Open Access
ISSN:1546-2218(print)
ISSN:1546-2226(online)
Publication Frequency:Monthly
Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials. This journal is published monthly by Tech Science Press.
SCI: 2021 Impact Factor 3.860; Scopus CiteScore (Impact per Publication 2021): 4.9; SNIP (Source Normalized Impact per Paper 2021): 1.277; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.
Refers to the articles published on the journal within the last three years that have gained the most viewing times to date (Statistics provided by TSP database)
Refers to articles published on the journal since 2020 that have received the most frequent citation to date (Statistics provided by TSP database)
Open Access
ARTICLE
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 (QoS). SDN having an overall… More >
Open Access
ARTICLE
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 Software Defined Networks is used… More >
Open Access
ARTICLE
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. Considering this, a novel strategy… More >
Open Access
ARTICLE
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 WSN-based IoT. Three consecutive segments… More >
Open Access
ARTICLE
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 model demonstrate that the relative… More >
Open Access
ARTICLE
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 blockchain ledger guarantees that the… More >
Open Access
ARTICLE
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 Forest (RF) algorithms were employed… More >
Open Access
ARTICLE
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 in real-time by acquiring railway… More >
Open Access
ARTICLE
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 developing more sophisticated and efficient… More >
Open Access
ARTICLE
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. There are two automated diagnostic… More >
Open Access
ARTICLE
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 rate, and forehead temperature for… More >
Open Access
ARTICLE
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 projection covariance matrix adaptive evolution… More >
Open Access
ARTICLE
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 difficult to understand. This study… More >
Open Access
ARTICLE
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 the raw tweets into a… More >
Open Access
ARTICLE
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 according to their ranking positions… More >
Open Access
ARTICLE
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. Both methods have limitations, such… More >
Open Access
ARTICLE
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 two types of hyper-chaotic maps.… More >
Open Access
ARTICLE
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 no failure state, to avoid… More >
Open Access
ARTICLE
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 combines a meta-learning approach with… More >
Open Access
ARTICLE
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 based on an iterated n-player… More >
Open Access
ARTICLE
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 their covisibility. For both approaches,… More >
Open Access
ARTICLE
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 a hardware-based solution with a… More >
Open Access
ARTICLE
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 data. They have to be… More >
Open Access
ARTICLE
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 improve the efficiency of high-dimensional… More >
Open Access
ARTICLE
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 of iteration. To verify DFA’s… More >
Open Access
ARTICLE
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 of practical applications for various… More >
Open Access
REVIEW
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 identical state. This paper extensively… More >
Open Access
ARTICLE
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 lard with chicken, lamb, and… More >
Open Access
ARTICLE
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 for Chinese text is almost… More >
Open Access
ARTICLE
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 sizes and numbers. Therefore, MSCNN… More >
Open Access
ARTICLE
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 into smaller portions to be… More >
Open Access
ARTICLE
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 content, such as News titles… More >
Open Access
ARTICLE
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 resource consumption caused by frequent… More >
Open Access
ARTICLE
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 relation is valid between an… More >
Open Access
ARTICLE
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 data. The trained models yielded… More >
Open Access
ARTICLE
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 makes both of these dynamic… More >
Open Access
ARTICLE
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 analyzed, a practically feasible methodology… More >
Open Access
ARTICLE
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 to reduce the complexity of… More >
Open Access
ARTICLE
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 will not be able to… More >
Open Access
ARTICLE
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 achieve a person’s privacy-preserving preferences.… More >
Open Access
ARTICLE
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 of nodes by improving… More >
Open Access
ARTICLE
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 authentication schemes are centralized, relying… More >
Open Access
ARTICLE
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 index is an essential consideration… More >
Open Access
ARTICLE
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 the other two. Thirdly, features… More >
Open Access
ARTICLE
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 spectrum scarcity in wireless networks,… More >
Open Access
ARTICLE
