Home / Journals / CMC / Vol.75, No.2, 2023
Special lssues
Table of Content
  • Open AccessOpen Access

    ARTICLE

    WiMA: Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

    Sohaib Manzoor1, Hira Manzoor2, Saddaf Rubab3,4, Muhammad Attique Khan5, Majed Alhaisoni6, Abdullah Alqahtani7, Ye Jin Kim8, Byoungchol Chang9,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2347-2363, 2023, DOI:10.32604/cmc.2023.034044
    Abstract Despite the planned installation and operations of the traditional IEEE 802.11 networks, they still experience degraded performance due to the number of inefficiencies. One of the main reasons is the received signal strength indicator (RSSI) association problem, in which the user remains connected to the access point (AP) unless the RSSI becomes too weak. In this paper, we propose a multi-criterion association (WiMA) scheme based on software defined networking (SDN) in Wi-Fi networks. An association solution based on multi-criterion such as AP load, RSSI, and channel occupancy is proposed to satisfy the quality of service More >

  • Open AccessOpen Access

    ARTICLE

    An IoT Environment Based Framework for Intelligent Intrusion Detection

    Hamza Safwan1, Zeshan Iqbal1, Rashid Amin1, Muhammad Attique Khan2, Majed Alhaisoni3, Abdullah Alqahtani4, Ye Jin Kim5, Byoungchol Chang6,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2365-2381, 2023, DOI:10.32604/cmc.2023.033896
    Abstract Software-defined networking (SDN) represents a paradigm shift in network traffic management. It distinguishes between the data and control planes. APIs are then used to communicate between these planes. The controller is central to the management of an SDN network and is subject to security concerns. This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks. Overfitting, low accuracy, and efficient feature selection is all discussed. We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory (LSTM). In this study, a new dataset based specifically on More >

  • Open AccessOpen Access

    ARTICLE

    SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

    Abida Sharif1, Muhammad Imran Sharif1, Muhammad Attique Khan2, Nisar Ali2, Abdullah Alqahtani3, Majed Alhaisoni4, Ye Jin Kim5, Byoungchol Chang6,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2383-2396, 2023, DOI:10.32604/cmc.2023.033894
    Abstract The content-centric networking (CCN) architecture allows access to the content through name, instead of the physical location where the content is stored, which makes it a more robust and flexible content-based architecture. Nevertheless, in CCN, the broadcast nature of vehicles on the Internet of Vehicles (IoV) results in latency and network congestion. The IoV-based content distribution is an emerging concept in which all the vehicles are connected via the internet. Due to the high mobility of vehicles, however, IoV applications have different network requirements that differ from those of many other networks, posing new challenges. More >

  • Open AccessOpen Access

    ARTICLE

    An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

    Mohammed Mubarak Mustafa1, Ahmed Abelmonem Khalifa2,3, Korhan Cengiz4,5, Nikola Ivković6,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2397-2412, 2023, DOI:10.32604/cmc.2023.036275
    Abstract The performance of Wireless Sensor Networks (WSNs) is an important fragment of the Internet of Things (IoT), where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical resources. By grouping hubs, a clustering convention offers a useful solution for ensuring energy-saving of hubs and Hybrid Media Access Control (HMAC) during the course of the organization. Nevertheless, current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions negatively. In this investigation, we recommend an Improved Energy-Proficient Algorithm (IEPA) for HMAC throughout the lifetime of the… More >

  • Open AccessOpen Access

    ARTICLE

    Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

    Salah-ud-din Khokhar1,2,*, QinKe Peng1, Muhammad Yasir Noor3
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2413-2427, 2023, DOI:10.32604/cmc.2023.036148
    Abstract For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested More >

  • Open AccessOpen Access

    ARTICLE

    Stackelberg Game-Based Resource Allocation with Blockchain for Cold-Chain Logistics System

    Yang Zhang1, Chaoyang Li2,*, Xiangjun Xin2
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2429-2442, 2023, DOI:10.32604/cmc.2023.037139
    Abstract Cold-chain logistics system (CCLS) plays the role of collecting and managing the logistics data of frozen food. However, there always exist problems of information loss, data tampering, and privacy leakage in traditional centralized systems, which influence frozen food security and people’s health. The centralized management form impedes the development of the cold-chain logistics industry and weakens logistics data availability. This paper first introduces a distributed CCLS based on blockchain technology to solve the centralized management problem. This system aggregates the production base, storage, transport, detection, processing, and consumer to form a cold-chain logistics union. The… More >

