Home / Journals / CMC / Vol.70, No.2, 2022
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  • Open AccessOpen Access

    ARTICLE

    Torsional Wave in a Dissipative Cylindrical Shell Under Initial Stresses

    Mahmoud M. Selim1,2,*, Khaled A. Gepreel3
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3021-3030, 2022, DOI:10.32604/cmc.2022.019579
    Abstract The dispersion relation of torsional wave in a dissipative, incompressible cylindrical shell of infinite length incorporating initial stresses effects is investigated. The governing equation and closed form solutions are derived with the aid of Biot's principle. Phase velocity and damping of torsional wave are obtained analytically and the influences of dissipation and initial stresses are studied in details. We proposed a new method for obtaining the phase and damping velocities of torsional wave in a complex form. Numerical results analyzing the torsional wave propagation incorporating initial stress effects are analyzed and presented in graphs. The analytical and numerical solutions reveal… More >

  • Open AccessOpen Access

    ARTICLE

    Flow-Shop Scheduling with Transportation Capacity and Time Consideration

    Chia-Nan Wang1, Glen Andrew Porter2, Ching-Chien Huang3,*, Viet Tinh Nguyen4, Syed Tam Husain4
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3031-3048, 2022, DOI:10.32604/cmc.2022.020222
    (This article belongs to this Special Issue: Big Data for Supply Chain Management in the Service and Manufacturing Sectors)
    Abstract Planning and scheduling is one of the most important activity in supply chain operation management. Over the years, there have been multiple researches regarding planning and scheduling which are applied to improve a variety of supply chains. This includes two commonly used methods which are mathematical programming models and heuristics algorithms. Flowshop manufacturing systems are seen normally in industrial environments but few have considered certain constraints such as transportation capacity and transportation time within their supply chain. A two-stage flowshop of a single processing machine and a batch processing machine are considered with their capacity and transportation time between two… More >

  • Open AccessOpen Access

    ARTICLE

    Modified Differential Box Counting in Breast Masses for Bioinformatics Applications

    S. Sathiya Devi1, S. Vidivelli2,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3049-3066, 2022, DOI:10.32604/cmc.2022.019917
    Abstract Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer. The present research work is useful in image processing for characterizing shape and gray-scale complexity. The proposed Modified Differential Box Counting (MDBC) extract Fractal features such as Fractal Dimension (FD), Lacunarity, and Succolarity for shape characterization. In traditional DBC method, the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels. The problem is overcome by the proposed MDBC method that uses box over counting and under counting that covers the… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Reference Free Adaptive Learning Process for Speech Enhancement Applications

    Girika Jyoshna1,*, Md. Zia Ur Rahman1, L. Koteswararao2
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3067-3080, 2022, DOI:10.32604/cmc.2022.020160
    Abstract In issues like hearing impairment, speech therapy and hearing aids play a major role in reducing the impairment. Removal of noise signals from speech signals is a key task in hearing aids as well as in speech therapy. During the transmission of speech signals, several noise components contaminate the actual speech components. This paper addresses a new adaptive speech enhancement (ASE) method based on a modified version of singular spectrum analysis (MSSA). The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference component. The MSSA adopts three key steps for generating the reference… More >

  • Open AccessOpen Access

    ARTICLE

    VISPNN: VGG-Inspired Stochastic Pooling Neural Network

    Shui-Hua Wang1, Muhammad Attique Khan2, Yu-Dong Zhang3,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3081-3097, 2022, DOI:10.32604/cmc.2022.019447
    (This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
    Abstract Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol. This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately. Methods We propose the VGG-Inspired stochastic pooling neural network (VISPNN) model based on three components: (i) a VGG-inspired mainstay network, (ii) the stochastic pooling technique, which aims to outperform traditional max pooling and average pooling, and (iii) an improved 20-way data augmentation (Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise, horizontal shear, vertical shear, rotation, Gamma correction, random translation, and scaling on both raw image and its horizontally mirrored image).… More >

  • Open AccessOpen Access

    ARTICLE

    Unified Detection of Obfuscated and Native Android Malware

    Pagnchakneat C. Ouk1, Wooguil Pak2,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3099-3116, 2022, DOI:10.32604/cmc.2022.020202
    Abstract The Android operating system has become a leading smartphone platform for mobile and other smart devices, which in turn has led to a diversity of malware applications. The amount of research on Android malware detection has increased significantly in recent years and many detection systems have been proposed. Despite these efforts, however, most systems can be thwarted by sophisticated Android malware adopting obfuscation or native code to avoid discovery by anti-virus tools. In this paper, we propose a new static analysis technique to address the problems of obfuscating and native malware applications. The proposed system provides a unified technique for… More >

