Home / Journals / CMC / Vol.71, No.1, 2022
Table of Content
  • Open Access

    ARTICLE

    Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

    Fahd N. Al-Wesabi1,2,*, Imran Khan3, Saleem Latteef Mohammed4, Huda Farooq Jameel4, Mohammad Alamgeer5, Ali M. Al-Sharafi6, Byung Seo Kim7
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1-15, 2022, DOI:10.32604/cmc.2022.018469
    Abstract With the rapid development of the next-generation mobile network, the number of terminal devices and applications is growing explosively. Therefore, how to obtain a higher data rate, wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of scholars. Device-to-Device (D2D) communication technology and other frontier communication technologies have emerged. Device-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular networks. It has become one of the key technologies of the fifth-generation mobile communications system(5G). D2D communication technology which is introduced into cellular networks can effectively improve… More >

  • Open Access

    ARTICLE

    Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning

    Khalid Mahmood Aamir1, Muhammad Ramzan1,2, Saima Skinadar1, Hikmat Ullah Khan3, Usman Tariq4, Hyunsoo Lee5, Yunyoung Nam5,*, Muhammad Attique Khan6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 17-33, 2022, DOI:10.32604/cmc.2022.018613
    (This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
    Abstract This paper focuses on detecting diseased signals and arrhythmias classification into two classes: ventricular tachycardia and premature ventricular contraction. The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person. The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency (IF). Once a signal taken from a patient is detected, then the classifier takes that signal as input and classifies the target disease by predicting the class label. While applying the classifier, templates are designed separately for ventricular tachycardia and… More >

  • Open Access

    ARTICLE

    Automatic License Plate Recognition System for Vehicles Using a CNN

    Parneet Kaur1, Yogesh Kumar1, Shakeel Ahmed2,*, Abdulaziz Alhumam2, Ruchi Singla3, Muhammad Fazal Ijaz4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 35-50, 2022, DOI:10.32604/cmc.2022.017681
    Abstract Automatic License Plate Recognition (ALPR) systems are important in Intelligent Transportation Services (ITS) as they help ensure effective law enforcement and security. These systems play a significant role in border surveillance, ensuring safeguards, and handling vehicle-related crime. The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network (CNN). A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other. CNNs are popular for license plate character recognition. However, little has been reported on the results of these systems with regard to unusual varieties… More >

  • Open Access

    ARTICLE

    Novel Architecture of OneM2M-Based Convergence Platform for Mixed Reality and IoT

    Seungwoon Lee1, Woogeun Kil1, Byeong-hee Roh1,*, SJ Kim2, Jin-suk Kang3
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 51-69, 2022, DOI:10.32604/cmc.2022.019635
    Abstract There have been numerous works proposed to merge augmented reality/mixed reality (AR/MR) and Internet of Things (IoT) in various ways. However, they have focused on their specific target applications and have limitations on interoperability or reusability when utilizing them to different domains or adding other devices to the system. This paper proposes a novel architecture of a convergence platform for AR/MR and IoT systems and services. The proposed architecture adopts the oneM2M IoT standard as the basic framework that converges AR/MR and IoT systems and enables the development of application services used in general-purpose environments without being subordinate to specific… More >

  • Open Access

    ARTICLE

    Modeling and Optimization of Surface Roughness of Epoxy/Nanoparticles Composite Coating

    A. F. Mohamed1,2, J. Abu Alsoud1, Mujahed Al-Dhaifallah3,*, Hegazy Rezk4,5, Mohamed K. Hassan1,6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 71-83, 2022, DOI:10.32604/cmc.2022.019257
    Abstract In power plants, flue gases can cause severe corrosion damage in metallic parts such as flue ducts, heat exchangers, and boilers. Coating is an effective technique to prevent this damage. A robust fuzzy model of the surface roughness (Ra and Rz) of flue gas ducts coated by protective composite coating from epoxy and nanoparticles was constructed based on the experimental dataset. The proposed model consists of four nanoparticles (ZnO, ZrO2, SiO2, and NiO) with 2%, 4%, 6%, and 8%, respectively. Response surface methodology (RSM) was used to optimize the process parameters and identify the optimal conditions for minimum surface roughness… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Approach for Arabic Visual Speech Recognition

    Nadia H. Alsulami1,*, Amani T. Jamal1, Lamiaa A. Elrefaei2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 85-108, 2022, DOI:10.32604/cmc.2022.019450
    (This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
    Abstract Lip-reading technologies are rapidly progressing following the breakthrough of deep learning. It plays a vital role in its many applications, such as: human-machine communication practices or security applications. In this paper, we propose to develop an effective lip-reading recognition model for Arabic visual speech recognition by implementing deep learning algorithms. The Arabic visual datasets that have been collected contains 2400 records of Arabic digits and 960 records of Arabic phrases from 24 native speakers. The primary purpose is to provide a high-performance model in terms of enhancing the preprocessing phase. Firstly, we extract keyframes from our dataset. Secondly, we produce… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

    Sathish Babu Pandu1,*, A. Sagai Francis Britto2, Pudi Sekhar3, P. Vijayarajan4, Amani Abdulrahman Albraikan5, Fahd N. Al-Wesabi6, Mesfer Al Duhayyim7
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 109-124, 2022, DOI:10.32604/cmc.2022.021015
    Abstract Solar energy has gained attention in the past two decades, since it is an effective renewable energy source that causes no harm to the environment. Solar Irradiation Prediction (SIP) is essential to plan, schedule, and manage photovoltaic power plants and grid-based power generation systems. Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time. In this scenario, commonly available Artificial Intelligence (AI) technique can be trained over past values of irradiance as well as weather-related parameters such as temperature, humidity, wind… More >

  • Open Access

    ARTICLE

    Benchmarking Performance of Document Level Classification and Topic Modeling

    Muhammad Shahid Bhatti1,*, Azmat Ullah1, Rohaya Latip2, Abid Sohail1, Anum Riaz1, Rohail Hassan3
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 125-141, 2022, DOI:10.32604/cmc.2022.020083
    (This article belongs to this Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    Abstract Text classification of low resource language is always a trivial and challenging problem. This paper discusses the process of Urdu news classification and Urdu documents similarity. Urdu is one of the most famous spoken languages in Asia. The implementation of computational methodologies for text classification has increased over time. However, Urdu language has not much experimented with research, it does not have readily available datasets, which turn out to be the primary reason behind limited research and applying the latest methodologies to the Urdu. To overcome these obstacles, a medium-sized dataset having six categories is collected from authentic Pakistani news… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

    Anwer Mustafa Hilal1, Badria Sulaiman Alfurhood2, Fahd N. Al-Wesabi3,4, Manar Ahmed Hamza1,*, Mesfer Al Duhayyim5, Huda G. Iskandar4,6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 143-157, 2022, DOI:10.32604/cmc.2022.021502
    Abstract Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examine massive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment analysis (SA) in social networking… More >

  • Open Access

    ARTICLE

    Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

    Dezheng Zhang1,2, Qi Jia1,2, Shibing Yang1,2, Xinliang Han2, Cong Xu3, Xin Liu1,4, Yonghong Xie1,2,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 159-170, 2022, DOI:10.32604/cmc.2022.017295
    Abstract Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine (TCM). We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM. We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph. There are two kinds of path patterns in the knowledge graph: one-hop and two-hop. The one-hop path pattern maps the symptom to syndromes immediately. The two-hop path pattern maps the symptom to syndromes through the nature of disease, etiology, and pathomechanism to support the diagnostic reasoning. Considering the different support strengths for the… More >

  • Open Access

    ARTICLE

    AGWO-CNN Classification for Computer-Assisted Diagnosis of Brain Tumors

    T. Jeslin1,*, J. Arul Linsely2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 171-182, 2022, DOI:10.32604/cmc.2022.020255
    (This article belongs to this Special Issue: Advanced signal acquisition and processing for Internet of Medical Things)
    Abstract Brain cancer is the premier reason for cancer deaths all over the world. The diagnosis of brain cancer at an initial stage is mediocre, as the radiologist is ineffectual. Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful. Therefore, the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules. The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region. In the beginning stage, an… More >

  • Open Access

    ARTICLE

    Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

    Afiya Kiran1, Ahmad Karim2,*, Yasser Obaid Alharbi3, Diaa Mohammed Uliyan3
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 183-197, 2022, DOI:10.32604/cmc.2022.021860
    Abstract Wireless Sensor Networks (WSNs) can be termed as an auto-configured and infrastructure-less wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure and motion etc. WSNs may comprise thousands of Internet of Things (IoT) devices to sense and collect data from its surrounding, process the data and take an automated and mechanized decision. On the other side the proliferation of these devices will soon cause radio spectrum shortage. So, to facilitate these networks, we integrate Cognitive Radio (CR) functionality in these networks. CR can sense the unutilized spectrum of licensed users and then use these empty… More >

  • Open Access

    ARTICLE

    An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

    Abdelhameed Ibrahim1,*, Hattan F. Abutarboush2, Ali Wagdy Mohamed3,4, Mohamad Fouad1, El-Sayed M. El-kenawy5,6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 199-213, 2022, DOI:10.32604/cmc.2022.021886
    (This article belongs to this Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)
    Abstract Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average… More >

  • Open Access

    ARTICLE

    ILipo-PseAAC: Identification of Lipoylation Sites Using Statistical Moments and General PseAAC

    Talha Imtiaz Baig1,*, Yaser Daanial Khan1, Talha Mahboob Alam2, Bharat Biswal3, Hanan Aljuaid4, Durdana Qaiser Gillani5
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 215-230, 2022, DOI:10.32604/cmc.2022.021849
    (This article belongs to this Special Issue: Machine Learning Applications in Medical, Finance, Education and Cyber Security)
    Abstract Lysine Lipoylation is a protective and conserved Post Translational Modification (PTM) in proteomics research like prokaryotes and eukaryotes. It is connected with many biological processes and closely linked with many metabolic diseases. To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level, the computational methods and several other factors play a key role in this purpose. Usually, most of the techniques and different traditional experimental models have a very high cost. They are time-consuming; so, it is required to construct a predictor model to extract lysine lipoylation sites. This study proposes a model that… More >

  • Open Access

    ARTICLE

    Disturbance Evaluation in Power System Based on Machine Learning

    Emad M. Ahmed1,*, Mohamed A. Ahmed1, Ziad M. Ali2,3, Imran Khan4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 231-254, 2022, DOI:10.32604/cmc.2022.022005
    Abstract The operation complexity of the distribution system increases as a large number of distributed generators (DG) and electric vehicles were introduced, resulting in higher demands for fast online reactive power optimization. In a power system, the characteristic selection criteria for power quality disturbance classification are not universal. The classification effect and efficiency needs to be improved, as does the generalization potential. In order to categorize the quality in the power signal disturbance, this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances. Then, a multi-label classification algorithm… More >

  • Open Access

    ARTICLE

    Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

    R. Krishnaswamy1,*, B. Sivakumar2, B. Viswanathan3, Fahd N. Al-Wesabi4,5, Marwa Obayya6, Anwer Mustafa Hilal7
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 255-268, 2022, DOI:10.32604/cmc.2022.021995
    Abstract Automatic biomedical signal recognition is an important process for several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patterns of the ECG signals. In order to raise the diagnostic accuracy and reduce the diagnostic time, automated computer aided diagnosis model is necessary. With the advancements of artificial intelligence (AI) techniques, large quantity of biomedical datasets can be easily examined for decision making. In this aspect, this paper presents an intelligent biomedical ECG signal processing (IBECG-SP) technique for CVD diagnosis. The proposed IBECG-SP technique examines… More >