  • Open AccessOpen Access

    ARTICLE

    Prediction of NFT Sale Price Fluctuations on OpenSea Using Machine Learning Approaches

    Zixiong Wang, Qiuying Chen, Sang-Joon Lee*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2443-2459, 2023, DOI:10.32604/cmc.2023.037553
    Abstract The rapid expansion of the non-fungible token (NFT) market has attracted many investors. However, studies on the NFT price fluctuations have been relatively limited. To date, the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction. The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea, the world’s largest NFT marketplace. We used Python programs to collect data and summarized them as: NFT information, collection information, and related account information. AdaBoost and Random… More >

  • Open AccessOpen Access

    ARTICLE

    Embedded System Development for Detection of Railway Track Surface Deformation Using Contour Feature Algorithm

    Tarique Rafique Memon1,2,*, Tayab Din Memon3,4, Imtiaz Hussain Kalwar5, Bhawani Shankar Chowdhry1
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2461-2477, 2023, DOI:10.32604/cmc.2023.035413
    Abstract Derailment of trains is not unusual all around the world, especially in developing countries, due to unidentified track or rolling stock faults that cause massive casualties each year. For this purpose, a proper condition monitoring system is essential to avoid accidents and heavy losses. Generally, the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment. Therefore, in this paper, we present the development of a novel embedded system prototype for condition monitoring of railway track. The proposed prototype system works… More >

  • Open AccessOpen Access

    ARTICLE

    Network Intrusion Detection Model Using Fused Machine Learning Technique

    Fahad Mazaed Alotaibi*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2479-2490, 2023, DOI:10.32604/cmc.2023.033792
    Abstract With the progress of advanced technology in the industrial revolution encompassing the Internet of Things (IoT) and cloud computing, cyberattacks have been increasing rapidly on a large scale. The rapid expansion of IoT and networks in many forms generates massive volumes of data, which are vulnerable to security risks. As a result, cyberattacks have become a prevalent and danger to society, including its infrastructures, economy, and citizens’ privacy, and pose a national security risk worldwide. Therefore, cyber security has become an increasingly important issue across all levels and sectors. Continuous progress is being made in More >

  • Open AccessOpen Access

    ARTICLE

    Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

    Ashir Javeed1,2, Ana Luiza Dallora2, Johan Sanmartin Berglund2,*, Arif Ali4, Peter Anderberg2,3, Liaqat Ali5
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2491-2508, 2023, DOI:10.32604/cmc.2023.033783
    Abstract Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables.… More >

  • Open AccessOpen Access

    ARTICLE

    Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

    Shu-Yin Chiang*, Dong-Ye Wu
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2509-2526, 2023, DOI:10.32604/cmc.2023.036466
    Abstract Physiological signals indicate a person’s physical and mental state at any given time. Accordingly, many studies extract physiological signals from the human body with non-contact methods, and most of them require facial feature points. However, under COVID-19, wearing a mask has become a must in many places, so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research. In this study, RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate, blood pressure, respiratory… More >

  • Open AccessOpen Access

    ARTICLE

    A New Model for Network Security Situation Assessment of the Industrial Internet

    Ming Cheng1, Shiming Li1,3,*, Yuhe Wang1, Guohui Zhou1, Peng Han1, Yan Zhao2
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2527-2555, 2023, DOI:10.32604/cmc.2023.036427
    Abstract To address the problem of network security situation assessment in the Industrial Internet, this paper adopts the evidential reasoning (ER)algorithm and belief rule base (BRB) method to establish an assessment model. First, this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge. Second, the evaluation indicators are fused with expert knowledge and the ER algorithm. According to the fusion results, a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established, and the… More >

  • Open AccessOpen Access

    ARTICLE

    Prediction of Uncertainty Estimation and Confidence Calibration Using Fully Convolutional Neural Network

    Karim Gasmi1,*, Lassaad Ben Ammar2,, Hmoud Elshammari4, Fadwa Yahya2
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2557-2573, 2023, DOI:10.32604/cmc.2023.033270
    Abstract Convolution neural networks (CNNs) have proven to be effective clinical imaging methods. This study highlighted some of the key issues within these systems. It is difficult to train these systems in a limited clinical image databases, and many publications present strategies including such learning algorithm. Furthermore, these patterns are known for making a highly reliable prognosis. In addition, normalization of volume and losses of dice have been used effectively to accelerate and stabilize the training. Furthermore, these systems are improperly regulated, resulting in more confident ratings for correct and incorrect classification, which are inaccurate and… More >

