Home / Journals / CMC / Vol.75, No.1, 2023
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  • Open AccessOpen Access

    ARTICLE

    Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

    Aswathy Ravikumar, Harini Sriraman*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 891-909, 2023, DOI:10.32604/cmc.2023.035848
    Abstract Pneumonia is an acute lung infection that has caused many fatalities globally. Radiologists often employ chest X-rays to identify pneumonia since they are presently the most effective imaging method for this purpose. Computer-aided diagnosis of pneumonia using deep learning techniques is widely used due to its effectiveness and performance. In the proposed method, the Synthetic Minority Oversampling Technique (SMOTE) approach is used to eliminate the class imbalance in the X-ray dataset. To compensate for the paucity of accessible data, pre-trained transfer learning is used, and an ensemble Convolutional Neural Network (CNN) model is developed. The ensemble model consists of all… More >

  • Open AccessOpen Access

    ARTICLE

    Isolation Enhancement in a Compact Four-Element MIMO Antenna for Ultra-Wideband Applications

    Awais Khan1,2,*, Shahid Bashir1, Salman Ghafoor3, Hatem Rmili4,5, Jawad Mirza6, Ammar Ahmad1
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 911-925, 2023, DOI:10.32604/cmc.2023.033866
    Abstract Mutual coupling reduction or isolation enhancement in antenna arrays is an important area of research as it severely affects the performance of an antenna. In this paper, a new type of compact and highly isolated Multiple-Input-Multiple-Output (MIMO) antenna for ultra-wideband (UWB) applications is presented. The design consists of four radiators that are orthogonally positioned and confined to a compact 40 × 40 × 0.8 mm3 space. The final antenna design uses an inverted L shape partial ground to produce an acceptable reflection coefficient (S11 < −10 dB) in an entire UWB band (3.1–10.6) giga hertz (GHz). Moreover, the inter-element isolation… More >

  • Open AccessOpen Access

    ARTICLE

    Gaussian Blur Masked ResNet2.0 Architecture for Diabetic Retinopathy Detection

    Swagata Boruah1, Archit Dehloo1, Prajul Gupta2, Manas Ranjan Prusty3,*, A. Balasundaram3
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 927-942, 2023, DOI:10.32604/cmc.2023.035143
    Abstract Diabetic Retinopathy (DR) is a serious hazard that can result in irreversible blindness if not addressed in a timely manner. Hence, numerous techniques have been proposed for the accurate and timely detection of this disease. Out of these, Deep Learning (DL) and Computer Vision (CV) methods for multiclass categorization of color fundus images diagnosed with Diabetic Retinopathy have sparked considerable attention. In this paper, we attempt to develop an extended ResNet152V2 architecture-based Deep Learning model, named ResNet2.0 to aid the timely detection of DR. The APTOS-2019 dataset was used to train the model. This consists of 3662 fundus images belonging… More >

  • Open AccessOpen Access

    ARTICLE

    Self-Tuning Parameters for Decision Tree Algorithm Based on Big Data Analytics

    Manar Mohamed Hafez1,*, Essam Eldin F. Elfakharany1, Amr A. Abohany2, Mostafa Thabet3
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 943-958, 2023, DOI:10.32604/cmc.2023.034078
    Abstract Big data is usually unstructured, and many applications require the analysis in real-time. Decision tree (DT) algorithm is widely used to analyze big data. Selecting the optimal depth of DT is time-consuming process as it requires many iterations. In this paper, we have designed a modified version of a (DT). The tree aims to achieve optimal depth by self-tuning running parameters and improving the accuracy. The efficiency of the modified (DT) was verified using two datasets (airport and fire datasets). The airport dataset has 500000 instances and the fire dataset has 600000 instances. A comparison has been made between the… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

    M. Zeeshan Sarwar1, Mohammed Hamad Alatiyyah2, Ahmad Jalal1, Mohammad Shorfuzzaman3, Nawal Alsufyani3, Jeongmin Park4,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 959-976, 2023, DOI:10.32604/cmc.2023.035442
    Abstract In the last decade, there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D cameras. They enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given scenarios. Depth cues can improve the quality of object detection and recognition. The main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D datasets. The proposed methodology extracts image normally from depth maps and then… More >

  • Open AccessOpen Access

    ARTICLE

    Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

    Nagaraju Jajam, Nagendra Panini Challa*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 977-997, 2023, DOI:10.32604/cmc.2023.036098
    Abstract In the insurance sector, a massive volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining existing ones. The success of retention initiatives is determined not only by the accuracy of forecasting churners but also by the timing of the forecast. Previous works on churn forecast presented models for anticipating churn quarterly or monthly with an emphasis on customers’ static behavior. This paper’s objective is to calculate daily churn based on dynamic variations in client behavior. Training excellent models to… More >