  • Open AccessOpen Access

    ARTICLE

    DLBT: Deep Learning-Based Transformer to Generate Pseudo-Code from Source Code

    Walaa Gad1,*, Anas Alokla1, Waleed Nazih2, Mustafa Aref1, Abdel-badeeh Salem1
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3117-3132, 2022, DOI:10.32604/cmc.2022.019884
    (This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
    Abstract Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language. Pseudo-code explains and describes the content of the code without using syntax or programming language technologies. However, writing Pseudo-code to each code instruction is laborious. Recently, neural machine translation is used to generate textual descriptions for the source code. In this paper, a novel deep learning-based transformer (DLBT) model is proposed for automatic Pseudo-code generation from the source code. The proposed model uses deep learning which is based on Neural Machine Translation (NMT) to work as a language… More >

  • Open AccessOpen Access

    ARTICLE

    A Fractional Fourier Based Medical Image Authentication Approach

    Fayez Alqahtani1,*, Mohammed Amoon2,3, Walid El-Shafai4
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3133-3150, 2022, DOI:10.32604/cmc.2022.020454
    Abstract Patient medical information in all forms is crucial to keep private and secure, particularly when medical data communication occurs through insecure channels. Therefore, there is a bad need for protecting and securing the color medical images against impostors and invaders. In this paper, an optical medical image security approach is introduced. It is based on the optical bit-plane Jigsaw Transform (JT) and Fractional Fourier Transform (FFT). Different kernels with a lone lens and a single arbitrary phase code are exploited in this security approach. A preceding bit-plane scrambling process is conducted on the input color medical images prior to the… More >

  • Open AccessOpen Access

    ARTICLE

    Generating Synthetic Data to Reduce Prediction Error of Energy Consumption

    Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3151-3167, 2022, DOI:10.32604/cmc.2022.020143
    (This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
    Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a technique used for increasing the… More >

  • Open AccessOpen Access

    ARTICLE

    Methods for the Efficient Energy Management in a Smart Mini Greenhouse

    Vasyl Teslyuk1,*, Ivan Tsmots1, Michal Gregus ml.2, Taras Teslyuk3, Iryna Kazymyra1
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3169-3187, 2022, DOI:10.32604/cmc.2022.019869
    Abstract To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption. One of the possible ways is proposed in this research. A multi-level hierarchical system for energy efficiency management of the enterprise is designed, it is based on the modular principle providing rapid modernization. The novelty of the work is the development of new and improvement of the existing methods and models, in particular: 1) models for dynamic analysis of IT tools for data acquisition and processing (DAAP) in multilevel energy management systems, which are based on Petri nets; 2) method of synthesis of… More >

  • Open AccessOpen Access

    ARTICLE

    Data Analytics for the Identification of Fake Reviews Using Supervised Learning

    Saleh Nagi Alsubari1, Sachin N. Deshmukh1, Ahmed Abdullah Alqarni2, Nizar Alsharif3, Theyazn H. H. Aldhyani4,*, Fawaz Waselallah Alsaade5, Osamah I. Khalaf6
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3189-3204, 2022, DOI:10.32604/cmc.2022.019625
    (This article belongs to this Special Issue: Application of Big Data Analytics in the Management of Business)
    Abstract Fake reviews, also known as deceptive opinions, are used to mislead people and have gained more importance recently. This is due to the rapid increase in online marketing transactions, such as selling and purchasing. E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased. New customers usually go through the posted reviews or comments on the website before making a purchase decision. However, the current challenge is how new individuals can distinguish truthful reviews from fake ones, which later deceive customers, inflict losses, and tarnish the reputation of companies. The present paper… More >

  • Open AccessOpen Access

    ARTICLE

    Database Recovery Technique for Mobile Computing: A Game Theory Approach

    Magda M. Madbouly1, Yasser F. Mokhtar2, Saad M. Darwish1,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3205-3219, 2022, DOI:10.32604/cmc.2022.019440
    Abstract Contact between mobile hosts and database servers presents many problems in the Mobile Database System (MDS). It is harmed by a variety of causes, including handoff, inadequate capacity, frequent transaction updates, and repeated failures, both of which contribute to serious issues with the information system’s consistency. However, error tolerance technicality allows devices to continue performing their functions in the event of a failure. The aim of this paper is to identify the optimal recovery approach from among the available state-of-the-art techniques in MDS by employing game theory. Several of the presented recovery protocols are chosen and evaluated in order to… More >