  • Open AccessOpen Access

    ARTICLE

    Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

    Badriyya B. Al-onazi1, Abdulkhaleq Q. A. Hassan2, Mohamed K. Nour3, Mesfer Al Duhayyim4,*, Abdullah Mohamed5, Amgad Atta Abdelmageed6, Ishfaq Yaseen6, Gouse Pasha Mohammed6
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2575-2591, 2023, DOI:10.32604/cmc.2023.033531
    Abstract Sentiment Analysis (SA), a Machine Learning (ML) technique, is often applied in the literature. The SA technique is specifically applied to the data collected from social media sites. The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process. In this background, the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets (QPSODL-SAAT). The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic. Initially, the data pre-processing is performed to convert… More >

  • Open AccessOpen Access

    ARTICLE

    Advanced DAG-Based Ranking (ADR) Protocol for Blockchain Scalability

    Tayyaba Noreen1,*, Qiufen Xia1, Muhammad Zeeshan Haider2
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2593-2613, 2023, DOI:10.32604/cmc.2023.036139
    Abstract In the past decade, blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention. However, current blockchain systems face the problems of limited throughput, poor scalability, and high latency. Due to the failure of consensus algorithms in managing nodes’identities, blockchain technology is considered inappropriate for many applications, e.g., in IoT environments, because of poor scalability. This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking (ADR) protocol to improve blockchain scalability and throughput. The ADR protocol uses the directed acyclic graph ledger, where nodes are placed… More >

  • Open AccessOpen Access

    ARTICLE

    Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

    Navin Ranjan1, Sovit Bhandari1, Yeong-Chan Kim1,2, Hoon Kim1,2,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2615-2639, 2023, DOI:10.32604/cmc.2023.026086
    Abstract Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame sequence. It is one of the crucial issues in computer vision and has many real-world applications, mainly focused on predicting future scenarios to avoid undesirable outcomes. However, modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene, such as occlusions, camera movements, delay and illumination. Direct frame synthesis or optical-flow estimation are common approaches used by researchers. However, researchers mainly focused on video prediction using one of the approaches.… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Color-Image Encryption Method Using DNA Sequence and Chaos Cipher

    Ghofran Kh. Shraida1, Hameed A. Younis1, Taief Alaa Al-Amiedy2, Mohammed Anbar2,*, Hussain A. Younis3,4, Iznan H. Hasbullah2
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2641-2654, 2023, DOI:10.32604/cmc.2023.035793
    Abstract Nowadays, high-resolution images pose several challenges in the context of image encryption. The encryption of huge images’ file sizes requires high computational resources. Traditional encryption techniques like, Data Encryption Standard (DES), and Advanced Encryption Standard (AES) are not only inefficient, but also less secure. Due to characteristics of chaos theory, such as periodicity, sensitivity to initial conditions and control parameters, and unpredictability. Hence, the characteristics of deoxyribonucleic acid (DNA), such as vast parallelism and large storage capacity, make it a promising field. This paper presents an efficient color image encryption method utilizing DNA encoding with… More >

  • Open AccessOpen Access

    ARTICLE

    Identifying Counterexamples Without Variability in Software Product Line Model Checking

    Ling Ding1, Hongyan Wan2,*, Luokai Hu1, Yu Chen1
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2655-2670, 2023, DOI:10.32604/cmc.2023.035542
    Abstract Product detection based on state abstraction technologies in the software product line (SPL) is more complex when compared to a single system. This variability constitutes a new complexity, and the counterexample may be valid for some products but spurious for others. In this paper, we found that spurious products are primarily due to the failure states, which correspond to the spurious counterexamples. The violated products correspond to the real counterexamples. Hence, identifying counterexamples is a critical problem in detecting violated products. In our approach, we obtain the violated products through the genuine counterexamples, which have… More >

  • Open AccessOpen Access

    ARTICLE

    Meta-Learning Multi-Scale Radiology Medical Image Super-Resolution

    Liwei Deng1, Yuanzhi Zhang1, Xin Yang2,*, Sijuan Huang2, Jing Wang3,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2671-2684, 2023, DOI:10.32604/cmc.2023.036642
    Abstract High-resolution medical images have important medical value, but are difficult to obtain directly. Limited by hardware equipment and patient’s physical condition, the resolution of directly acquired medical images is often not high. Therefore, many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical images. However, current super-resolution algorithms only work on a single scale, and multiple networks need to be trained when super-resolution images of different scales are needed. This definitely raises the cost of acquiring high-resolution medical images. Thus, we propose a multi-scale super-resolution algorithm using meta-learning. The algorithm… More >