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 fuzzy weighted Heronian mean (FHFWHM)… More >
Open Access
ARTICLE
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 performance due to the hyperparameters… More >
Open Access
ARTICLE
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 installation and operation are high,… More >
Open Access
ARTICLE
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, most of the existing vulnerability… More >
Open Access
ARTICLE
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 infrared (IR) images using machine… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3291-3315, 2023, DOI:10.32604/cmc.2023.035835
Abstract Internet of things (IoT) devices are being increasingly used in numerous areas. However, the low priority on security and various IoT types have made these devices vulnerable to attacks. To prevent this, recent studies have analyzed firmware in an emulation environment that does not require actual devices and is efficient for repeated experiments. However, these studies focused only on major firmware architectures and rarely considered exotic firmware. In addition, because of the diversity of firmware, the emulation success rate is not high in terms of large-scale analyses. In this study, we propose the adaptive emulation framework for multi-architecture (AEMA). In… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3317-3337, 2023, DOI:10.32604/cmc.2023.035690
Abstract In this paper, the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, their distribution and generation are investigated. First, the optimal placement and capacity of the energy storage is taken into consideration, then, the charge-discharge strategy for this equipment is determined. Here, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to calculate the minimum and maximum load in the network with the presence of energy storage systems. The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach. Ultimately, the battery… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3339-3358, 2023, DOI:10.32604/cmc.2023.035074
Abstract Due to an increase in agricultural mislabeling and carelesshandling of non-perishable foods in recent years, consumers have been calling for the food sector to be more transparent. Due to information dispersion between divisions and the propensity to record inaccurate data, current traceability solutions typically fail to provide reliable farm-to-fork histories ofproducts. The three most enticing characteristics of blockchain technology areopenness, integrity, and traceability, which make it a potentially crucial tool for guaranteeing the integrity and correctness of data. In this paper, we suggest a permissioned blockchain system run by organizations, such as regulatory bodies, to promote the origin-tracking of shelf-stable… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3359-3374, 2023, DOI:10.32604/cmc.2023.034221
Abstract Recent economic growth and development have considerably raised energy consumption over the globe. Electric load prediction approaches become essential for effective planning, decision-making, and contract evaluation of the power systems. In order to achieve effective forecasting outcomes with minimum computation time, this study develops an improved whale optimization with deep learning enabled load prediction (IWO-DLELP) scheme for energy storage systems (ESS) in smart grid platform. The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS. The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3375-3398, 2023, DOI:10.32604/cmc.2023.036587
Abstract The cyber-criminal compromises end-hosts (bots) to configure a network of bots (botnet). The cyber-criminals are also looking for an evolved architecture that makes their techniques more resilient and stealthier such as Peer-to-Peer (P2P) networks. The P2P botnets leverage the privileges of the decentralized nature of P2P networks. Consequently, the P2P botnets exploit the resilience of this architecture to be arduous against take-down procedures. Some P2P botnets are smarter to be stealthy in their Command-and-Control mechanisms (C2) and elude the standard discovery mechanisms. Therefore, the other side of this cyberwar is the monitor. The P2P botnet monitoring is an exacting mission… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3399-3412, 2023, DOI:10.32604/cmc.2023.026687
Abstract In recent times, web intelligence (WI) has become a hot research topic, which utilizes Artificial Intelligence (AI) and advanced information technologies on the Web and Internet. The users post reviews on social media and are employed for sentiment analysis (SA), which acts as feedback to business people and government. Proper SA on the reviews helps to enhance the quality of the services and products, however, web intelligence techniques are needed to raise the company profit and user fulfillment. With this motivation, this article introduces a new modified pigeon inspired optimization based feature selection (MPIO-FS) with Bayesian deep learning (BDL), named… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3413-3429, 2023, DOI:10.32604/cmc.2023.035303
Abstract Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities. Several models have been available in the literature for sign language detection and classification for enhanced outcomes. But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks. This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning (ASLGC-DHOML) model. The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures. The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3431-3452, 2023, DOI:10.32604/cmc.2023.035897
Abstract The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment, photographing habits, camera resolution and other factors, resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits. Developing an anti-counterfeiting code authentication algorithm based on mobile phones is of great commercial value. Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes, the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy. To address the small differences in texture features, low response speed and excessively large deep learning… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3453-3464, 2023, DOI:10.32604/cmc.2023.034704
