, 2022, DOI:10.32604/cmc.2022.028504
    Abstract Hitherto, Rice (Oryza Sativa) has been one of the most demanding food crops in the world, cultivated in larger quantities, but loss in both quality and quantity of yield due to abiotic and biotic stresses has become a major concern. During cultivation, the crops are most prone to biotic stresses such as bacterial, viral, fungal diseases and pests. These stresses can drastically damage the crop. Lately and erroneously recognized crop diseases can increase fertilizers costs and major yield loss which results in high financial loss and adverse impact on nation’s economy. The proven methods of molecular biology can provide accurate… More >

  • Open Access

    ARTICLE

    Impact of Accumulated Temperature on Wetland Vegetation Area in Poyang Lake

    Xin Yao1, Junyu Zhu1, Hong Zeng2, Wenzheng Yu1,*, Hanxiaoya Zhang3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1915-1926, 2022, DOI:10.32604/cmc.2022.026777
    Abstract Accumulated temperature, which is now widely used in agronomy, is an important ecological factor to the growth of plants, but few relative studies have been found on the vegetation area of floodplain grasslands in Poyang Lake. This research used the classification and regression tree (CART) to classify normalized vegetation area index derived from MODIS LAI (Moderate Resolution Imaging Spectroradiometer Leaf Area Index) images from 2008 to 2014, according to different climate indexes, such as mean daily air temperature (n), accumulated temperature (jw), daily maximum temperature (g), daily minimum temperature (d), accumulative precipitation (j), water level (s) and average water level… More >

  • Open Access

    ARTICLE

    Content Based Automated File Organization Using Machine Learning Approaches

    Syed Ali Raza1,2, Sagheer Abbas1, Taher M. Ghazal3,4, Muhammad Adnan Khan5,6, Munir Ahmad1, Hussam Al Hamadi7,*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1927-1942, 2022, DOI:10.32604/cmc.2022.029400
    Abstract In the world of big data, it's quite a task to organize different files based on their similarities. Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user. Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities. Then, the browsing process can be performed quickly and easily. This research aims to design a system to automatically organize files based on their similarities in terms of content. The proposed… More >

  • Open Access

    ARTICLE

    P-ACOHONEYBEE: A Novel Load Balancer for Cloud Computing Using Mathematical Approach

    Sunday Adeola Ajagbe1, Mayowa O. Oyediran2, Anand Nayyar3,*, Jinmisayo A. Awokola4, Jehad F. Al-Amri5
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1943-1959, 2022, DOI:10.32604/cmc.2022.028331
    Abstract Cloud computing is a collection of disparate resources or services, a web of massive infrastructures, which is aimed at achieving maximum utilization with higher availability at a minimized cost. One of the most attractive applications for cloud computing is the concept of distributed information processing. Security, privacy, energy saving, reliability and load balancing are the major challenges facing cloud computing and most information technology innovations. Load balancing is the process of redistributing workload among all nodes in a network; to improve resource utilization and job response time, while avoiding overloading some nodes when other nodes are underloaded or idle is… More >

  • Open Access

    ARTICLE

    Computer Vision Technology for Fault Detection Systems Using Image Processing

    Abed Saif Alghawli*
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1961-1976, 2022, DOI:10.32604/cmc.2022.028990
    Abstract In the period of Industries 4.0, cyber-physical systems (CPSs) were a major study area. Such systems frequently occur in manufacturing processes and people’s everyday lives, and they communicate intensely among physical elements and lead to inconsistency. Due to the magnitude and importance of the systems they support, the cyber quantum models must function effectively. In this paper, an image-processing-based anomalous mobility detecting approach is suggested that may be added to systems at any time. The expense of glitches, failures or destroyed products is decreased when anomalous activities are detected and unplanned scenarios are avoided. The presently offered techniques are not… More >

  • Open Access

    ARTICLE

    High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection

    Krishan Kumar1,*, Mohamed Abouhawwash2,3, Amit Kant Pandit1, Shubham Mahajan1, Mofreh A. Hogo4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1977-1993, 2022, DOI:10.32604/cmc.2022.027850
    Abstract The high-efficiency video coder (HEVC) is one of the most advanced techniques used in growing real-time multimedia applications today. However, they require large bandwidth for transmission through bandwidth, and bandwidth varies with different video sequences/formats. This paper proposes an adaptive information-based variable quantization matrix (AI-VQM) developed for different video formats having variable energy levels. The quantization method is adapted based on video sequence using statistical analysis, improving bit budget, quality and complexity reduction. Further, to have precise control over bit rate and quality, a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K… More >