  • Open AccessOpen Access

    ARTICLE

    Enhanced Parallelized DNA-Coded Stream Cipher Based on Multiplayer Prisoners’ Dilemma

    Khaled M. Suwais*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2685-2704, 2023, DOI:10.32604/cmc.2023.036161
    Abstract Data encryption is essential in securing exchanged data between connected parties. Encryption is the process of transforming readable text into scrambled, unreadable text using secure keys. Stream ciphers are one type of an encryption algorithm that relies on only one key for decryption and as well as encryption. Many existing encryption algorithms are developed based on either a mathematical foundation or on other biological, social or physical behaviours. One technique is to utilise the behavioural aspects of game theory in a stream cipher. In this paper, we introduce an enhanced Deoxyribonucleic acid (DNA)-coded stream cipher More >

  • Open AccessOpen Access

    ARTICLE

    Clustering Reference Images Based on Covisibility for Visual Localization

    Sangyun Lee1, Junekoo Kang2, Hyunki Hong2,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2705-2725, 2023, DOI:10.32604/cmc.2023.034136
    Abstract In feature-based visual localization for small-scale scenes, local descriptors are used to estimate the camera pose of a query image. For large and ambiguous environments, learning-based hierarchical networks that employ local as well as global descriptors to reduce the search space of database images into a smaller set of reference views have been introduced. However, since global descriptors are generated using visual features, reference images with some of these features may be erroneously selected. In order to address this limitation, this paper proposes two clustering methods based on how often features appear as well as… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Authentication Scheme for UAV-Assisted Mobile Edge Computing

    Maryam Alhassan, Abdul Raouf Khan*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2727-2740, 2023, DOI:10.32604/cmc.2023.037129
    Abstract Preserving privacy is imperative in the new unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture to ensure that sensitive information is protected and kept secure throughout the communication. Simultaneously, efficiency must be considered while developing such a privacy-preserving scheme because the devices involved in these architectures are resource constrained. This study proposes a lightweight and efficient authentication scheme for the UAV-assisted MEC environment. The proposed scheme is a hardware-based password-less authentication mechanism that is based on the fact that temporal and memory-related efficiency can be significantly improved while maintaining the data security by adopting… More >

  • Open AccessOpen Access

    ARTICLE

    A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

    Muhammad Usman Sana1,*, Zhanli Li1, Tayybah Kiren2, Hannan Bin Liaqat3, Shahid Naseem3, Atif Saeed4
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2741-2757, 2023, DOI:10.32604/cmc.2023.036093
    Abstract Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s… More >

  • Open AccessOpen Access

    ARTICLE

    Quantum Fuzzy Regression Model for Uncertain Environment

    Tiansu Chen1,2, Shi bin Zhang1,2, Qirun Wang3, Yan Chang1,2,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2759-2773, 2023, DOI:10.32604/cmc.2023.033284
    Abstract In the era of big data, traditional regression models cannot deal with uncertain big data efficiently and accurately. In order to make up for this deficiency, this paper proposes a quantum fuzzy regression model, which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation. In this paper, data envelopment analysis (DEA) is used to calculate the degree of importance of each data point. Meanwhile, Harrow, Hassidim and Lloyd (HHL) algorithm and quantum swap circuits are used to… More >

  • Open AccessOpen Access

    ARTICLE

    Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

    Dongyang Li1, Shiyu Du2,*, Yiming Zhang2, Meiting Zhao3
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2775-2803, 2023, DOI:10.32604/cmc.2023.035911
    Abstract Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way… More >

  • Open AccessOpen Access

    ARTICLE

    Semantic Document Layout Analysis of Handwritten Manuscripts

    Emad Sami Jaha*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2805-2831, 2023, DOI:10.32604/cmc.2023.036169
    Abstract A document layout can be more informative than merely a document’s visual and structural appearance. Thus, document layout analysis (DLA) is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives. This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis (SDLA) by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts. The proposed SDLA approach enables the derivation of implicit information and semantic characteristics, which can be effectively utilized in dozens… More >

