Home / Journals / CMC / Vol.71, No.1, 2022
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  • Open AccessOpen 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… More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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 More >

  • Open AccessOpen 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 More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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 the 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.… More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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.… More >

  • Open AccessOpen 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.… More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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 More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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 the 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 More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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… More >

  • Open AccessOpen 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 the 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… More >

  • Open AccessOpen 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 More >

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    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… More >

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    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.… More >

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    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… More >

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    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… More >

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    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… More >

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    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… More >

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    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… More >

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    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… More >

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    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 the 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… More >

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    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.

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    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… More >

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    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 the 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… More >

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    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… More >

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    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 the 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… More >

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    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… More >

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    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… More >

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    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 the 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… More >

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    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 the 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… More >

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