  • Open Access

    ARTICLE

    Automated Machine Learning for Epileptic Seizure Detection Based on EEG Signals

    Jian Liu1, Yipeng Du1, Xiang Wang1,*, Wuguang Yue2, Jim Feng3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1995-2011, 2022, DOI:10.32604/cmc.2022.029073
    Abstract Epilepsy is a common neurological disease and severely affects the daily life of patients. The automatic detection and diagnosis system of epilepsy based on electroencephalogram (EEG) is of great significance to help patients with epilepsy return to normal life. With the development of deep learning technology and the increase in the amount of EEG data, the performance of deep learning based automatic detection algorithm for epilepsy EEG has gradually surpassed the traditional hand-crafted approaches. However, the neural architecture design for epilepsy EEG analysis is time-consuming and laborious, and the designed structure is difficult to adapt to the changing EEG collection… More >

  • Open Access

    ARTICLE

    Diabetes Prediction Using Derived Features and Ensembling of Boosting Classifiers

    R. Rajkamal1,*, Anitha Karthi2, Xiao-Zhi Gao3
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2013-2033, 2022, DOI:10.32604/cmc.2022.027142
    Abstract Diabetes is increasing commonly in people’s daily life and represents an extraordinary threat to human well-being. Machine Learning (ML) in the healthcare industry has recently made headlines. Several ML models are developed around different datasets for diabetic prediction. It is essential for ML models to predict diabetes accurately. Highly informative features of the dataset are vital to determine the capability factors of the model in the prediction of diabetes. Feature engineering (FE) is the way of taking forward in yielding highly informative features. Pima Indian Diabetes Dataset (PIDD) is used in this work, and the impact of informative features in… More >

  • Open Access

    ARTICLE

    Application of the Fictitious Domain Method for Navier-Stokes Equations

    Almas Temirbekov1, Zhadra Zhaksylykova2,*, Yerzhan Malgazhdarov3, Syrym Kasenov1
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2035-2055, 2022, DOI:10.32604/cmc.2022.027830
    Abstract To apply the fictitious domain method and conduct numerical experiments, a boundary value problem for an ordinary differential equation is considered. The results of numerical calculations for different values of the iterative parameter τ and the small parameter ε are presented. A study of the auxiliary problem of the fictitious domain method for Navier-Stokes equations with continuation into a fictitious subdomain by higher coefficients with a small parameter is carried out. A generalized solution of the auxiliary problem of the fictitious domain method with continuation by higher coefficients with a small parameter is determined. After all the above mathematical studies,… More >

  • Open Access

    ARTICLE

    Whale Optimization Algorithm Strategies for Higher Interaction Strength T-Way Testing

    Ali Abdullah Hassan1,*, Salwani Abdullah1, Kamal Z. Zamli2, Rozilawati Razali1
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2057-2077, 2022, DOI:10.32604/cmc.2022.026310
    Abstract Much of our daily tasks have been computerized by machines and sensors communicating with each other in real-time. There is a reasonable risk that something could go wrong because there are a lot of sensors producing a lot of data. Combinatorial testing (CT) can be used in this case to reduce risks and ensure conformance to specifications. Numerous existing meta-heuristic-based solutions aim to assist the test suite generation for combinatorial testing, also known as t-way testing (where t indicates the interaction strength), viewed as an optimization problem. Much previous research, while helpful, only investigated a small number of interaction strengths… More >

  • Open Access

    ARTICLE

    Optimizing Fresh Logistics Distribution Route Based on Improved Ant Colony Algorithm

    Daqing Wu1,2, Ziwei Zhu1, Dong Hu3,*, Romany Fouad Mansour4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2079-2095, 2022, DOI:10.32604/cmc.2022.027794
    Abstract With the rapid development of the fresh cold chain logistics distribution and the prevalence of low carbon concept, this paper proposed an optimization model of low carbon fresh cold chain logistics distribution route considering customer satisfaction, and combined with time, space, weight, distribution rules and other constraints to optimize the distribution model. At the same time, transportation cost, penalty cost, overloading cost, carbon tax cost and customer satisfaction were considered as the components of the objective function, and the thought of cost efficiency was taken into account, so as to establish a distribution model based on the ratio of minimum… More >

  • Open Access

    ARTICLE

    Fast and Efficient Security Scheme for Blockchain-Based IoT Networks

    K. A. Fasila*, Sheena Mathew
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2097-2114, 2022, DOI:10.32604/cmc.2022.029637
    Abstract