  • Open AccessOpen Access

    REVIEW

    A Review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

    Norah M. Alshahrani1,2,*, M. L. Mat Kiah1,*, B. B. Zaidan3, A. H. Alamoodi4, Abdu Saif5
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2833-2858, 2023, DOI:10.32604/cmc.2023.036138
    Abstract A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However, research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an… More >

  • Open AccessOpen Access

    ARTICLE

    Infrared Spectroscopy-Based Chemometric Analysis for Lard Differentiation in Meat Samples

    Muhammad Aadil Siddiqui1,*, M. H. Md Khir1, Zaka Ullah2, Muath Al Hasan2, Abdul Saboor3, Saeed Ahmed Magsi1
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2859-2871, 2023, DOI:10.32604/cmc.2023.034164
    Abstract One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing… More >

  • Open AccessOpen Access

    ARTICLE

    Syntax-Based Aspect Sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

    Zhaoliang Wu1, Shanyu Tang2, Xiaoli Feng1, Jiajun Zou3, Fulian Yin1,4,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2873-2888, 2023, DOI:10.32604/cmc.2023.037060
    Abstract Aspect-Based Sentiment Analysis (ABSA) is one of the essential research in the field of Natural Language Processing (NLP), of which Aspect Sentiment Quad Prediction (ASQP) is a novel and complete subtask. ASQP aims to accurately recognize the sentiment quad in the target sentence, which includes the aspect term, the aspect category, the corresponding opinion term, and the sentiment polarity of opinion. Nevertheless, existing approaches lack knowledge of the sentence’s syntax, so despite recent innovations in ASQP, it is poor for complex cyber comment processing. Also, most research has focused on processing English text, and ASQP… More >

  • Open AccessOpen Access

    ARTICLE

    MSCNN-LSTM Model for Predicting Return Loss of the UHF Antenna in HF-UHF RFID Tag Antenna

    Zhao Yang1, Yuan Zhang1, Lei Zhu2,*, Lei Huang1, Fangyu Hu3, Yanping Du1, Xiaowei Li1
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2889-2904, 2023, DOI:10.32604/cmc.2023.037297
    Abstract High-frequency (HF) and ultrahigh-frequency (UHF) dual-band radio frequency identification (RFID) tags with both near-field and far-field communication can meet different application scenarios. However, it is time-consuming to calculate the return loss of a UHF antenna in a dual-band tag antenna using electromagnetic (EM) simulators. To overcome this, the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory (MSCNN-LSTM) for predicting the return loss of UHF antennas instead of EM simulators. In the proposed MSCNN-LSTM, the MSCNN has three branches, which include three convolution layers with different kernel… More >

  • Open AccessOpen Access

    ARTICLE

    Blockchain Mobile Wallet with Secure Offline Transactions

    Raed Saeed Rasheed1, Khalil Hamdi Ateyeh Al-Shqeerat2,*, Ahmed Salah Ghorab3, Fuad Salama AbuOwaimer4, Aiman Ahmed AbuSamra1
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2905-2919, 2023, DOI:10.32604/cmc.2023.036691
    Abstract There has been an increase in the adoption of mobile payment systems worldwide in the past few years. However, poor Internet connection in rural regions continues to be an obstacle to the widespread use of such technologies. On top of that, there are significant problems with the currently available offline wallets; for instance, the payee cannot verify the number of coins received without access to the Internet. Additionally, it has been demonstrated that some existing systems are susceptible to false token generation, and some do not even permit the user to divide the offline token… More >

  • Open AccessOpen Access

    ARTICLE

    Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

    Muhammad Asfand-e-Yar1,*, Qadeer Hashir1,*, Syed Hassan Tanvir1, Wajeeha Khalil2
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2921-2938, 2023, DOI:10.32604/cmc.2023.034741
    Abstract The growth of the internet and technology has had a significant effect on social interactions. False information has become an important research topic due to the massive amount of misinformed content on social networks. It is very easy for any user to spread misinformation through the media. Therefore, misinformation is a problem for professionals, organizers, and societies. Hence, it is essential to observe the credibility and validity of the News articles being shared on social media. The core challenge is to distinguish the difference between accurate and false information. Recent studies focus on News article… More >

  • Open AccessOpen Access

    ARTICLE

    A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

    Zerui Zhen1, Zihao Wu2, Lei Feng1,*, Wenjing Li1, Feng Qi1, Shixuan Guo1
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2939-2955, 2023, DOI:10.32604/cmc.2023.036505
    Abstract Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the… More >