  • Open AccessOpen Access

    ARTICLE

    Smart Devices Based Multisensory Approach for Complex Human Activity Recognition

    Muhammad Atif Hanif1, Tallha Akram1, Aamir Shahzad2, Muhammad Attique Khan3, Usman Tariq4, Jung-In Choi5, Yunyoung Nam6,*, Zanib Zulfiqar7
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3221-3234, 2022, DOI:10.32604/cmc.2022.019815
    (This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
    Abstract Sensors based Human Activity Recognition (HAR) have numerous applications in eHeath, sports, fitness assessments, ambient assisted living (AAL), human-computer interaction and many more. The human physical activity can be monitored by using wearable sensors or external devices. The usage of external devices has disadvantages in terms of cost, hardware installation, storage, computational time and lighting conditions dependencies. Therefore, most of the researchers used smart devices like smart phones, smart bands and watches which contain various sensors like accelerometer, gyroscope, GPS etc., and adequate processing capabilities. For the task of recognition, human activities can be broadly categorized as basic and complex… More >

  • Open AccessOpen Access

    ARTICLE

    A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification

    Javaria Tahir1, Syed Rameez Naqvi2,*, Khursheed Aurangzeb3, Musaed Alhussein3
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3235-3250, 2022, DOI:10.32604/cmc.2022.018949
    (This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
    Abstract Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white populations. It has been reported a number of times and is now widely accepted, that early detection of melanoma increases the chances of the subject’s survival. Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques. In this work, we propose a framework that accurately segments, and later classifies, the lesion using improved image segmentation and fusion methods. The proposed technique takes an image and passes it through two… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Scale Network for Thoracic Organs Segmentation

    Muhammad Ibrahim Khalil1, Samabia Tehsin1, Mamoona Humayun2, N.Z Jhanjhi3,4,*, Mohammed A. AlZain5
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3251-3265, 2022, DOI:10.32604/cmc.2022.020561
    Abstract Medical Imaging Segmentation is an essential technique for modern medical applications. It is the foundation of many aspects of clinical diagnosis, oncology, and computer-integrated surgical intervention. Although significant successes have been achieved in the segmentation of medical images, DL (deep learning) approaches. Manual delineation of OARs (organs at risk) is vastly dominant but it is prone to errors given the complex irregularities in shape, low texture diversity between tissues and adjacent blood area, patient-wide location of organisms, and weak soft tissue contrast across adjacent organs in CT images. Till now several models have been implemented on multi organs segmentation but… More >

  • Open AccessOpen Access

    ARTICLE

    Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

    Seonjae Kim, Dongsan Jun*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3267-3279, 2022, DOI:10.32604/cmc.2022.020651
    (This article belongs to this Special Issue: Integrity and Multimedia Data Management in Healthcare Applications using IoT)
    Abstract Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multi-scale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. In order to optimize the… More >

  • Open AccessOpen Access

    ARTICLE

    Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection

    Nazar Hussain1, Muhammad Attique Khan1, Usman Tariq2, Seifedine Kadry3,*, MuhammadAsfand E. Yar4, Almetwally M. Mostafa5, Abeer Ali Alnuaim6, Shafiq Ahmad7
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3281-3294, 2022, DOI:10.32604/cmc.2022.019036
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Agriculture is an important research area in the field of visual recognition by computers. Plant diseases affect the quality and yields of agriculture. Early-stage identification of crop disease decreases financial losses and positively impacts crop quality. The manual identification of crop diseases, which are mostly visible on leaves, is a very time-consuming and costly process. In this work, we propose a new framework for the recognition of cucumber leaf diseases. The proposed framework is based on deep learning and involves the fusion and selection of the best features. In the feature extraction phase, VGG (Visual Geometry Group) and Inception V3… More >

  • Open AccessOpen Access

    ARTICLE

    Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

    Mohamed Ibrahim Waly1, Mohamed Yacin Sikkandar1, Mohamed Abdelkader Aboamer1, Seifedine Kadry2, Orawit Thinnukool3,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3295-3309, 2022, DOI:10.32604/cmc.2022.020713
    Abstract Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

    Khattab Alheeti1, Abdulkareem Alaloosy1, Haitham Khalaf2, Abdulkareem Alzahrani3,*, Duaa Al_Dosary4
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3311-3319, 2022, DOI:10.32604/cmc.2022.019773
    Abstract Self-driving and semi-self-driving cars play an important role in our daily lives. The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information. However, external infrastructures also play significant roles in the transmission and reception of control data, cooperative awareness messages, and caution notifications. In this case, roadside units are considered one of the most important communication peripherals. Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost, bandwidth, connectivity, and radio coverage area. In this paper, a new distributed roadside unit is proposed… More >

  • Open AccessOpen Access

    ARTICLE

    A Compact Tri-Band Antenna Based on Inverted-L Stubs for Smart Devices

    Niamat Hussain1, Anees Abbas1, Sang-Myeong Park1, Seong Gyoon Park2, Nam Kim1,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3321-3331, 2022, DOI:10.32604/cmc.2022.020688
    (This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
    Abstract We designed and constructed a novel, compact tri-band monopole antenna for intelligent devices. Multiband behavior was achieved by placing inverted-L shaped stubs of various lengths in a triangular monopole antenna fed by a coplanar waveguide. The resonance frequency of each band can be controlled by varying the length of the corresponding stub. Three bands, at 2.4 (2.37–2.51), 3.5 (3.34–3.71), and 5.5 (4.6–6.4) GHz, were easily obtained using three stubs of different lengths. For miniaturization, a portion of the longest stub (at 2.4 GHz) was printed on the opposite side of the substrate, and connected to the main stub via a… More >

