Home / Journals / CMC / Vol.72, No.3, 2022
Table of Content
  • Open Access

    ARTICLE

    A Sustainable WSN System with Heuristic Schemes in IIoT

    Wenjun Li1, Siyang Zhang1, Guangwei Wu2, Aldosary Saad3, Amr Tolba3,4, Gwang-jun Kim5,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4215-4231, 2022, DOI:10.32604/cmc.2022.024204
    Abstract Recently, the development of Industrial Internet of Things has taken the advantage of 5G network to be more powerful and more intelligent. However, the upgrading of 5G network will cause a variety of issues increase, one of them is the increased cost of coverage. In this paper, we propose a sustainable wireless sensor networks system, which avoids the problems brought by 5G network system to some extent. In this system, deploying relays and selecting routing are for the sake of communication and charging. The main aim is to minimize the total energy-cost of communication under the precondition, where each terminal… More >

  • Open Access

    ARTICLE

    A New Fuzzy Controlled Antenna Network Proposal for Small Satellite Applications

    Chafaa Hamrouni1,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4233-4248, 2022, DOI:10.32604/cmc.2022.023453
    Abstract This research contributes to small satellite system development based on electromagnetic modeling and an integrated meta-materials antenna networks design for multimedia transmission contents. It includes an adaptive nonsingular mode tracking control design for small satellites systems using fuzzy waveless antenna networks. By analyzing and modeling based on electromagnetic methods, propagation properties of guided waves from metallic structures with simple or complex forms charge partially or entirely by anisotropic materials such as metamaterials. We propose a system control rule to omit uncertainties, including the inevitable approximation errors resulting from the finite number of fuzzy signal power value basis functions in antenna… More >

  • Open Access

    ARTICLE

    Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

    Muhammad Irfan1, Ali Raza2,*, Faisal Althobiani3, Nasir Ayub4,5, Muhammad Idrees6, Zain Ali7, Kashif Rizwan4, Abdullah Saeed Alwadie1, Saleh Mohammed Ghonaim3, Hesham Abdushkour3, Saifur Rahman1, Omar Alshorman1, Samar Alqhtani8
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4249-4265, 2022, DOI:10.32604/cmc.2022.025863
    Abstract In the Smart Grid (SG) residential environment, consumers change their power consumption routine according to the price and incentives announced by the utility, which causes the prices to deviate from the initial pattern. Thereby, electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability. Due to the massive amount of data, big data analytics for forecasting becomes a hot topic in the SG domain. In this paper, the changing and non-linearity of consumer consumption pattern complex data is taken as input. To minimize the computational cost and complexity of the data, the… More >

  • Open Access

    ARTICLE

    Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

    Jaber S. Alzahrani1, Reem M. Alshehri2, Mohammad Alamgeer3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4, Ishfaq Yaseen4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4267-4281, 2022, DOI:10.32604/cmc.2022.027369
    Abstract Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby… More >

  • Open Access

    ARTICLE

    A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning

    Soban Arshad1, Khalid Iqbal1,*, Sheneela Naz2, Sadaf Yasmin1, Zobia Rehman2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4283-4301, 2022, DOI:10.32604/cmc.2022.025442
    Abstract Telecom industry relies on churn prediction models to retain their customers. These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers. Retention not only contributes to the profit of an organization, but it is also important for upholding a position in the competitive market. In the past, numerous churn prediction models have been proposed, but the current models have a number of flaws that prevent them from being used in real-world large-scale telecom datasets. These schemes, fail to incorporate frequently changing requirements. Data sparsity, noisy data, and the… More >

  • Open Access

    ARTICLE

    Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

    Saman Riaz1, Ali Arshad2, Shahab S. Band3,*, Amir Mosavi4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4303-4321, 2022, DOI:10.32604/cmc.2022.024819
    Abstract The way towards generating a website front end involves a designer settling on an idea for what kind of layout they want the website to have, then proceeding to plan and implement each aspect one by one until they have converted what they initially laid out into its Html front end form, this process can take a considerable time, especially considering the first draft of the design is traditionally never the final one. This process can take up a large amount of resource real estate, and as we have laid out in this paper, by using a Model consisting of… More >

  • Open Access

    ARTICLE

    Low Complexity Encoder with Multilabel Classification and Image Captioning Model

    Mahmoud Ragab1,2,3,*, Abdullah Addas4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4323-4337, 2022, DOI:10.32604/cmc.2022.026602
    Abstract Due to the advanced development in the multimedia-on-demand traffic in different forms of audio, video, and images, has extremely moved on the vision of the Internet of Things (IoT) from scalar to Internet of Multimedia Things (IoMT). Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT, which are commonly employed in diverse application areas, especially for capturing remote sensing (RS) images. At the same time, the interpretation of the captured RS image also plays a crucial issue, which can be addressed by the multi-label classification and Computational Linguistics based image… More >

  • Open Access

    ARTICLE

    Task Scheduling Optimization in Cloud Computing by Rao Algorithm

    A. Younes1,*, M. Kh. Elnahary1, Monagi H. Alkinani2, Hamdy H. El-Sayed1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4339-4356, 2022, DOI:10.32604/cmc.2022.022824
    Abstract Cloud computing is currently dominated within the space of high-performance distributed computing and it provides resource polling and on-demand services through the web. So, task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user's services demand modification dynamically. The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions. In heterogeneous multiprocessor systems, task assignments and schedules have a significant impact on system operation. Within the heuristic-based task scheduling algorithm, the different processes will lead to a… More >

  • Open Access

    ARTICLE

    Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory

    Noureen Fatima1, Rashid Jahangir2, Ghulam Mujtaba1, Adnan Akhunzada3,*, Zahid Hussain Shaikh4, Faiza Qureshi1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4357-4374, 2022, DOI:10.32604/cmc.2022.023830
    Abstract The Coronavirus Disease 2019 (COVID-19) pandemic poses the worldwide challenges surpassing the boundaries of country, religion, race, and economy. The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) testing. Nevertheless, this testing method is accurate enough for the diagnosis of COVID-19. However, it is time-consuming, expensive, expert-dependent, and violates social distancing. In this paper, this research proposed an effective multi-modality-based and feature fusion-based (MMFF) COVID-19 detection technique through deep neural networks. In multi-modality, we have utilized the cough samples, breathe samples and sound samples of healthy as well as COVID-19 patients from… More >

  • Open Access

    ARTICLE

    Multi-Agent Deep Reinforcement Learning-Based Resource Allocation in HPC/AI Converged Cluster

    Jargalsaikhan Narantuya1,*, Jun-Sik Shin2, Sun Park2, JongWon Kim2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4375-4395, 2022, DOI:10.32604/cmc.2022.023318
    Abstract As the complexity of deep learning (DL) networks and training data grows enormously, methods that scale with computation are becoming the future of artificial intelligence (AI) development. In this regard, the interplay between machine learning (ML) and high-performance computing (HPC) is an innovative paradigm to speed up the efficiency of AI research and development. However, building and operating an HPC/AI converged system require broad knowledge to leverage the latest computing, networking, and storage technologies. Moreover, an HPC-based AI computing environment needs an appropriate resource allocation and monitoring strategy to efficiently utilize the system resources. In this regard, we introduce a… More >

  • Open Access

    ARTICLE

    Imbalanced Classification in Diabetics Using Ensembled Machine Learning

    M. Sandeep Kumar1, Mohammad Zubair Khan2,*, Sukumar Rajendran1, Ayman Noor3, A. Stephen Dass1, J. Prabhu1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4397-4409, 2022, DOI:10.32604/cmc.2022.025865
    Abstract Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar. The risk of diabetics can be lowered if the diabetic is found at the early stage. In recent days, several machine learning models were developed to predict the diabetic presence at an early stage. In this paper, we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics. The proposed method uses both the concept of over-sampling and under-sampling along with model weighting… More >

  • Open Access

    ARTICLE

    A Method for Detecting Non-Mask Wearers Based on Regression Analysis

    Dokyung Hwang1, Hyeonmin Ro1, Naejoung Kwak2, Jinsang Hwang3, Dongju Kim1,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4411-4431, 2022, DOI:10.32604/cmc.2022.025378
    Abstract A novel practical and universal method of mask-wearing detection has been proposed to prevent viral respiratory infections. The proposed method quickly and accurately detects mask and facial regions using well-trained You Only Look Once (YOLO) detector, then applies image coordinates of the detected bounding box (bbox). First, the data that is used to train our model is collected under various circumstances such as light disturbances, distances, time variations, and different climate conditions. It also contains various mask types to detect in general and universal application of the model. To detect mask-wearing status, it is important to detect facial and mask… More >

  • Open Access

    ARTICLE

    Importance of Adaptive Photometric Augmentation for Different Convolutional Neural Network

    Saraswathi Sivamani1, Sun Il Chon1, Do Yeon Choi1, Dong Hoon Lee2, Ji Hwan Park1,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4433-4452, 2022, DOI:10.32604/cmc.2022.026759
    Abstract Existing segmentation and augmentation techniques on convolutional neural network (CNN) has produced remarkable progress in object detection. However, the nominal accuracy and performance might be downturned with the photometric variation of images that are directly ignored in the training process, along with the context of the individual CNN algorithm. In this paper, we investigate the effect of a photometric variation like brightness and sharpness on different CNN. We observe that random augmentation of images weakens the performance unless the augmentation combines the weak limits of photometric variation. Our approach has been justified by the experimental result obtained from the PASCAL… More >

  • Open Access

    ARTICLE

    Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome

    Ankur Dumka1, Parag Verma2, Rajesh Singh3, Anuj Bhardwaj4, Khalid Alsubhi5, Divya Anand6,7,*, Irene Delgado Noya7,8, Silvia Aparicio Obregon7,9
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4453-4466, 2022, DOI:10.32604/cmc.2022.023974
    Abstract In December 2019, a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia. The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9, 2020, named Novel Coronavirus 2019 (nCoV-2019). This nCoV-2019 is now known as COVID-19. There is a big list of infections of this coronavirus which is present in the form of a big family. This virus can cause several diseases that usually develop with a serious problem. According to the World… More >

  • Open Access

    ARTICLE

    Detecting IoT Botnet in 5G Core Network Using Machine Learning

    Ye-Eun Kim1, Min-Gyu Kim2, Hwankuk Kim2,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4467-4488, 2022, DOI:10.32604/cmc.2022.026581
    Abstract As Internet of Things (IoT) devices with security issues are connected to 5G mobile networks, the importance of IoT Botnet detection research in mobile network environments is increasing. However, the existing research focused on AI-based IoT Botnet detection research in wired network environments. In addition, the existing research related to IoT Botnet detection in ML-based mobile network environments have been conducted up to 4G. Therefore, this paper conducts a study on ML-based IoT Botnet traffic detection in the 5G core network. The binary and multiclass classification was performed to compare simple normal/malicious detection and normal/three-type IoT Botnet malware detection. In… More >

  • Open Access

    ARTICLE

    Multi-Target Track Initiation in Heavy Clutter

    Li Xu1,2,*, Ruzhen Lou1, Chuanbin Zhang1, Bo Lang3, Weiyue Ding4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4489-4507, 2022, DOI:10.32604/cmc.2022.027400
    Abstract In the heavy clutter environment, the information capacity is large, the relationships among information are complicated, and track initiation often has a high false alarm rate or missing alarm rate. Obviously, it is a difficult task to get a high-quality track initiation in the limited measurement cycles. This paper studies the multi-target track initiation in heavy clutter. At first, a relaxed logic-based clutter filter algorithm is presented. In the algorithm, the raw measurement is filtered by using the relaxed logic method. We not only design a kind of incremental and adaptive filtering gate, but also add the angle extrapolation based… More >

