Home / Journals / CMC / Vol.74, No.1, 2023
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  • Open AccessOpen Access

    ARTICLE

    Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

    Poonam*, Suman Sangwan
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1783-1799, 2023, DOI:10.32604/cmc.2023.031729
    Abstract This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification… More >

  • Open AccessOpen Access

    ARTICLE

    Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

    Yun Fen Yong1,*, Chee Keong Tan1, Ian Kim Teck Tan2, Su Wei Tan1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1801-1818, 2023, DOI:10.32604/cmc.2023.032710
    Abstract A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations.… More >

  • Open AccessOpen Access

    ARTICLE

    Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

    Zakarya Farea Shaaf1,*, Muhammad Mahadi Abdul Jamil1, Radzi Ambar1, Ahmed Abdu Alattab2,3, Anwar Ali Yahya3,4, Yousef Asiri4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1819-1835, 2023, DOI:10.32604/cmc.2023.031900
    Abstract The automatic localization of the left ventricle (LV) in short-axis magnetic resonance (MR) images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest (ROI). The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration. Nevertheless, this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices. Thus, this study proposed a region-based convolutional network (Faster R-CNN) for the LV localization from… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Siamese Network for Few/Zero-Shot Handwritten Character Recognition Tasks

    Nagwa Elaraby*, Sherif Barakat, Amira Rezk
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.032288
    Abstract Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks. It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks (CNNs) for learning a distance function that can map input data from the input space to the feature space. Instead of determining the class of each sample, the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not. The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly… More >

  • Open AccessOpen Access

    ARTICLE

    Optimization of the Placement and Size of Photovoltaic Source

    Maawiya Ould Sidi1,*, Mustafa Mosbah2, Rabie Zine3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1855-1870, 2023, DOI:10.32604/cmc.2023.030032
    Abstract This paper presents a new optimization study of the placement and size of a photovoltaic source (PVS) in a distribution grid, based on annual records of meteorological parameters (irradiance, temperature). Based on the recorded data, the production output as well as the daily average power (24-h vector) of the PVS is extracted over the year. When a power vector is available, it can be used as an input when searching for the optimal size of the PVS. This allows to take into account the constraint of the variation of the power generated by this source considering the variation of the… More >

  • Open AccessOpen Access

    ARTICLE

    Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

    Atif Ikram1,2,*, Masita Abdul Jalil1, Amir Bin Ngah1, Saqib Raza6, Ahmad Salman Khan3, Yasir Mahmood3,4, Nazri Kama4, Azri Azmi4, Assad Alzayed5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1871-1886, 2023, DOI:10.32604/cmc.2023.030818
    Abstract Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value. One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’ projects. The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients. The projects belong to OSMO vendors, having offices in… More >

  • Open AccessOpen Access

    ARTICLE

    Improved Key Node Recognition Method of Social Network Based on PageRank Algorithm

    Lei Hong1, Yiji Qian1,*, Chaofan Gong2, Yurui Zhang1, Xin Zhou3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1887-1903, 2023, DOI:10.32604/cmc.2023.029180
    Abstract The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture, and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to expand. There are key node users in social networks. Compared with ordinary users, their influence is greater, their radiation range is wider, and their information transmission capabilities are better. The key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking sites. In order to solve the problems of incomplete evaluation factors, poor… More >

  • Open AccessOpen Access

    ARTICLE

    Dipper Throated Optimization for Detecting Black-Hole Attacks in MANETs

    Reem Alkanhel1, El-Sayed M. El-kenawy2,3, Abdelaziz A. Abdelhamid4,5, Abdelhameed Ibrahim6, Mostafa Abotaleb7, Doaa Sami Khafaga8,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1905-1921, 2023, DOI:10.32604/cmc.2023.032157
    Abstract In terms of security and privacy, mobile ad-hoc network (MANET) continues to be in demand for additional debate and development. As more MANET applications become data-oriented, implementing a secure and reliable data transfer protocol becomes a major concern in the architecture. However, MANET’s lack of infrastructure, unpredictable topology, and restricted resources, as well as the lack of a previously permitted trust relationship among connected nodes, contribute to the attack detection burden. A novel detection approach is presented in this paper to classify passive and active black-hole attacks. The proposed approach is based on the dipper throated optimization (DTO) algorithm, which… More >

  • Open AccessOpen Access

    ARTICLE

    GrCol-PPFL: User-Based Group Collaborative Federated Learning Privacy Protection Framework