  • Open AccessOpen Access

    ARTICLE

    MPFracNet: A Deep Learning Algorithm for Metacarpophalangeal Fracture Detection with Varied Difficulties

    Geng Qin1, Ping Luo1, Kaiyuan Li1, Yufeng Sun1, Shiwei Wang1, Xiaoting Li1,2,3, Shuang Liu1,2,3, Linyan Xue1,2,3,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 999-1015, 2023, DOI:10.32604/cmc.2023.035777
    Abstract Due to small size and high occult, metacarpophalangeal fracture diagnosis displays a low accuracy in terms of fracture detection and location in X-ray images. To efficiently detect metacarpophalangeal fractures on X-ray images as the second opinion for radiologists, we proposed a novel one-stage neural network named MPFracNet based on RetinaNet. In MPFracNet, a deformable bottleneck block (DBB) was integrated into the bottleneck to better adapt to the geometric variation of the fractures. Furthermore, an integrated feature fusion module (IFFM) was employed to obtain more in-depth semantic and shallow detail features. Specifically, Focal Loss and Balanced L1 Loss were introduced to… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Method for Heat Recovery Process and Temperature Optimization

    Basim Kareem Naser1, Mohammed Dauwed2,*, Ahmed Alkhayyat3, Mustafa Musa Jaber4,5, Shahad Alyousif6,7, Mohammed Hasan Ali8
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1017-1031, 2023, DOI:10.32604/cmc.2023.033957
    Abstract Flue gas heat loss accounts for a significant component of the overall heat loss for coal-fired boilers in power plants. The flue gas absorbs more heat as the exhaust gas temperature rises, which reduces boiler efficiency and raises coal consumption. Additionally, if the exhaust gas temperature is too high, a lot of water must be used to cool the flue gas for the wet flue gas desulfurization system to function well, which has an impact on the power plant’s ability to operate profitably. It is consequently vital to take steps to lower exhaust gas temperatures in order to increase boiler… More >

  • Open AccessOpen Access

    ARTICLE

    Minimizing FWM Impact in DWDM ROF DP-DQPSK System for Optical

    Esra Ehsan*, Razali Ngah, Nurul Ashikin Binti Daud
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1033-1050, 2023, DOI:10.32604/cmc.2023.034229
    Abstract The demonstration of a higher data rate transmission system was a major aspect to be considered by researchers in recent years. The most relevant aspect to be studied and analyzed is the need for a reliable system to handle nonlinear impairments and reduce them. Therefore, this paper examines the influence of Four-Wave Mixing (FWM) impairment on the proposed high data rate Dual polarization–Differential Quadrature phase shift keying (DP-DQPSK) system using the Optisystem software. In the beginning, the impact of varied input power on the proposed system’s performance was evaluated in terms of QF and BER metrics. More power is used… More >

  • Open AccessOpen Access

    ARTICLE

    Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

    Shijun Fu1,*, Hongji Fu2
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1051-1071, 2023, DOI:10.32604/cmc.2023.034495
    Abstract This paper is to explore the problems of intelligent connected vehicles (ICVs) autonomous driving decision-making under a 5G-V2X structured road environment. Through literature review and interviews with autonomous driving practitioners, this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system. Secondly, situated on this framework, it builds a hierarchical finite state machine (HFSM) model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method (EWM) and analytic hierarchy process method (AHP) and by employing a model fusion approach between the technique for… More >

  • Open AccessOpen Access

    ARTICLE

    Estimation of Weibull Distribution Parameters for Wind Speed Characteristics Using Neural Network Algorithm

    Musaed Alrashidi*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1073-1088, 2023, DOI:10.32604/cmc.2023.036170
    Abstract Harvesting the power coming from the wind provides a green and environmentally friendly approach to producing electricity. To facilitate the ongoing advancement in wind energy applications, deep knowledge about wind regime behavior is essential. Wind speed is typically characterized by a statistical distribution, and the two-parameters Weibull distribution has shown its ability to represent wind speeds worldwide. Estimation of Weibull parameters, namely scale and shape parameters, is vital to describe the observed wind speeds data accurately. Yet, it is still a challenging task. Several numerical estimation approaches have been used by researchers to obtain c and k. However, utilizing such… More >

  • Open AccessOpen Access

    ARTICLE

    HRNetO: Human Action Recognition Using Unified Deep Features Optimization Framework

    Tehseen Ahsan1,*, Sohail Khalid1, Shaheryar Najam1, Muhammad Attique Khan2, Ye Jin Kim3, Byoungchol Chang4
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1089-1105, 2023, DOI:10.32604/cmc.2023.034563
    Abstract Human action recognition (HAR) attempts to understand a subject’s behavior and assign a label to each action performed. It is more appealing because it has a wide range of applications in computer vision, such as video surveillance and smart cities. Many attempts have been made in the literature to develop an effective and robust framework for HAR. Still, the process remains difficult and may result in reduced accuracy due to several challenges, such as similarity among actions, extraction of essential features, and reduction of irrelevant features. In this work, we proposed an end-to-end framework using deep learning and an improved… More >