  • Open Access

    ARTICLE

    Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

    Elias Hossain1, Md. Shazzad Hossain2, Md. Selim Hossain3, Sabila Al Jannat4, Moontahina Huda5, Sameer Alsharif6, Osama S. Faragallah7, Mahmoud M. A. Eid8, Ahmed Nabih Zaki Rashed9,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4509-4523, 2022, DOI:10.32604/cmc.2022.025977
    Abstract Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate… More >

  • Open Access

    ARTICLE

    Efficient Routing Protection Algorithm Based on Optimized Network Topology

    Haijun Geng1,2, Zikun Jin1, Jiangyuan Yao3,*, Han Zhang4, Zhiguo Hu6, Bo Yang5, Yingije Guo7, Wei Wang1, Qidong Zhang1, Guoao Duan8
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4525-4540, 2022, DOI:10.32604/cmc.2022.027725
    Abstract Network failures are unavoidable and occur frequently. When the network fails, intra-domain routing protocols deploying on the Internet need to undergo a long convergence process. During this period, a large number of messages are discarded, which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers (ISP). Therefore, improving the availability of intra-domain routing is a trending research question to be solved. Industry usually employs routing protection algorithms to improve intra-domain routing availability. However, existing routing protection schemes compute as many backup paths as possible to reduce message loss due to… More >

  • Open Access

    ARTICLE

    Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

    Manar Ahmed Hamza1,*, Noha Negm2, Shaha Al-Otaibi3, Amel A. Alhussan4, Mesfer Al Duhayyim5, Fuad Ali Mohammed Al-Yarimi2, Mohammed Rizwanullah1, Ishfaq Yaseen1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4541-4555, 2022, DOI:10.32604/cmc.2022.027048
    Abstract Machine learning (ML) becomes a familiar topic among decision makers in several domains, particularly healthcare. Effective design of ML models assists to detect and classify the occurrence of diseases using healthcare data. Besides, the parameter tuning of the ML models is also essential to accomplish effective classification results. This article develops a novel red colobuses monkey optimization with kernel extreme learning machine (RCMO-KELM) technique for epileptic seizure detection and classification. The proposed RCMO-KELM technique initially extracts the chaotic, time, and frequency domain features in the actual EEG signals. In addition, the min-max normalization approach is employed for the pre-processing of… More >

  • Open Access

    ARTICLE

    Fuzzy Decision Model: Evaluating and Selecting Open Banking Business Partners

    Ngo Quang Trung, Nguyen Van Thanh*, Nguyen Viet Tinh, Syed Tam Husain
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4557-4570, 2022, DOI:10.32604/cmc.2022.022417
    Abstract The finance supply chain has always been a different supply chain compared to product supply chain being a service supply chain. Open Banking (OB) is one of the most important milestones since the beginning of financial technology innovation and service supply chain. As these are activities provided by traditional banks, non-bank financial institutions also provide financial service with access to consumer banking, transactional and other financial data to develop financial applications and services tailored to their customers. The development of financial technology, “Open banking”, promotes financial services to begin this transformation. However, evaluating and selecting open banking business partners from… More >

  • Open Access

    ARTICLE

    Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

    Ayman A. El-Saleh1, Abdulraqeb Alhammadi2,*, Ibraheem Shayea3, Azizul Azizan4, Wan Haslina Hassan2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4571-4587, 2022, DOI:10.32604/cmc.2022.024642
    Abstract Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultra-low latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate the minimum opinion score (MOS)… More >

  • Open Access

    ARTICLE

    Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

    Souad Larabi-Marie-Sainte1, Eatedal Alabdulkreem2, Mohammad Alamgeer3, Mohamed K Nour4, Anwer Mustafa Hilal5,*, Mesfer Al Duhayyim6, Abdelwahed Motwakel5, Ishfaq Yaseen5
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4589-4601, 2022, DOI:10.32604/cmc.2022.027922
    Abstract Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. The CBADL-BEESC model performs feature… More >

  • Open Access

    ARTICLE

    Research on Multi-View Image Reconstruction Technology Based on Auto-Encoding Learning

    Tao Zhang1, Shaokui Gu1, Jinxing Niu1,*, Yi Cao2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4603-4614, 2022, DOI:10.32604/cmc.2022.027079
    Abstract Traditional three-dimensional (3D) image reconstruction method, which highly dependent on the environment and has poor reconstruction effect, is easy to lead to mismatch and poor real-time performance. The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology. To solve the problem, a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper. The algorithm first extracts the feature information of multiple two-dimensional (2D) images based on scale and rotation invariance parameters of Scale-invariant feature transform (SIFT) operator. Secondly, self-encoding learning neural network is introduced into the… More >

  • Open Access

    ARTICLE

    Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

    Erssa Arif1,*, Syed Khuram Shahzad2, Muhammad Waseem Iqbal3, Muhammad Arfan Jaffar4, Abdullah S. Alshahrani5, Ahmed Alghamdi6
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4615-4630, 2022, DOI:10.32604/cmc.2022.027571
    Abstract The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision. Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc. Therefore, automatic weapons detection is a vital requirement now a day. The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net. Real time datasets, from local surveillance department's test sessions are used for model training and testing. Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism.… More >

  • Open Access

    ARTICLE

    Compact Interlaced Dual Circularly Polarized Sequentially Rotated Dielectric-Resonator Antenna Array

    Yazeed Qasaymeh*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4631-4643, 2022, DOI:10.32604/cmc.2022.026111
    Abstract In this study, a compact 2 × 2 interlaced sequentially rotated dual-polarized dielectric-resonator antenna array is proposed for 5.8 GHz applications. The array is composed of a novel unit elements that are made of rectangular dielectric resonator (RDR) coupled to an eye slot for generating the orthogonal modes, and to acquire circular polarization (CP) radiation. For the purpose of miniaturization and achieving dual polarized resonance, the array is fed by two interlaced ports and each port excites two radiating elements. The first port feeds horizontal elements to obtain left hand circular polarization (LHCP). The second port feeds vertical elements to obtain right hand… More >

  • Open Access

    ARTICLE

    Multi-Scale Attention-Based Deep Neural Network for Brain Disease Diagnosis

    Yin Liang1,*, Gaoxu Xu1, Sadaqat ur Rehman2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4645-4661, 2022, DOI:10.32604/cmc.2022.026999
    Abstract Whole brain functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in the diagnosis of brain disorders such as autism spectrum disorder (ASD). Recently, an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification. However, the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification. In this paper, we proposed a multi-scale attention-based deep neural network (MSA-DNN) model to classify FC patterns for the ASD diagnosis.… More >

  • Open Access

    ARTICLE

    Secure Irrigation System for Olive Orchards Using Internet of Things

    Ayman Massaoudi*, Abdelwahed Berguiga, Ahlem Harchay
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4663-4673, 2022, DOI:10.32604/cmc.2022.026972
    Abstract Smart irrigation system, also referred as precision irrigation system, is an attractive solution to save the limited water resources as well as to improve crop productivity and quality. In this work, by using Internet of things (IoT), we aim to design a smart irrigation system for olive groves. In such IoT system, a huge number of low-power and low-complexity devices (sensors, actuators) are interconnected. Thus, a great challenge is to satisfy the increasing demands in terms of spectral efficiency. Moreover, securing the IoT system is also a critical challenge, since several types of cybersecurity threats may pose. In this paper,… More >

  • Open Access

    ARTICLE

    Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

    Mohammed Hadwan1,2,3,*, Mohammed A. Al-Hagery4, Mohammed Al-Sarem5, Faisal Saeed5,6
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4675-4689, 2022, DOI:10.32604/cmc.2022.027311
    Abstract Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information from users’ reviews is an… More >

  • Open Access

    ARTICLE

    Optimal Machine Learning Enabled Intrusion Detection in Cyber-Physical System Environment

    Bassam A. Y. Alqaralleh1,*, Fahad Aldhaban1, Esam A. AlQarallehs2, Ahmad H. Al-Omari3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4691-4707, 2022, DOI:10.32604/cmc.2022.026556
    Abstract Cyber-attacks on cyber-physical systems (CPSs) resulted to sensing and actuation misbehavior, severe damage to physical object, and safety risk. Machine learning (ML) models have been presented to hinder cyberattacks on the CPS environment; however, the non-existence of labelled data from new attacks makes their detection quite interesting. Intrusion Detection System (IDS) is a commonly utilized to detect and classify the existence of intrusions in the CPS environment, which acts as an important part in secure CPS environment. Latest developments in deep learning (DL) and explainable artificial intelligence (XAI) stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication.… More >

  • Open Access

    ARTICLE

    Research on Facial Expression Capture Based on Two-Stage Neural Network

    Zhenzhou Wang1, Shao Cui1, Xiang Wang1,*, JiaFeng Tian2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4709-4725, 2022, DOI:10.32604/cmc.2022.027767
    Abstract To generate realistic three-dimensional animation of virtual character, capturing real facial expression is the primary task. Due to diverse facial expressions and complex background, facial landmarks recognized by existing strategies have the problem of deviations and low accuracy. Therefore, a method for facial expression capture based on two-stage neural network is proposed in this paper which takes advantage of improved multi-task cascaded convolutional networks (MTCNN) and high-resolution network. Firstly, the convolution operation of traditional MTCNN is improved. The face information in the input image is quickly filtered by feature fusion in the first stage and Octave Convolution instead of the… More >

  • Open Access

    ARTICLE

    Mathematical Modelling of Rotavirus Disease Through Efficient Methods

    Ali Raza*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4727-4740, 2022, DOI:10.32604/cmc.2022.027044
    Abstract The design of evolutionary approaches has a vital role in the recent development of scientific literature. To tackle highly nonlinear complex problems, nonlinear ordinary differential equations, partial differential equations, stochastic differential equations, and many more may called computational algorithms. The rotavirus causes may include severe diarrhea, vomiting, and fever leading to rapid dehydration. By the report of the World Health Organization (WHO), approximately 600,000 children die worldwide each year, 80 percent of whom live in developing countries. Two million children are hospitalized each year. In Asia, up to 45 percent of the children hospitalized for diarrhea may be infected with… More >

  • Open Access

    ARTICLE

    WDBM: Weighted Deep Forest Model Based Bearing Fault Diagnosis Method

    Letao Gao1,*, Xiaoming Wang2, Tao Wang3, Mengyu Chang4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4741-4754, 2022, DOI:10.32604/cmc.2022.027204
    Abstract In the research field of bearing fault diagnosis, classical deep learning models have the problems of too many parameters and high computing cost. In addition, the classical deep learning models are not effective in the scenario of small data. In recent years, deep forest is proposed, which has less hyper parameters and adaptive depth of deep model. In addition, weighted deep forest (WDF) is proposed to further improve deep forest by assigning weights for decisions trees based on the accuracy of each decision tree. In this paper, weighted deep forest model-based bearing fault diagnosis method (WDBM) is proposed. The WDBM… More >

  • Open Access

    ARTICLE

    Automatic Eyewitness Identification During Disasters by Forming a Feature-Word Dictionary