  • Open AccessOpen Access

    ARTICLE

    Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

    Zibo Wang1,3, Chaobin Huo2, Yaofang Zhang1,3, Shengtao Cheng1,3, Yilu Chen1,3, Xiaojie Wei5, Chao Li4, Bailing Wang1,3,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2957-2979, 2023, DOI:10.32604/cmc.2023.035694
    Abstract With the growing discovery of exposed vulnerabilities in the Industrial Control Components (ICCs), identification of the exploitable ones is urgent for Industrial Control System (ICS) administrators to proactively forecast potential threats. However, it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods. To address these challenges, we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph (KG) in which relation paths contain abundant potential evidence to support the reasoning. The reasoning task in this work refers to determining whether a specific… More >

  • Open AccessOpen Access

    ARTICLE

    Neural Machine Translation Models with Attention-Based Dropout Layer

    Huma Israr1,*, Safdar Abbas Khan1, Muhammad Ali Tahir1, Muhammad Khuram Shahzad1, Muneer Ahmad1, Jasni Mohamad Zain2,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2981-3009, 2023, DOI:10.32604/cmc.2023.035814
    Abstract In bilingual translation, attention-based Neural Machine Translation (NMT) models are used to achieve synchrony between input and output sequences and the notion of alignment. NMT model has obtained state-of-the-art performance for several language pairs. However, there has been little work exploring useful architectures for Urdu-to-English machine translation. We conducted extensive Urdu-to-English translation experiments using Long short-term memory (LSTM)/Bidirectional recurrent neural networks (Bi-RNN)/Statistical recurrent unit (SRU)/Gated recurrent unit (GRU)/Convolutional neural network (CNN) and Transformer. Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively, with a scalable data set, make precise predictions on unseen… More >

  • Open AccessOpen Access

    ARTICLE

    Dynamic S-Box Generation Using Novel Chaotic Map with Nonlinearity Tweaking

    Amjad Hussain Zahid1, Muhammad Junaid Arshad2, Musheer Ahmad3,*, Naglaa F. Soliman4, Walid El-Shafai5,6
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3011-3026, 2023, DOI:10.32604/cmc.2023.037516
    Abstract A substitution box (S-Box) is a crucial component of contemporary cryptosystems that provide data protection in block ciphers. At the moment, chaotic maps are being created and extensively used to generate these S-Boxes as a chaotic map assists in providing disorder and resistance to combat cryptanalytical attempts. In this paper, the construction of a dynamic S-Box using a cipher key is proposed using a novel chaotic map and an innovative tweaking approach. The projected chaotic map and the proposed tweak approach are presented for the first time and the use of parameters in their working… More >

  • Open AccessOpen Access

    ARTICLE

    Delivery Invoice Information Classification System for Joint Courier Logistics Infrastructure

    Youngmin Kim1, Sunwoo Hwang2, Jaemin Park1, Joouk Kim2,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3027-3044, 2023, DOI:10.32604/cmc.2023.027877
    Abstract With the growth of the online market, demand for logistics and courier cargo is increasing rapidly. Accordingly, in the case of urban areas, road congestion and environmental problems due to cargo vehicles are mainly occurring. The joint courier logistics system, a plan to solve this problem, aims to establish an efficient logistics transportation system by utilizing one joint logistics delivery terminal by several logistics and delivery companies. However, several courier companies use different types of courier invoices. Such a system has a problem of information data transmission interruption. Therefore, the data processing process was systematically More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

    Omar Abdulghafoor1, Musbah Shaat2, Ibraheem Shayea3, Ahmad Hamood1, Abdelzahir Abdelmaboud4, Ashraf Osman Ibrahim5, Fadhil Mukhlif6,*, Herish Badal1, Norafida Ithnin6, Ali Khadim Lwas7
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3045-3064, 2023, DOI:10.32604/cmc.2023.033888
    Abstract The computational complexity of resource allocation processes, in cognitive radio networks (CRNs), is a major issue to be managed. Furthermore, the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users, CRs, and primary users, PUs, exist in the identical geographical area. Hence, this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarios while limiting interference to PUs to allowable threshold. Hence, this paper, compared to other frameworks proposed in the literature, proposes a two-step approach… More >