  • Open Access

    ARTICLE

    Optimal Parameter Estimation of Transmission Line Using Chaotic Initialized Time-Varying PSO Algorithm

    Abdullah Shoukat1, Muhammad Ali Mughal1,*, Saifullah Younus Gondal1, Farhana Umer2, Tahir Ejaz3, Ashiq Hussain1
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 269-285, 2022, DOI:10.32604/cmc.2022.021575
    (This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
    Abstract Transmission line is a vital part of the power system that connects two major points, the generation, and the distribution. For an efficient design, stable control, and steady operation of the power system, adequate knowledge of the transmission line parameters resistance, inductance, capacitance, and conductance is of great importance. These parameters are essential for transmission network expansion planning in which a new parallel line is needed to be installed due to increased load demand or the overhead line is replaced with an underground cable. This paper presents a method to optimally estimate the parameters using the input-output quantities i.e., voltages,… More >

  • Open Access

    ARTICLE

    Hyperuricemia Prediction Using Photoplethysmogram and Arteriograph

    Hafifah Ab Hamid1, Nazrul Anuar Nayan1,*, Mohd Zubir Suboh1, Nurin Izzati Mohamad Azizul1, Mohamad Nazhan Mohd Nizar1, Amilia Aminuddin2, Mohd Shahrir Mohamed Said3, Saharuddin Ahmad4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 287-304, 2022, DOI:10.32604/cmc.2022.021987
    (This article belongs to this Special Issue: Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics)
    Abstract Hyperuricemia is an alarming issue that contributes to cardiovascular disease. Uric acid (UA) level was proven to be related to pulse wave velocity, a marker of arterial stiffness. A hyperuricemia prediction method utilizing photoplethysmogram (PPG) and arteriograph by using machine learning (ML) is proposed. From the literature search, there is no available papers found that relates PPG with UA level even though PPG is highly associated with vessel condition. The five phases in this research are data collection, signal preprocessing including denoising and signal quality indexes, features extraction for PPG and SDPPG waveform, statistical analysis for feature selection and classification… More >

  • Open Access

    ARTICLE

    An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

    T. Gopalakrishnan1, Periakaruppan Sudhakaran2, K. C. Ramya3, K. Sathesh Kumar4, Fahd N. Al-Wesabi5,6,*, Manal Abdullah Alohali7, Anwer Mustafa Hilal8
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 305-320, 2022, DOI:10.32604/cmc.2022.020914
    Abstract Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such as muscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging (MRI). Among these techniques, Muscle MRI recommends the diagnosis of muscular dystrophy through identification of the patterns that exist in muscle fatty replacement. But the patterns overlap among various diseases whereas there is a lack of knowledge prevalent with regards to disease-specific patterns. Therefore, artificial intelligence techniques can be used in the diagnosis of muscular dystrophies, which enables us to analyze, learn, and predict for the future. In this scenario,… More >

  • Open Access

    ARTICLE

    Cluster-Based Stable BSM Dissemination System for Safe Autonomous Platooning

    Jaehwan Lee, Suhwan Kwak, Seungwoo Park, Sangoh Park*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 321-338, 2022, DOI:10.32604/cmc.2022.021237
    Abstract Recently, the importance of vehicle safety supporting system has been highlighted as autonomous driving and platooning has attracted the researchers. To ensure driving safety, each vehicle must broadcast a basic safety message (BSM) every 100 ms. However, stable BSM exchange is difficult because of the changing environment and limited bandwidth of vehicular wireless communication. The increasing number of vehicles on the road increases the competition to access wireless networks for BSM exchange; this increases the packet collision rate. An increased packet collision rate impairs the transmission and reception of BSM information, which can easily cause a traffic accident. We propose… More >

  • Open Access

    ARTICLE

    Performance of Gradient-Based Optimizer for Optimum Wind Cube Design

    Alaa A. K. Ismaeel1,2, Essam H. Houssein3, Amir Y. Hassan4, Mokhtar Said5,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 339-353, 2022, DOI:10.32604/cmc.2022.021517
    Abstract Renewable energy is a safe and limitless energy source that can be utilized for heating, cooling, and other purposes. Wind energy is one of the most important renewable energy sources. Power fluctuation of wind turbines occurs due to variation of wind velocity. A wind cube is used to decrease power fluctuation and increase the wind turbine’s power. The optimum design for a wind cube is the main contribution of this work. The decisive design parameters used to optimize the wind cube are its inner and outer radius, the roughness factor, and the height of the wind turbine hub. A Gradient-Based… More >

  • Open Access

    ARTICLE

    Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

    Jakkrit Onshaunjit, Jakkree Srinonchat*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 355-367, 2022, DOI:10.32604/cmc.2022.017698
    Abstract An ideal printed circuit board (PCB) defect inspection system can detect defects and classify PCB defect types. Existing defect inspection technologies can identify defects but fail to classify all PCB defect types. This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types. In the proposed algorithmic scheme, fuzzy c-means clustering is used for image segmentation via image subtraction prior to defect detection. Arithmetic and logic operations, the circle hough transform (CHT), morphological reconstruction (MR), and connected component labeling (CCL) are used in defect classification. The algorithmic scheme achieves 100% defect detection and 99.05%… More >

  • Open Access

    ARTICLE

    Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm

    Noor Gul1,2, Saeed Ahmed1,3, Atif Elahi4, Su Min Kim1, Junsu Kim1,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 369-387, 2022, DOI:10.32604/cmc.2022.022260
    (This article belongs to this Special Issue: Artificial Intelligence Convergence Networks Leveraging Software-Defined Networking)
    Abstract Since the introduction of the Internet of Things (IoT), several researchers have been exploring its productivity to utilize and organize the spectrum assets. Cognitive radio (CR) technology is characterized as the best aspirant for wireless communications to augment IoT competencies. In the CR networks, secondary users (SUs) opportunistically get access to the primary users (PUs) spectrum through spectrum sensing. The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs. Therefore, several cooperative SUs are engaged in cooperative spectrum sensing (CSS) to ensure reliable sensing results. In CSS, security is still a major concern for… More >

  • Open Access

    ARTICLE

    Multilingual Sentiment Mining System to Prognosticate Governance

    Muhammad Shahid Bhatti1,*, Saman Azhar1, Abid Sohail1, Mohammad Hijji2, Hamna Ayemen1, Areesha Ramzan1
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 389-406, 2022, DOI:10.32604/cmc.2022.021384
    (This article belongs to this Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    Abstract In the age of the internet, social media are connecting us all at the tip of our fingers. People are linkedthrough different social media. The social network, Twitter, allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights. This paper serves the purpose of text processing of a multilingual dataset including Urdu, English, and Roman Urdu. Explore machine learning solutions for sentiment analysis and train models, collect the data on government from Twitter, apply sentiment analysis, and provide a python library that classifies text sentiment.… More >

  • Open Access

    ARTICLE

    An Efficient Internet Traffic Classification System Using Deep Learning for IoT

    Muhammad Basit Umair1, Zeshan Iqbal1, Muhammad Bilal2, Jamel Nebhen4, Tarik Adnan Almohamad3, Raja Majid Mehmood5,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 407-422, 2022, DOI:10.32604/cmc.2022.020727
    (This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
    Abstract Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper… More >

  • Open Access

    ARTICLE

    IoT & AI Enabled Three-Phase Secure and Non-Invasive COVID 19 Diagnosis System

    Anurag Jain1, Kusum Yadav2, Hadeel Fahad Alharbi2, Shamik Tiwari1,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 423-438, 2022, DOI:10.32604/cmc.2022.020238
    (This article belongs to this Special Issue: Emerging Trends and Real-World Applications of Intelligent Computing Techniques)
    Abstract Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital role in diagnosing the COVID-19… More >

  • Open Access

    ARTICLE

    Network Quality Assessment in Heterogeneous Wireless Settings: An Optimization Approach

    Sultan H. Almotiri*, Mohammed A. Al Ghamdi
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 439-455, 2022, DOI:10.32604/cmc.2022.021012
    (This article belongs to this Special Issue: Intelligent Computing Techniques for Communication Systems)
    Abstract The identification of an effective network which can efficiently meet the service requirements of the target, while maintaining ultimate performance at an increased level is significant and challenging in a fully interconnected wireless medium. The wrong selection can contribute to unwanted situations like frustrated users, slow service, traffic congestion issues, missed and/or interrupted calls, and wastefulness of precious network components. Conventional schemes estimate the handoff need and cause the network screening process by a single metric. The strategies are not effective enough because traffic characteristics, user expectations, network terminology and other essential device metrics are not taken into account. This… More >

  • Open Access

    ARTICLE

    Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

    Milos Mravik1, T. Vetriselvi2, K. Venkatachalam3,*, Marko Sarac1, Nebojsa Bacanin1, Sasa Adamovic1
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 457-471, 2022, DOI:10.32604/cmc.2022.020687
    Abstract At present, the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level. Accurate prediction of diabetes patients is an important research area. Many researchers have proposed techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is a key concept in preprocessing. Thus, the features that are relevant to the disease are used for prediction. This condition improves the prediction accuracy. Selecting the right features in the whole feature set is a complicated process, and many researchers are concentrating on it to produce a predictive model with high accuracy.… More >

  • Open Access

    ARTICLE

    A Cost-Efficient Environment Monitoring Robotic Vehicle for Smart Industries

    Arfat Ahmad Khan1, Chitapong Wechtaisong1,*, Faizan Ahmed Khan2, Nadeem Ahmad3
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 473-487, 2022, DOI:10.32604/cmc.2022.020903
    Abstract Environmental monitoring is essential for accessing and avoiding the undesirable situations in industries along with ensuring the safety of workers. Moreover, inspecting and monitoring of environmental parameters by humans lead to various health concerns, which in turn brings to the requirement of monitoring the environment by robotics. In this paper, we have designed and implemented a cost-efficient robotic vehicle for the computation of various environmental parameters such as temperature, radiation, smoke, and pressure with the help of sensors. Furthermore, the robotic vehicle is designed in such a way that it can be dually controlled by using the remote control along… More >

  • Open Access

    ARTICLE

    Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

    R. D. Pubudu L. Indrasiri1, Ernesto Lee2, Vaibhav Rupapara3, Furqan Rustam4, Imran Ashraf5,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 489-515, 2022, DOI:10.32604/cmc.2022.019636
    Abstract Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques.… More >

  • Open Access

    ARTICLE

    Pseudo NLP Joint Spam Classification Technique for Big Data Cluster

    WooHyun Park1, Nawab Muhammad Faseeh Qureshi2,*, Dong Ryeol Shin1
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 517-535, 2022, DOI:10.32604/cmc.2022.021421
    (This article belongs to this Special Issue: Big Data Security Using Artificial Intelligence-based Approaches)
    Abstract Spam mail classification considered complex and error-prone task in the distributed computing environment. There are various available spam mail classification approaches such as the naive Bayesian classifier, logistic regression and support vector machine and decision tree, recursive neural network, and long short-term memory algorithms. However, they do not consider the document when analyzing spam mail content. These approaches use the bag-of-words method, which analyzes a large amount of text data and classifies features with the help of term frequency-inverse document frequency. Because there are many words in a document, these approaches consume a massive amount of resources and become infeasible… More >