    Shahzad Nazir1, Muhammad Asif1,*, Shahbaz Ahmad1, Hanan Aljuaid2, Shahbaz Ahmad1, Yazeed Ghadi3, Zubair nawaz4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4755-4769, 2022, DOI:10.32604/cmc.2022.026145
    Abstract Social media provide digitally interactional technologies to facilitate information sharing and exchanging individuals. Precisely, in case of disasters, a massive corpus is placed on platforms such as Twitter. Eyewitness accounts can benefit humanitarian organizations and agencies, but identifying the eyewitness Tweets related to the disaster from millions of Tweets is difficult. Different approaches have been developed to address this kind of problem. The recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic rules. However, these approaches suffer from limitations as they are dataset-dependent and not scalable. In this paper, we… More >

  • Open Access

    ARTICLE

    MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

    Bobbinpreet1, Sultan Aljahdali2,*, Tripti Sharma1, Bhawna Goyal1, Ayush Dogra3, Shubham Mahajan4, Amit Kant Pandit4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4771-4787, 2022, DOI:10.32604/cmc.2022.023150
    Abstract In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a… More >

  • Open Access

    ARTICLE

    Protected Fair Secret Sharing Based Bivariate Asymmetric Polynomials in Satellite Network

    Yanyan Han1,2, Jiangping Yu3, Guangyu Hu4, Chenglei Pan4, Dingbang Xie5, Chao Guo1,2,6,*, Abdul Waheed7
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4789-4802, 2022, DOI:10.32604/cmc.2022.027496
    Abstract Verifiable secret sharing mainly solves the cheating behavior between malicious participants and the ground control center in the satellite network. The verification stage can verify the effectiveness of secret shares issued by the ground control center to each participant and verify the effectiveness of secret shares shown by participants. We use a lot of difficult assumptions based on mathematical problems in the verification stage, such as solving the difficult problem of the discrete logarithm, large integer prime factorization, and so on. Compared with other verifiable secret sharing schemes designed for difficult problems under the same security, the verifiable secret sharing… More >

  • Open Access

    ARTICLE

    Impact Analysis of Resilience Against Malicious Code Attacks via Emails

    Chulwon Lee1, Kyungho Lee2,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4803-4816, 2022, DOI:10.32604/cmc.2022.025310
    Abstract The damage caused by malicious software is increasing owing to the COVID-19 pandemic, such as ransomware attacks on information technology and operational technology systems based on corporate networks and social infrastructures and spear-phishing attacks on business or research institutes. Recently, several studies have been conducted to prevent further phishing emails in the workplace because malware attacks employ emails as the primary means of penetration. However, according to the latest research, there appears to be a limitation in blocking email spoofing through advanced blocking systems such as spam email filtering solutions and advanced persistent threat systems. Therefore, experts believe that it… More >

  • Open Access

    ARTICLE

    Motion-Planning Algorithm for a Hyper-Redundant Manipulator in Narrow Spaces

    Lei Zhang1,2,*, Shouzhi Huang1,2, Zhaocai Du3, Guangyao Ouyang1,2, Heping Chen4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4817-4832, 2022, DOI:10.32604/cmc.2022.026845
    Abstract In this study, a hyper-redundant manipulator was designed for detection and searching in narrow spaces for aerospace and earthquake rescue applications. A forward kinematics equation for the hyper-redundant manipulator was derived using the homogeneous coordinate transformation method. Based on the modal function backbone curve method and the known path, an improved modal method for the backbone curves was proposed. First, the configuration of the backbone curve for the hyper-redundant manipulator was divided into two parts: a mode function curve segment of the mode function and a known path segment. By changing the discrete points along the known path, the backbone… More >

  • Open Access

    ARTICLE

    Breast Cancer Detection in Saudi Arabian Women Using Hybrid Machine Learning on Mammographic Images

    Yassir Edrees Almalki11, Ahmad Shaf2, Tariq Ali2, Muhammad Aamir2, Sharifa Khalid Alduraibi3, Shoayea Mohessen Almutiri4, Muhammad Irfan5, Mohammad Abd Alkhalik Basha6, Alaa Khalid Alduraibi3, Abdulrahman Manaa Alamri7, Muhammad Zeeshan Azam8, Khalaf Alshamrani9,*, Hassan A. Alshamrani9
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4833-4851, 2022, DOI:10.32604/cmc.2022.027111
    Abstract Breast cancer (BC) is the most common cause of women’s deaths worldwide. The mammography technique is the most important modality for the detection of BC. To detect abnormalities in mammographic images, the Breast Imaging Reporting and Data System (BI-RADs) is used as a baseline. The correct allocation of BI-RADs categories for mammographic images is always an interesting task, even for specialists. In this work, to detect and classify the mammogram images in BI-RADs, a novel hybrid model is presented using a convolutional neural network (CNN) with the integration of a support vector machine (SVM). The dataset used in this research… More >

  • Open Access

    ARTICLE

    Fuzzy Multi-Criteria Decision Making for Solar Power Plant Location Selection

    Thai Hoang Tuyet Nhi1, Chia-Nan Wang1, Nguyen Van Thanh2,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4853-4865, 2022, DOI:10.32604/cmc.2022.026374
    Abstract Vietnam is one of Southeast Asian countries with a rapid GDP growth rate, ranging from 6.5% to 7% annually, leading to an average increase in energy demand of 11% per year. This demand creates many new opportunities in the energy industry, especially renewable energy, to ensure sustainable development in the future for the country with applications of solar energy growing at the present, and other opportunities to expand in the future. In Vietnam, thanks to favorable weather, climate, terrain characteristics and many preferential support policies, there are many great opportunities in the field of solar energy exploitation and application. Location… More >

  • Open Access

    ARTICLE

    A Novel Method for Precipitation Nowcasting Based on ST-LSTM

    Wei Fang1,2,*, Liang Shen1, Victor S. Sheng3, Qiongying Xue1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4867-4877, 2022, DOI:10.32604/cmc.2022.027197
    Abstract Precipitation nowcasting is of great significance for severe convective weather warnings. Radar echo extrapolation is a commonly used precipitation nowcasting method. However, the traditional radar echo extrapolation methods are encountered with the dilemma of low prediction accuracy and extrapolation ambiguity. The reason is that those methods cannot retain important long-term information and fail to capture short-term motion information from the long-range data stream. In order to solve the above problems, we select the spatiotemporal long short-term memory (ST-LSTM) as the recurrent unit of the model and integrate the 3D convolution operation in it to strengthen the model's ability to capture… More >

  • Open Access

    ARTICLE

    Hybrid Single Image Super-Resolution Algorithm for Medical Images

    Walid El-Shafai1,2, Ehab Mahmoud Mohamed3,4,*, Medien Zeghid3,5, Anas M. Ali1,6, Moustafa H. Aly7
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4879-4896, 2022, DOI:10.32604/cmc.2022.028364
    Abstract High-quality medical microscopic images used for diseases detection are expensive and difficult to store. Therefore, low-resolution images are favorable due to their low storage space and ease of sharing, where the images can be enlarged when needed using Super-Resolution (SR) techniques. However, it is important to maintain the shape and size of the medical images while enlarging them. One of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image resolution. Consequently, this paper suggests a multi-SR and classification framework based on Generative Adversarial Network (GAN) to… More >

  • Open Access

    ARTICLE

    Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

    Shi Qiu1, Bin Li2,*, Tao Zhou3, Feng Li4, Ting Liang5
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4897-4910, 2022, DOI:10.32604/cmc.2022.026855
    Abstract Lung is an important organ of human body. More and more people are suffering from lung diseases due to air pollution. These diseases are usually highly infectious. Such as lung tuberculosis, novel coronavirus COVID-19, etc. Lung nodule is a kind of high-density globular lesion in the lung. Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis, which is inefficient. For this reason, the use of computer-assisted diagnosis of lung nodules has become the current main trend. In the process of computer-aided diagnosis, how to reduce the false positive… More >

  • Open Access

    ARTICLE

    Network Traffic Obfuscation System for IIoT-Cloud Control Systems

    Yangjae Lee1, Sung Hoon Baek2, Jung Taek Seo3, Ki-Woong Park1,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4911-4929, 2022, DOI:10.32604/cmc.2022.026657
    Abstract One of the latest technologies enabling remote control, operational efficiency upgrades, and real-time big-data monitoring in an industrial control system (ICS) is the IIoT-Cloud ICS, which integrates the Industrial Internet of Things (IIoT) and the cloud into the ICS. Although an ICS benefits from the application of IIoT and the cloud in terms of cost reduction, efficiency improvement, and real-time monitoring, the application of this technology to an ICS poses an unprecedented security risk by exposing its terminal devices to the outside world. An adversary can collect information regarding senders, recipients, and prime-time slots through traffic analysis and use it… More >

  • Open Access

    ARTICLE

    A Dynamic Multi-ary Query Tree Protocol for Passive RFID Anti-collision

    Gang Li1, Haoyang Sun1, Zhenbing Li1, Peiqi Wu1, Daniele Inserra1,*, Jian Su2, Xiaochuan Fang3, Guangjun Wen1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4931-4944, 2022, DOI:10.32604/cmc.2022.026654
    Abstract

    In this paper, a dynamic multi-ary query tree (DMQT) anti-collision protocol for Radio Frequency Identification (RFID) systems is proposed for large scale passive RFID tag identification. The proposed DMQT protocol is based on an iterative process between the reader and tags which identifies the position of collision bits through map commands and dynamically encodes them to optimize slots allocation through query commands. In this way, the DMQT completely eliminates empty slots and greatly reduces collision slots, which in turn reduces the identification time and energy costs. In addition and differently to other known protocols, the DMQT does not need to… More >

  • Open Access

    ARTICLE

    Forecasting Mental Stress Using Machine Learning Algorithms

    Elias Hossain1, Abdulwahab Alazeb2,*, Naif Al Mudawi2, Sultan Almakdi2, Mohammed Alshehri2, M. Gazi Golam Faruque3, Wahidur Rahman3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4945-4966, 2022, DOI:10.32604/cmc.2022.027058
    Abstract Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology of this proposed study is… More >

  • Open Access

    ARTICLE

    A Traceable Capability-based Access Control for IoT

    Chao Li1, Fan Li1,2, Cheng Huang3, Lihua Yin1,*, Tianjie Luo1,2, Bin Wang4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4967-4982, 2022, DOI:10.32604/cmc.2022.023496
    Abstract Delegation mechanism in Internet of Things (IoT) allows users to share some of their permissions with others. Cloud-based delegation solutions require that only the user who has registered in the cloud can be delegated permissions. It is not convenient when a permission is delegated to a large number of temporarily users. Therefore, some works like CapBAC delegate permissions locally in an offline way. However, this is difficult to revoke and modify the offline delegated permissions. In this work, we propose a traceable capability-based access control approach (TCAC) that can revoke and modify permissions by tracking the trajectories of permissions delegation.… More >

  • Open Access

    ARTICLE

    Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

    Seon Hong Lee1, Haein Lee1, Jang Hyun Kim2,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4983-4997, 2022, DOI:10.32604/cmc.2022.027943
    Abstract Metaverse is one of the main technologies in the daily lives of several people, such as education, tour systems, and mobile application services. Particularly, the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere. To provide an improved service, it is important to analyze online reviews that contain user satisfaction. Several previous studies have utilized traditional methods, such as the structural equation model (SEM) and technology acceptance method (TAM) for exploring user satisfaction, using limited survey data. These methods may not be appropriate for analyzing the users of mobile applications. To overcome this… More >