  • Open AccessOpen Access

    ARTICLE

    Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

    Ali Arshad1,*, Muhammad Nadeem2, Saman Riaz1, Syeda Wajiha Zahra2, Ashit Kumar Dutta3, Zaid Alzaid4, Rana Alabdan5, Badr Almutairi6, Sultan Almotairi4,7
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3065-3089, 2023, DOI:10.32604/cmc.2023.035695
    Abstract There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data, but these techniques and algorithms cannot be used to protect data from an attacker. Cloud cryptography is the best way to transmit data in a secure and reliable format. Various researchers have developed various mechanisms to transfer data securely, which can convert data from readable to unreadable, but these algorithms are not sufficient to provide complete data security. Each algorithm has some data security issues. If some effective data protection techniques are used, the attacker… More >

  • Open AccessOpen Access

    ARTICLE

    A Privacy-Preserving System Design for Digital Presence Protection

    Eric Yocam1, Ahmad Alomari2, Amjad Gawanmeh3,*, Wathiq Mansoor3
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3091-3110, 2023, DOI:10.32604/cmc.2023.032826
    Abstract A person’s privacy has become a growing concern, given the nature of an expansive reliance on real-time video activities with video capture, stream, and storage. This paper presents an innovative system design based on a privacy-preserving model. The proposed system design is implemented by employing an enhanced capability that overcomes today’s single parameter-based access control protection mechanism for digital privacy preservation. The enhanced capability combines multiple access control parameters: facial expression, resource, environment, location, and time. The proposed system design demonstrated that a person’s facial expressions combined with a set of access control rules can More >

  • Open AccessOpen Access

    ARTICLE

    An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

    Wenlong Zhu1,*, Yu Miao1, Shuangshuang Yang2, Zuozheng Lian1, Lianhe Cui1
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3111-3131, 2023, DOI:10.32604/cmc.2023.036159
    Abstract Influence maximization of temporal social networks (IMT) is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread. To solve the IMT problem, we propose an influence maximization algorithm based on an improved K-shell method, namely improved K-shell in temporal social networks (KT). The algorithm takes into account the global and local structures of temporal social networks. First, to obtain the kernel value Ks of each node, in the global scope, it layers the network according to the temporal characteristic… More >

  • Open AccessOpen Access

    ARTICLE

    Blockchain-Based Decentralized Authentication Model for IoT-Based E-Learning and Educational Environments

    Osama A. Khashan1,*, Sultan Alamri2, Waleed Alomoush3, Mutasem K. Alsmadi4, Samer Atawneh2, Usama Mir5
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3133-3158, 2023, DOI:10.32604/cmc.2023.036217
    Abstract In recent times, technology has advanced significantly and is currently being integrated into educational environments to facilitate distance learning and interaction between learners. Integrating the Internet of Things (IoT) into education can facilitate the teaching and learning process and expand the context in which students learn. Nevertheless, learning data is very sensitive and must be protected when transmitted over the network or stored in data centers. Moreover, the identity and the authenticity of interacting students, instructors, and staff need to be verified to mitigate the impact of attacks. However, most of the current security and… More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent System Application to Monitor the Smart City Building Lighting

    Tzu-Chia Chen1, Ngakan Ketut Acwin Dwijendra2,*, Saurabh Singhal3, R. Sivaraman4, Amr Mamdouh5
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3159-3169, 2023, DOI:10.32604/cmc.2023.035418
    Abstract A smart city incorporates infrastructure methods that are environmentally responsible, such as smart communications, smart grids, smart energy, and smart buildings. The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality, with energy resources taking precedence. To achieve optimal energy management in the multidimensional system of a city tribe, it is necessary not only to identify and study the vast majority of energy elements, but also to define their implicit interdependencies. This is because optimal energy management is required to reach this objective. The lighting… More >

  • Open AccessOpen Access

    ARTICLE

    Smart Fraud Detection in E-Transactions Using Synthetic Minority Oversampling and Binary Harris Hawks Optimization

    Chandana Gouri Tekkali, Karthika Natarajan*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3171-3187, 2023, DOI:10.32604/cmc.2023.036865
    Abstract Fraud Transactions are haunting the economy of many individuals with several factors across the globe. This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions. This research proposes a novel methodology through three stages. Firstly, Synthetic Minority Oversampling Technique (SMOTE) is applied to get balanced data. Secondly, SMOTE is fed to the nature-inspired Meta Heuristic (MH) algorithm, namely Binary Harris Hawks Optimization (BinHHO), Binary Aquila Optimization (BAO), and Binary Grey Wolf Optimization (BGWO), for feature selection. BinHHO has performed well when compared with More >