    Jieren Cheng1, Zhenhao Liu1,*, Yiming Shi1, Ping Luo1,2, Victor S. Sheng3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1923-1939, 2023, DOI:10.32604/cmc.2023.032758
    Abstract With the increasing number of smart devices and the development of machine learning technology, the value of users’ personal data is becoming more and more important. Based on the premise of protecting users’ personal privacy data, federated learning (FL) uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data. However, since FL can still leak the user's original data by exchanging gradient information. The existing privacy protection strategy will increase the uplink time due to encryption measures. It is a huge challenge in terms of communication. When there are a… More >

  • Open AccessOpen Access

    ARTICLE

    A Survey on Image Semantic Segmentation Using Deep Learning Techniques

    Jieren Cheng1,3, Hua Li2,*, Dengbo Li3, Shuai Hua2, Victor S. Sheng4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1941-1957, 2023, DOI:10.32604/cmc.2023.032757
    Abstract Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture. This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Firstly, the commonly used image segmentation… More >

  • Open AccessOpen Access

    ARTICLE

    Smart Home IoT Privacy and Security Preservation via Machine Learning Techniques

    Mubarak Almutairi*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1959-1983, 2023, DOI:10.32604/cmc.2023.031155
    Abstract The development and use of Internet of Things (IoT) devices have grown significantly in recent years. Advanced IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT devices. Corporations have begun to embrace the IoT concept. Identifying true and suitable devices, security faults that might be used for bad reasons, and administration of such devices are only a few of the issues that IoT, a new concept in technological progress, provides. In some ways, IoT device traffic differs from regular device traffic. Devices with particular features can be classified into… More >

  • Open AccessOpen Access

    ARTICLE

    Robust and Reusable Fuzzy Extractors from Non-Uniform Learning with Errors Problem

    Joo Woo1, Jonghyun Kim1, Jong Hwan Park2,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1985-2003, 2023, DOI:10.32604/cmc.2023.033102
    Abstract A fuzzy extractor can extract an almost uniform random string from a noisy source with enough entropy such as biometric data. To reproduce an identical key from repeated readings of biometric data, the fuzzy extractor generates a helper data and a random string from biometric data and uses the helper data to reproduce the random string from the second reading. In 2013, Fuller et al. proposed a computational fuzzy extractor based on the learning with errors problem. Their construction, however, can tolerate a sub-linear fraction of errors and has an inefficient decoding algorithm, which causes the reproducing time to increase significantly. In 2016,… More >

  • Open AccessOpen Access

    ARTICLE

    Performance Enhancement of Adaptive Neural Networks Based on Learning Rate

    Swaleha Zubair1, Anjani Kumar Singha1, Nitish Pathak2, Neelam Sharma3, Shabana Urooj4,*, Samia Rabeh Larguech4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2005-2019, 2023, DOI:10.32604/cmc.2023.031481
    Abstract Deep learning is the process of determining parameters that reduce the cost function derived from the dataset. The optimization in neural networks at the time is known as the optimal parameters. To solve optimization, it initialize the parameters during the optimization process. There should be no variation in the cost function parameters at the global minimum. The momentum technique is a parameters optimization approach; however, it has difficulties stopping the parameter when the cost function value fulfills the global minimum (non-stop problem). Moreover, existing approaches use techniques; the learning rate is reduced during the iteration period. These techniques are monotonically… More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Global Optimization Algorithm for Feature Selection

    Ahmad Taher Azar1,2,*, Zafar Iqbal Khan2, Syed Umar Amin2, Khaled M. Fouad1,3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2021-2037, 2023, DOI:10.32604/cmc.2023.032183
    Abstract This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has introduced the benefits of Parallel computing into the combined power of TVAC (Time-Variant Acceleration Coefficients) and IW (Inertial Weight). Proposed algorithm has been tested against linear, non-linear, traditional, and multiswarm based optimization algorithms. An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO. Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIW-PSO vs. IW based Particle Swarm Optimization (PSO) algorithms, TVAC based PSO algorithms, traditional PSO, Genetic algorithms (GA),… More >

  • Open AccessOpen Access

    ARTICLE

    Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

    Iftikhar Naseer1,*, Tehreem Masood1, Sheeraz Akram1, Arfan Jaffar1, Muhammad Rashid2, Muhammad Amjad Iqbal3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2039-2054, 2023, DOI:10.32604/cmc.2023.032927
    Abstract Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT… More >

  • Open AccessOpen Access

    ARTICLE

    Optimization Scheme of Trusted Task Offloading in IIoT Scenario Based on DQN

    Xiaojuan Wang1, Zikui Lu1,*, Siyuan Sun2, Jingyue Wang1, Luona Song3, Merveille Nicolas4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2055-2071, 2023, DOI:10.32604/cmc.2023.031750
    Abstract With the development of the Industrial Internet of Things (IIoT), end devices (EDs) are equipped with more functions to capture information. Therefore, a large amount of data is generated at the edge of the network and needs to be processed. However, no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing (MEC) devices, the data is short of security and may be changed during transmission. In view of this challenge, this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security. Blockchain… More >