  • Open Access

    ARTICLE

    Optimized Deep Learning Model for Fire Semantic Segmentation

    Songbin Li1,*, Peng Liu1, Qiandong Yan1, Ruiling Qian2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4999-5013, 2022, DOI:10.32604/cmc.2022.026498
    Abstract Recent convolutional neural networks (CNNs) based deep learning has significantly promoted fire detection. Existing fire detection methods can efficiently recognize and locate the fire. However, the accurate flame boundary and shape information is hard to obtain by them, which makes it difficult to conduct automated fire region analysis, prediction, and early warning. To this end, we propose a fire semantic segmentation method based on Global Position Guidance (GPG) and Multi-path explicit Edge information Interaction (MEI). Specifically, to solve the problem of local segmentation errors in low-level feature space, a top-down global position guidance module is used to restrain the offset… More >

  • Open Access

    ARTICLE

    Intelligent Networks for Chaotic Fractional-Order Nonlinear Financial Model

    Prem Junswang1, Zulqurnain Sabir2, Muhammad Asif Zahoor Raja3, Waleed Adel4,5, Thongchai Botmart6,*, Wajaree Weera6
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5015-5030, 2022, DOI:10.32604/cmc.2022.027523
    Abstract The purpose of this paper is to present a numerical approach based on the artificial neural networks (ANNs) for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented system. The method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation (LMB), named the ANNs-LMB. This technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference solution. Fifteen numbers neurons have been used to solve the fractional-order chaotic financial model. The selection of the… More >

  • Open Access

    ARTICLE

    Lightweight Authentication Protocol Based on Physical Unclonable Function

    Hanguang Luo1, Tao Zou1,*, Chunming Wu2, Dan Li3, Shunbin Li1, Chu Chu4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5031-5040, 2022, DOI:10.32604/cmc.2022.027118
    Abstract In the emerging Industrial Internet of Things (IIoT), authentication problems have become an urgent issue for massive resource-constrained devices because traditional costly security mechanisms are not suitable for them. The security protocol designed for resource-constrained systems should not only be secure but also efficient in terms of usage of energy, storage, and processing. Although recently many lightweight schemes have been proposed, to the best of our knowledge, they are unable to address the problem of privacy preservation with the resistance of Denial of Service (DoS) attacks in a practical way. In this paper, we propose a lightweight authentication protocol based… More >

  • Open Access

    ARTICLE

    Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

    Maqsood Ahmad1,2, Noorhaniza Wahid1, Rahayu A Hamid1, Saima Sadiq2, Arif Mehmood3, Gyu Sang Choi4,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5041-5058, 2022, DOI:10.32604/cmc.2022.026077
    Abstract Mental health signifies the emotional, social, and psychological well-being of a person. It also affects the way of thinking, feeling, and situation handling of a person. Stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life. Mental health problems are rising globally and constituting a burden on healthcare systems. Early diagnosis can help the professionals in the treatment that may lead to complications if they remain untreated. The machine learning… More >

  • Open Access

    ARTICLE

    A Hybrid Grey DEMATEL and PLS-SEM Model to Investigate COVID-19 Vaccination Intention

    Phi-Hung Nguyen1,2,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5059-5078, 2022, DOI:10.32604/cmc.2022.027630
    Abstract The main objective of this study is to comprehensively investigate individuals’ vaccination intention against COVID-19 during the second wave of COVID-19 spread in Vietnam using a novel hybrid approach. First, the Decision-Making Trial and Evaluation Laboratory based on Grey Theory (DEMATEL-G) was employed to explore the critical factors of vaccination intention among individuals. Second, Partial Least Squares-Structural Equation Modeling (PLS-SEM) was applied to test the hypotheses of individual behavioral intention to get the vaccine to prevent the outbreak of COVID-19. A panel of 661 valid respondents was collected from June 2021 to July 2021, and confidentiality was maintained for all… More >

  • Open Access

    ARTICLE

    CWoT-Share: Context-Based Web of Things Resource Sharing in Blockchain Environment

    Yangqun Li1,2,*, Jin Qi1,2, Lijuan Min1,2, Hongzhi Yang1,2, Chenyang Zhou1,2, Bonan Jin3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5079-5098, 2022, DOI:10.32604/cmc.2022.027281
    Abstract Web of Things (WoT) resources are not only numerous, but also have a wide range of applications and deployments. The centralized WoT resource sharing mechanism lacks flexibility and scalability, and hence cannot satisfy requirement of distributed resource sharing in large-scale environment. In response to this problem, a trusted and secure mechanism for WoT resources sharing based on context and blockchain (CWoT-Share) was proposed. Firstly, the mechanism can respond quickly to the changes of the application environment by dynamically determining resource access control rules according to the context. Then, the flexible resource charging strategies, which reduced the fees paid by the… More >

  • Open Access

    ARTICLE

    Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

    Bareera Zafar1, Syed Abbas Zilqurnain Naqvi1, Muhammad Ahsan1, Allah Ditta2,*, Ummul Baneen1, Muhammad Adnan Khan3,4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5099-5116, 2022, DOI:10.32604/cmc.2022.027874
    Abstract This research proposes a method called enhanced collaborative and geometric multi-kernel learning (E-CGMKL) that can enhance the CGMKL algorithm which deals with multi-class classification problems with non-linear data distributions. CGMKL combines multiple kernel learning with softmax function using the framework of multi empirical kernel learning (MEKL) in which empirical kernel mapping (EKM) provides explicit feature construction in the high dimensional kernel space. CGMKL ensures the consistent output of samples across kernel spaces and minimizes the within-class distance to highlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them and try… More >

  • Open Access

    ARTICLE

    Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

    Hiam Alquran1,2, Wan Azani Mustafa3,4,*, Isam Abu Qasmieh2, Yasmeen Mohd Yacob3,4, Mohammed Alsalatie5, Yazan Al-Issa6, Ali Mohammad Alqudah2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5117-5134, 2022, DOI:10.32604/cmc.2022.025692
    Abstract Cervical cancer is screened by pap smear methodology for detection and classification purposes. Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues. In this paper, we proposed the first system that it ables to classify the pap smear images into a seven classes problem. Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells. Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine (SVM) classifier. The success of this proposed system… More >

  • Open Access

    ARTICLE

    Safety Analysis of Riding at Intersection Entrance Using Video Recognition Technology

    Xingjian Xue1,*, Linjuan Ge2, Longxin Zeng2, Weiran Li2, Rui Song2, Neal N. Xiong3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5135-5148, 2022, DOI:10.32604/cmc.2022.027356
    Abstract To study riding safety at intersection entrance, video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian method. It is analyzed the relationship among the width of non-motorized lanes at the entrance lane of the intersection, the vehicle-bicycle soft isolation form of the entrance lane of intersection, the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles, the speed of right-turning motor vehicles, and straight-going non-motor vehicles, and the conflict between right-turning motor vehicles and straight-going non-motor vehicles. Due to the traditional statistical methods, to overcome the discreteness of vehicle-bicycle conflict data and the differences… More >

  • Open Access

    ARTICLE

    Handling Big Data in Relational Database Management Systems

    Kamal ElDahshan1, Eman Selim2, Ahmed Ismail Ebada2, Mohamed Abouhawwash3,4, Yunyoung Nam5,*, Gamal Behery2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5149-5164, 2022, DOI:10.32604/cmc.2022.028326
    Abstract Currently, relational database management systems (RDBMSs) face different challenges in application development due to the massive growth of unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, which do not adhere to the relational model. The migration from relational databases to NoSQL databases is challenging due to the data complexity. This study aims to enhance the storage performance of RDBMSs in handling a variety of data. The paper presents two approaches. The first approach proposes a convenient representation of unstructured data storage. Several extensive experiments were implemented to assess the efficiency… More >

  • Open Access

    ARTICLE

    A Truck Scheduling Problem for Multi-Crossdocking System with Metaheuristics

    Phan Nguyen Ky Phuc1, Nguyen Van Thanh2,*, Duong Bao Tram1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5165-5178, 2022, DOI:10.32604/cmc.2022.027967
    Abstract The cross-docking is a very important subject in logistics and supply chain managements. According to the definition, cross-docking is a process dealing with transhipping inventory, in which goods and products are unloaded from an inbound truck and process through a flow-center to be directly loaded onto an outbound truck. Cross-docking is favored due to its advantages in reducing the material handing cost, the needs to store the product in warehouse, as well decreasing the labor cost by eliminating packaging, storing, pick-location and order picking. In cross-docking, products can be consolidated and transported as a full load, reducing overall distribution costs.… More >

  • Open Access

    ARTICLE

    Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

    Md. Maniruzzaman1, Jungpil Shin1,*, Md. Al Mehedi Hasan1, Akira Yasumura2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5179-5195, 2022, DOI:10.32604/cmc.2022.028339
    Abstract Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric and neurobehavioral disorders in children, affecting 11% of children worldwide. This study aimed to propose a machine learning (ML)-based algorithm for discriminating ADHD from healthy children using their electroencephalography (EEG) signals. The study included 61 children with ADHD and 60 healthy children aged 7–12 years. Different morphological and time-domain features were extracted from EEG signals. The t-test (p-value < 0.05) and least absolute shrinkage and selection operator (LASSO) were used to select potential features of children with ADHD and enhance the classification accuracy. The selected potential features were… More >

  • Open Access

    ARTICLE

    Fault Tolerance in the Joint EDF-RMS Algorithm: A Comparative Simulation Study

    Rashmi Sharma1, Nitin Nitin2, Deepak Dahiya3,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5197-5213, 2022, DOI:10.32604/cmc.2022.025059
    Abstract Failure is a systemic error that affects overall system performance and may eventually crash across the entire configuration. In Real-Time Systems (RTS), deadline is the key to successful completion of the program. If tasks effectively meet the deadline, it means the system is working in pristine order. However, missing the deadline means a systemic fault due to which the system can crash (hard RTS) or degrade inclusive performance (soft RTS). To fine-tune the RTS, tolerance is the critical issue and must be handled with extreme care. This article explains the context of fault tolerance with improvised Joint EDF-RMS algorithm in… More >

  • Open Access

    ARTICLE

    Conflict Resolution Strategy in Handover Management for 4G and 5G Networks

    Abdulraqeb Alhammadi1,*, Wan Haslina Hassan1, Ayman A. El-Saleh2, Ibraheem Shayea3, Hafizal Mohamad4, Yousef Ibrahim Daradkeh5
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5215-5232, 2022, DOI:10.32604/cmc.2022.024713
    Abstract Fifth-generation (5G) cellular networks offer high transmission rates in dense urban environments. However, a massive deployment of small cells will be required to provide wide-area coverage, which leads to an increase in the number of handovers (HOs). Mobility management is an important issue that requires considerable attention in heterogeneous networks, where 5G ultra-dense small cells coexist with current fourth-generation (4G) networks. Although mobility robustness optimization (MRO) and load balancing optimization (LBO) functions have been introduced in the 3GPP standard to address HO problems, non-robust and nonoptimal algorithms for selecting appropriate HO control parameters (HCPs) still exist, and an optimal solution… More >