Abstract With the advancement of technology, shielding for terahertz (THz) electronic and communication equipment is increasingly important. The metamaterial absorption technique is mostly used to shield electromagnetic interference (EMI) in THz sensing technologies. The most widely used THz metamaterial absorbers suffer from their narrowband properties and the involvement of complex fabrication techniques. Materials with multifunctional properties, such as adjustable conductivity, broad bandwidth, high flexibility, and robustness, are driving future development to meet THz shielding applications. In this article, a theoretical simulation approach based on finite difference time domain (FDTD) is utilized to study the absorption and shielding characteristics of a two-dimensional… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3465-3477, 2023, DOI:10.32604/cmc.2023.037162
Abstract Image forgery is a crucial part of the transmission of misinformation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains a major challenge in research.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3479-3495, 2023, DOI:10.32604/cmc.2023.033647
Abstract Reading and writing are the main interaction methods with web content. Text simplification tools are helpful for people with cognitive impairments, new language learners, and children as they might find difficulties in understanding the complex web content. Text simplification is the process of changing complex text into more readable and understandable text. The recent approaches to text simplification adopted the machine translation concept to learn simplification rules from a parallel corpus of complex and simple sentences. In this paper, we propose two models based on the transformer which is an encoder-decoder structure that achieves state-of-the-art (SOTA) results in machine translation.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3497-3515, 2023, DOI:10.32604/cmc.2023.035878
Abstract Driver identification in intelligent transport systems has immense demand, considering the safety and convenience of traveling in a vehicle. The rapid growth of driver assistance systems (DAS) and driver identification system propels the need for understanding the root causes of automobile accidents. Also, in the case of insurance, it is necessary to track the number of drivers who commonly drive a car in terms of insurance pricing. It is observed that drivers with frequent records of paying “fines” are compelled to pay higher insurance payments than drivers without any penalty records. Thus driver identification act as an important information source… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3517-3535, 2023, DOI:10.32604/cmc.2023.035410
Abstract The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3537-3551, 2023, DOI:10.32604/cmc.2023.030557
Abstract In the proposed paper, a parallel structure type Generative Adversarial Network (GAN) using edge and texture information is proposed. In the existing GAN-based model, many learning iterations had to be given to obtaining an output that was somewhat close to the original data, and noise and distortion occurred in the output image even when learning was performed. To solve this problem, the proposed model consists of two generators and three discriminators to propose a network in the form of a parallel structure. In the network, each edge information and texture information were received as inputs, learning was performed, and each… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3553-3569, 2023, DOI:10.32604/cmc.2023.037028
Abstract Recently, deep learning has achieved remarkable results in fields that require human cognitive ability, learning ability, and reasoning ability. Activation functions are very important because they provide the ability of artificial neural networks to learn complex patterns through nonlinearity. Various activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning process. However, it takes a lot of time and effort for researchers to use the existing activation function in their research. Therefore, in this paper, we propose a universal activation function (UA) so that researchers can easily create… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3571-3588, 2023, DOI:10.32604/cmc.2023.037386
Abstract Intrusion Detection System (IDS) in the cloud Computing (CC) environment has received paramount interest over the last few years. Among the latest approaches, Deep Learning (DL)-based IDS methods allow the discovery of attacks with the highest performance. In the CC environment, Distributed Denial of Service (DDoS) attacks are widespread. The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic, resulting in financial losses. Although various researchers have proposed many detection techniques, there are possible obstacles in terms of detection performance due to the use of insignificant traffic features. Therefore, in this paper,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3589-3605, 2023, DOI:10.32604/cmc.2023.032097
Abstract Many Internet of things application scenarios have the characteristics of limited hardware resources and limited energy supply, which are not suitable for traditional security technology. The security technology based on the physical mechanism has attracted extensive attention. How to improve the key generation rate has always been one of the urgent problems to be solved in the security technology based on the physical mechanism. In this paper, superlattice technology is introduced to the security field of Internet of things, and a high-speed symmetric key generation scheme based on superlattice for Internet of things is proposed. In order to ensure the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3607-3624, 2023, DOI:10.32604/cmc.2023.037532