  • Open AccessOpen Access

    ARTICLE

    Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

    Sumin Xie1, Ling Liu2,*, Guang Sun2, Bin Pan2, Lin Lang2, Peng Guo3
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1107-1117, 2023, DOI:10.32604/cmc.2023.034917
    Abstract The rapidly escalating sophistication of e-commerce fraud in recent years has led to an increasing reliance on fraud detection methods based on machine learning. However, fraud detection methods based on conventional machine learning approaches suffer from several problems, including an excessively high number of network parameters, which decreases the efficiency and increases the difficulty of training the network, while simultaneously leading to network overfitting. In addition, the sparsity of positive fraud incidents relative to the overwhelming proportion of negative incidents leads to detection failures in trained networks. The present work addresses these issues by proposing a convolutional neural network (CNN)… More >

  • Open AccessOpen Access

    ARTICLE

    Construction and Application of Cloud Computing Model for Reciprocal and Collaborative Knowledge Management

    Jingqi Li1,*, Yijie Bian1, Jun Guan2, Lu Yang2
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1119-1137, 2023, DOI:10.32604/cmc.2023.035369
    Abstract Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management. Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms. With the rapid development and application of cloud computing and big data technology, knowledge management is faced with many problems, such as how to combine with the new generation of information technology, how to achieve integration with organizational business processes, and so on. To solve such problems, this paper proposes a reciprocal collaborative knowledge management model (RCKM model) based on cloud computing… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient EMD-Based Reversible Data Hiding Technique Using Dual Stego Images

    Ahmad A. Mohammad*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1139-1156, 2023, DOI:10.32604/cmc.2023.035964
    Abstract Exploiting modification direction (EMD) based data hiding techniques (DHTs) provide moderate data hiding capacity and high-quality stego images. The overflow problem and the cyclic nature of the extraction function essentially hinder their application in several fields in which reversibility is necessary. Thus far, the few EMD reversible DHTs are complex and numerically demanding. This paper presents a novel EMD-based reversible DHT using dual-image. Two novel 2 × 4 modification lookup tables are introduced, replacing the reference matrix used in similar techniques and eliminating the numerically demanding search step in similar techniques. In the embedding step, one of the modification tables modifies a… More >

  • Open AccessOpen Access

    ARTICLE

    A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

    Guo-Wen Sun1, Gang Xiang2,3, Wei He1,4,*, Kai Tang1, Zi-Yi Wang1, Hai-Long Zhu1
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1157-1177, 2023, DOI:10.32604/cmc.2023.035667
    Abstract Wireless sensor networks (WSNs) operate in complex and harsh environments; thus, node faults are inevitable. Therefore, fault diagnosis of the WSNs node is essential. Affected by the harsh working environment of WSNs and wireless data transmission, the data collected by WSNs contain noisy data, leading to unreliable data among the data features extracted during fault diagnosis. To reduce the influence of unreliable data features on fault diagnosis accuracy, this paper proposes a belief rule base (BRB) with a self-adaptive quality factor (BRB-SAQF) fault diagnosis model. First, the data features required for WSN node fault diagnosis are extracted. Second, the quality… More >

  • Open AccessOpen Access

    ARTICLE

    Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework

    Samra Rehman1, Muhammad Attique Khan1, Majed Alhaisoni2, Ammar Armghan3, Fayadh Alenezi3, Abdullah Alqahtani4, Khean Vesal5, Yunyoung Nam5,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1179-1194, 2023, DOI:10.32604/cmc.2023.035324
    Abstract Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye observation. The authors present computer vision techniques for detecting and classifying fruit leaf diseases. Examples of computer vision techniques are preprocessing original images for visualization of infected regions, feature extraction from raw or segmented images, feature fusion, feature selection, and classification. The following are the major challenges identified by researchers in the literature: (i) low-contrast infected regions extract irrelevant and redundant information, which misleads classification accuracy; (ii) irrelevant and redundant information may increase computational time and reduce the designed model’s accuracy. This paper proposed… More >

  • Open AccessOpen Access

    ARTICLE

    Congestion Control Using In-Network Telemetry for Lossless Datacenters

    Jin Wang1, Dongzhi Yuan1, Wangqing Luo1, Shuying Rao1, R. Simon Sherratt2, Jinbin Hu1,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1195-1212, 2023, DOI:10.32604/cmc.2023.035932
    Abstract In the Ethernet lossless Data Center Networks (DCNs) deployed with Priority-based Flow Control (PFC), the head-of-line blocking problem is still difficult to prevent due to PFC triggering under burst traffic scenarios even with the existing congestion control solutions. To address the head-of-line blocking problem of PFC, we propose a new congestion control mechanism. The key point of Congestion Control Using In-Network Telemetry for Lossless Datacenters (ICC) is to use In-Network Telemetry (INT) technology to obtain comprehensive congestion information, which is then fed back to the sender to adjust the sending rate timely and accurately. It is possible to control congestion… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimized Deep Learning Approach for Improving Airline Services