  • Open Access

    ARTICLE

    Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

    Nawaf Alhebaishi1, Abdulrhman M. Alshareef1, Tawfiq Hasanin1, Raed Alsini1, Gyanendra Prasad Joshi2, Seongsoo Cho3, Doo Ill Chul4,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5233-5250, 2022, DOI:10.32604/cmc.2022.025596
    Abstract In recent times, internet of things (IoT) applications on the cloud might not be the effective solution for every IoT scenario, particularly for time sensitive applications. A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices. Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge. One of the considerations of the edge computing environment is resource management that involves resource scheduling, load balancing, task scheduling, and quality of service (QoS) to accomplish improved performance. With this motivation, this paper presents new… More >

  • Open Access

    ARTICLE

    Compiler IR-Based Program Encoding Method for Software Defect Prediction

    Yong Chen1, Chao Xu1,*, Jing Selena He2, Sheng Xiao3, Fanfan Shen1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5251-5272, 2022, DOI:10.32604/cmc.2022.026750
    Abstract With the continuous expansion of software applications, people's requirements for software quality are increasing. Software defect prediction is an important technology to improve software quality. It often encodes the software into several features and applies the machine learning method to build defect prediction classifiers, which can estimate the software areas is clean or buggy. However, the current encoding methods are mainly based on the traditional manual features or the AST of source code. Traditional manual features are difficult to reflect the deep semantics of programs, and there is a lot of noise information in AST, which affects the expression of… More >

  • Open Access

    ARTICLE

    Intelligent Deep Transfer Learning Based Malaria Parasite Detection and Classification Model Using Biomedical Image

    Ahmad Alassaf, Mohamed Yacin Sikkandar*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5273-5285, 2022, DOI:10.32604/cmc.2022.025577
    Abstract Malaria is a severe disease caused by Plasmodium parasites, which can be detected through blood smear images. The early identification of the disease can effectively reduce the severity rate. Deep learning (DL) models can be widely employed to analyze biomedical images, thereby minimizing the misclassification rate. With this objective, this study developed an intelligent deep-transfer-learning-based malaria parasite detection and classification (IDTL-MPDC) model on blood smear images. The proposed IDTL-MPDC technique aims to effectively determine the presence of malarial parasites in blood smear images. In addition, the IDTL-MPDC technique derives median filtering (MF) as a pre-processing step. In addition, a residual… More >

  • Open Access

    ARTICLE

    Performance Analysis of Multilayer Coil Based MI Waveguide Communication System

    Sandeep N. Dandu1, Vinay Kumar2, Joydeep Sengupta1, Neeraj D. Bokde3,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5287-5300, 2022, DOI:10.32604/cmc.2022.026390
    Abstract In the non-conventional media like underwater and underground, the Radio Frequency (RF) communication technique does not perform well due to large antenna size requirement and high path loss. In such media, magnetic induction (MI) communication technique is very promising due to small coil size and constant channel behavior. Unlike the RF technique, the communication range in MI technique is relatively less. To enhance this range, a waveguide technique is already brought in practice. This technique employs single layer coils to enhance the performance of MI waveguide. To further enhance the system functioning, in this paper, we investigated the performance of… More >

  • Open Access

    ARTICLE

    Two-Dimensional Projection-Based Wireless Intrusion Classification Using Lightweight EfficientNet

    Muhamad Erza Aminanto1,2,*, Ibnu Rifqi Purbomukti3, Harry Chandra2, Kwangjo Kim4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5301-5314, 2022, DOI:10.32604/cmc.2022.026749
    Abstract Internet of Things (IoT) networks leverage wireless communication protocols, which adversaries can exploit. Impersonation attacks, injection attacks, and flooding are several examples of different attacks existing in Wi-Fi networks. Intrusion Detection System (IDS) became one solution to distinguish those attacks from benign traffic. Deep learning techniques have been intensively utilized to classify the attacks. However, the main issue of utilizing deep learning models is projecting the data, notably tabular data, into an image. This study proposes a novel projection from wireless network attacks data into a grid-based image for feeding one of the Convolutional Neural Network (CNN) models, EfficientNet. We… More >

  • Open Access

    ARTICLE

    Improved Lightweight Deep Learning Algorithm in 3D Reconstruction

    Tao Zhang1,*, Yi Cao2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5315-5325, 2022, DOI:10.32604/cmc.2022.027083
    Abstract The three-dimensional (3D) reconstruction technology based on structured light has been widely used in the field of industrial measurement due to its many advantages. Aiming at the problems of high mismatch rate and poor real-time performance caused by factors such as system jitter and noise, a lightweight stripe image feature extraction algorithm based on You Only Look Once v4 (YOLOv4) network is proposed. First, Mobilenetv3 is used as the backbone network to effectively extract features, and then the Mish activation function and Complete Intersection over Union (CIoU) loss function are used to calculate the improved target frame regression loss, which… More >

  • Open Access

    ARTICLE

    Intelligent Sign Language Recognition System for E-Learning Context

    Muhammad Jamil Hussain1, Ahmad Shaoor1, Suliman A. Alsuhibany2, Yazeed Yasin Ghadi3, Tamara al Shloul4, Ahmad Jalal1, Jeongmin Park5,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5327-5343, 2022, DOI:10.32604/cmc.2022.025953
    Abstract In this research work, an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and lines. This feature set has the ability to increase the overall performance of machine learning algorithms in an efficient way. The hand gesture recognition based on these features has been implemented for usage in real-time. The feature set used hand landmarks, which were generated using media-pipe (MediaPipe) and open computer vision (openCV) on each frame of the incoming video. The overall algorithm has been tested on two well-known ASL-alphabet (American Sign Language) and ISL-HS… More >

  • Open Access

    ARTICLE

    XGBRS Framework Integrated with Word2Vec Sentiment Analysis for Augmented Drug Recommendation

    Shweta Paliwal1, Amit Kumar Mishra2,*, Ram Krishn Mishra3, Nishad Nawaz4, M. Senthilkumar5
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5345-5362, 2022, DOI:10.32604/cmc.2022.025858
    Abstract Machine Learning is revolutionizing the era day by day and the scope is no more limited to computer science as the advancements are evident in the field of healthcare. Disease diagnosis, personalized medicine, and Recommendation system (RS) are among the promising applications that are using Machine Learning (ML) at a higher level. A recommendation system helps inefficient decision-making and suggests personalized recommendations accordingly. Today people share their experiences through reviews and hence designing of recommendation system based on users’ sentiments is a challenge. The recommendation system has gained significant attention in different fields but considering healthcare, little is being done… More >

  • Open Access

    ARTICLE

    A Steganography Model Data Protection Method Based on Scrambling Encryption

    Xintao Duan1,*, Zhiqiang Shao1, Wenxin Wang1, En Zhang1, Dongli Yue1, Chuan Qin2, Haewoon Nam3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5363-5375, 2022, DOI:10.32604/cmc.2022.027807
    Abstract At present, the image steganography method based on CNN has achieved good results. The trained model and its parameters are of great value. Once leaked, the secret image will be exposed. To protect the security of steganographic network model parameters in the transmission process, an idea based on network model parameter scrambling is proposed in this paper. Firstly, the sender trains the steganography network and extraction network, encrypts the extraction network parameters with the key shared by the sender and the receiver, then sends the extraction network and parameters to the receiver through the public channel, and the receiver recovers… More >

  • Open Access

    ARTICLE

    Improving Method of Anomaly Detection Performance for Industrial IoT Environment

    Junwon Kim1, Jiho Shin2, Ki-Woong Park3, Jung Taek Seo4,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5377-5394, 2022, DOI:10.32604/cmc.2022.026619
    Abstract Industrial Control System (ICS), which is based on Industrial IoT (IIoT), has an intelligent mobile environment that supports various mobility, but there is a limit to relying only on the physical security of the ICS environment. Due to various threat factors that can disrupt the workflow of the IIoT, machine learning-based anomaly detection technologies are being presented; it is also essential to study for increasing detection performance to minimize model errors for promoting stable ICS operation. In this paper, we established the requirements for improving the anomaly detection performance in the IIoT-based ICS environment by analyzing the related cases. After… More >

  • Open Access

    ARTICLE

    A Blockchain-Based Framework for Secure Storage and Sharing of Resumes

    Huanrong Tang1, Changlin Hu1, Tianming Liu2, Jianquan Ouyang1,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5395-5413, 2022, DOI:10.32604/cmc.2022.028284
    Abstract In response to problems in the centralized storage of personal resumes on third-party recruitment platforms, such as inadequate privacy protection, inability of individuals to accurately authorize downloads, and inability to determine who downloaded the resume and when, this study proposes a blockchain-based framework for secure storage and sharing of resumes. Users can employ an authorized access mechanism to protect their privacy rights. The proposed framework uses smart contracts, interplanetary file system, symmetric encryption, and digital signatures to protect, verify, and share resumes. Encryption keys are split and stored in multiple depositories through secret-sharing technology to improve the security of these… More >

  • Open Access

    ARTICLE

    Incremental Learning Model for Load Forecasting without Training Sample

    Charnon Chupong, Boonyang Plangklang*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5415-5427, 2022, DOI:10.32604/cmc.2022.028416
    Abstract This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine (OS-ELM), which can learn and adapt automatically according to new arrival input. However, the use of OS-ELM requires a sufficient amount of initial training sample data, which makes OS-ELM inoperable if sufficiently accurate sample data cannot be obtained. To solve this problem, a synthesis of the initial training sample is proposed. The synthesis of the initial sample is achieved by taking the first data received at the start of working and adding random noises to that data to create new and sufficient… More >

  • Open Access

    ARTICLE

    A Searchable Encryption Scheme Based on Lattice for Log Systems in Blockchain

    Gang Xu1, Yibo Cao1, Shiyuan Xu1, Xin Liu2,*, Xiu-Bo Chen3, Yiying Yu1, Xiaojun Wang4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5429-5441, 2022, DOI:10.32604/cmc.2022.028562
    Abstract With the increasing popularity of cloud storage, data security on the cloud has become increasingly visible. Searchable encryption has the ability to realize the privacy protection and security of data in the cloud. However, with the continuous development of quantum computing, the standard Public-key Encryption with Keyword Search (PEKS) scheme cannot resist quantum-based keyword guessing attacks. Further, the credibility of the server also poses a significant threat to the security of the retrieval process. This paper proposes a searchable encryption scheme based on lattice cryptography using blockchain to address the above problems. Firstly, we design a lattice-based encryption primitive to… More >

  • Open Access

    ARTICLE

    Arabic Music Genre Classification Using Deep Convolutional Neural Networks (CNNs)

    Laiali Almazaydeh1,*, Saleh Atiewi2, Arar Al Tawil3, Khaled Elleithy4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5443-5458, 2022, DOI:10.32604/cmc.2022.025526
    Abstract Genres are one of the key features that categorize music based on specific series of patterns. However, the Arabic music content on the web is poorly defined into its genres, making the automatic classification of Arabic audio genres challenging. For this reason, in this research, our objective is first to construct a well-annotated dataset of five of the most well-known Arabic music genres, which are: Eastern Takht, Rai, Muwashshah, the poem, and Mawwal, and finally present a comprehensive empirical comparison of deep Convolutional Neural Networks (CNNs) architectures on Arabic music genres classification. In this work, to utilize CNNs to develop… More >

  • Open Access

    ARTICLE

    ENSOCOM: Ensemble of Multi-Output Neural Network’s Components for Multi-Label Classification