Abstract Due to its decentralized, tamper-proof, and trust-free characteristics, blockchain is used in the Internet of Things (IoT) to guarantee the reliability of data. However, some technical flaws in blockchain itself prevent the development of these applications, such as the issue with linearly growing storage capacity of blockchain systems. On the other hand, there is a lack of storage resources for sensor devices in IoT, and numerous sensor devices will generate massive data at ultra-high speed, which makes the storage problem of the IoT enabled by blockchain more prominent. There are various solutions to reduce the storage burden by modifying the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3625-3642, 2023, DOI:10.32604/cmc.2023.036754
Abstract A model that can obtain rapid and accurate detection of coronavirus disease 2019 (COVID-19) plays a significant role in treating and preventing the spread of disease transmission. However, designing such a model that can balance the detection accuracy and weight parameters of memory well to deploy a mobile device is challenging. Taking this point into account, this paper fuses the convolutional neural network and residual learning operations to build a multi-class classification model, which improves COVID-19 pneumonia detection performance and keeps a trade-off between the weight parameters and accuracy. The convolutional neural network can extract the COVID-19 feature information by… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3643-3656, 2023, DOI:10.32604/cmc.2023.037507
Abstract Inventory counting is crucial to manufacturing industries in terms of inventory management, production, and procurement planning. Many companies currently require workers to manually count and track the status of materials, which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees. This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material. The type of material of interest is metal sheet, whose shape is simple, a large rectangular shape, yet difficult to detect. The use of computer vision… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3657-3675, 2023, DOI:10.32604/cmc.2023.034929
Abstract The present paper aims at validating a Model Predictive Control (MPC), based on the Mixed Logical Dynamical (MLD) model, for Hybrid Dynamic Systems (HDSs) that explicitly involve continuous dynamics and discrete events. The proposed benchmark system is a three-tank process, which is a typical case study of HDSs. The MLD-MPC controller is applied to the level control of the considered tank system. The study is initially focused on the MLD approach that allows consideration of the interacting continuous dynamics with discrete events and includes the operating constraints. This feature of MLD modeling is very advantageous when an MPC controller synthesis… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3677-3693, 2023, DOI:10.32604/cmc.2023.037363
Abstract The major mortality factor relevant to the intestinal tract is the growth of tumorous cells (polyps) in various parts. More specifically, colonic polyps have a high rate and are recognized as a precursor of colon cancer growth. Endoscopy is the conventional technique for detecting colon polyps, and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate. The automated diagnosis of polyps in a computer-aided diagnosis (CAD) method is implemented using statistical analysis. Nowadays, Deep Learning, particularly through Convolution Neural networks… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3695-3712, 2023, DOI:10.32604/cmc.2023.036820
Abstract Finding clusters based on density represents a significant class of clustering algorithms. These methods can discover clusters of various shapes and sizes. The most studied algorithm in this class is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects. It requires two input parameters: epsilon (fixed neighborhood radius) and MinPts (the lowest number of objects in epsilon). However, it can’t handle clusters of various densities since it uses a global value for epsilon. This article proposes an adaptation of the DBSCAN method so… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3713-3728, 2023, DOI:10.32604/cmc.2023.037463
Abstract In recent years, cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low storage. Cross-modal retrieval technology can be applied to search engines, cross-modal medical processing, etc. The existing main method is to use a multi-label matching paradigm to finish the retrieval tasks. However, such methods do not use fine-grained information in the multi-modal data, which may lead to sub-optimal results. To avoid cross-modal matching turning into label matching, this paper proposes an end-to-end fine-grained cross-modal hash retrieval method, which can focus more on the fine-grained semantic information of multi-modal data. First,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3729-3741, 2023, DOI:10.32604/cmc.2023.036020
Abstract Smartphones have now become an integral part of our everyday lives. User authentication on smartphones is often accomplished by mechanisms (like face unlock, pattern, or pin password) that authenticate the user’s identity. These technologies are simple, inexpensive, and fast for repeated logins. However, these technologies are still subject to assaults like smudge assaults and shoulder surfing. Users’ touch behavior while using their cell phones might be used to authenticate them, which would solve the problem. The performance of the authentication process may be influenced by the attributes chosen (from these behaviors). The purpose of this study is to present an… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3743-3759, 2023, DOI:10.32604/cmc.2023.035126
Abstract Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers’ (and/or benign equivalents’) dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3761-3780, 2023, DOI:10.32604/cmc.2023.037686