  • Open AccessOpen Access

    ARTICLE

    AI Based Traffic Flow Prediction Model for Connected and Autonomous Electric Vehicles

    P. Thamizhazhagan1,*, M. Sujatha2, S. Umadevi3, K. Priyadarshini4, Velmurugan Subbiah Parvathy5, Irina V. Pustokhina6, Denis A. Pustokhin7
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3333-3347, 2022, DOI:10.32604/cmc.2022.020197
    Abstract There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gained momentum in the recent years among potential users. Connected and Autonomous Electric Vehicle (CAEV) technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking. Therefore, Traffic Flow Prediction (TFP) is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning (DL) techniques. In this view, the current research paper presents an artificial intelligence-based parallel autoencoder for TFP, abbreviated as AIPAE-TFP model in… More >

  • Open AccessOpen Access

    ARTICLE

    Allocation and Migration of Virtual Machines Using Machine Learning

    Suruchi Talwani1, Khaled Alhazmi2,*, Jimmy Singla1, Hasan J. Alyamani3, Ali Kashif Bashir4
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3349-3364, 2022, DOI:10.32604/cmc.2022.020473
    (This article belongs to this Special Issue: Pervasive Computing and Communication: Challenges, Technologies & Opportunities)
    Abstract Cloud computing promises the advent of a new era of service boosted by means of virtualization technology. The process of virtualization means creation of virtual infrastructure, devices, servers and computing resources needed to deploy an application smoothly. This extensively practiced technology involves selecting an efficient Virtual Machine (VM) to complete the task by transferring applications from Physical Machines (PM) to VM or from VM to VM. The whole process is very challenging not only in terms of computation but also in terms of energy and memory. This research paper presents an energy aware VM allocation and migration approach to meet… More >

  • Open AccessOpen Access

    ARTICLE

    Magneto-Thermoelasticity with Thermal Shock Considering Two Temperatures and LS Model

    F. S. Bayones1, S. M. Abo-Dahab2,3, N. S. Hussein4, A. M. Abd-Alla5,*, H. A. Alshehri1
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3365-3381, 2022, DOI:10.32604/cmc.2022.019711
    Abstract The present investigation is intended to demonstrate the magnetic field, relaxation time, hydrostatic initial stress, and two temperature on the thermal shock problem. The governing equations are formulated in the context of Lord-Shulman theory with the presence of bodily force, two temperatures, thermal shock, and hydrostatic initial stress. We obtained the exact solution using the normal mode technique with appropriate boundary conditions. The field quantities are calculated analytically and displayed graphically under thermal shock problem with effect of external parameters respect to space coordinates. The results obtained are agreeing with the previous results obtained by others when the new parameters… More >

  • Open AccessOpen Access

    ARTICLE

    Identification of Composite-Metal Bolted Structures with Nonlinear Contact Effect

    Mohammad Ghalandari1, Ibrahim Mahariq2, Majid Pourghasem3, Hasan Mulki2, Fahd Jarad4,5,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3383-3397, 2022, DOI:10.32604/cmc.2022.020245
    Abstract The middle layer model has been used in recent years to better describe the connection behavior in composite structures. The influencing parameters including low pre-screw and high preload have the main effects on nonlinear behavior of the connection as well as the amplitude of the excitation force applied to the structure. Therefore, in this study, the effects of connection behavior on the general structure in two sections of increasing damping and reducing the stiffness of the structures that lead to non-linear phenomena have been investigated. Due to the fact that in composite structure we are faced to the limitation of… More >

  • Open AccessOpen Access

    ARTICLE

    Data Fusion-Based Machine Learning Architecture for Intrusion Detection

    Muhammad Adnan Khan, Taher M. Ghazal2,3, Sang-Woong Lee1,*, Abdur Rehman4
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3399-3413, 2022, DOI:10.32604/cmc.2022.020173
    Abstract In recent years, the infrastructure of Wireless Internet of Sensor Networks (WIoSNs) has been more complicated owing to developments in the internet and devices’ connectivity. To effectively prepare, control, hold and optimize wireless sensor networks, a better assessment needs to be conducted. The field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data analysis. This study investigates the methodology of Real Time Sequential Deep Extreme Learning Machine (RTS-DELM) implemented to wireless Internet of Things (IoT) enabled sensor networks for the detection of any intrusion activity. Data fusion… More >