  • Open Access

    REVIEW

    Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms

    Haykel Marouani1,2, Omar Al-mutiri1,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 537-571, 2022, DOI:10.32604/cmc.2022.020098
    Abstract The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field. New algorithms are continually being designed on the basis of observations of nature, wildlife, and humanity. In this paper, we review eight major evolutionary algorithms that emulate the behavior of civilization, ants, bees, fishes, and birds (i.e., genetic algorithms, bee colony optimization, simulated annealing, particle swarm optimization, biogeography-based optimization, artificial immune system optimization, cuckoo algorithm and imperialist competitive algorithm). We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems. Results from a literature survey show the best… More >

  • Open Access

    ARTICLE

    Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

    Najib Ahmed Mohammed1, Ali Mohammed Mansoor1,*, Rodina Binti Ahmad1, Saaidal Razalli Bin Azzuhri2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 573-592, 2022, DOI:10.32604/cmc.2022.020462
    Abstract Mission critical Machine-type Communication (mcMTC), also referred to as Ultra-reliable Low Latency Communication (URLLC), has become a research hotspot. It is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected devices. While the reduction in physical (PHY) layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability, the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper layers. The mcMTC has a significant technical impact on… More >

  • Open Access

    ARTICLE

    Binocular Vision Positioning Method for Safety Monitoring of Solitary Elderly

    Lihua Zhu1, Yan Zhang1, Yu Wang1,*, Cheire Cheng2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 593-609, 2022, DOI:10.32604/cmc.2022.022053
    Abstract In nowadays society, the safety of the elderly population is becoming a pressing concern, especially for those who live alone. There might be daily risks such as accidental falling or treatment attack on them. Aiming at these problems, indoor positioning could be a critical way to monitor their states. With the rapidly development of the imaging techniques, wearable and portable cameras are very popular, which could be set on human individual. And in view of the advantages of the visual positioning, the authors propose a binocular visual positioning algorithm to real-timely locate the elderly indoor. In this paper, the imaging… More >

  • Open Access

    ARTICLE

    Left-Handed Characteristics Tunable C-Shaped Varactor Loaded Textile Metamaterial for Microwave Applications

    Samir Salem Al-Bawri1, Mohammad Tariqul Islam2,*, Kabir Hossain3,4, Thennarasan Sabapathy3,4, Muzammil Jusoh3,4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 611-628, 2022, DOI:10.32604/cmc.2022.021244
    Abstract This paper presents a textile-based C-shaped split-ring resonators (SRR) metamaterial (MTM) unit cells with an electrical tunability function. The proposed MTM was composed of two symmetrical C-shaped SRR combined with a central diagonal metal bar, whereas the RF varactor diode is placed on the backside of the splitted ground plane. Stopband behavior of single and array MTM unit cells were analyzed while the achieved negative index physical characteristics were widely studies. Though four different MTM arrays (i.e., 1 × 1, 1 × 2, 2 × 1, and 2 × 2) were analyzed in simulation, a 2 × 2-unit cell array… More >

  • Open Access

    ARTICLE

    An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model

    Savita Khurana1, Gaurav Sharma2, Neha Miglani3, Aman Singh4, Abdullah Alharbi5, Wael Alosaimi5, Hashem Alyami6, Nitin Goyal7,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 629-649, 2022, DOI:10.32604/cmc.2022.021884
    Abstract COVID-19, being the virus of fear and anxiety, is one of the most recent and emergent of various respiratory disorders. It is similar to the MERS-COV and SARS-COV, the viruses that affected a large population of different countries in the year 2012 and 2002, respectively. Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty. The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson… More >

  • Open Access

    ARTICLE

    Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

    A. Devipriya1, P. Prabu2, K. Venkatachalam3, Ahmed Zohair Ibrahim4,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 651-666, 2022, DOI:10.32604/cmc.2022.020820
    Abstract This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed… More >

  • Open Access

    ARTICLE

    Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

    Anwer Mustafa Hilal1,*, Lamia Osman Widaa2, Fahd N. Al-Wesabi3, Mohammad Medani3, Manar Ahmed Hamza1, Mesfer Al Duhayyim4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 667-681, 2022, DOI:10.32604/cmc.2022.021338
    Abstract The emergence of Beyond 5G (B5G) and 6G networks translated personal and industrial operations highly effective, reliable, and gainful by speeding up the growth of next generation Internet of Things (IoT). Industrial equipment in 6G encompasses a huge number of wireless sensors, responsible for collecting massive quantities of data. At the same time, 6G network can take real-world intelligent decisions and implement automated equipment operations. But the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be taken. This has become mandatory for optimal resource allocation in 6G-enabled industrial applications. In this… More >

  • Open Access

    ARTICLE

    Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

    K. S. Bhuvaneshwari1, J. Uma2, K. Venkatachalam3, Mehedi Masud4, Mohamed Abouhawwash5,6,*, T. Logeswaran7
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 683-695, 2022, DOI:10.32604/cmc.2022.021477
    Abstract Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic with the advancement of the… More >

  • Open Access

    ARTICLE

    An Optimized Algorithm for CR-MIMO Wireless Networks

    Imran Khan1, Fahd N. Al-Wesabi2, Marwa Obayya3, Anwer Mustafa Hilal4, Manar Ahmed Hamza4, Mohammed Rizwanullah4, Fahad Ahmed Al-Zahrani5, Hirofumi Amano6, Samih M. Mostafa7,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 697-715, 2022, DOI:10.32604/cmc.2022.021847
    Abstract With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU’ service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing… More >

  • Open Access

    ARTICLE

    Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

    Siti Hajar Khairuddin, Mohd Hilmi Hasan*, Emilia Akashah P. Akhir, Manzoor Ahmed Hashmani
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 717-734, 2022, DOI:10.32604/cmc.2022.020666
    (This article belongs to this Special Issue: Future Generation of Artificial Intelligence and Intelligent Internet of Things)
    Abstract Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for… More >

  • Open Access

    ARTICLE

    Fuzzy Based Hybrid Focus Value Estimation for Multi Focus Image Fusion

    Muhammad Ahmad1,*, M. Arfan Jaffar1, Fawad Nasim1, Tehreem Masood1, Sheeraz Akram2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 735-752, 2022, DOI:10.32604/cmc.2022.019691
    Abstract

    Due to limited depth-of-field of digital single-lens reflex cameras, the scene content within a limited distance from the imaging plane remains in focus while other objects closer to or further away from the point of focus appear as blurred (out-of-focus) in the image. Multi-Focus Image Fusion can be used to reconstruct a fully focused image from two or more partially focused images of the same scene. In this paper, a new Fuzzy Based Hybrid Focus Measure (FBHFM) for multi-focus image fusion has been proposed. Optimal block size is very critical step for multi-focus image fusion. Particle Swarm Optimization (PSO) algorithm… More >

  • Open Access

    ARTICLE

    Chaos-Based Cryptographic Mechanism for Smart Healthcare IoT Systems

    Muhammad Samiullah1, Waqar Aslam1, Arif Mehmood1, Muhammad Saeed Ahmad2, Shafiq Ahmad3, Adel M. Al-Shayea3, Muhammad Shafiq4,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 753-769, 2022, DOI:10.32604/cmc.2022.020432
    Abstract Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things (IoT) in healthcare (HIoT). Due to more and more online security and data hijacking attacks, the confidentiality, integrity and availability of data are considered serious issues in HIoT applications. In this regard, lightweight block ciphers (LBCs) are promising in resource-constrained environment where security is the primary consideration. The prevalent challenge while designing an LBC for the HIoT environment is how to ascertain platform performance, cost, and security. Most of the existing LBCs primarily focus on… More >

  • Open Access

    ARTICLE

    DDoS Detection in SDN using Machine Learning Techniques

    Muhammad Waqas Nadeem, Hock Guan Goh*, Vasaki Ponnusamy, Yichiet Aun
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 771-789, 2022, DOI:10.32604/cmc.2022.021669
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of the SDN that has a… More >

  • Open Access

    ARTICLE

    FPGA Implementation of Deep Leaning Model for Video Analytics

    P. N. Palanisamy*, N. Malmurugan
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 791-808, 2022, DOI:10.32604/cmc.2022.019921
    Abstract In recent years, deep neural networks have become a fascinating and influential research subject, and they play a critical role in video processing and analytics. Since, video analytics are predominantly hardware centric, exploration of implementing the deep neural networks in the hardware needs its brighter light of research. However, the computational complexity and resource constraints of deep neural networks are increasing exponentially by time. Convolutional neural networks are one of the most popular deep learning architecture especially for image classification and video analytics. But these algorithms need an efficient implement strategy for incorporating more real time computations in terms of… More >

  • Open Access

    ARTICLE

    Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

    Abdulsamad A. AL-Marghilani*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 809-824, 2022, DOI:10.32604/cmc.2022.021185
    (This article belongs to this Special Issue: Intelligent Big Data Management and Machine Learning Techniques for IoT-Enabled Pervasive Computing)
    Abstract Presently, suspect prediction of crime scenes can be considered as a classification task, which predicts the suspects based on the time, space, and type of crime. Performing digital forensic investigation in a big data environment poses several challenges to the investigational officer. Besides, the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime scenes. The sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program (composite sketches) based on the verbal explanation given by the eyewitness or victim. Since this suspect… More >

  • Open Access

    ARTICLE

    Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

    Mesfer Al Duhayyim1, Marwa Obayya2, Fahd N. Al-Wesabi3,4, Anwer Mustafa Hilal5,*, Mohammed Rizwanullah5, Majdy M. Eltahir6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 825-842, 2022, DOI:10.32604/cmc.2022.021490
    Abstract With technological advancements in 6G and Internet of Things (IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellular networks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timely solutions for real-time applications such as medicine, tracking, surveillance, etc. Energy efficiency, data collection, and route planning are crucial processes to improve the network communication. These processes are highly difficult owing to high mobility, presence of non-stationary links, dynamic topology, and energy-restricted UAVs. With this motivation, the current research paper presents a novel Energy Aware Data Collection with… More >

  • Open Access

    ARTICLE

    A New Task Scheduling Scheme Based on Genetic Algorithm for Edge Computing

    Zhang Nan1, Li Wenjing1,*, Liu Zhu1, Li Zhi1, Liu Yumin1, Nurun Nahar2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 843-854, 2022, DOI:10.32604/cmc.2022.017504
    Abstract With the continuous evolution of smart grid and global energy interconnection technology, amount of intelligent terminals have been connected to power grid, which can be used for providing resource services as edge nodes. Traditional cloud computing can be used to provide storage services and task computing services in the power grid, but it faces challenges such as resource bottlenecks, time delays, and limited network bandwidth resources. Edge computing is an effective supplement for cloud computing, because it can provide users with local computing services with lower latency. However, because the resources in a single edge node are limited, resource-intensive tasks… More >