    Internet of Things (IoT) has become widely used nowadays and tremendous increase in the number of users raises its security requirements as well. The constraints on resources such as low computational capabilities and power requirements demand lightweight cryptosystems. Conventional algorithms are not applicable in IoT network communications because of the constraints mentioned above. In this work, a novel and efficient scheme for providing security in IoT applications is introduced. The scheme proposes how security can be enhanced in a distributed IoT application by providing multilevel protection and dynamic key generation in the data uploading and transfer phases. Existing works rely… More >

  • Open Access

    ARTICLE

    A Novel Method for Thermoelectric Generator Based on Neural Network

    Mohammad Saraireh1,*, A. M. Maqableh2, Manar Jaradat3, Omar A. Saraereh4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2115-2133, 2022, DOI:10.32604/cmc.2022.029978
    Abstract The growing need for renewable energy and zero carbon dioxide emissions has fueled the development of thermoelectric generators with improved power generating capability. Along with the endeavor to develop thermoelectric materials with greater figures of merit, the geometrical and structural optimization of thermoelectric generators is equally critical for maximum power output and efficiency. Green energy strategies that are constantly updated are a viable option for addressing the global energy issue while also protecting the environment. There have been significant focuses on the development of thermoelectric modules for a range of solar, automotive, military, and aerospace applications in recent years due… More >

  • Open Access

    ARTICLE

    CNTFET Based Grounded Active Inductor for Broadband Applications

    Muhammad I. Masud1,2,*, Nasir Shaikh-Husin2, Iqbal A. Khan1, Abu K. Bin A’Ain2
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2135-2149, 2022, DOI:10.32604/cmc.2022.026831
    Abstract A new carbon nanotube field effect transistor (CNTFET) based grounded active inductor (GAI) circuit is presented in this work. The suggested GAI offers a tunable inductance with a very wide inductive bandwidth, high quality factor (QF) and low power dissipation. The tunability of the realized circuit is achieved through CNTFET based varactor. The proposed topology shows inductive behavior in the frequency range of 0.1–101 GHz and achieves to a maximum QF of 9125. The GAI operates at 0.7 V with 0.337 mW of power consumption. To demonstrate the performance of GAI, a broadband low noise amplifier (LNA) circuit is designed by utilizing… More >

  • Open Access

    ARTICLE

    CNN-BiLSTM-Attention Model in Forecasting Wave Height over South-East China Seas

    Lina Wang1,2,*, Xilin Deng1, Peng Ge1, Changming Dong2,3, Brandon J. Bethel3, Leqing Yang1, Jinyue Xia4
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2151-2168, 2022, DOI:10.32604/cmc.2022.027415
    Abstract Though numerical wave models have been applied widely to significant wave height prediction, they consume massive computing memory and their accuracy needs to be further improved. In this paper, a two-dimensional (2D) significant wave height (SWH) prediction model is established for the South and East China Seas. The proposed model is trained by Wave Watch III (WW3) reanalysis data based on a convolutional neural network, the bi-directional long short-term memory and the attention mechanism (CNN-BiLSTM-Attention). It adopts the convolutional neural network to extract spatial features of original wave height to reduce the redundant information input into the BiLSTM network. Meanwhile,… More >

  • Open Access

    ARTICLE

    Hyper-Parameter Optimization of Semi-Supervised GANs Based-Sine Cosine Algorithm for Multimedia Datasets

    Anas Al-Ragehi1, Said Jadid Abdulkadir1,2,*, Amgad Muneer1,2, Safwan Sadeq3, Qasem Al-Tashi4,5
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2169-2186, 2022, DOI:10.32604/cmc.2022.027885
    Abstract Generative Adversarial Networks (GANs) are neural networks that allow models to learn deep representations without requiring a large amount of training data. Semi-Supervised GAN Classifiers are a recent innovation in GANs, where GANs are used to classify generated images into real and fake and multiple classes, similar to a general multi-class classifier. However, GANs have a sophisticated design that can be challenging to train. This is because obtaining the proper set of parameters for all models-generator, discriminator, and classifier is complex. As a result, training a single GAN model for different datasets may not produce satisfactory results. Therefore, this study… More >

  • Open Access

    ARTICLE

    Voice to Face Recognition Using Spectral ERB-DMLP Algorithms

    Fauzi A. Bala1,2,*, Osman N. Ucan1, Oguz Bayat1
    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2187-2204, 2022, DOI:10.32604/cmc.2022.024205
    Abstract Designing an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented equipment. This authentication is also vital to prevent any security threats or risks like compromises of business server, release of confidential data etc. Though conventional works attempted to accomplish better authentication, they lacked with respect to accuracy. Hence, the study aims to enhance the recognition rate by introducing a voice recognition system as a personal authentication based on Deep Learning (DL) due to its ability… More >

Share Link

WeChat scan