  • Open AccessOpen Access

    ARTICLE

    Treatment of Polio Delayed Epidemic Model via Computer Simulations

    Muhammad Naveed1,*, Dumitru Baleanu2,3, Ali Raza4, Muhammad Rafiq5, Atif Hassan Soori1
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3415-3431, 2022, DOI:10.32604/cmc.2022.020112
    (This article belongs to this Special Issue: Pervasive Computing and Communication: Challenges, Technologies & Opportunities)
    Abstract Through the study, the nonlinear delayed modelling has vital significance in the different field of allied sciences like computational biology, computational chemistry, computational physics, computational economics and many more. Polio is a contagious viral illness that in its most severe form causes nerve injury leading to paralysis, difficulty breathing and sometimes death. In recent years, developing regions like Asia, Africa and sub-continents facing a dreadful situation of poliovirus. That is the reason we focus on the treatment of the polio epidemic model with different delay strategies in this article. Polio delayed epidemic model is categorized into four compartments like susceptible,… More >

  • Open AccessOpen Access

    ARTICLE

    A Study of Cellular Neural Networks with Vertex-Edge Topological Descriptors

    Sadia Husain1, Muhammad Imran2,*, Ali Ahmad1, Yasir Ahmad1, Kashif Elahi3
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3433-3447, 2022, DOI:10.32604/cmc.2022.020384
    Abstract The Cellular Neural Network (CNN) has various parallel processing applications, image processing, non-linear processing, geometric maps, high-speed computations. It is an analog paradigm, consists of an array of cells that are interconnected locally. Cells can be arranged in different configurations. Each cell has an input, a state, and an output. The cellular neural network allows cells to communicate with the neighbor cells only. It can be represented graphically; cells will represent by vertices and their interconnections will represent by edges. In chemical graph theory, topological descriptors are used to study graph structure and their biological activities. It is a single… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimal DF Based Method for Transient Stability Analysis

    Z. A. Zaki1, Emad M. Ahmed1,*, Ziad M. Ali2,3, Imran Khan4
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3449-3471, 2022, DOI:10.32604/cmc.2022.020263
    Abstract The effect of energy on the natural environment has become increasingly severe as human consumption of fossil energy has increased. The capacity of the synchronous generators to keep working without losing synchronization when the system is exposed to severe faults such as short circuits is referred to as the power system's transient stability. As the power system's safe and stable operation and mechanism of action become more complicated, higher demands for accurate and rapid power system transient stability analysis are made. Current methods for analyzing transient stability are less accurate because they do not account for misclassification of unstable samples.… More >

  • Open AccessOpen Access

    ARTICLE

    Price Prediction of Seasonal Items Using Machine Learning and Statistical Methods

    Mohamed Ali Mohamed, Ibrahim Mahmoud El-Henawy, Ahmad Salah*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3473-3489, 2022, DOI:10.32604/cmc.2022.020782
    Abstract Price prediction of goods is a vital point of research due to how common e-commerce platforms are. There are several efforts conducted to forecast the price of items using classic machine learning algorithms and statistical models. These models can predict prices of various financial instruments, e.g., gold, oil, cryptocurrencies, stocks, and second-hand items. Despite these efforts, the literature has no model for predicting the prices of seasonal goods (e.g., Christmas gifts). In this context, we framed the task of seasonal goods price prediction as a regression problem. First, we utilized a real online trailer dataset of Christmas gifts and then… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects

    Jibran Mir1, Azhar Mahmood2,*, Shaheen Khatoon3
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3491-3509, 2022, DOI:10.32604/cmc.2022.019952
    (This article belongs to this Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)
    Abstract Aspect's extraction is a critical task in aspect-based sentiment analysis, including explicit and implicit aspects identification. While extensive research has identified explicit aspects, little effort has been put forward on implicit aspects extraction due to the complexity of the problem. Moreover, existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’ dependency problems. Therefore, in this paper, a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed. The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF (Bidirectional Long Short Memory-Conditional Random Field), which serve… More >

  • Open AccessOpen Access

    ARTICLE

    Applying Apache Spark on Streaming Big Data for Health Status Prediction

    Ahmed Ismail Ebada1, Ibrahim Elhenawy2, Chang-Won Jeong3, Yunyoung Nam4,*, Hazem Elbakry1, Samir Abdelrazek1
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3511-3527, 2022, DOI:10.32604/cmc.2022.019458
    Abstract Big data applications in healthcare have provided a variety of solutions to reduce costs, errors, and waste. This work aims to develop a real-time system based on big medical data processing in the cloud for the prediction of health issues. In the proposed scalable system, medical parameters are sent to Apache Spark to extract attributes from data and apply the proposed machine learning algorithm. In this way, healthcare risks can be predicted and sent as alerts and recommendations to users and healthcare providers. The proposed work also aims to provide an effective recommendation system by using streaming medical data, historical… More >