    Shimaa Ouf*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1213-1233, 2023, DOI:10.32604/cmc.2023.034399
    Abstract The aviation industry is one of the most competitive markets. The most common approach for airline service providers is to improve passenger satisfaction. Passenger satisfaction in the aviation industry occurs when passengers’ expectations are met during flights. Airline service quality is critical in attracting new passengers and retaining existing ones. It is crucial to identify passengers’ pain points and enhance their satisfaction with the services offered. The airlines used a variety of techniques to improve service quality. They used data analysis approaches to analyze the passenger point data. These solutions have focused simply on surveys; consequently, deep-learning approaches have received… More >

  • Open AccessOpen Access

    ARTICLE

    Clustering-Aided Supervised Malware Detection with Specialized Classifiers and Early Consensus

    Murat Dener*, Sercan Gulburun
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1235-1251, 2023, DOI:10.32604/cmc.2023.036357
    Abstract One of the most common types of threats to the digital world is malicious software. It is of great importance to detect and prevent existing and new malware before it damages information assets. Machine learning approaches are used effectively for this purpose. In this study, we present a model in which supervised and unsupervised learning algorithms are used together. Clustering is used to enhance the prediction performance of the supervised classifiers. The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score. In the first stage of the model, the… More >

  • Open AccessOpen Access

    ARTICLE

    Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

    Waleed Aldosari*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1253-1269, 2023, DOI:10.32604/cmc.2023.036111
    Abstract This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks on Wireless Sensor Networks (WSNs). Jamming is a type of Denial of Service (DoS) attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly. To defend and prevent such attacks, the first step is to detect them. The current detection approaches use centralized techniques to detect jamming, where each node collects information and forwards it to the base station. As a result, overhead and communication costs… More >

  • Open AccessOpen Access

    ARTICLE

    Machine Learning Techniques for Detecting Phishing URL Attacks

    Diana T. Mosa1,2, Mahmoud Y. Shams3,*, Amr A. Abohany2, El-Sayed M. El-kenawy4, M. Thabet5
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1271-1290, 2023, DOI:10.32604/cmc.2023.036422
    Abstract Cyber Attacks are critical and destructive to all industry sectors. They affect social engineering by allowing unapproved access to a Personal Computer (PC) that breaks the corrupted system and threatens humans. The defense of security requires understanding the nature of Cyber Attacks, so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber Attacks. Cyber-Security proposes appropriate actions that can handle and block attacks. A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private information. One of the online security challenges… More >

  • Open AccessOpen Access

    ARTICLE

    An Automated System for Early Prediction of Miscarriage in the First Trimester Using Machine Learning

    Sumayh S. Aljameel1, Malak Aljabri1,2, Nida Aslam1, Dorieh M. Alomari3,*, Arwa Alyahya1, Shaykhah Alfaris1, Maha Balharith1, Hiessa Abahussain1, Dana Boujlea1, Eman S. Alsulmi4
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1291-1304, 2023, DOI:10.32604/cmc.2023.035710
    Abstract Currently, the risk factors of pregnancy loss are increasing and are considered a major challenge because they vary between cases. The early prediction of miscarriage can help pregnant ladies to take the needed care and avoid any danger. Therefore, an intelligent automated solution must be developed to predict the risk factors for pregnancy loss at an early stage to assist with accurate and effective diagnosis. Machine learning (ML)-based decision support systems are increasingly used in the healthcare sector and have achieved notable performance and objectiveness in disease prediction and prognosis. Thus, we developed a model to help obstetricians predict the… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Objective Optimization of External Louvers in Buildings

    Tzu-Chia Chen1, Ngakan Ketut Acwin Dwijendra2, I. Wayan Parwata3, Agata Iwan Candra4, Elsayed M. Tag El Din5,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1305-1316, 2023, DOI:10.32604/cmc.2023.033274
    Abstract Because solar energy is among the renewable energies, it has traditionally been used to provide lighting in buildings. When solar energy is effectively utilized during the day, the environment is not only more comfortable for users, but it also utilizes energy more efficiently for both heating and cooling purposes. Because of this, increasing the building’s energy efficiency requires first controlling the amount of light that enters the space. Considering that the only parts of the building that come into direct contact with the sun are the windows, it is essential to make use of louvers in order to regulate the… More >