  • Open Access

    ARTICLE

    Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

    Visvasam Devadoss Ambeth Kumar1, Chetan Swarup2, Indhumathi Murugan1, Abhishek Kumar3, Kamred Udham Singh4, Teekam Singh5, Ramu Dubey6,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 855-869, 2022, DOI:10.32604/cmc.2022.021582
    (This article belongs to this Special Issue: Applications of Machine Learning for Big Data)
    Abstract Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main aim of this research is… More >

  • Open Access

    ARTICLE

    Energy-Efficient Resource Optimization for Massive MIMO Networks Considering Network Load

    Samira Mujkic1,*, Suad Kasapovic1, Mohammed Abuibaid2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 871-888, 2022, DOI:10.32604/cmc.2022.021441
    (This article belongs to this Special Issue: Radio Networks for new Disruptive Digital Services in Fourth Industrial Revolution)
    Abstract This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output (MIMO) network in which each base station (BS) is equipped with a large number of antennas and each base station (BS) adapts the number of antennas to the daily load profile (DLP). This paper takes into consideration user location distribution (ULD) variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO system. ULD variation is modeled by dividing the cell into two coverage areas with different user densities: boundary focused (BF) and center focused (CF) ULD. All cells are assumed identical in terms… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Scheme for Multi-Channel Sleep Stage Classification

    Wei Pei1, Yan Li1, Siuly Siuly1,*, Peng Wen2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 889-905, 2022, DOI:10.32604/cmc.2022.021830
    (This article belongs to this Special Issue: Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics)
    Abstract Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on sleep staging are either single… More >

  • Open Access

    ARTICLE

    Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19

    Yuan Ai Ho1, Chee Keong Tan1,*, Yin Hoe Ng2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 907-924, 2022, DOI:10.32604/cmc.2022.021756
    (This article belongs to this Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
    Abstract The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease (i.e., COVID-19). As a countermeasure, contact tracing and social distancing are essential to prevent the transmission of the virus, which can be achieved using indoor location analytics. Based on the indoor location analytics, the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19. Given the indoor location data, the clustering can be applied to cluster spatial data, spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.… More >

  • Open Access

    ARTICLE

    Identification of Anomalous Behavioral Patterns in Crowd Scenes

    Muhammad Asif Nauman*, Muhammad Shoaib
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 925-939, 2022, DOI:10.32604/cmc.2022.022147
    Abstract Real time crowd anomaly detection and analyses has become an active and challenging area of research in computer vision since the last decade. The emerging need of crowd management and crowd monitoring for public safety has widen the countless paths of deep learning methodologies and architectures. Although, researchers have developed many sophisticated algorithms but still it is a challenging and tedious task to manage and monitor crowd in real time. The proposed research work focuses on detection of local and global anomaly detection of crowd. Fusion of spatial-temporal features assist in differentiation of feature trained using Mask R-CNN with Resnet101… More >

  • Open Access

    ARTICLE

    Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization

    Alaa A. El-Demerdash, Sherif E. Hussein, John FW Zaki*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 941-959, 2022, DOI:10.32604/cmc.2022.021839
    Abstract Sentiment analysis attracts the attention of Egyptian Decision-makers in the education sector. It offers a viable method to assess education quality services based on the students’ feedback as well as that provides an understanding of their needs. As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels, this research uses a dataset for tweets' sentiments to assess a few machine learning techniques. After dataset preprocessing to remove symbols, necessary stemming and lemmatization is performed for features extraction. This is followed by several machine learning techniques and a proposed Long Short-Term Memory… More >

  • Open Access

    ARTICLE

    Analysis of Flow Structure in Microturbine Operating at Low Reynolds Number

    Mohamed Omri1,*, Yusuf Al-Turki2, Ahmed A. Alghamdi1, Amrid Amnache3, Luc G. Fréchette3
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 961-977, 2022, DOI:10.32604/cmc.2022.021406
    (This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract In this paper, three-dimensional flows in laminar subsonic cascades at relatively low Reynolds numbers (Re < 2500) are presented, based on numerical calculations. The stator and rotor blade designs are those for a MEMS-based Rankine microturbine power-plant-on-a-chip with 109-micron chord blades. Blade passage calculations in 3D were done for different Reynolds numbers, tip clearances (from 0 to 20%) and incidences (0° to 15°) to determine the impact of aerodynamic conditions on the flow patterns. These conditions are applied to a blade passage for a stationary outer casing. The 3D blade passage without tip clearance indicates the presence of two large… More >

  • Open Access

    ARTICLE

    Data Warehouse Design for Big Data in Academia

    Alex Rudniy*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 979-992, 2022, DOI:10.32604/cmc.2022.016676
    Abstract This paper describes the process of design and construction of a data warehouse (“DW”) for an online learning platform using three prominent technologies, Microsoft SQL Server, MongoDB and Apache Hive. The three systems are evaluated for corpus construction and descriptive analytics. The case also demonstrates the value of evidence-centered design principles for data warehouse design that is sustainable enough to adapt to the demands of handling big data in a variety of contexts. Additionally, the paper addresses maintainability-performance tradeoff, storage considerations and accessibility of big data corpora. In this NSF-sponsored work, the data were processed, transformed, and stored in the… More >

  • Open Access

    ARTICLE

    Polarization Insensitive Broadband Zero Indexed Nano-Meta Absorber for Optical Region Applications

    Ismail Hossain1, Md Samsuzzaman2, Ahasanul Hoque3, Mohd Hafiz Baharuddin3, Norsuzlin Binti Mohd Sahar1, Mohammad Tariqul Islam3,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 993-1009, 2022, DOI:10.32604/cmc.2022.021435
    Abstract Broadband response metamaterial absorber (MMA) remains a challenge among researchers. A nanostructured new zero-indexed metamaterial (ZIM) absorber is presented in this study, constructed with a hexagonal shape resonator for optical region applications. The design consists of a resonator and dielectric layers made with tungsten and quartz (Fused). The proposed absorbent exhibits average absorption of more than 0.8972 (89.72%) within the visible wavelength of 450–600 nm and nearly perfect absorption of 0.99 (99%) at 461.61 nm. Based on computational analysis, the proposed absorber can be characterized as ZIM. The developments of ZIM absorbers have demonstrated plasmonic resonance characteristics and a perfect… More >

  • Open Access

    ARTICLE

    Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

    Izzat Al-Darraji1,2, Ayad A. Kakei2, Ayad Ghany Ismaeel3, Georgios Tsaramirsis4, Fazal Qudus Khan5, Princy Randhawa6, Muath Alrammal4, Sadeeq Jan7,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1011-1024, 2022, DOI:10.32604/cmc.2022.022451
    Abstract Robotic manipulators are widely used in applications that require fast and precise motion. Such devices, however, are prompt to nonlinear control issues due to the flexibility in joints and the friction in the motors within the dynamics of their rigid part. To address these issues, the Linear Matrix Inequalities (LMIs) and Parallel Distributed Compensation (PDC) approaches are implemented in the Takagy–Sugeno Fuzzy Model (T-SFM). We propose the following methodology; initially, the state space equations of the nonlinear manipulator model are derived. Next, a Takagy–Sugeno Fuzzy Model (T-SFM) technique is used for linearizing the state space equations of the nonlinear manipulator.… More >

  • Open Access

    ARTICLE

    Heat Transfer of Casson Fluid over a Vertical Plate with Arbitrary Shear Stress and Exponential Heating

    Dolat Khan1, Gohar Ali1, Arshad Khan2, Ilyas Khan3,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1025-1034, 2022, DOI:10.32604/cmc.2022.012635
    Abstract The basic objective of this work is to study the heat transfer of Casson fluid of non-Newtonian nature. The fluid is considered over a vertical plate such that the plate exhibits arbitrary wall shear stress at the boundary. Heat transfers due to exponential plate heating and natural convection are due to buoyancy force. Magnetohydrodynamic (MHD) analysis in the occurrence of a uniform magnetic field is also considered. The medium over the plate is porous and hence Darcy’s law is applied. The governing equations are established for the velocity and temperature fields by the usual Boussinesq approximation. The problem is first… More >

  • Open Access

    ARTICLE

    OTP-Based Software-Defined Cloud Architecture for Secure Dynamic Routing

    Tae Woo Kim1, Yi Pan2, Jong Hyuk Park1,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1035-1049, 2022, DOI:10.32604/cmc.2022.015546
    (This article belongs to this Special Issue: Emerging Trends in Cyber Security for Communication Networks)
    Abstract In the current era, anyone can freely access the Internet thanks to the development of information and communication technology. The cloud is attracting attention due to its ability to meet continuous user demands for resources. Additionally, Cloud is effective for systems with large data flow such as the Internet of Things (IoT) systems and Smart Cities. Nonetheless, the use of traditional networking technology in the cloud causes network traffic overload and network security problems. Therefore, the cloud requires efficient networking technology to solve the existing challenges. In this paper, we propose one-time password-based software-defined cloud architecture for secure dynamic routing… More >

  • Open Access

    ARTICLE

    A Novel Cryptocurrency Prediction Method Using Optimum CNN

    Syed H. Hasan1, Syeda Huyam Hasan2, Mohammed Salih Ahmed3, Syed Hamid Hasan4,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1051-1063, 2022, DOI:10.32604/cmc.2022.020823
    (This article belongs to this Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)
    Abstract In recent years, cryptocurrency has become gradually more significant in economic regions worldwide. In cryptocurrencies, records are stored using a cryptographic algorithm. The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media. Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels. Therefore, this work focuses on extracting Tweets and gathering data from different sources to classify them into positive, negative, and neutral categories, and further examining the correlations between cryptocurrency movements… More >

  • Open Access

    ARTICLE

    A Quantum Algorithm for Evaluating the Hamming Distance

    Mohammed Zidan1,2,*, Manal G. Eldin3, Mahmoud Y. Shams4, Mohamed Tolan5,6, Ayman Abd-Elhamed2,7, Mahmoud Abdel-Aty8
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1065-1078, 2022, DOI:10.32604/cmc.2022.020103
    (This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract We present a novel quantum algorithm to evaluate the hamming distance between two unknown oracles via measuring the degree of entanglement between two ancillary qubits. In particular, we use the power of the entanglement degree based quantum computing model that preserves at most the locality of interactions within the quantum model structure. This model uses one of two techniques to retrieve the solution of a quantum computing problem at hand. In the first technique, the solution of the problem is obtained based on whether there is an entanglement between the two ancillary qubits or not. In the second, the solution… More >

  • Open Access

    ARTICLE

    Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

    Mesfer Al Duhayyim1, Hadeel Alsolai2, Fahd N. Al-Wesabi3,4, Nadhem Nemri3, Hany Mahgoub3, Anwer Mustafa Hilal5, Manar Ahmed Hamza5,*, Mohammed Rizwanullah5
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1079-1094, 2022, DOI:10.32604/cmc.2022.021199
    Abstract Recently, Financial Technology (FinTech) has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm. Financial crisis prediction (FCP) is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution. At the same time, the development of the internet of things (IoT) has altered the mode of human interaction with the physical world. The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process. This paper presents a novel multi-objective squirrel search… More >