  • Open AccessOpen Access

    ARTICLE

    Airstacknet: A Stacking Ensemble-Based Approach for Air Quality Prediction

    Amel Ksibi1, Amina Salhi1, Ala Saleh Alluhaidan1,*, Sahar A. El-Rahman2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2073-2096, 2023, DOI:10.32604/cmc.2023.032566
    Abstract The quality of the air we breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals. Unfortunately, personal air quality measurement remains challenging. In this study, we investigate the use of first-person photos for the prediction of air quality. The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction. AirStackNet consists of two layers and four regression models, where the first layer generates meta-data from… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

    Hamayun Khan1,*, Irfan Ud Din2, Arshad Ali3, Mohammad Husain3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2097-2113, 2023, DOI:10.32604/cmc.2023.032999
    Abstract Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities on-chip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper… More >

  • Open AccessOpen Access

    ARTICLE

    Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

    Daniyar Sultan1,2, Aigerim Toktarova3,*, Ainur Zhumadillayeva4, Sapargali Aldeshov5,6, Shynar Mussiraliyeva1, Gulbakhram Beissenova6,7, Abay Tursynbayev8, Gulmira Baenova4, Aigul Imanbayeva6
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2115-2131, 2023, DOI:10.32604/cmc.2023.032993
    Abstract Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying… More >

  • Open AccessOpen Access

    ARTICLE

    Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

    Ahmed Y. Hamed1, M. Kh. Elnahary1,*, Faisal S. Alsubaei2, Hamdy H. El-Sayed1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2133-2148, 2023, DOI:10.32604/cmc.2023.032215
    Abstract Cloud computing has taken over the high-performance distributed computing area, and it currently provides on-demand services and resource polling over the web. As a result of constantly changing user service demand, the task scheduling problem has emerged as a critical analytical topic in cloud computing. The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions. Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system. The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing… More >

  • Open AccessOpen Access

    ARTICLE

    Solar Energy Harvesting Using a Timer-Based Relay Selection

    Raed Alhamad1,*, Hatem Boujemaa2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2149-2159, 2023, DOI:10.32604/cmc.2023.033222
    Abstract In this paper, the throughput and delay of cooperative communications are derived when solar energy is used and relay node is selected using a timer. The source and relays harvest energy from sun using a photo voltaic system. The harvested power is used by the source to transmit data to the relays. Then, a selected relay amplifies the signal to the destination. Opportunistic, partial and reactive relay selection are used. The relay transmits when its timer elapses. The timer is set to a value proportional to the inverse of its Signal to Noise Ratio (SNR). Therefore, the relay with largest… More >

  • Open AccessOpen Access

    ARTICLE

    Power Prediction of VLSI Circuits Using Machine Learning

    E. Poovannan*, S. Karthik
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2161-2177, 2023, DOI:10.32604/cmc.2023.032512
    Abstract The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit. A big database is needed to undertake important analytical work like statistical method, heat research, and IR-drop research that results in extended running times. This unit focuses on the assessment of test strength. Because of the enormous number of successful designs for current models and the unnecessary time required for every test, maximum energy ratings with all tests cannot be achieved. Nevertheless, test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip. Generally, effective… More >

  • Open AccessOpen Access

    ARTICLE

    Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

    R. Poonguzhali1, Sultan Ahmad2, P. Thiruvannamalai Sivasankar3, S. Anantha Babu3, Pranav Joshi4, Gyanendra Prasad Joshi5, Sung Won Kim6,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2179-2194, 2023, DOI:10.32604/cmc.2023.032816
    Abstract Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors (BT). A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate. The location and classification of BTs from huge medicinal images database, obtained from routine medical tasks with manual processes are a higher cost together in effort and time. An automatic recognition, place, and classifier process was desired and useful. This study introduces an Automated Deep Residual U-Net Segmentation with Classification model (ADRU-SCM) for Brain Tumor Diagnosis. The presented ADRU-SCM model majorly focuses on… More >

  • Open AccessOpen Access

    ARTICLE

    Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

    S. Rama Sree1, Inderjeet Kaur2, Alexey Tikhonov3, E. Laxmi Lydia4, Ahmed A. Thabit5, Zahraa H. Kareem6, Yousif Kerrar Yousif7, Ahmed Alkhayyat8,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2195-2209, 2023, DOI:10.32604/cmc.2023.032586
    Abstract Autism spectrum disorder (ASD) is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills, recurrent conduct, and communication. Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD. Recognition of ASD related to objective pathogenic mutation screening is the initial step against prior intervention and efficient treatment of children who were affected. Nowadays, healthcare and machine learning (ML) industries are combined for determining the existence of various diseases. This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and… More >