  • Open AccessOpen Access

    ARTICLE

    A 37 GHz Millimeter-Wave Antenna Array for 5G Communication Terminals

    Jalal Khan1, Sadiq Ullah1,*, Usman Ali1, Ladislau Matekovits2,3,4, Farooq Ahmad Tahir5, Muhammad Inam Abbasi6
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1317-1330, 2023, DOI:10.32604/cmc.2023.029879
    Abstract This work presents, design and specific absorption rate (SAR) analysis of a 37 GHz antenna, for 5th Generation (5G) applications. The proposed antenna comprises of 4-elements of rectangular patch and an even distribution. The radiating element is composed of copper material supported by Rogers RT5880 substrate of thickness, 0.254 mm, dielectric constant (εr), 2.2, and loss tangent, 0.0009. The 4-elements array antenna is compact in size with a dimension of 8 mm × 20 mm in length and width. The radiating patch is excited with a 50 ohms connector i.e., K-type. The antenna resonates in the frequency band of 37 GHz, that covers the 5G applications. The antenna… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks

    Anubha1, Ravneet Preet Singh Bedi2, Arfat Ahmad Khan3,*, Mohd Anul Haq4, Ahmad Alhussen5, Zamil S. Alzamil4
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1331-1351, 2023, DOI:10.32604/cmc.2023.033181
    Abstract Mobile adhoc networks have grown in prominence in recent years, and they are now utilized in a broader range of applications. The main challenges are related to routing techniques that are generally employed in them. Mobile Adhoc system management, on the other hand, requires further testing and improvements in terms of security. Traditional routing protocols, such as Adhoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), employ the hop count to calculate the distance between two nodes. The main aim of this research work is to determine the optimum method for sending packets while also extending life time of… More >

  • Open AccessOpen Access

    ARTICLE

    Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

    Sarah B. Basahel1, Saleh Bajaba2, Mohammad Yamin3, Sachi Nandan Mohanty4, E. Laxmi Lydia5,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1353-1369, 2023, DOI:10.32604/cmc.2023.036453
    Abstract The current advancement in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT) transformed the traditional healthcare system into smart healthcare. Healthcare services could be enhanced by incorporating key techniques like AI and IoT. The convergence of AI and IoT provides distinct opportunities in the medical field. Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population. Therefore, earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support. Lately, the emergence of IoT, AI, smartphones, wearables, and so… More >

  • Open AccessOpen Access

    ARTICLE

    Improved Whale Optimization with Local-Search Method for Feature Selection

    Malek Alzaqebah1,2,*, Mutasem K. Alsmadi3, Sana Jawarneh4, Jehad Saad Alqurni5, Mohammed Tayfour3, Ibrahim Almarashdeh3, Rami Mustafa A. Mohammad6, Fahad A. Alghamdi3, Nahier Aldhafferi6, Abdullah Alqahtani6, Khalid A. Alissa7, Bashar A. Aldeeb8, Usama A. Badawi3, Maram Alwohaibi1,2, Hayat Alfagham3
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1371-1389, 2023, DOI:10.32604/cmc.2023.033509
    Abstract Various feature selection algorithms are usually employed to improve classification models’ overall performance. Optimization algorithms typically accompany such algorithms to select the optimal set of features. Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics. The present paper presents two Stages of Local Search models for feature selection based on WOA (Whale Optimization Algorithm) and Great Deluge (GD). GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search. Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.… More >

  • Open AccessOpen Access

    ARTICLE

    Human Verification over Activity Analysis via Deep Data Mining

    Kumar Abhishek1,*, Sheikh Badar ud din Tahir2
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1391-1409, 2023, DOI:10.32604/cmc.2023.035894
    Abstract Human verification and activity analysis (HVAA) are primarily employed to observe, track, and monitor human motion patterns using red-green-blue (RGB) images and videos. Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times. Numerous models rely on various parameters, such as the detection rate, position, and direction of human body components in RGB images. This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features. To use human interaction image sequences as input data, we first perform a few denoising steps. Then, human-to-human analyses are employed… More >

  • Open AccessOpen Access

    ARTICLE

    RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

    Pan Zhang, Hongwei Deng*, Zhong Chen
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1411-1430, 2023, DOI:10.32604/cmc.2023.034876
    Abstract In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection between space and channel and… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning-Enabled Brain Stroke Classification on Computed Tomography Images

    Azhar Tursynova1, Batyrkhan Omarov1,2, Natalya Tukenova3,*, Indira Salgozha4, Onergul Khaaval3, Rinat Ramazanov5, Bagdat Ospanov5
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1431-1446, 2023, DOI:10.32604/cmc.2023.034400
    Abstract In the field of stroke imaging, deep learning (DL) has enormous untapped potential. When clinically significant symptoms of a cerebral stroke are detected, it is crucial to make an urgent diagnosis using available imaging techniques such as computed tomography (CT) scans. The purpose of this work is to classify brain CT images as normal, surviving ischemia or cerebral hemorrhage based on the convolutional neural network (CNN) model. In this study, we propose a computer-aided diagnostic system (CAD) for categorizing cerebral strokes using computed tomography images. Horizontal flip data magnification techniques were used to obtain more accurate categorization. Image Data Generator… More >