    Khudran M. Alzhrani*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5459-5479, 2022, DOI:10.32604/cmc.2022.028512
    Abstract Multitasking and multioutput neural networks models jointly learn related classification tasks from a shared structure. Hard parameters sharing is a multitasking approach that shares hidden layers between multiple task-specific outputs. The output layers’ weights are essential in transforming aggregated neurons outputs into tasks labels. This paper redirects the multioutput network research to prove that the ensemble of output layers prediction can improve network performance in classifying multi-label classification tasks. The network’s output layers initialized with different weights simulate multiple semi-independent classifiers that can make non-identical label sets predictions for the same instance. The ensemble of a multi-output neural network that… More >

  • Open Access

    ARTICLE

    Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

    Yanhua Lu1, Xuehui Gong2,*, Andrew Byron Kipnis3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5481-5497, 2022, DOI:10.32604/cmc.2022.027089
    Abstract Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University, the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation (BP) neural network to solve nonlinear problems and have the ability of global approximation and generalization. By analyzing the influence of different uses, different building surfaces and different energy-saving schemes on the change of building energy consumption, the grey correlation method is used to determine the main influencing factors affecting each building energy consumption, including uses, building surfaces and energy-saving schemes, which are used as… More >

  • Open Access

    ARTICLE

    Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

    Masoud Alajmi1, Mohamed K. Nour2, Siwar Ben Haj Hassine3, Mimouna Abdullah Alkhonaini4, Manar Ahmed Hamza5,*, Ishfaq Yaseen5, Abu Sarwar Zamani5, Mohammed Rizwanullah5
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5499-5513, 2022, DOI:10.32604/cmc.2022.026187
    Abstract Recently, cyber physical system (CPS) has gained significant attention which mainly depends upon an effective collaboration with computation and physical components. The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems (CPES). At the same time, the rising ubiquity of wireless sensor networks (WSN) in several application areas makes it a vital part of the design of CPES. Since security and energy efficiency are the major challenging issues in CPES, this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms (EASCPS-MA). The presented EASCPS-MA technique… More >

  • Open Access

    ARTICLE

    Power Allocation in NOMA-CR for 5G Enabled IoT Networks

    Mohammed Basheri1, Mohammad Haseeb Zafar1,2,3,*, Imran Khan3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5515-5530, 2022, DOI:10.32604/cmc.2022.027532
    Abstract In the power domain, non-orthogonal multiple access (NOMA) supports multiple users on the same time-frequency resources, assigns different transmission powers to different users, and differentiates users by user channel gains. Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information, and multi-user detection algorithms, such as successive interference cancellation (SIC) is performed at the receiving end to demodulate the necessary user signals. In contrast to the orthogonal transmission method, the non-orthogonal method can achieve higher spectrum utilization. However, it will increase the receiver complexity. With the development of microelectronics technology,… More >

  • Open Access

    ARTICLE

    STTAR: A Traffic- and Thermal-Aware Adaptive Routing for 3D Network-on-Chip Systems

    Juan Fang1,*, Yunfei Mao1, Min Cai1, Li’ang Zhao1, Huijie Chen1, Wei Xiang2,3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5531-5545, 2022, DOI:10.32604/cmc.2022.027177
    Abstract Since the three-dimensional Network on Chip (3D NoC) uses through-silicon via technology to connect the chips, each silicon layer is conducted through heterogeneous thermal, and 3D NoC system suffers from thermal problems. To alleviate the seriousness of the thermal problem, the distribution of data packets usually relies on traffic information or historical temperature information. However, thermal problems in 3D NoC cannot be solved only based on traffic or temperature information. Therefore, we propose a Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) that applies traffic load and temperature information to routing. First, the STTAR dynamically adjusts the input and output buffer… More >

  • Open Access

    ARTICLE

    An Innovative Approach Utilizing Binary-View Transformer for Speech Recognition Task

    Muhammad Babar Kamal1, Arfat Ahmad Khan2, Faizan Ahmed Khan3, Malik Muhammad Ali Shahid4, Chitapong Wechtaisong2,*, Muhammad Daud Kamal5, Muhammad Junaid Ali6, Peerapong Uthansakul2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5547-5562, 2022, DOI:10.32604/cmc.2022.024590
    Abstract The deep learning advancements have greatly improved the performance of speech recognition systems, and most recent systems are based on the Recurrent Neural Network (RNN). Overall, the RNN works fine with the small sequence data, but suffers from the gradient vanishing problem in case of large sequence. The transformer networks have neutralized this issue and have shown state-of-the-art results on sequential or speech-related data. Generally, in speech recognition, the input audio is converted into an image using Mel-spectrogram to illustrate frequencies and intensities. The image is classified by the machine learning mechanism to generate a classification transcript. However, the audio… More >

  • Open Access

    ARTICLE

    Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction

    Ashit Kumar Dutta1, S. Srinivasan2, S. N. Kumar3, T. S. Balaji4,5, Won Il Lee6, Gyanendra Prasad Joshi7, Sung Won Kim8,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5563-5576, 2022, DOI:10.32604/cmc.2022.027707
    Abstract Due to the advances of intelligent transportation system (ITSs), traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control, navigation, route mapping, etc. The traffic prediction model aims to predict the traffic conditions based on the past traffic data. For more accurate traffic prediction, this study proposes an optimal deep learning-enabled statistical analysis model. This study offers the design of optimal convolutional neural network with attention long short term memory (OCNN-ALSTM) model for traffic prediction. The proposed OCNN-ALSTM technique primarily pre-processes the traffic data by the use of… More >

  • Open Access

    ARTICLE

    Automated Artificial Intelligence Empowered Colorectal Cancer Detection and Classification Model

    Mahmoud Ragab1,2,3,*, Ashwag Albukhari2,4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5577-5591, 2022, DOI:10.32604/cmc.2022.026715
    Abstract Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large intestine. The histopathologist generally investigates the colon biopsy at the time of colonoscopy or surgery. Early detection of colorectal cancer is helpful to maintain the concept of accumulating cancer cells. In medical practices, histopathological investigation of tissue specimens generally takes place in a conventional way, whereas automated tools that use Artificial Intelligence (AI) techniques can produce effective results in disease detection performance. In this background, the current study presents an Automated AI-empowered Colorectal Cancer Detection and Classification (AAI-CCDC) technique. The proposed… More >

  • Open Access

    ARTICLE

    Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

    Esam A. AlQaralleh1, Fahad Aldhaban2, Halah Nasseif2, Malek Z. Alksasbeh3, Bassam A. Y. Alqaralleh2,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5593-5605, 2022, DOI:10.32604/cmc.2022.026666
    Abstract Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes. The use of deep learning (DL) technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification. The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention. Human action recognition (HAR) is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level. The advancements of the DL models help to accomplish improved recognition performance. In this view,… More >

  • Open Access

    ARTICLE

    Iterative Semi-Supervised Learning Using Softmax Probability

    Heewon Chung, Jinseok Lee*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5607-5628, 2022, DOI:10.32604/cmc.2022.028154
    Abstract For the classification problem in practice, one of the challenging issues is to obtain enough labeled data for training. Moreover, even if such labeled data has been sufficiently accumulated, most datasets often exhibit long-tailed distribution with heavy class imbalance, which results in a biased model towards a majority class. To alleviate such class imbalance, semi-supervised learning methods using additional unlabeled data have been considered. However, as a matter of course, the accuracy is much lower than that from supervised learning. In this study, under the assumption that additional unlabeled data is available, we propose the iterative semi-supervised learning algorithms, which… More >

  • Open Access

    ARTICLE

    An Adaptive Real-Time Third Order Sliding Mode Control for Nonlinear Systems

    Ahmed M. Elmogy1,2,*, Amany Sarhan2, Wael M. Elawady2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5629-5641, 2022, DOI:10.32604/cmc.2022.025247
    Abstract As most real world systems are significantly nonlinear in nature, developing robust controllers have attracted many researchers for decades. Robust controllers are the controllers that are able to cope with the inherent uncertainties of the nonlinear systems. Many control methods have been developed for this purpose. Sliding mode control (SMC) is one of the most commonly used methods in developing robust controllers. This paper presents a higher order SMC (HOSMC) approach to mitigate the chattering problem of the traditional SMC techniques. The developed approach combines a third order SMC with an adaptive PID (proportional, integral, derivative) sliding surface to overcome… More >

  • Open Access

    ARTICLE

    Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

    Hajer Faris1, Musaria Karim Mahmood2, Osama Ahmad Alomari3,*, Ashraf Elnagar4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5643-5661, 2022, DOI:10.32604/cmc.2022.026228
    Abstract Wireless sensor networks (WSNs) are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data, and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol. Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node. All routing protocols are large consumers of energy, as they represent the main source of energy cost through data exchange operation. Cluster-based hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in… More >

  • Open Access

    ARTICLE

    Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

    S. Vinson Joshua1, A. Selwin Mich Priyadharson1, Raju Kannadasan2, Arfat Ahmad Khan3, Worawat Lawanont3,*, Faizan Ahmed Khan4, Ateeq Ur Rehman5, Muhammad Junaid Ali6
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5663-5679, 2022, DOI:10.32604/cmc.2022.027178
    Abstract The exponential growth of population in developing countries like India should focus on innovative technologies in the Agricultural process to meet the future crisis. One of the vital tasks is the crop yield prediction at its early stage; because it forms one of the most challenging tasks in precision agriculture as it demands a deep understanding of the growth pattern with the highly nonlinear parameters. Environmental parameters like rainfall, temperature, humidity, and management practices like fertilizers, pesticides, irrigation are very dynamic in approach and vary from field to field. In the proposed work, the data were collected from paddy fields… More >

  • Open Access

    ARTICLE

    Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

    Mujeeb Ur Rehman1,*, Maha Driss2,3, Abdukodir Khakimov4, Sohail Khalid1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5681-5697, 2022, DOI:10.32604/cmc.2022.025840
    Abstract Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence, this article presents an AI-based… More >

  • Open Access

    ARTICLE

    Experimental Analysis of Methods Used to Solve Linear Regression Models

    Mua’ad Abu-Faraj1,*, Abeer Al-Hyari2, Ziad Alqadi3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5699-5712, 2022, DOI:10.32604/cmc.2022.027364
    Abstract Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement processes. Regression is one of the most important types of supervised machine learning, in which labeled data is used to build a prediction model, regression can be classified into three different categories: linear, polynomial, and logistic. In this research paper, different methods will be implemented to solve the linear regression problem, where there is a linear relationship between the target and the predicted output.… More >

  • Open Access

    ARTICLE

    Frequency Domain Adaptive Learning Algorithm for Thoracic Electrical Bioimpedance Enhancement

    Md Zia Ur Rahman1,*, S. Rooban1, P. Rohini2, M. V. S. Ramprasad3, Pradeep Vinaik Kodavanti3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5713-5726, 2022, DOI:10.32604/cmc.2022.027672
    Abstract The Thoracic Electrical Bioimpedance (TEB) helps to determine the stroke volume during cardiac arrest. While measuring cardiac signal it is contaminated with artifacts. The commonly encountered artifacts are Baseline wander (BW) and Muscle artifact (MA), these are physiological and non-stationary. As the nature of these artifacts is random, adaptive filtering is needed than conventional fixed coefficient filtering techniques. To address this, a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario. The proposed block least mean square (BLMS) algorithm is mathematically normalized with reference to data and error. This normalization leads, block… More >