  • Open Access

    ARTICLE

    Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

    R. Joshua Samuel Raj1, M. Varalatchoumy2, V. L. Helen Josephine3, A. Jegatheesan4, Seifedine Kadry5, Maytham N. Meqdad6, Yunyoung Nam7,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1095-1109, 2022, DOI:10.32604/cmc.2022.021859
    Abstract Internet of Things (IoT) is transforming the technical setting of conventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to the IoT enabled models are resource-limited and necessitate crisp responses, low latencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentioned challenges. But the intrinsic high latency of CC makes it nonviable. The longer latency degrades the outcome of IoT based smart systems. CC is an emergent dispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems.… More >

  • Open Access

    ARTICLE

    Cardiovascular Disease Prediction Among the Malaysian Cohort Participants Using Electrocardiogram

    Mohd Zubir Suboh1,2, Nazrul Anuar Nayan1,3,*, Noraidatulakma Abdullah4,5, Nurul Ain Mhd Yusof4, Mariatul Akma Hamid4, Azwa Shawani Kamalul Arinfin4, Syakila Mohd Abd Daud4, Mohd Arman Kamaruddin4, Rosmina Jaafar1, Rahman Jamal4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1111-1132, 2022, DOI:10.32604/cmc.2022.022123
    (This article belongs to this Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    Abstract A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The Malaysian Cohort project. An automated peak detection algorithm to detect nine fiducial points of electrocardiogram (ECG) was developed. Forty-eight features were extracted in both time and frequency domains, including statistical features obtained from heart rate variability and Poincare plot analysis. These include five new features derived from spectrum counts of five different frequency ranges. Feature selection was then made based on p-value and correlation matrix. Selected features were used as input for five classifiers of… More >

  • Open Access

    ARTICLE

    Covid-19 CT Lung Image Segmentation Using Adaptive Donkey and Smuggler Optimization Algorithm

    P. Prabu1, K. Venkatachalam2, Ala Saleh Alluhaidan3,*, Radwa Marzouk4, Myriam Hadjouni5, Sahar A. El_Rahman5,6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1133-1152, 2022, DOI:10.32604/cmc.2022.020919
    Abstract COVID’19 has caused the entire universe to be in existential health crisis by spreading globally in the year 2020. The lungs infection is detected in Computed Tomography (CT) images which provide the best way to increase the existing healthcare schemes in preventing the deadly virus. Nevertheless, separating the infected areas in CT images faces various issues such as low-intensity difference among normal and infectious tissue and high changes in the characteristics of the infection. To resolve these issues, a new inf-Net (Lung Infection Segmentation Deep Network) is designed for detecting the affected areas from the CT images automatically. For the… More >

  • Open Access

    ARTICLE

    Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

    Jehangir Arshad1, Ayesha Khan1, Mariam Aftab1, Mujtaba Hussain1, Ateeq Ur Rehman2, Shafiq Ahmad3, Adel M. Al-Shayea3, Muhammad Shafiq4,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1153-1169, 2022, DOI:10.32604/cmc.2022.021917
    Abstract Controlling feedback control systems in continuous action spaces has always been a challenging problem. Nevertheless, reinforcement learning is mainly an area of artificial intelligence (AI) because it has been used in process control for more than a decade. However, the existing algorithms are unable to provide satisfactory results. Therefore, this research uses a reinforcement learning (RL) algorithm to manage the control system. We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient (DDPG). The actor-critic scenario using DDPG is implemented to build the RL agent. In addition, a framework has been created for traditional… More >

  • Open Access

    ARTICLE

    Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction

    R. Joshua Samuel Raj1,*, J. Prince Antony Joel2, Salem Alelyani3, Mohammed Saleh Alsaqer3, C. Anand Deva Durai4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1171-1188, 2022, DOI:10.32604/cmc.2022.021667
    Abstract Human Adaptive Mechatronics (HAM) includes human and computer system in a closed loop. Elderly person with disabilities, normally carry out their daily routines with some assistance to move their limbs. With the short fall of human care takers, mechatronics devices are used with the likes of exoskeleton and exosuits to assist them. The rehabilitation and occupational therapy equipments utilize the electromyography (EMG) signals to measure the muscle activity potential. This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system. Limb characteristics extraction… More >

  • Open Access

    ARTICLE

    Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

    Wafaa Alsaggaf1,*, Felwa Abukhodair1, Amani Tariq Jamal2, Sayed Abdel-Khalek3, Romany F. Mansour4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1189-1203, 2022, DOI:10.32604/cmc.2022.022469
    Abstract In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model… More >

  • Open Access

    ARTICLE

    Deep Learning Based Audio Assistive System for Visually Impaired People

    S. Kiruthika Devi*, C. N. Subalalitha
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1205-1219, 2022, DOI:10.32604/cmc.2022.020827
    Abstract Vision impairment is a latent problem that affects numerous people across the globe. Technological advancements, particularly the rise of computer processing abilities like Deep Learning (DL) models and emergence of wearables pave a way for assisting visually-impaired persons. The models developed earlier specifically for visually-impaired people work effectually on single object detection in unconstrained environment. But, in real-time scenarios, these systems are inconsistent in providing effective guidance for visually-impaired people. In addition to object detection, extra information about the location of objects in the scene is essential for visually-impaired people. Keeping this in mind, the current research work presents an… More >

  • Open Access

    ARTICLE

    Dynamic Encryption and Secure Transmission of Terminal Data Files

    Ruchun Jia1,*, Yang Xin2, Bo Liu3, Qin Qin4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1221-1232, 2022, DOI:10.32604/cmc.2022.019318
    Abstract Data is the last defense line of security, in order to prevent data loss, no matter where the data is stored, copied or transmitted, it is necessary to accurately detect the data type, and further clarify the form and encryption structure of the data transmission process to ensure the accuracy of the data, so as to prevent data leakage, take the data characteristics as the core, use transparent encryption and decryption technology as the leading, and According to the data element characteristics such as identity authentication, authority management, outgoing management, file audit and external device management, the terminal data is… More >

  • Open Access

    ARTICLE

    An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

    Mesfer Al Duhayyim1, Radwa Marzouk2,3, Fahd N. Al-Wesabi4, Maram Alrajhi5, Manar Ahmed Hamza6,*, Abu Sarwar Zamani6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1233-1247, 2022, DOI:10.32604/cmc.2022.021652
    Abstract Recent advancements in computer technologies for data processing, collection, and storage have offered several chances to improve the abilities in production, services, communication, and researches. Data mining (DM) is an interdisciplinary field commonly used to extract useful patterns from the data. At the same time, educational data mining (EDM) is a kind of DM concept, which finds use in educational sector. Recently, artificial intelligence (AI) techniques can be used for mining a large amount of data. At the same time, in DM, the feature selection process becomes necessary to generate subset of features and can be solved by the use… More >

  • Open Access

    ARTICLE

    Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

    Sumaya Alkubaisi1,*, Said Ghoul2, Oguz Ata1
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1249-1262, 2022, DOI:10.32604/cmc.2022.019434
    (This article belongs to this Special Issue: Application of Big Data Analytics in the Management of Business)
    Abstract Most large-scale systems including self-adaptive systems utilize feature models (FMs) to represent their complex architectures and benefit from the reuse of commonalities and variability information. Self-adaptive systems (SASs) are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite contexts. However, reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and resources. The process of configuration reuse can be a better alternative to some contexts to reduce computational time, effort and error-prone. Nevertheless, systems’ complexity can be reduced while the development process of systems by reusing elements or components. FMs… More >

  • Open Access

    ARTICLE

    Bilateral Coupled Epsilon Negative Metamaterial for Dual Band Wireless Communications

    Md Mhedi Hasan1, Mohammad Tariqul Islam1,*, Md Moniruzzaman1, Mohd Hafiz Baharuddin1, Norsuzlin Binti Mohd Sahar2, Md Samsuzzaman3
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1263-1281, 2022, DOI:10.32604/cmc.2022.021388
    Abstract This work presents a dual band epsilon negative (ENG) metamaterial with a bilateral coupled split ring resonator (SRR) for use in C and X band wireless communication systems. The traditional split-ring resonator (SRR) has been amended with this engineered structure. The proposed metamaterial unit cell is realized on the 1.6 mm thick FR-4 printed media with a dimension of 10 × 10 mm2. The resonating patch built with a square split outer ring. Two interlinked inner rings are coupled vertically to the outer ring to extend its electrical length as well as to tune the resonance frequency. Numerical simulation is… More >

  • Open Access

    ARTICLE

    Dynamic Audio-Visual Biometric Fusion for Person Recognition

    Najlaa Hindi Alsaedi*, Emad Sami Jaha
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1283-1311, 2022, DOI:10.32604/cmc.2022.021608
    Abstract Biometric recognition refers to the process of recognizing a person’s identity using physiological or behavioral modalities, such as face, voice, fingerprint, gait, etc. Such biometric modalities are mostly used in recognition tasks separately as in unimodal systems, or jointly with two or more as in multimodal systems. However, multimodal systems can usually enhance the recognition performance over unimodal systems by integrating the biometric data of multiple modalities at different fusion levels. Despite this enhancement, in real-life applications some factors degrade multimodal systems’ performance, such as occlusion, face poses, and noise in voice data. In this paper, we propose two algorithms… More >

  • Open Access

    ARTICLE

    Design of Nonlinear Components Over a Mordell Elliptic Curve on Galois Fields

    Hafeez ur Rehman1,*, Tariq Shah1, Amer Aljaedi2, Mohammad Mazyad Hazzazi3, Adel R. Alharbi2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1313-1329, 2022, DOI:10.32604/cmc.2022.022224
    Abstract Elliptic curve cryptography ensures more safety and reliability than other public key cryptosystems of the same key size. In recent years, the use of elliptic curves in public-key cryptography has increased due to their complexity and reliability. Different kinds of substitution boxes are proposed to address the substitution process in the cryptosystems, including dynamical, static, and elliptic curve-based methods. Conventionally, elliptic curve-based S-boxes are based on prime field but in this manuscript; we propose a new technique of generating S-boxes based on mordell elliptic curves over the Galois field . This technique affords a higher number of possibilities to generate… More >

  • Open Access

    ARTICLE

    Ultra-Wideband Annular Ring Fed Rectangular Dielectric Resonator Antenna for Millimeter Wave 5G Applications

    Abinash Gaya1, Mohd. Haizal Jamaluddin1,*, Ayman A. Althuwayb2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1331-1348, 2022, DOI:10.32604/cmc.2022.022041
    (This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
    Abstract In this article an ultra-wideband rectangular Dielectric Resonator Antenna is designed for millimeter wave 5G frequency band applications. Indoor 5G communications require antenna system with wide bandwidth and high efficiency to enhance the throughput in the channel. To fulfill such requirements a Dielectric Resonator Antenna (DRA) is designed here which has achieved an ultra-wide bandwidth of 20.15% (22.32–27.56 GHz) which is 5.24 GHz of bandwidth centered at 26 GHz as resonating frequency. This covers the complete band 30 (24.3–27.5 GHz) of 5G spectrum. 26 and 28 GHz are considered as most popular frequencies in millimeter wave 5G communications. The aperture… More >