  • Open AccessOpen Access

    ARTICLE

    Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution Model

    Debnath Bhattacharyya1, Eali. Stephen Neal Joshua2, N. Thirupathi Rao2, Yung-cheol Byun3,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1447-1465, 2023, DOI:10.32604/cmc.2023.031878
    Abstract Lung cancer is the leading cause of mortality in the world affecting both men and women equally. When a radiologist just focuses on the patient’s body, it increases the amount of strain on the radiologist and the likelihood of missing pathological information such as abnormalities are increased. One of the primary objectives of this research work is to develop computer-assisted diagnosis and detection of lung cancer. It also intends to make it easier for radiologists to identify and diagnose lung cancer accurately. The proposed strategy which was based on a unique image feature, took into consideration the spatial interaction of… More >

  • Open AccessOpen Access

    ARTICLE

    Novel Double-Damped Tuned AC Filters in HVDC Systems

    Rana Shaheer Mehmood1, Asif Hussain1, Usman Ali2, Muhammad Tariq Mahmood3,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1467-1482, 2023, DOI:10.32604/cmc.2023.033280
    Abstract This paper presents a performance analysis of novel double-damped tuned alternating current (AC) filters in high voltage direct current (HVDC) systems. The proposed double-damped tuned AC filters offer the advantages of improved performance of HVDC systems in terms of better power quality, high power factor, and lower total harmonic distortion (THD). The system under analysis consists of an 878 km long HVDC transmission line connecting converter stations at Matiari and Lahore, two major cities in Pakistan. The main focus of this research is to design a novel AC filter using the equivalent impedance method of two single-tuned and double-damped tuned… More >

  • Open AccessOpen Access

    ARTICLE

    A Semantic Adversarial Network for Detection and Classification of Myopic Maculopathy

    Qaisar Abbas1, Abdul Rauf Baig1,*, Ayyaz Hussain2
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1483-1499, 2023, DOI:10.32604/cmc.2023.036366
    Abstract The diagnosis of eye disease through deep learning (DL) technology is the latest trend in the field of artificial intelligence (AI). Especially in diagnosing pathologic myopia (PM) lesions, the implementation of DL is a difficult task because of the classification complexity and definition system of PM. However, it is possible to design an AI-based technique that can identify PM automatically and help doctors make relevant decisions. To achieve this objective, it is important to have adequate resources such as a high-quality PM image dataset and an expert team. The primary aim of this research is to design and train the… More >

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    ARTICLE

    Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

    Samah Ibrahim AlShathri, Dina S. M. Hassan*, Samia Allaoua Chelloug
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1501-1526, 2023, DOI:10.32604/cmc.2023.035602
    Abstract Task offloading is a key strategy in Fog Computing (FC). The definition of resource-constrained devices no longer applies to sensors and Internet of Things (IoT) embedded system devices alone. Smart and mobile units can also be viewed as resource-constrained devices if the power, cloud applications, and data cloud are included in the set of required resources. In a cloud-fog-based architecture, a task instance running on an end device may need to be offloaded to a fog node to complete its execution. However, in a busy network, a second offloading decision is required when the fog node becomes overloaded. The possibility… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Meta-Heuristic Optimization Algorithm in White Blood Cells Classification

    Khaled A. Fathy, Humam K. Yaseen*, Mohammad T. Abou-Kreisha, Kamal A. ElDahshan
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1527-1545, 2023, DOI:10.32604/cmc.2023.036322
    Abstract Some human diseases are recognized through of each type of White Blood Cell (WBC) count, so detecting and classifying each type is important for human healthcare. The main aim of this paper is to propose a computer-aided WBCs utility analysis tool designed, developed, and evaluated to classify WBCs into five types namely neutrophils, eosinophils, lymphocytes, monocytes, and basophils. Using a computer-artificial model reduces resource and time consumption. Various pre-trained deep learning models have been used to extract features, including AlexNet, Visual Geometry Group (VGG), Residual Network (ResNet), which belong to different taxonomy types of deep learning architectures. Also, Binary Border… More >

  • Open AccessOpen Access

    ARTICLE

    TRS Scheduling for Improved QoS Performance in Cloud System

    G. John Samuel Babu1, M. Baskar2,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1547-1559, 2023, DOI:10.32604/cmc.2023.033300
    Abstract Numerous methods are analysed in detail to improve task scheduling and data security performance in the cloud environment. The methods involve scheduling according to the factors like makespan, waiting time, cost, deadline, and popularity. However, the methods are inappropriate for achieving higher scheduling performance. Regarding data security, existing methods use various encryption schemes but introduce significant service interruption. This article sketches a practical Real-time Application Centric TRS (Throughput-Resource utilization–Success) Scheduling with Data Security (RATRSDS) model by considering all these issues in task scheduling and data security. The method identifies the required resource and their claim time by receiving the service… More >