  • Open AccessOpen Access

    ARTICLE

    Utilizing the Improved QPSO Algorithm to Build a WSN Monitoring System

    Wen-Tsai Sung1, Sung-Jung Hsiao2,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3529-3548, 2022, DOI:10.32604/cmc.2022.020613
    Abstract This research uses the improved Quantum Particle Swarm Optimization (QPSO) algorithm to build an Internet of Things (IoT) life comfort monitoring system based on wireless sensing networks. The purpose is to improve the quality of intelligent life. The functions of the system include automatic basketball court lighting system, monitoring of infants’ sleeping posture and accidental falls of the elderly, human thermal comfort measurement and other related life comfort services, etc. On the hardware system of the IoT, this research is based on the latest version of ZigBee 3.0, which uses optical sensors, 3-axis accelerometers, and temperature/humidity sensors in the IoT… More >

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    ARTICLE

    Effect of Direct Statistical Contrast Enhancement Technique on Document Image Binarization

    Wan Azani Mustafa1,2,*, Haniza Yazid3, Ahmed Alkhayyat4, Mohd Aminudin Jamlos3, Hasliza A. Rahim3
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3549-3564, 2022, DOI:10.32604/cmc.2022.019801
    Abstract Background: Contrast enhancement plays an important role in the image processing field. Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image. Objective: This paper proposed a novel method based on statistical data from the local mean and local standard deviation. Method: The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories: background, foreground, and problematic (contrast & luminosity) region. Experimental results from both visual and objective aspects show that the proposed method can normalize the contrast… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Autonomous Defense System Using Machine Learning on Edge Device

    Jaehyuk Cho*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3565-3588, 2022, DOI:10.32604/cmc.2022.020826
    (This article belongs to this Special Issue: Analysis, Processing, and Applications of Fuzzy System and Deep Learning)
    Abstract As a large amount of data needs to be processed and speed needs to be improved, edge computing with ultra-low latency and ultra-connectivity is emerging as a new paradigm. These changes can lead to new cyber risks, and should therefore be considered for a security threat model. To this end, we constructed an edge system to study security in two directions, hardware and software. First, on the hardware side, we want to autonomically defend against hardware attacks such as side channel attacks by configuring field programmable gate array (FPGA) which is suitable for edge computing and identifying communication status to… More >

  • Open AccessOpen Access

    ARTICLE

    Epilepsy Radiology Reports Classification Using Deep Learning Networks

    Sengul Bayrak1,2, Eylem Yucel2,*, Hidayet Takci3
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3589-3607, 2022, DOI:10.32604/cmc.2022.018742
    (This article belongs to this Special Issue: Machine Learning Applications in Medical, Finance, Education and Cyber Security)
    Abstract The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing technique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given… More >

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    ARTICLE

    Reversible Logic Based MOS Current Mode Logic Implementation in Digital Circuits

    S. Sharmila Devi1,*, V. Bhanumathi2
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3609-3624, 2022, DOI:10.32604/cmc.2022.020426
    Abstract Now a days, MOS Current Mode Logic (MCML) has emerged as a better alternative to Complementary Metal Oxide Semiconductor (CMOS) logic in digital circuits. Recent works have only traditional logic gates that have issues with information loss. Reversible logic is incorporated with MOS Current Mode Logic (MCML) in this proposed work to solve this problem, which is used for multiplier design, D Flip-Flop (DFF) and register. The minimization of power and area is the main aim of the work. In reversible logic, the count of outputs and inputs is retained as the same value for creating one-to-one mapping. A unique… More >

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    ARTICLE

    Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System

    Jae-Hyun Ro1, Jong-Gyu Ha2, Woon-Sang Lee2, Young-Hwan You3, Hyoung-Kyu Song2,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3625-3636, 2022, DOI:10.32604/cmc.2022.020596
    Abstract This paper proposes the multiple-input multiple-output (MIMO) detection scheme by using the deep neural network (DNN) based ensemble machine learning for higher error performance in wireless communication systems. For the MIMO detection based on the ensemble machine learning, all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models. In the offline learning, the received signals and channel coefficients are set to input data, and the labels which correspond to transmit symbols are set to output data. In the online learning, the perfectly learned models are used for signal… More >

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    ARTICLE

    An IoT Based Secure Patient Health Monitoring System

    Kusum Yadav1, Ali Alharbi1, Anurag Jain2,*, Rabie A. Ramadan1
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3637-3652, 2022, DOI:10.32604/cmc.2022.020614
    (This article belongs to this Special Issue: Integrity and Multimedia Data Management in Healthcare Applications using IoT)
    Abstract Internet of things (IoT) field has emerged due to the rapid growth of artificial intelligence and communication technologies. The use of IoT technology in modern healthcare environments is convenient for doctors and patients as it can be used in real-time monitoring of patients, proper administration of patient information, and healthcare management. However, the usage of IoT in the healthcare domain will become a nightmare if patient information is not securely maintained while transferring over an insecure network or storing at the administrator end. In this manuscript, the authors have developed a secure IoT healthcare monitoring system using the Blockchain-based XOR… More >