  • Open Access

    ARTICLE

    Parametric Study of Hip Fracture Risk Using QCT-Based Finite Element Analysis

    Hossein Bisheh1,2, Yunhua Luo1,3, Timon Rabczuk2,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1349-1369, 2022, DOI:10.32604/cmc.2022.018262
    Abstract Various parameters such as age, height, weight, and body mass index (BMI) influence the hip fracture risk in the elderly which is the most common injury during the sideways fall. This paper presents a parametric study of hip fracture risk based on the gender, age, height, weight, and BMI of subjects using the subject-specific QCT-based finite element modelling and simulation of single-leg stance and sideways fall loadings. Hip fracture risk is estimated using the strain energy failure criterion as a combination of bone stresses and strains leading to more accurate and reasonable results based on the bone failure mechanism. Understanding… More >

  • Open Access

    ARTICLE

    Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

    Samah Ibrahim Alshathri1,*, Desiree Juby Vincent2, V. S. Hari2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1371-1386, 2022, DOI:10.32604/cmc.2022.022458
    Abstract Invoice document digitization is crucial for efficient management in industries. The scanned invoice image is often noisy due to various reasons. This affects the OCR (optical character recognition) detection accuracy. In this paper, letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method. A stacked denoising autoencoder (SDAE) is implemented with two hidden layers each in encoder network and decoder network. In order to capture the most salient features of training samples, a undercomplete autoencoder is designed with non-linear encoder and decoder function. This autoencoder is regularized for denoising application using a combined… More >

  • Open Access

    ARTICLE

    An Integrated Deep Learning Framework for Fruits Diseases Classification

    Abdul Majid1, Muhammad Attique Khan1, Majed Alhaisoni2, Muhammad Asfand E. yar3, Usman Tariq4, Nazar Hussain1, Yunyoung Nam5,*, Seifedine Kadry6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1387-1402, 2022, DOI:10.32604/cmc.2022.017701
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Agriculture has been an important research area in the field of image processing for the last five years. Diseases affect the quality and quantity of fruits, thereby disrupting the economy of a country. Many computerized techniques have been introduced for detecting and recognizing fruit diseases. However, some issues remain to be addressed, such as irrelevant features and the dimensionality of feature vectors, which increase the computational time of the system. Herein, we propose an integrated deep learning framework for classifying fruit diseases. We consider seven types of fruits, i.e., apple, cherry, blueberry, grapes, peach, citrus, and strawberry. The proposed method… More >

  • Open Access

    ARTICLE

    SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification

    Mohd Anul Haq*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1403-1425, 2022, DOI:10.32604/cmc.2022.021968
    (This article belongs to this Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)
    Abstract Rapid industrialization and urbanization are rapidly deteriorating ambient air quality, especially in the developing nations. Air pollutants impose a high risk on human health and degrade the environment as well. Earlier studies have used machine learning (ML) and statistical modeling to classify and forecast air pollution. However, these methods suffer from the complexity of air pollution dataset resulting in a lack of efficient classification and forecasting of air pollution. ML-based models suffer from improper data pre-processing, class imbalance issues, data splitting, and hyperparameter tuning. There is a gap in the existing ML-based studies on air pollution due to improper data… More >

  • Open Access

    ARTICLE

    Effective Video Summarization Approach Based on Visual Attention

    Hilal Ahmad1, Habib Ullah Khan2, Sikandar Ali3,*, Syed Ijaz Ur Rahman1, Fazli Wahid3, Hizbullah Khattak4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1427-1442, 2022, DOI:10.32604/cmc.2022.021158
    (This article belongs to this Special Issue: Application of Machine-Learning in Computer Vision)
    Abstract Video summarization is applied to reduce redundancy and develop a concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes, the frames that stand out visually are extracted as key frames based on human attention modeling theories. The schemes for modeling visual attention have proven to be effective for video summaries. Nevertheless, the high cost of computing in such techniques restricts their usability in everyday situations. In this context, we propose a method based on KFE (key frame extraction) technique, which is recommended based on an efficient and… More >

  • Open Access

    ARTICLE

    Novel Image Encryption and Compression Scheme for IoT Environment

    Mesfer Al Duhayyim1, Fahd N. Al-Wesabi2, Radwa Marzouk3, Manar Ahmed Hamza4, Anwer Mustafa Hilal4,*, Majdy M. Eltahir2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1443-1457, 2022, DOI:10.32604/cmc.2022.021873
    Abstract Latest advancements made in the processing abilities of smart devices have resulted in the designing of Intelligent Internet of Things (IoT) environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless Multimedia Surveillance Networks (WMSNs) form a vital part in IoT-assisted environment since it contains visual sensors that examine the surroundings from a number of overlapping views by capturing the images incessantly. Since IoT devices generate a massive quantity of digital media, it is therefore required to save the media, especially images, in a secure way. In order to achieve security,… More >

  • Open Access

    ARTICLE

    Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

    Anupam Garg1, Anshu Parashar1, Dipto Barman2, Sahil Jain3, Divya Singhal3, Mehedi Masud4, Mohamed Abouhawwash5,6,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1459-1471, 2022, DOI:10.32604/cmc.2022.022170
    (This article belongs to this Special Issue: Applications of Intelligent Systems in Computer Vision)
    Abstract Autism Spectrum Disorder (ASD) is a developmental disorder whose symptoms become noticeable in early years of the age though it can be present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completely but can be reduced if detected early. An early diagnosis is hampered by the variation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergence of deep learning approaches in various fields, medical experts can be assisted in early diagnosis of ASD. It… More >

  • Open Access

    ARTICLE

    Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

    Pudi Sekhar1, T. J. Benedict Jose2, Velmurugan Subbiah Parvathy3, E. Laxmi Lydia4, Seifedine Kadry5, Kuntha Pin6, Yunyoung Nam7,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1473-1487, 2022, DOI:10.32604/cmc.2022.022110
    Abstract With the incorporation of distributed energy systems in the electric grid, transactive energy market (TEM) has become popular in balancing the demand as well as supply adaptively over the grid. The classical grid can be updated to the smart grid by the integration of Information and Communication Technology (ICT) over the grids. The TEM allows the Peer-to-Peer (P2P) energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them. At the same time, there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of… More >

  • Open Access

    ARTICLE

    Design of Automatic Batch Calibration and Correction System for IMU

    Lihua Zhu1, Qifan Yun1, Zhiqiang Wu1,*, Cheire Cheng2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1489-1501, 2022, DOI:10.32604/cmc.2022.022091
    Abstract Thanks to its light weight, low power consumption, and low price, the inertial measurement units (IMUs) have been widely used in civil and military applications such as autopilot, robotics, and tactical weapons. The calibration is an essential procedure before the IMU is put in use, which is generally used to estimate the error parameters such as the bias, installation error, scale factor of the IMU. Currently, the manual one-by-one calibration is still the mostly used manner, which is low in efficiency, time-consuming, and easy to introduce mis-operation. Aiming at this issue, this paper designs an automatic batch calibration method for… More >

  • Open Access

    ARTICLE

    Piezoresistive Prediction of CNTs-Embedded Cement Composites via Machine Learning Approaches

    Jinho Bang1, SongEe Park2, Haemin Jeon2,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1503-1519, 2022, DOI:10.32604/cmc.2022.020485
    (This article belongs to this Special Issue: Applications of Machine Learning for Big Data)
    Abstract Conductive cementitious composites are innovated materials that have improved electrical conductivity compared to general types of cement, and are expected to be used in a variety of future infrastructures with unique functionalities such as self-heating, electromagnetic shielding, and piezoelectricity. In the present study, machine learning methods that have been recently applied in various fields were proposed for the prediction of piezoelectric characteristics of carbon nanotubes (CNTs)-incorporated cement composites. Data on the resistivity change of CNTs/cement composites according to various water/binder ratios, loading types, and CNT content were considered as training values. These data were applied to numerous machine learning techniques… More >

  • Open Access

    ARTICLE

    Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

    Zuhaira Muhammad Zain1,*, Sapiah Sakri1, Nurul Halimatul Asmak Ismail2, Reza M. Parizi3
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1521-1546, 2022, DOI:10.32604/cmc.2022.022085
    Abstract Developing successful software with no defects is one of the main goals of software projects. In order to provide a software project with the anticipated software quality, the prediction of software defects plays a vital role. Machine learning, and particularly deep learning, have been advocated for predicting software defects, however both suffer from inadequate accuracy, overfitting, and complicated structure. In this paper, we aim to address such issues in predicting software defects. We propose a novel structure of 1-Dimensional Convolutional Neural Network (1D-CNN), a deep learning architecture to extract useful knowledge, identifying and modelling the knowledge in the data sequence,… More >

  • Open Access

    ARTICLE

    Switched-Beam Optimization for an Indoor Visible Light Communication Using Genetic Algorithm

    Ladathunya Pumkaew, Monthippa Uthansakul*, Peerapong Uthansakul
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1547-1566, 2022, DOI:10.32604/cmc.2022.022556
    Abstract Nowadays, Visible Light Communication (VLC) is an attractive alternative technology for wireless communication because it can use some simple Light Emitting Diodes (LEDs) instead of antennas. Typically, indoor VLC is designed to transmit only one dataset through multiple LED beams at a time. As a result, the number of users per unit of time (throughput) is relatively low. Therefore, this paper proposes the design of an indoor VLC system using switched-beam technique through computer simulation. The LED lamps are designed to be arranged in a circular array and the signal can be transmitted through the beam of each LED lamp… More >

  • Open Access

    ARTICLE

    Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET

    E. Ahila Devi1, K. C. Ramya2, K. Sathesh Kumar3, Sultan Ahmad4, Seifedine Kadry5, Hyung Ju Park6, Byeong-Gwon Kang6,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1567-1580, 2022, DOI:10.32604/cmc.2022.022539
    Abstract A mobile ad hoc network (MANET) involves a group of wireless mobile nodes which create an impermanent network with no central authority and infrastructure. The nodes in the MANET are highly mobile and it results in adequate network topology, link loss, and increase the re-initialization of the route discovery process. Route planning in MANET is a multi-hop communication process due to the restricted transmission range of the nodes. Location aided routing (LAR) is one of the effective routing protocols in MANET which suffers from the issue of high energy consumption. Though few research works have focused on resolving energy consumption… More >

  • Open Access

    ARTICLE

    Relation-Aware Entity Matching Using Sentence-BERT

    Huchen Zhou1, Wenfeng Huang1, Mohan Li1,*, Yulin Lai2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1581-1595, 2022, DOI:10.32604/cmc.2022.020695
    Abstract A key aspect of Knowledge fusion is Entity Matching. The objective of this study was to investigate how to identify heterogeneous expressions of the same real-world entity. In recent years, some representative works have used deep learning methods for entity matching, and these methods have achieved good results. However, the common limitation of these methods is that they assume that different attribute columns of the same entity are independent, and inputting the model in the form of paired entity records will cause repeated calculations. In fact, there are often potential relations between different attribute columns of different entities. These relations… More >

  • Open Access

    ARTICLE

    Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

    Usman Ali, Muhammad Tariq Mahmood*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1597-1611, 2022, DOI:10.32604/cmc.2022.022219
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operator is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on local binary pattern (LBP) with adaptive threshold for blur detection. The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur, that may… More >

  • Open Access

    ARTICLE

    Gain Enhancement of Dielectric Resonator Antenna Using Electromagnetic Bandgap Structure