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    ARTICLE

    An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

    Wen Deng1, Guangjun Liang1,2,3,*, Chenfei Yu1, Kefan Yao1, Chengrui Wang1, Xuan Zhang1
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1561-1576, 2023, DOI:10.32604/cmc.2023.035016
    Abstract With the frequent occurrence of telecommunications and network fraud crimes in recent years, new frauds have emerged one after another which has caused huge losses to the people. However, due to the lack of an effective preventive mechanism, the police are often in a passive position. Using technologies such as web crawlers, feature engineering, deep learning, and artificial intelligence, this paper proposes a user portrait fraud warning scheme based on Weibo public data. First, we perform preliminary screening and cleaning based on the keyword “defrauded” to obtain valid fraudulent user Identity Documents (IDs). The basic information and account information of… More >

  • Open AccessOpen Access

    ARTICLE

    LexDeep: Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

    Azlinah Mohamed1,3,*, Zuhaira Muhammad Zain2, Hadil Shaiba2,*, Nazik Alturki2, Ghadah Aldehim2, Sapiah Sakri2, Saiful Farik Mat Yatin1, Jasni Mohamad Zain1
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1577-1601, 2023, DOI:10.32604/cmc.2023.034746
    Abstract The COVID-19 pandemic has spread globally, resulting in financial instability in many countries and reductions in the per capita gross domestic product. Sentiment analysis is a cost-effective method for acquiring sentiments based on household income loss, as expressed on social media. However, limited research has been conducted in this domain using the LexDeep approach. This study aimed to explore social trend analytics using LexDeep, which is a hybrid sentiment analysis technique, on Twitter to capture the risk of household income loss during the COVID-19 pandemic. First, tweet data were collected using Twint with relevant keywords before (9 March 2019 to… More >

  • Open AccessOpen Access

    ARTICLE

    An Active Image Forgery Detection Approach Based on Edge Detection

    Hüseyin Bilal Macit1, Arif Koyun2,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1603-1619, 2023, DOI:10.32604/cmc.2023.036216
    Abstract Recently, digital images have become the most used data, thanks to high internet speed and high resolution, cheap and easily accessible digital cameras. We generate, transmit and store millions of images every second. Most of these images are insignificant images containing only personal information. However, in many fields such as banking, finance, public institutions, and educational institutions, the images of many valuable objects like ID cards, photographs, credit cards, and transaction receipts are stored and transmitted to the digital environment. These images are very significant and must be secured. A valuable image can be maliciously modified by an attacker. The… More >

  • Open AccessOpen Access

    ARTICLE

    SiamDLA: Dynamic Label Assignment for Siamese Visual Tracking

    Yannan Cai, Ke Tan, Zhenzhong Wei*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1621-1640, 2023, DOI:10.32604/cmc.2023.036177
    Abstract Label assignment refers to determining positive/negative labels for each sample to supervise the training process. Existing Siamese-based trackers primarily use fixed label assignment strategies according to human prior knowledge; thus, they can be sensitive to predefined hyperparameters and fail to fit the spatial and scale variations of samples. In this study, we first develop a novel dynamic label assignment (DLA) module to handle the diverse data distributions and adaptively distinguish the foreground from the background based on the statistical characteristics of the target in visual object tracking. The core of DLA module is a two-step selection mechanism. The first step… More >

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    ARTICLE

    Short Term Traffic Flow Prediction Using Hybrid Deep Learning

    Mohandu Anjaneyulu, Mohan Kubendiran*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1641-1656, 2023, DOI:10.32604/cmc.2023.035056
    Abstract Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In this paper, we have proposed… More >

  • Open AccessOpen Access

    ARTICLE

    MCMOD: The Multi-Category Large-Scale Dataset for Maritime Object Detection

    Zihao Sun1,*, Xiao Hu2, Yining Qi2, Yongfeng Huang2, Songbin Li3
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1657-1669, 2023, DOI:10.32604/cmc.2023.036558
    Abstract The marine environment is becoming increasingly complex due to the various marine vehicles, and the diversity of maritime objects poses a challenge to marine environmental governance. Maritime object detection technology plays an important role in this segment. In the field of computer vision, there is no sufficiently comprehensive public dataset for maritime objects in the contrast to the automotive application domain. The existing maritime datasets either have no bounding boxes (which are made for object classification) or cover limited varieties of maritime objects. To fulfil the vacancy, this paper proposed the Multi-Category Large-Scale Dataset for Maritime Object Detection (MCMOD) which… More >

  • Open AccessOpen Access

    ARTICLE

    Detection of Worker’s Safety Helmet and Mask and Identification of Worker Using Deeplearning