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    ARTICLE

    Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS

    Inam Ullah Khan1, Muhammad Abul Hassan2, Muhammad Fayaz3, Jeonghwan Gwak4,5,6,7,*, Muhammad Adnan Aziz1
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3653-3666, 2022, DOI:10.32604/cmc.2022.020697
    (This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)
    Abstract Most interesting area is the growing demand of flying-IoT mergers with smart cities. However, aerial vehicles, especially unmanned aerial vehicles (UAVs), have limited capabilities for maintaining node energy efficiency. In order to communicate effectively, IoT is a key element for smart cities. While improving network performance, routing protocols can be deployed in flying-IoT to improve latency, packet drop rate, packet delivery, power utilization, and average-end-to-end delay. Furthermore, in literature, proposed techniques are very much complex which cannot be easily implemented in real-world applications. This issue leads to the development of lightweight energy-efficient routing in flying-IoT networks. This paper addresses the… More >

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    ARTICLE

    An Efficient AES 32-Bit Architecture Resistant to Fault Attacks

    Hassen Mestiri1,2,3,*, Imen Barraj4,5, Abdullah Alsir Mohamed1, Mohsen Machhout3
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3667-3683, 2022, DOI:10.32604/cmc.2022.020716
    Abstract The Advanced Encryption Standard cryptographic algorithm, named AES, is implemented in cryptographic circuits to ensure high security level to any system which required confidentiality and secure information exchange. One of the most effective physical attacks against the hardware implementation of AES is fault attacks which can extract secret data. Until now, a several AES fault detection schemes against fault injection attacks have been proposed. In this paper, so as to ensure a high level of security against fault injection attacks, a new efficient fault detection scheme based on the AES architecture modification has been proposed. For this reason, the AES… More >

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    ARTICLE

    Cross-Layer Hidden Markov Analysis for Intrusion Detection

    K. Venkatachalam1, P. Prabu2, B. Saravana Balaji3, Byeong-Gwon Kang4, Yunyoung Nam4,*, Mohamed Abouhawwash5,6
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3685-3700, 2022, DOI:10.32604/cmc.2022.019502
    Abstract Ad hoc mobile cloud computing networks are affected by various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment. Given the issues above, route failure is prevalent in ad hoc mobile cloud computing networks, which increases energy consumption and delay and reduces stability. These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network. To address these weaknesses, which raise many concerns about privacy and security, this study formulated clustering-based storage and search optimization approaches using cross-layer analysis. The proposed approaches were formed by cross-layer analysis based on intrusion… More >

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    ARTICLE

    IoT Devices Authentication Using Artificial Neural Network

    Syed Shabih Ul Hasan1, Anwar Ghani1, Ikram Ud Din2, Ahmad Almogren3,*, Ayman Altameem4
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3701-3716, 2022, DOI:10.32604/cmc.2022.020624
    (This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)
    Abstract User authentication is one of the critical concerns of information security. Users tend to use strong textual passwords, but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile phones. Textual passwords are slightly unprotected and are easily attackable. The attacks include dictionary, shoulder surfing, and brute force. Graphical passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of use. This paper proposes a Process-based Pattern Authentication (PPA) system for Internet of Things (IoT) devices that does not require a server to maintain… More >

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    ARTICLE

    A Hybrid Approach for COVID-19 Detection Using Biogeography-Based Optimization and Deep Learning

    K. Venkatachalam1, Siuly Siuly2, M. Vinoth Kumar3, Praveen Lalwani1, Manas Kumar Mishra1, Enamul Kabir4,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3717-3732, 2022, DOI:10.32604/cmc.2022.018487
    Abstract The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron… More >

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    ARTICLE

    Graphical Transformation of OWL Ontologies to Event-B Formal Models

    Eman H. Alkhammash*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3733-3750, 2022, DOI:10.32604/cmc.2022.015987
    (This article belongs to this Special Issue: Machine Learning for Data Analytics)
    Abstract Formal methods use mathematical models to develop systems. Ontologies are formal specifications that provide reusable domain knowledge representations. Ontologies have been successfully used in several data-driven applications, including data analysis. However, the creation of formal models from informal requirements demands skill and effort. Ambiguity, inconsistency, imprecision, and incompleteness are major problems in informal requirements. To solve these problems, it is necessary to have methods and approaches for supporting the mapping of requirements to formal specifications. The purpose of this paper is to present an approach that addresses this challenge by using the Web Ontology Language (OWL) to construct Event-B formal… More >