  • Open Access

    ARTICLE

    Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

    Esam A. AlQaralleh1, Fahad Aldhaban2, Halah Nasseif2, Bassam A.Y. Alqaralleh2,*, Tamer AbuKhalil3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5727-5740, 2022, DOI:10.32604/cmc.2022.026780
    Abstract Recent technological advancements have been used to improve the quality of living in smart cities. At the same time, automated detection of vehicles can be utilized to reduce crime rate and improve public security. On the other hand, the automatic identification of vehicle license plate (LP) character becomes an essential process to recognize vehicles in real time scenarios, which can be achieved by the exploitation of optimal deep learning (DL) approaches. In this article, a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition (HMODL-ALPCR) technique has been presented for smart city environments. The major… More >

  • Open Access

    ARTICLE

    Deep Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Control Using 3D Hand Gestures

    Fawad Salam Khan1,4, Mohd Norzali Haji Mohd1,*, Saiful Azrin B. M. Zulkifli2, Ghulam E Mustafa Abro2, Suhail Kazi3, Dur Muhammad Soomro1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5741-5759, 2022, DOI:10.32604/cmc.2022.024927
    Abstract The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent upgrades. Reinforcement learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle (UAV) required maximum accuracy. In this paper, we designed a hybrid framework, which is based on Reinforcement Learning and Deep Learning where the traditional electronic flight controller is replaced by using 3D hand gestures. The algorithm is designed to take the input from 3D hand gestures and integrate with the Deep Deterministic Policy Gradient (DDPG) to receive the best reward and take… More >

  • Open Access

    ARTICLE

    Simulation and Modelling of Water Injection for Reservoir Pressure Maintenance

    Rishi Dewan1, Adarsh Kumar2, Mohammad Khalid Imam Rahmani3, Surbhi Bhatia4, Md Ezaz Ahmed3,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5761-5776, 2022, DOI:10.32604/cmc.2022.024762
    Abstract Water injection has shown to be one of the most successful, efficient, and cost-effective reservoir management strategies. By re-injecting treated and filtered water into reservoirs, this approach can help maintain reservoir pressure, increase hydrocarbon output, and reduce the environmental effect. The goal of this project is to create a water injection model utilizing Eclipse reservoir simulation software to better understand water injection methods for reservoir pressure maintenance. A basic reservoir model is utilized in this investigation. For simulation designs, the reservoir length, breadth, and thickness may be changed to different levels. The water-oil contact was discovered at 7000 feet, and the… More >

  • Open Access

    ARTICLE

    3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data

    Siddiqui Muhammad Yasir1, Amin Muhammad Sadiq2, Hyunsik Ahn3,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5777-5791, 2022, DOI:10.32604/cmc.2022.025909
    Abstract 3D object recognition is a challenging task for intelligent and robot systems in industrial and home indoor environments. It is critical for such systems to recognize and segment the 3D object instances that they encounter on a frequent basis. The computer vision, graphics, and machine learning fields have all given it a lot of attention. Traditionally, 3D segmentation was done with hand-crafted features and designed approaches that didn’t achieve acceptable performance and couldn’t be generalized to large-scale data. Deep learning approaches have lately become the preferred method for 3D segmentation challenges by their great success in 2D computer vision. However,… More >

  • Open Access

    ARTICLE

    Brain Tumor Segmentation using Multi-View Attention based Ensemble Network

    Noreen Mushtaq1, Arfat Ahmad Khan2, Faizan Ahmed Khan3, Muhammad Junaid Ali4, Malik Muhammad Ali Shahid5, Chitapong Wechtaisong2,*, Peerapong Uthansakul2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5793-5806, 2022, DOI:10.32604/cmc.2022.024316
    Abstract Astrocytoma IV or glioblastoma is one of the fatal and dangerous types of brain tumors. Early detection of brain tumor increases the survival rate and helps in reducing the fatality rate. Various imaging modalities have been used for diagnosing by expert radiologists, and Medical Resonance Image (MRI) is considered a better option for detecting brain tumors as MRI is a non-invasive technique and provides better visualization of the brain region. One of the challenging issues is to identify the tumorous region from the MRI scans correctly. Manual segmentation is performed by medical experts, which is a time-consuming task and got… More >

  • Open Access

    ARTICLE

    Subcarrier BD with Cooperative Communication for MIMO-NOMA System

    Jung-In Baik, Ji-Hwan Kim, Beom-Sik Shin, Ji-Hye Oh, Hyoung-Kyu Song*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5807-5821, 2022, DOI:10.32604/cmc.2022.028434
    Abstract With the rapid evolution of Internet of things (IoT), many edge devices require simultaneous connection in 5G communication era. To afford massive data of IoT devices, multiple input multiple output non-orthogonal multiple access (MIMO-NOMA) method has been considered as a promising technology. However, there are numerous drawbacks due to error propagation and inter-user interferences. Therefore, proposed scheme aims to improve the reliability of the MIMO-NOMA system with digital beamforming and intra-cluster cooperative multi point (CoMP) to efficiently support IoT system. In the conventional MIMO-NOMA system, user entities are grouped into clusters. Block diagonalization (BD) is applied to efficiently eliminate the… More >

  • Open Access

    ARTICLE

    An Advanced Stochastic Numerical Approach for Host-Vector-Predator Nonlinear Model

    Prem Junswang1, Zulqurnain Sabir2, Muhammad Asif Zahoor Raja3, Soheil Salahshour4, Thongchai Botmart5,*, Wajaree Weera5
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5823-5843, 2022, DOI:10.32604/cmc.2022.027629
    Abstract A novel design of the computational intelligent framework is presented to solve a class of host-vector-predator nonlinear model governed with set of ordinary differential equations. The host-vector-predator nonlinear model depends upon five groups or classes, host plant susceptible and infected populations, vectors population of susceptible and infected individuals and the predator population. An unsupervised artificial neural network is designed using the computational framework of local and global search competencies of interior-point algorithm and genetic algorithms. For solving the host-vector-predator nonlinear model, a merit function is constructed using the differential model and its associated boundary conditions. The optimization of this merit… More >

  • Open Access

    ARTICLE

    A Mutual Authentication and Cross Verification Protocol for Securing Internet-of-Drones (IoD)

    Saeed Ullah Jan1, Irshad Ahmed Abbasi2,*, Fahad Algarni3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5845-5869, 2022, DOI:10.32604/cmc.2022.026179
    Abstract With the rapid miniaturization in sensor technology, Internet-of-Drones (IoD) has delighted researchers towards information transmission security among drones with the control station server (CSS). In IoD, the drone is different in shapes, sizes, characteristics, and configurations. It can be classified on the purpose of its deployment, either in the civilian or military domain. Drone’s manufacturing, equipment installation, power supply, multi-rotor system, and embedded sensors are not issues for researchers. The main thing is to utilize a drone for a complex and sensitive task using an infrastructure-less/self-organization/resource-less network type called Flying Ad Hoc Network (FANET). Monitoring data transmission traffic, emergency and… More >

  • Open Access

    ARTICLE

    Automated Service Search Model for the Social Internet of Things

    Farhan Amin, Seong Oun Hwang*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5871-5888, 2022, DOI:10.32604/cmc.2022.028342
    Abstract The social internet of things (SIoT) is one of the emerging paradigms that was proposed to solve the problems of network service discovery, navigability, and service composition. The SIoT aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings. In IoT, an object can offer multiple services and different objects can offer the same services with different parameters and interest factors. The proliferation of offered services led to difficulties during service customization and service filtering. This problem is known as service explosion. The selection of suitable service that fits the requirements… More >

  • Open Access

    ARTICLE

    A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

    Esraa Hassan1,3,*, Mahmoud Y. Shams1, Noha A. Hikal2, Samir Elmougy3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5889-5907, 2022, DOI:10.32604/cmc.2022.025629
    Abstract Infectious diseases are an imminent danger that faces human beings around the world. Malaria is considered a highly contagious disease. The diagnosis of various diseases, including malaria, was performed manually, but it required a lot of time and had some human errors. Therefore, there is a need to investigate an efficient and fast automatic diagnosis system. Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis. This study proposed a Convolutional Neural Network (CNN) model to detect malaria automatically. A Malaria Convolutional Neural Network (MCNN)… More >

  • Open Access

    ARTICLE

    Deobfuscating Mobile Malware for Identifying Concealed Behaviors

    Dongho Lee, Geochang Jeon, Sunjun Lee, Haehyun Cho*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5909-5923, 2022, DOI:10.32604/cmc.2022.026395
    Abstract The smart phone market is continuously increasing and there are more than 6 billion of smart phone users worldwide with the aid of the 5G technology. Among them Android occupies 87% of the market share. Naturally, the widespread Android smartphones has drawn the attention of the attackers who implement and spread malware. Consequently, currently the number of malware targeting Android mobile phones is ever increasing. Therefore, it is a critical task to find and detect malicious behaviors of malware in a timely manner. However, unfortunately, attackers use a variety of obfuscation techniques for malware to evade or delay detection. When… More >

  • Open Access

    ARTICLE

    Printed Surface Defect Detection Model Based on Positive Samples

    Xin Zihao1, Wang Hongyuan1,*, Qi Pengyu1, Du Weidong2, Zhang Ji1, Chen Fuhua3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5925-5938, 2022, DOI:10.32604/cmc.2022.026943
    Abstract For a long time, the detection and extraction of printed surface defects has been a hot issue in the print industry. Nowadays, defect detection of a large number of products still relies on traditional image processing algorithms such as scale invariant feature transform (SIFT) and oriented fast and rotated brief (ORB), and researchers need to design algorithms for specific products. At present, a large number of defect detection algorithms based on object detection have been applied but need lots of labeling samples with defects. Besides, there are many kinds of defects in printed surface, so it is difficult to enumerate… More >

  • Open Access

    ARTICLE

    Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

    Shaher Alshammrei1, Sahbi Boubaker2,*, Lioua Kolsi1,3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5939-5954, 2022, DOI:10.32604/cmc.2022.028165
    Abstract Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots (MRs) in both research and education. In this paper, an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm. To achieve this research objectives, first, the MR obstacle-free environment is modeled as a diagraph including nodes, edges and weights. Second, Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point. During its movement, the robot should follow the previously obtained path and stop at each node to test if there… More >

  • Open Access

    ARTICLE

    Online Rail Fastener Detection Based on YOLO Network

    Jun Li1, Xinyi Qiu1, Yifei Wei1,*, Mei Song1, Xiaojun Wang2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5955-5967, 2022, DOI:10.32604/cmc.2022.027947
    Abstract Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production. Track is the basic equipment of railway transportation, and its performance directly affects the service lifetime of railway lines and vehicles. The anomaly detection of rail fasteners is in a priority, while the traditional manual method is extremely inefficient and dangerous to workers. Therefore, this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners, but also to recognize the fasteners states. To be more specific, this paper mainly studies the rail fastener detection based… More >

  • Open Access

    ARTICLE

    Securing Copyright Using 3D Objects Blind Watermarking Scheme

    Hussein Abulkasim1,*, Mona Jamjoom2, Safia Abbas2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5969-5983, 2022, DOI:10.32604/cmc.2022.027999
    Abstract Recently, securing Copyright has become a hot research topic due to rapidly advancing information technology. As a host cover, watermarking methods are used to conceal or embed sensitive information messages in such a manner that it was undetectable to a human observer in contemporary times. Digital media covers may often take any form, including audio, video, photos, even DNA data sequences. In this work, we present a new methodology for watermarking to hide secret data into 3-D objects. The technique of blind extraction based on reversing the steps of the data embedding process is used. The implemented technique uses the… More >