  • Open AccessOpen Access

    ARTICLE

    Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

    Farrukh Shahzad Ahmed, Awais Majeed, Tamim Ahmed Khan*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2211-2238, 2023, DOI:10.32604/cmc.2023.032664
    Abstract Test Case Prioritization (TCP) techniques perform better than other regression test optimization techniques including Test Suite Reduction (TSR) and Test Case Selection (TCS). Many TCP techniques are available, and their performance is usually measured through a metric Average Percentage of Fault Detection (APFD). This metric is value-neutral because it only works well when all test cases have the same cost, and all faults have the same severity. Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results. Therefore, using the right metric for performance evaluation of TCP… More >

  • Open AccessOpen Access

    ARTICLE

    Generation of OAM Waves and Analysis of Mode Purity for 5G Sub-6 GHz Applications

    Shehab Khan Noor1, Arif Mawardi Ismail1, Mohd Najib Mohd Yasin1,*, Mohamed Nasrun Osman1, Nurulazlina Ramli2, Ali H. Rambe3, J. Iqbal4,5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2239-2259, 2023, DOI:10.32604/cmc.2023.031170
    Abstract This article presents the generation of Orbital Angular Momentum (OAM) vortex waves with mode 1 using Uniform Circular Array (UCA) antenna. Two different designs, namely, UCA-1 (4-element array antenna) and UCA-2 (8-element array antenna), were designed and fabricated using FR-4 substrate to generate OAM mode 1 at 3.5 GHz (5G mid-band). The proposed antenna arrays comprised rectangular microstrip patch elements with inset fed technique. The elements were excited by a carefully designed feeding phase shift network to provide similar output energy at output ports with desired phase shift value. The generated OAM waves were confirmed by measuring the null in… More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

    Hanadi AlZaabi1, Khaled Shaalan1, Taher M. Ghazal2,3,*, Muhammad A. Khan4,5, Sagheer Abbas6, Beenu Mago7, Mohsen A. A. Tomh6, Munir Ahmad6
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2261-2278, 2023, DOI:10.32604/cmc.2023.031834
    Abstract Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer’s energy requirements. In many regions, energy utilization in the housing area is 30%–40%. The growth of smart homes has raised the requirement for intelligence in applications such as… More >

  • Open AccessOpen Access

    ARTICLE

    An Integrated and Comprehensive Fuzzy Multi-Criteria Model for Electronic Wallet Selection

    Phuoc Van Nguyen*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2279-2301, 2023, DOI:10.32604/cmc.2023.030019
    Abstract Electronic wallet (E-wallet), which combines software, hardware, and human interaction, is a modern platform for electronic payment. Financial services managers can create more effective strategies for advancing existing positions by evaluating the success of e-wallet services. The objective of this research is to create an integrated model for assessing E-wallet services using the Fuzzy Analytic Hierarchy Process (FAHP) in conjunction with a technique for extent analysis and the ARAS approach (Additive Ratio Assessment). In this study, E-wallet service providers are ranked and evaluated using the ARAS approach and the FAHP priority weights. In addition, a case study from Vietnam illustrates… More >

  • Open AccessOpen Access

    ARTICLE

    Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

    K. Kalyani1, Velmurugan Subbiah Parvathy2, Hikmat A. M. Abdeljaber3, T. Satyanarayana Murthy4, Srijana Acharya5, Gyanendra Prasad Joshi6, Sung Won Kim7,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2303-2316, 2023, DOI:10.32604/cmc.2023.033162
    Abstract In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning… More >

  • Open AccessOpen Access

    ARTICLE

    Query Optimization Framework for Graph Database in Cloud Dew Environment

    Tahir Alyas1, Ali Alzahrani2, Yazed Alsaawy2, Khalid Alissa3, Qaiser Abbas2, Nadia Tabassum4,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2317-2330, 2023, DOI:10.32604/cmc.2023.032454
    Abstract The query optimizer uses cost-based optimization to create an execution plan with the least cost, which also consumes the least amount of resources. The challenge of query optimization for relational database systems is a combinatorial optimization problem, which renders exhaustive search impossible as query sizes rise. Increases in CPU performance have surpassed main memory, and disk access speeds in recent decades, allowing data compression to be used—strategies for improving database performance systems. For performance enhancement, compression and query optimization are the two most factors. Compression reduces the volume of data, whereas query optimization minimizes execution time. Compressing the database reduces… More >

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