    Amor Smida1,2,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1613-1623, 2022, DOI:10.32604/cmc.2022.022289
    (This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
    Abstract High gain antennas are highly desirable for long-range wireless communication systems. In this paper, a compact, low profile, and high gain dielectric resonator antenna is proposed, fabricated, experimentally tested, and verified. The proposed antenna system has a cylindrical dielectric resonator antenna with a height of 9 mm and a radius of 6.35 mm as a radiating element. The proposed dielectric resonator antenna is sourced with a slot while the slot is excited with a rectangular microstrip transmission line. The microstrip transmission line is designed for a 50 Ω impedance to provide maximum power to the slot. As a result, the… More >

  • Open Access

    ARTICLE

    Dynamic Automated Infrastructure for Efficient Cloud Data Centre

    R. Dhaya1,*, R. Kanthavel2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1625-1639, 2022, DOI:10.32604/cmc.2022.022213
    Abstract We propose a dynamic automated infrastructure model for the cloud data centre which is aimed as an efficient service stipulation for the enormous number of users. The data center and cloud computing technologies have been at the moment rendering attention to major research and development efforts by companies, governments, and academic and other research institutions. In that, the difficult task is to facilitate the infrastructure to construct the information available to application-driven services and make business-smart decisions. On the other hand, the challenges that remain are the provision of dynamic infrastructure for applications and information anywhere. Further, developing technologies to… More >

  • Open Access

    ARTICLE

    Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing

    Manoj Kumar*, Suman
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1641-1660, 2022, DOI:10.32604/cmc.2022.021793
    (This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
    Abstract Cloud computing has gained widespread popularity over the last decade. Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users. Meta-heuristic techniques in cloud computing have exhibited high performance in comparison to traditional scheduling algorithms. This paper presents a novel hybrid Nesterov Accelerated Gradient-based Cuckoo Search Algorithm (NAGCSA) to address the scheduling issue in cloud computing. Nesterov Accelerated Gradient can address trapping at local minima in CSA by updating the position using future approximation. The local search in the proposed algorithm is performed by using Nesterov Accelerated Gradient, while the global search is performed by using… More >

  • Open Access

    ARTICLE

    Decoding of Factorial Experimental Design Models Implemented in Production Process

    Borislav Savkovic1, Pavel Kovac1, Branislav Dudic2,3,*, Michal Gregus2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1661-1675, 2022, DOI:10.32604/cmc.2022.021642
    Abstract The paper deals with factorial experimental design models decoding. For the ease of calculation of the experimental mathematical models, it is convenient first to code the independent variables. When selecting independent variables, it is necessary to take into account the range covered by each. A wide range of choices of different variables is presented in this paper. After calculating the regression model, its variables must be returned to their original values for the model to be easy recognized and represented. In the paper, the procedures of simple first order models, with interactions and with second order models, are presented, which… More >

  • Open Access

    ARTICLE

    Decagonal C-Shaped CSRR Textile-Based Metamaterial for Microwave Applications

    Kabir Hossain1,2, Thennarasan Sabapathy1,2,*, Muzammil Jusoh1,2, Ping Jack Soh1,3, Samir Salem Al-Bawri4, Mohamed Nasrun Osman1,2, Hasliza A. Rahim1,2, Danai Torrungrueng5, Prayoot Akkaraekthalin6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1677-1693, 2022, DOI:10.32604/cmc.2022.022227
    Abstract This paper introduces a decagonal C-shaped complementary split-ring resonator (CSRR) textile-based metamaterial (MTM). The overall size of the proposed sub-wavelength MTM unit cell is 0.28λ0 × 0.255λ0 at 3 GHz. Its stopband behaviour was first studied prior analysing the negative index properties of the proposed MTM. It is worth noting that in this work a unique way the experiments were completed. For both simulations and measurements, the proposed MTM exhibited negative-permittivity and negative-refractive index characteristics with an average bandwidth of more than 3 GHz (considering 1.7 to 8.2 GHz as the measurements were carried out within this range). In simulations, the MTM… More >

  • Open Access

    ARTICLE

    Lightweight Key Management Scheme Using Fuzzy Extractor for Wireless Mobile Sensor Network

    Eid Rehman1, Ibrahima Kalil Toure2, Kashif Sultan3, Muhammad Asif4, Muhammad Habib1, Najam Ul Hasan5, Oh-Young Song6,*, Aaqif Afzaal Abbasi1
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1695-1712, 2022, DOI:10.32604/cmc.2022.021641
    Abstract

    The mature design of wireless mobile sensor network makes it to be used in vast verities of applications including from home used to the security surveillance. All such types of applications based on wireless mobile sensor network are generally using real time data, most of them are interested in real time communication directly from cluster head of cluster instead of a base station in cluster network. This would be possible if an external user allows to directly access real time data from the cluster head in cluster wireless mobile sensor network instead of accessing data from base station. But this… More >

  • Open Access

    ARTICLE

    IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

    P. Suresh1,*, R. H. Aswathy1, Sridevi Arumugam2, Amani Abdulrahman Albraikan3, Fahd N. Al-Wesabi4, Anwer Mustafa Hilal5, Mohammad Alamgeer6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1713-1728, 2022, DOI:10.32604/cmc.2022.021789
    Abstract In India, water wastage in agricultural fields becomes a challenging issue and it is needed to minimize the loss of water in the irrigation process. Since the conventional irrigation system needs massive quantity of water utilization, a smart irrigation system can be designed with the help of recent technologies such as machine learning (ML) and the Internet of Things (IoT). With this motivation, this paper designs a novel IoT enabled deep learning enabled smart irrigation system (IoTDL-SIS) technique. The goal of the IoTDL-SIS technique focuses on the design of smart irrigation techniques for effectual water utilization with less human interventions.… More >

  • Open Access

    ARTICLE

    DNNBoT: Deep Neural Network-Based Botnet Detection and Classification

    Mohd Anul Haq, Mohd Abdul Rahim Khan*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1729-1750, 2022, DOI:10.32604/cmc.2022.020938
    (This article belongs to this Special Issue: Advanced IoT Industrial Solutions and Cyber Security Threats in Communication Networks)
    Abstract The evolution and expansion of IoT devices reduced human efforts, increased resource utilization, and saved time; however, IoT devices create significant challenges such as lack of security and privacy, making them more vulnerable to IoT-based botnet attacks. There is a need to develop efficient and faster models which can work in real-time with efficiency and stability. The present investigation developed two novels, Deep Neural Network (DNN) models, DNNBoT1 and DNNBoT2, to detect and classify well-known IoT botnet attacks such as Mirai and BASHLITE from nine compromised industrial-grade IoT devices. The utilization of PCA was made to feature extraction and improve… More >

  • Open Access

    ARTICLE

    Prediction of COVID-19 Transmission in the United States Using Google Search Trends

    Meshrif Alruily1, Mohamed Ezz1,2, Ayman Mohamed Mostafa1,3, Nacim Yanes1,4, Mostafa Abbas5, Yasser El-Manzalawy5,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1751-1768, 2022, DOI:10.32604/cmc.2022.020714
    (This article belongs to this Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
    Abstract Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources. Due to the exponential spread of the COVID-19 infection worldwide, several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature. To accelerate scientific and public health insights into the spread and impact of COVID-19, Google released the Google COVID-19 search trends symptoms open-access dataset. Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19… More >

  • Open Access

    ARTICLE

    Development of PCCNN-Based Network Intrusion Detection System for EDGE Computing

    Mohd Anul Haq, Mohd Abdul Rahim Khan*, Talal AL-Harbi
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1769-1788, 2022, DOI:10.32604/cmc.2022.018708
    (This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
    Abstract Intrusion Detection System (IDS) plays a crucial role in detecting and identifying the DoS and DDoS type of attacks on IoT devices. However, anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion detection. Also, we found many difficulty levels when applying IDS to IoT devices for identifying attempted attacks. Given this background, we designed a solution to detect intrusions using the Convolutional Neural Network (CNN) for Enhanced Data rates for GSM Evolution (EDGE) Computing. We created two separate categories to handle the attack and non-attack events in the system. The findings of this study indicate that this approach was… More >

  • Open Access

    ARTICLE

    MELex: The Construction of Malay-English Sentiment Lexicon

    Nurul Husna Mahadzir1, Mohd Faizal Omar2, Mohd Nasrun Mohd Nawi3,*, Anas A. Salameh4, Kasmaruddin Che Hussin5, Abid Sohail6
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1789-1805, 2022, DOI:10.32604/cmc.2022.021131
    (This article belongs to this Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    Abstract Currently, the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment lexicon. Thus, this issue is addressed in this paper in order to enhance the accuracy of sentiment analysis. In this study, a new lexicon for sentiment analysis is constructed. A detailed review of existing approaches has been conducted, and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. Constructing MELex involves three activities: seed words selection, polarity assignment, and synonym expansions. Our approach differs from previous works in that MELex can analyze text for the two most widely… More >

  • Open Access

    ARTICLE

    Convolutional Neural Network-Based Identity Recognition Using ECG at Different Water Temperatures During Bathing

    Jianbo Xu, Wenxi Chen*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1807-1819, 2022, DOI:10.32604/cmc.2022.021154
    (This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
    Abstract This study proposes a convolutional neural network (CNN)-based identity recognition scheme using electrocardiogram (ECG) at different water temperatures (WTs) during bathing, aiming to explore the impact of ECG length on the recognition rate. ECG data was collected using non-contact electrodes at five different WTs during bathing. Ten young student subjects (seven men and three women) participated in data collection. Three ECG recordings were collected at each preset bathtub WT for each subject. Each recording is 18 min long, with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. The R peaks were… More >

  • Open Access

    ARTICLE

    Intelligent Machine Learning Based EEG Signal Classification Model

    Mesfer Al Duhayyim1, Haya Mesfer Alshahrani2, Fahd N. Al-Wesabi3, Mohammed Abdullah Al-Hagery4, Anwer Mustafa Hilal5,*, Abu Sarwar Zaman5
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1821-1835, 2022, DOI:10.32604/cmc.2022.021119
    Abstract In recent years, Brain-Computer Interface (BCI) system gained much popularity since it aims at establishing the communication between human brain and computer. BCI systems are applied in several research areas such as neuro-rehabilitation, robots, exoeskeletons, etc. Electroencephalography (EEG) is a technique commonly applied in capturing brain signals. It is incorporated in BCI systems since it has attractive features such as non-invasive nature, high time-resolution output, mobility and cost-effective. EEG classification process is highly essential in decision making process and it incorporates different processes namely, feature extraction, feature selection, and classification. With this motivation, the current research paper presents an Intelligent… More >

  • Open Access

    ARTICLE

    The Roll Stability Analysis of Semi-Trailer Based on the Wheel Force

    Dong Wang1,*, Siwei Chen1, Weigong Zhang1, Danjie Du2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1837-1848, 2022, DOI:10.32604/cmc.2022.023033
    Abstract It is different for the liquid tank semi-trailer to keep roll stability during turning or emergency voidance, and that may cause serious accidents. Although the scholars did lots of research about the roll stability of liquid tank semi-trailer in theory by calculating and simulation, how to make an effective early warning of rollover is still unsolved in practice. The reasons include the complex driving condition and the difficulty of the vehicle parameter obtaining. The feasible method used currently is evaluating the roll stability of a liquid tank semi-trailer by the lateral acceleration or the attitude of the vehicle. Unfortunately, the… More >