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    ARTICLE

    A Joint Resource Allocation Algorithm for D2D Communication

    Abdul Kadir Hamid1, Lamia Osman Widaa2, Fahd N. Al-Wesabi3,*, Imran Khan4, Anwer Mustafa Hilal5, Manar Ahmed Hamza5, Abu Sarwar Zaman5, Mohammed Rizwanullah5
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3751-3762, 2022, DOI:10.32604/cmc.2022.020136
    Abstract The emergence of multimedia services has meant a substantial increase in the number of devices in mobile networks and driving the demand for higher data transmission rates. The result is that, cellular networks must technically evolve to support such higher rates, to be equipped with greater capacity, and to increase the spectral and energy efficiency. Compared with 4G technology, the 5G networks are being designed to transmit up to 100 times more data volume with devices whose battery life is 10 times longer. Therefore, this new generation of networks has adopted a heterogeneous and ultra-dense architecture, where different technological advances… More >

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    ARTICLE

    Earthquake Risk Assessment Approach Using Multiple Spatial Parameters for Shelter Demands

    Wenquan Jin1, Naeem Iqbal2, Hee-Cheal Kang3, Dohyeun Kim2,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3763-3780, 2022, DOI:10.32604/cmc.2022.020336
    (This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)
    Abstract The earthquake is considered one of the most devastating disasters in any area of the world due to its potentially destructive force. Based on the various earthquake-related parameters, the risk assessment is enabled in advance to prevent future earthquake disasters. In this paper, for providing the shelter space demands to reduce the damage level and prevention costs, an earthquake risk assessment approach is proposed for deriving the risk index based on multiple spatial parameters in the gridded map. The proposed assessment approach is comprised of pre-processing, methodology model, and data visualization. The risk index model derives the earthquake risk index… More >

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    ARTICLE

    Fault Management Cyber-Physical Systems in Virtual Storage Model

    Kailash Kumar*, Ahmad Abdullah Aljabr
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3781-3801, 2022, DOI:10.32604/cmc.2022.020510
    Abstract On average, every two years, the amount of data existing globally doubles. Software development will be affected and improved by Cyber-Physical Systems (CPS). The number of problems remained even though developments helped Information Technology experts extract better value from their storage investments. Because of poor interoperability between different vendors and devices, countless numbers of Storage Area Networks were created. Network setup used for data storage includes a complex and rigid arrangement of routers, switch, hosts/servers, storage arrays. We have evaluated the performance of routing protocol Transmission Control Protocol (TCP) and Fibre Channel Protocol (FCP) under different network scenario by Network… More >

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    ARTICLE

    A Machine Learning Approach for Early COVID-19 Symptoms Identification

    Omer Ali1,2, Mohamad Khairi Ishak1,*, Muhammad Kamran Liaquat Bhatti2
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3803-3820, 2022, DOI:10.32604/cmc.2022.019797
    (This article belongs to this Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
    Abstract Symptom identification and early detection are the first steps towards a health condition diagnosis. The COVID-19 virus causes pneumonia-like symptoms such as fever, cough, and shortness of breath. Many COVID-19 contraction tests necessitate extensive clinical protocols in medical settings. Clinical studies help with the accurate analysis of COVID-19, where the virus has already spread to the lungs in most patients. The majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized tomography. This is heavily reliant on a larger clinical study and does not emphasize early symptom detection. The aim of this… More >

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    ARTICLE

    Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm

    Nitin Mittal1, Harbinder Singh1, Vikas Mittal2, Shubham Mahajan3, Amit Kant Pandit3, Mehedi Masud4, Mohammed Baz5, Mohamed Abouhawwash6,7,*
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3821-3835, 2022, DOI:10.32604/cmc.2022.020592
    Abstract Cognitive Radio (CR) has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency. To improve the overall performance of the CR system it is extremely important to adapt or reconfigure the system parameters. The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation. As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches, the performance of these algorithms is investigated in order to design an efficient CR system that is able… More >

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    ARTICLE

    Indoor Air Quality Control Using Backpropagated Neural Networks

    Raissa Uskenbayeva1, Aigerim Altayeva1,*, Faryda Gusmanova2, Gluyssya Abdulkarimova3, Saule Berkimbaeva4, Kuralay Dalbekova4, Azizah Suiman5, Akzhunis Zhanseitova6, Aliya Amreyeva2
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3837-3853, 2022, DOI:10.32604/cmc.2022.020491
    Abstract Providing comfortable indoor air quality control in residential construction is an exceedingly important issue. This is due to the structure of the fast response controller of air quality. The presented work shows the breakdown and creation of a mathematical model for an interactive, nonlinear system for the required comfortable air quality. Furthermore, the paper refers to designing traditional proportional integral derivative regulators and proportional, integral, derivative regulators with independent parameters based on a backpropagation neural network. In the end, we perform the experimental outputs of a suggested backpropagation neural network-based proportional, integral, derivative controller and analyze model results by applying… More >

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