  • Open AccessOpen Access

    ARTICLE

    Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

    Shakti Raj Chopra1, Parulpreet Singh1, Ahmed Alhussen2,*, Nitin Mittal3, MohdAnul Haq4
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3189-3201, 2023, DOI:10.32604/cmc.2023.030061
    Abstract Many extensive UAV communication networks have used UAV cooperative control. Wireless networking services can be offered using unmanned aerial vehicles (UAVs) as aerial base stations. Not only is coverage maximization, but also better connectivity, a fundamental design challenge that must be solved. The number of applications for unmanned aerial vehicles (UAVs) operating in unlicensed bands is fast expanding as the Internet of Things (IoT) develops. Those bands, however, have become overcrowded as the number of systems that use them grows. Cognitive Radio (CR) and spectrum allocation approaches have emerged as a potential approach for resolving… More >

  • Open AccessOpen Access

    ARTICLE

    Fermatean Hesitant Fuzzy Prioritized Heronian Mean Operator and Its Application in Multi-Attribute Decision Making

    Chuan-Yang Ruan1,2, Xiang-Jing Chen1, Li-Na Han3,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3203-3222, 2023, DOI:10.32604/cmc.2023.035480
    Abstract In real life, incomplete information, inaccurate data, and the preferences of decision-makers during qualitative judgment would impact the process of decision-making. As a technical instrument that can successfully handle uncertain information, Fermatean fuzzy sets have recently been used to solve the multi-attribute decision-making (MADM) problems. This paper proposes a Fermatean hesitant fuzzy information aggregation method to address the problem of fusion where the membership, non-membership, and priority are considered simultaneously. Combining the Fermatean hesitant fuzzy sets with Heronian Mean operators, this paper proposes the Fermatean hesitant fuzzy Heronian mean (FHFHM) operator and the Fermatean hesitant… More >

  • Open AccessOpen Access

    ARTICLE

    Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

    Alawi Alqushaibi1,2,*, Mohd Hilmi Hasan1,2, Said Jadid Abdulkadir1,2, Amgad Muneer1,2, Mohammed Gamal1,2, Qasem Al-Tashi3, Shakirah Mohd Taib1,2, Hitham Alhussian1,2
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3223-3238, 2023, DOI:10.32604/cmc.2023.035655
    Abstract Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

    Majdy M. Eltahir1, Adil Yousif2, Fadwa Alrowais3, Mohamed K. Nour4, Radwa Marzouk5, Hatim Dafaalla6, Asma Abbas Hassan Elnour6, Amira Sayed A. Aziz7, Manar Ahmed Hamza8,*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3239-3255, 2023, DOI:10.32604/cmc.2023.034037
    Abstract The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon… More >

  • Open AccessOpen Access

    ARTICLE

    SMINER: Detecting Unrestricted and Misimplemented Behaviors of Software Systems Based on Unit Test Cases

    Kyungmin Sim, Jeong Hyun Yi, Haehyun Cho*
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3257-3274, 2023, DOI:10.32604/cmc.2023.036695
    Abstract Despite the advances in automated vulnerability detection approaches, security vulnerabilities caused by design flaws in software systems are continuously appearing in real-world systems. Such security design flaws can bring unrestricted and misimplemented behaviors of a system and can lead to fatal vulnerabilities such as remote code execution or sensitive data leakage. Therefore, it is an essential task to discover unrestricted and misimplemented behaviors of a system. However, it is a daunting task for security experts to discover such vulnerabilities in advance because it is time-consuming and error-prone to analyze the whole code in detail. Also,… More >

  • Open AccessOpen Access

    ARTICLE

    Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

    Ali Raza1, Samia Allaoua Chelloug2,*, Mohammed Hamad Alatiyyah3, Ahmad Jalal1, Jeongmin Park4
    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3275-3289, 2023, DOI:10.32604/cmc.2023.029719
    Abstract Pedestrian detection and tracking are vital elements of today’s surveillance systems, which make daily life safe for humans. Thus, human detection and visualization have become essential inventions in the field of computer vision. Hence, developing a surveillance system with multiple object recognition and tracking, especially in low light and night-time, is still challenging. Therefore, we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night. In particular, we propose a system that tackles a two-fold problem by detecting multiple pedestrians in… More >

Per Page:

Share Link