  • Open Access

    ARTICLE

    Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

    Swati Singh1, Isha Gupta2, Sheifali Gupta2, Deepika Koundal3,*, Sultan Aljahdali4, Shubham Mahajan5, Amit Kant Pandit5
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1849-1866, 2022, DOI:10.32604/cmc.2022.021875
    (This article belongs to this Special Issue: Applications of Intelligent Systems in Computer Vision)
    Abstract In Agriculture Sciences, detection of diseases is one of the most challenging tasks. The mis-interpretations of plant diseases often lead to wrong pesticide selection, resulting in damage of crops. Hence, the automatic recognition of the diseases at earlier stages is important as well as economical for better quality and quantity of fruits. Computer aided detection (CAD) has proven as a supportive tool for disease detection and classification, thus allowing the identification of diseases and reducing the rate of degradation of fruit quality. In this research work, a model based on convolutional neural network with 19 convolutional layers has been proposed… More >

  • Open Access

    Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

    Zhe Sun1, Jiyuan Feng1, Lihua Yin1,*, Zixu Zhang2, Ran Li1, Yu Hu1, Chongning Na3
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1867-1886, 2022, DOI:10.32604/cmc.2022.022290
    Abstract Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data. However, the training mechanism for passing model parameters is still threatened by gradient inversion, inference attacks, etc. With a lightweight encryption overhead, function encryption is a viable secure aggregation technique in federation learning, which is often used in combination with differential privacy. The function encryption in federal learning still has the following problems: a) Traditional function encryption usually requires a trust third party (TTP) to assign the keys. If a TTP colludes with a server, the security aggregation mechanism can be… More >

  • Open Access

    ARTICLE

    Optimal Hybrid Precoding Based QoE for Partially Structured Massive MIMO System

    Farung Samklang, Peerapong Uthansakul, Monthippa Uthansakul*, Patikorn Anchuen
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1887-1902, 2022, DOI:10.32604/cmc.2022.022139
    Abstract Precoding is a beamforming technique that supports multi-stream transmission in which the RF chain plays a significant role as a digital precoding at the receiver for wireless communication. The traditional precoding contains only digital signal processing and each antenna connects to each RF chain, which provides high transmission efficiency but high cost and hardware complexity. Hybrid precoding is one of the most popular massive multiple input multiple output (MIMO) techniques that can save costs and avoid using complex hardware. At present, network services are currently in focus with a wide range of traffic volumes. In terms of the Quality of… More >

  • Open Access

    ARTICLE

    A Transfer Learning-Based Approach to Detect Cerebral Microbleeds

    Sitara Afzal, Imran Ullah Khan, Jong Weon Lee*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1903-1923, 2022, DOI:10.32604/cmc.2022.021930
    (This article belongs to this Special Issue: Application of Machine-Learning in Computer Vision)
    Abstract Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum vessels. Individuals and elderly people with brain injury and dementia can have small microbleeds in their brains. A recent study has shown that cerebral microbleeds could be remarkably risky in terms of life and can be riskier for patients with dementia. In this study, we proposed an efficient approach to automatically identify microbleeds by reducing the false positives in openly available susceptibility-weighted imaging (SWI) data samples. The proposed structure comprises two different pre-trained convolutional models with four stages. These stages include (i) skull removal and… More >

  • Open Access

    ARTICLE

    Fleet Optimization of Smart Electric Motorcycle System Using Deep Reinforcement Learning

    Patikorn Anchuen, Peerapong Uthansakul*, Monthippa Uthansakul, Settawit Poochaya
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1925-1943, 2022, DOI:10.32604/cmc.2022.022444
    Abstract Smart electric motorcycle-sharing systems based on the digital platform are one of the public transportations that we use in daily lives when the sharing economy is considered. This transportation provides convenience for users with low-cost systems while it also promotes an environmental conservation. Normally, users rent the vehicle to travel from the origin station to another station near their destination with a one-way trip in which the demand of renting and returning at each station is different. This leads to unbalanced vehicle rental systems. To avoid the full or empty inventory, the electric motorcycle-sharing rebalancing with the fleet optimization is… More >

  • Open Access

    ARTICLE

    Attribute Weighted Naïve Bayes Classifier

    Lee-Kien Foo*, Sook-Ling Chua, Neveen Ibrahim
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1945-1957, 2022, DOI:10.32604/cmc.2022.022011
    (This article belongs to this Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)
    Abstract The naïve Bayes classifier is one of the commonly used data mining methods for classification. Despite its simplicity, naïve Bayes is effective and computationally efficient. Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning, this assumption may not hold in real-world applications. Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption. While these methods improve the classification performance, they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time. One approach… More >

  • Open Access

    ARTICLE

    Multi-Path Service Function Chaining for Mobile Surveillance of Animal Husbandry

    Xi Chen1,3, Tao Wu2,*, Mehtab Afzal4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1959-1971, 2022, DOI:10.32604/cmc.2022.022344
    Abstract Animal husbandry is the pillar industry in some ethnic areas of China. However, the communication/networking infrastructure in these areas is often underdeveloped, thus the difficulty in centralized management, and challenges for the effective monitoring. Considering the dynamics of the field monitoring environment, as well as the diversity and mobility of monitoring targets, traditional WSN (Wireless Sensor Networks) or IoT (Internet of Things) is difficult to meet the surveillance needs. Mobile surveillance that features the collaboration of various functions (camera, sensing, image recognition, etc.) deployed on mobile devices is desirable in a volatile wireless environment. This paper proposes the service function… More >

  • Open Access

    ARTICLE

    Blood Pressure and Heart Rate Measurements Using Photoplethysmography with Modified LRCN

    Chih-Ta Yen1,*, Cheng-Hong Liao2
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1973-1986, 2022, DOI:10.32604/cmc.2022.022679
    Abstract In this study, single-channel photoplethysmography (PPG) signals were used to estimate the heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A deep learning model was proposed using a long-term recurrent convolutional network (LRCN) modified from a deep learning algorithm, the convolutional neural network model of the modified inception deep learning module, and a long short-term memory network (LSTM) to improve the model's accuracy of BP and HR measurements. The PPG data of 1,551 patients were obtained from the University of California Irvine Machine Learning Repository. How to design a filter of PPG signals and how to… More >

  • Open Access

    ARTICLE

    A Study on Classification and Detection of Small Moths Using CNN Model

    Sang-Hyun Lee*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1987-1998, 2022, DOI:10.32604/cmc.2022.022554
    (This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
    Abstract Currently, there are many limitations to classify images of small objects. In addition, there are limitations such as error detection due to external factors, and there is also a disadvantage that it is difficult to accurately distinguish between various objects. This paper uses a convolutional neural network (CNN) algorithm to recognize and classify object images of very small moths and obtain precise data images. A convolution neural network algorithm is used for image data classification, and the classified image is transformed into image data to learn the topological structure of the image. To improve the accuracy of the image classification… More >

  • Open Access

    ARTICLE

    Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

    Qaisar Abbas1,*, Mostafa E.A. Ibrahim1,2, Shakir Khan1, Abdul Rauf Baig1
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1999-2007, 2022, DOI:10.32604/cmc.2022.022553
    (This article belongs to this Special Issue: Artificial Intelligence Enabled Intelligent Transportation Systems)
    Abstract Traffic accidents are caused by driver fatigue or distraction in many cases. To prevent accidents, several low-cost hypovigilance (hypo-V) systems were developed in the past based on a multimodal-hybrid (physiological and behavioral) feature set. Similarly in this paper, real-time driver inattention and fatigue (Hypo-Driver) detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid features. The considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial expressions. To get enhanced visual facial features in uncontrolled environment, three cameras are deployed on multiview points (0°, 45°, and 90°) of… More >

  • Open Access

    ARTICLE

    Implementation and Validation of the Optimized Deduplication Strategy in Federated Cloud Environment

    Nipun Chhabra*, Manju Bala, Vrajesh Sharma
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 2019-2035, 2022, DOI:10.32604/cmc.2022.021797
    (This article belongs to this Special Issue: Future Generation of Artificial Intelligence and Intelligent Internet of Things)
    Abstract Cloud computing technology is the culmination of technical advancements in computer networks, hardware and software capabilities that collectively gave rise to computing as a utility. It offers a plethora of utilities to its clients worldwide in a very cost-effective way and this feature is enticing users/companies to migrate their infrastructure to cloud platform. Swayed by its gigantic capacity and easy access clients are uploading replicated data on cloud resulting in an unnecessary crunch of storage in datacenters. Many data compression techniques came to rescue but none could serve the purpose for the capacity as large as a cloud, hence, researches… More >

  • Open Access

    ARTICLE

    An Improved Sparrow Search Algorithm for Node Localization in WSN

    R. Thenmozhi1, Abdul Wahid Nasir2, Vijaya Krishna Sonthi3, T. Avudaiappan4, Seifedine Kadry5, Kuntha Pin6, Yunyoung Nam7,*
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 2037-2051, 2022, DOI:10.32604/cmc.2022.022203
    Abstract Wireless sensor networks (WSN) comprise a set of numerous cheap sensors placed in the target region. A primary function of the WSN is to avail the location details of the event occurrences or the node. A major challenge in WSN is node localization which plays an important role in data gathering applications. Since GPS is expensive and inaccurate in indoor regions, effective node localization techniques are needed. The major intention of localization is for determining the place of node in short period with minimum computation. To achieve this, bio-inspired algorithms are used and node localization is assumed as an optimization… More >

  • Open Access

    ARTICLE

    Metric-Based Resolvability of Quartz Structure

    Muhammad Imran1,*, Ali Ahmad2, Muhammad Azeem3, Kashif Elahi4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 2053-2071, 2022, DOI:10.32604/cmc.2022.022064
    Abstract Silica has three major varieties of crystalline. Quartz is the main and abundant ingredient in the crust of our earth. While other varieties are formed by the heating of quartz. Silica quartz is a rich chemical structure containing enormous properties. Any chemical network or structure can be transformed into a graph, where atoms become vertices and the bonds are converted to edges, between vertices. This makes a complex network easy to visualize to work on it. There are many concepts to work on chemical structures in terms of graph theory but the resolvability parameters of a graph are quite advance… More >

  • Open Access

    ARTICLE

    EfficientNet-Based Robust Recognition of Peach Plant Diseases in Field Images

    Haleem Farman1, Jamil Ahmad1,*, Bilal Jan2, Yasir Shahzad3, Muhammad Abdullah1, Atta Ullah4
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 2073-2089, 2022, DOI:10.32604/cmc.2022.018961
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    Abstract Plant diseases are a major cause of degraded fruit quality and yield losses. These losses can be significantly reduced with early detection of diseases to ensure their timely treatment, particularly in developing countries. In this regard, an expert system based on deep learning model where the expert knowledge, particularly the one acquired by plant pathologist, is recursively learned by the system and is applied using a smart phone application for use in the target field environment, is being proposed. In this paper, a robust disease detection method is developed based on convolutional neural network (CNN), where its powerful features extraction… More >

Share Link

WeChat scan