    NaeJoung Kwak1, DongJu Kim2,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1671-1686, 2023, DOI:10.32604/cmc.2023.035762
    Abstract This paper proposes a method for detecting a helmet for the safety of workers from risk factors and a mask worn indoors and verifying a worker’s identity while wearing a helmet and mask for security. The proposed method consists of a part for detecting the worker’s helmet and mask and a part for verifying the worker’s identity. An algorithm for helmet and mask detection is generated by transfer learning of Yolov5’s s-model and m-model. Both models are trained by changing the learning rate, batch size, and epoch. The model with the best performance is selected as the model for detecting… More >

  • Open AccessOpen Access

    ARTICLE

    Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

    Hong Li1,*, Jianjun Li1, Guohui Li1, Rong Gao2, Lingyu Yan2
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1687-1709, 2023, DOI:10.32604/cmc.2023.035239
    Abstract Expert Recommendation (ER) aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering (CQA) web services. How to model questions and users in the heterogeneous content network is critical to this task. Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues. Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling. However, they often fail… More >

  • Open AccessOpen Access

    ARTICLE

    ML and CFD Simulation of Flow Structure around Tandem Bridge Piers in Pressurized Flow

    Aliasghar Azma1, Ramin Kiyanfar2, Yakun Liu1,*, Masoumeh Azma3,*, Di Zhang1, Ze Cao1, Zhuoyue Li1
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1711-1733, 2023, DOI:10.32604/cmc.2023.036680
    Abstract Various regions are becoming increasingly vulnerable to the increased frequency of floods due to the recent changes in climate and precipitation patterns throughout the world. As a result, specific infrastructures, notably bridges, would experience significant flooding for which they were not intended and would be submerged. The flow field and shear stress distribution around tandem bridge piers under pressurized flow conditions for various bridge deck widths are examined using a series of three-dimensional (3D) simulations. It is indicated that scenarios with a deck width to pier diameter (Ld/p) ratio of 3 experience the highest levels of turbulent disturbance. In addition,… More >

  • Open AccessOpen Access

    ARTICLE

    Device Discovery in D2D Communication: Scenarios and Challenges

    Adeel Iqbal1, Ali Nauman2, Riaz Hussain1, Irfan Latif Khan1, Ali Khaqan1, Sana Shuja1, Sung Won Kim2,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1735-1750, 2023, DOI:10.32604/cmc.2023.034468
    Abstract Device to Device (D2D) communication is expected to be an essential part of 5G cellular networks. D2D communication enables close-proximity devices to establish a direct communication session. D2D communication offers many advantages, such as reduced latency, high data rates, range extension, and cellular offloading. The first step to establishing a D2D session is device discovery; an efficient device discovery will lead to efficient D2D communication. D2D device further needs to manage its mode of communication, perform resource allocation, manage its interference and most importantly control its power to improve the battery life of the device. This work has developed six… More >

  • Open AccessOpen Access

    ARTICLE

    TinyML-Based Classification in an ECG Monitoring Embedded System

    Eunchan Kim1, Jaehyuk Kim2, Juyoung Park3, Haneul Ko4, Yeunwoong Kyung5,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1751-1764, 2023, DOI:10.32604/cmc.2023.031663
    Abstract Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and… More >

  • Open AccessOpen Access

    ARTICLE

    Detecting Double JPEG Compressed Color Images via an Improved Approach

    Xiaojie Zhao1, Xiankui Meng1, Ruyong Ren2, Shaozhang Niu2,*, Zhenguang Gao3
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1765-1781, 2023, DOI:10.32604/cmc.2023.029552
    Abstract Detecting double Joint Photographic Experts Group (JPEG) compression for color images is vital in the field of image forensics. In previous researches, there have been various approaches to detecting double JPEG compression with different quantization matrices. However, the detection of double JPEG color images with the same quantization matrix is still a challenging task. An effective detection approach to extract features is proposed in this paper by combining traditional analysis with Convolutional Neural Networks (CNN). On the one hand, the number of nonzero pixels and the sum of pixel values of color space conversion error are provided with 12-dimensional features… More >

  • Open AccessOpen Access

    ARTICLE

    A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

    Saqib Ali1,2, Shazia Riaz2,3, Safoora2, Xiangyong Liu1, Guojun Wang1,*
    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1783-1800, 2023, DOI:10.32604/cmc.2023.035736
    Abstract Short-term load forecasting (STLF) is part and parcel of the efficient working of power grid stations. Accurate forecasts help to detect the fault and enhance grid reliability for organizing sufficient energy transactions. STLF ranges from an hour ahead prediction to a day ahead prediction. Various electric load forecasting methods have been used in literature for electricity generation planning to meet future load demand. A perfect balance regarding generation and utilization is still lacking to avoid extra generation and misusage of electric load. Therefore, this paper utilizes Levenberg–Marquardt (LM) based Artificial Neural Network (ANN) technique to forecast the short-term electricity load… More >

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