  • Open Access

    ARTICLE

    Signet Ring Cell Detection from Histological Images Using Deep Learning

    Muhammad Faheem Saleem1, Syed Muhammad Adnan Shah1, Tahira Nazir1, Awais Mehmood1, Marriam Nawaz1, Muhammad Attique Khan2, Seifedine Kadry3, Arnab Majumdar4, Orawit Thinnukool5,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5985-5997, 2022, DOI:10.32604/cmc.2022.023101
    Abstract Signet Ring Cell (SRC) Carcinoma is among the dangerous types of cancers, and has a major contribution towards the death ratio caused by cancerous diseases. Detection and diagnosis of SRC carcinoma at earlier stages is a challenging, laborious, and costly task. Automatic detection of SRCs in a patient's body through medical imaging by incorporating computing technologies is a hot topic of research. In the presented framework, we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning (DL) technique named Mask Region-based Convolutional Neural Network (Mask-RCNN). In the first… More >

  • Open Access

    ARTICLE

    UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

    Rania M Tawfik1, Hazem A. A. Nomer2, M. Saeed Darweesh1,*, Ali Wagdy Mohamed3,4, Hassan Mostafa5,6
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5999-6013, 2022, DOI:10.32604/cmc.2022.028234
    Abstract Unmanned Aerial Vehicles (UAVs) provide a reliable and energy-efficient solution for data collection from the Narrowband Internet of Things (NB-IoT) devices. However, the UAV’s deployment optimization, including locations of the UAV’s stop points, is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection efficiently. In this regard, this paper proposes Gaining-Sharing Knowledge (GSK) algorithm for optimizing the UAV’s deployment. In GSK, the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire deployment. The superiority of… More >

  • Open Access

    ARTICLE

    Sika Deer Facial Recognition Model Based on SE-ResNet

    He Gong1,3,4, Lin Chen1, Haohong Pan1, Shijun Li2,5, Yin Guo1, Lili Fu1, Tianli Hu1,3,4,*, Ye Mu1,3, Thobela Louis Tyasi6
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6015-6027, 2022, DOI:10.32604/cmc.2022.027160
    Abstract The scale of deer breeding has gradually increased in recent years and better information management is necessary, which requires the identification of individual deer. In this paper, a deer face dataset is produced using face images obtained from different angles, and an improved residual neural network (ResNet)-based recognition model is proposed to extract the features of deer faces, which have high similarity. The model is based on ResNet-50, which reduces the depth of the model, and the network depth is only 29 layers; the model connects Squeeze-and-Excitation (SE) modules at each of the four layers where the channel changes to… More >

  • Open Access

    ARTICLE

    A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

    Ankur Dumka1, Parag Verma2, Rajesh Singh3, Anil Kumar Bisht4, Divya Anand5,6,*, Hani Moaiteq Aljahdali7, Irene Delgado Noya6,8, Silvia Aparicio Obregon6,9
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6029-6044, 2022, DOI:10.32604/cmc.2022.024698
    Abstract Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the… More >

  • Open Access

    ARTICLE

    A New Intelligent Approach for Deaf/Dumb People based on Deep Learning

    Haitham Elwahsh1,*, Ahmed Elkhouly1, Emad Abouel Nasr2, Ali K. Kamrani3, Engy El-shafeiy4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6045-6060, 2022, DOI:10.32604/cmc.2022.026309
    Abstract

    People who are deaf or have difficulty speaking use sign language, which consists of hand gestures with particular motions that symbolize the “language” they are communicating. A gesture in a sign language is a particular movement of the hands with a specific shape from the fingers and whole hand. In this paper, we present an Intelligent for Deaf/Dumb People approach in real time based on Deep Learning using Gloves (IDLG). The approach IDLG offers scientific contributions based deep-learning, a multi-mode command techniques, real-time, and effective use, and high accuracy rates. For this purpose, smart gloves working in real time were… More >

  • Open Access

    ARTICLE

    Triple Key Security Algorithm Against Single Key Attack on Multiple Rounds

    Muhammad Akram1, Muhammad Waseem Iqbal2,*, Syed Ashraf Ali3, Muhammad Usman Ashraf4, Khalid Alsubhi5, Hani Moaiteq Aljahdali6
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6061-6077, 2022, DOI:10.32604/cmc.2022.028272
    Abstract In cipher algorithms, the encryption and decryption are based on the same key. There are some limitations in cipher algorithms, for example in polyalphabetic substitution cipher the key size must be equal to plaintext otherwise it will be repeated and if the key is known then encryption becomes useless. This paper aims to improve the said limitations by designing of Triple key security algorithm (TKS) in which the key is modified on polyalphabetic substitution cipher to maintain the size of the key and plaintext. Each plaintext character is substituted by an alternative message. The mode of substitution is transformed cyclically… More >

  • Open Access

    ARTICLE

    A 78-MHz BW Continuous-Time Sigma-Delta ADC with Programmable VCO Quantizer

    Sha Li1,2, Qiao Meng1,*, Irfan Tariq1, Xi Chen3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6079-6090, 2022, DOI:10.32604/cmc.2022.027404
    Abstract This article presents a high speed third-order continuous-time (CT) sigma-delta analog-to-digital converter (SDADC) based on voltage-controlled oscillator (VCO), featuring a digital programmable quantizer structure. To improve the overall performance, not only oversampling technique but also noise-shaping enhancing technique is used to suppress in-band noise. Due to the intrinsic first-order noise-shaping of the VCO quantizer, the proposed third-order SDADC can realize forth-order noise-shaping ideally. As a bright advantage, the proposed programmable VCO quantizer is digital-friendly, which can simplify the design process and improve anti-interference capability of the circuit. A 4-bit programmable VCO quantizer clocked at 2.5 GHz, which is proposed in a… More >

  • Open Access

    ARTICLE

    Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics

    Medhat A. Tawfeek1,2,*, Saad Alanazi1, A. A. Abd El-Aziz3,4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6091-6106, 2022, DOI:10.32604/cmc.2022.028391
    Abstract The alignment operation between many protein sequences or DNA sequences related to the scientific bioinformatics application is very complex. There is a trade-off in the objectives in the existing techniques of Multiple Sequence Alignment (MSA). The techniques that concern with speed ignore accuracy, whereas techniques that concern with accuracy ignore speed. The term alignment means to get the similarity in different sequences with high accuracy. The more growing number of sequences leads to a very complex and complicated problem. Because of the emergence; rapid development; and dependence on gene sequencing, sequence alignment has become important in every biological relationship analysis… More >

  • Open Access

    ARTICLE

    Wheat Breeding Strategies under Climate Change based on CERES-Wheat Model

    Jintao Cui1,2,*, Jihui Ding3, Sheng Deng4, Guangcheng Shao3, Weiguang Wang1,2, Xiaojun Wang5, Yesilekin Nebi6
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6107-6118, 2022, DOI:10.32604/cmc.2022.027611
    Abstract Climate change has inevitably had a negative impact on agricultural production and food security. Crop breeding improvement is an efficient option to adapt to future climate and increase grain production. To study the potential to provide valuable advice for breeding under climate change condition, the crop growth model was used as basis to investigate, the effects of the cultivar genotype parameters of the crop estimation through resource and environment synthesis-wheat (CERES-Wheat) model on yield under different climate scenarios. In this study, solar radiation had a positive effect on the yield of winter wheat, while the effects of daily temperature change… More >

  • Open Access

    ARTICLE

    An Efficient Stacked-LSTM Based User Clustering for 5G NOMA Systems

    S. Prabha Kumaresan1, Chee Keong Tan2,*, Yin Hoe Ng1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6119-6140, 2022, DOI:10.32604/cmc.2022.027223
    Abstract Non-orthogonal multiple access (NOMA) has been a key enabling technology for the fifth generation (5G) cellular networks. Based on the NOMA principle, a traditional neural network has been implemented for user clustering (UC) to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones. Consequently, the prediction of UC for the future ones is based on the current clustering information, which is never used again due to the lack of memory of the network. Therefore, to relate the input features of NOMA users and capture the dependency in the clustering… More >

  • Open Access

    ARTICLE

    Impact of Magnetic Field on a Peristaltic Flow with Heat Transfer of a Fractional Maxwell Fluid in a Tube

    Hanan S. Gafel*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6141-6153, 2022, DOI:10.32604/cmc.2022.017378
    Abstract Magnetic field and the fractional Maxwell fluids’ impacts on peristaltic flows within a circular cylinder tube with heat transfer was evaluated while assuming that they are preset with a low-Reynolds number and a long wavelength. Utilizing, the fractional calculus method, the problem was solved analytically. It was deduced for temperature, axial velocity, tangential stress, and heat transfer coefficient. Many emerging parameters and their effects on the aspects of the flow were illustrated, and the outcomes were expressed via graphs. A special focus was dedicated to some criteria, such as the wave amplitude's effect, Hartman and Grashof numbers, radius and relaxation–retardation… More >

  • Open Access

    ARTICLE

    A Study on Small Pest Detection Based on a CascadeR-CNN-Swin Model

    Man-Ting Li, Sang-Hyun Lee*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6155-6165, 2022, DOI:10.32604/cmc.2022.025714
    Abstract This study aims to detect and prevent greening disease in citrus trees using a deep neural network. The process of collecting data on citrus greening disease is very difficult because the vector pests are too small. In this paper, since the amount of data collected for deep learning is insufficient, we intend to use the efficient feature extraction function of the neural network based on the Transformer algorithm. We want to use the Cascade Region-based Convolutional Neural Networks (Cascade R-CNN) Swin model, which is a mixture of the transformer model and Cascade R-CNN model to detect greening disease occurring in… More >

  • Open Access

    ARTICLE

    Factors Affecting Internet Banking Adoption: An Application of Adaptive LASSO

    Hatice Jenkins1, Siamand Hesami1,*, Fulden Yesiltepe2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6167-6184, 2022, DOI:10.32604/cmc.2022.027293
    Abstract This research investigates a broad range of possible factors affecting the adoption of new technology in the banking industry using adaptive LASSO and a standard logit model. The research integrated the adoption of the innovation framework and the technology acceptance theory to develop a conceptual framework for the analysis. Primary data was collected from 400 bank customers in North Cyprus. Risk perception and other customer-specific factors such as perceived risk index and negative attitude toward new technologies index were formulated for the proposed conceptual model. The findings indicated that individuals with a negative attitude toward new technology are least likely… More >

  • Open Access

    ARTICLE

    Swarming Computational Approach for the Heartbeat Van Der Pol Nonlinear System

    Muhammad Umar1, Fazli Amin1, Soheil Salahshour2, Thongchai Botmart3, Wajaree Weera3, Prem Junswang4,*, Zulqurnain Sabir1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6185-6202, 2022, DOI:10.32604/cmc.2022.027970
    Abstract The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model (VP-HBM) using the feedforward artificial neural networks (ANNs) under the optimization of particle swarm optimization (PSO) hybridized with the active-set algorithm (ASA), i.e., ANNs-PSO-ASA. The global search PSO scheme and local refinement of ASA are used as an optimization procedure in this study. An error-based merit function is defined using the differential VP-HBM form as well as the initial conditions. The optimization of the merit function is accomplished using the hybrid computing performances of PSO-ASA. The designed performance of… More >

Share Link

WeChat scan