Abstract Prediction systems are an important aspect of intelligent decisions. In engineering practice, the complex system structure and the external environment cause many uncertain factors in the model, which influence the modeling accuracy of the model. The belief rule base (BRB) can implement nonlinear modeling and express a variety of uncertain information, including fuzziness, ignorance, randomness, etc. However, the BRB system also has two main problems: Firstly, modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy. Secondly, interpretability is not considered in the optimization process of current research, resulting in the destruction of the interpretability of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3781-3796, 2023, DOI:10.32604/cmc.2023.036437
Abstract As an essential component of intelligent transportation systems (ITS), electric vehicles (EVs) can store massive amounts of electric power in their batteries and send power back to a charging station (CS) at peak hours to balance the power supply and generate profits. However, when the system collects the corresponding power data, several severe security and privacy issues are encountered. The identity and private injection data may be maliciously intercepted by network attackers and be tampered with to damage the services of ITS and smart grids. Existing approaches requiring high computational overhead render them unsuitable for the resource-constrained Internet of Things… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3797-3815, 2023, DOI:10.32604/cmc.2023.034671
Abstract Several unique characteristics of Internet of Things (IoT) devices, such as distributed deployment and limited storage, make it challenging for standard centralized access control systems to enable access control in today’s large-scale IoT ecosystem. To solve these challenges, this study presents an IoT access control system called Ether-IoT based on the Ethereum Blockchain (BC) infrastructure with Attribute-Based Access Control (ABAC). Access Contract (AC), Cache Contract (CC), Device Contract (DC), and Policy Contract (PC) are the four central smart contracts (SCs) that are included in the proposed system. CC offers a way to save user characteristics in a local cache system… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3817-3832, 2023, DOI:10.32604/cmc.2023.037380
Abstract Speech plays an extremely important role in social activities. Many individuals suffer from a “speech barrier,” which limits their communication with others. In this study, an improved speech recognition method is proposed that addresses the needs of speech-impaired and deaf individuals. A basic improved connectionist temporal classification convolutional neural network (CTC-CNN) architecture acoustic model was constructed by combining a speech database with a deep neural network. Acoustic sensors were used to convert the collected voice signals into text or corresponding voice signals to improve communication. The method can be extended to modern artificial intelligence techniques, with multiple applications such as… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3833-3848, 2023, DOI:10.32604/cmc.2023.035686
Abstract Obesity poses several challenges to healthcare and the well-being of individuals. It can be linked to several life-threatening diseases. Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss. State-of-the-art technologies have the potential for long-term benefits in post-surgery living. In this work, an Internet of Things (IoT) framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight. The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3849-3865, 2023, DOI:10.32604/cmc.2023.037150
Abstract Data security and user privacy have become crucial elements in multi-tenant data centers. Various traffic types in the multi-tenant data center in the cloud environment have their characteristics and requirements. In the data center network (DCN), short and long flows are sensitive to low latency and high throughput, respectively. The traditional security processing approaches, however, neglect these characteristics and requirements. This paper proposes a fine-grained security enhancement mechanism (SEM) to solve the problem of heterogeneous traffic and reduce the traffic completion time (FCT) of short flows while ensuring the security of multi-tenant traffic transmission. Specifically, for short flows in DCN,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3867-3882, 2023, DOI:10.32604/cmc.2023.035498
Abstract Wheat is the most important cereal crop, and its low production incurs import pressure on the economy. It fulfills a significant portion of the daily energy requirements of the human body. The wheat disease is one of the major factors that result in low production and negatively affects the national economy. Thus, timely detection of wheat diseases is necessary for improving production. The CNN-based architectures showed tremendous achievement in the image-based classification and prediction of crop diseases. However, these models are computationally expensive and need a large amount of training data. In this research, a light weighted modified CNN architecture… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3883-3901, 2023, DOI:10.32604/cmc.2023.037413
Abstract The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in such a big quantity. Patients who test positive for Covid-19 are diagnosed via a nasal PCR test. In comparison, polymerase chain reaction (PCR) findings take a few hours to a few days. The PCR test is expensive, although the government may bear expenses in certain places. Furthermore, subsets of the population resist invasive testing like swabs. Therefore, chest X-rays or Computerized Vomography (CT) scans are preferred in most cases, and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3903-3922, 2023, DOI:10.32604/cmc.2023.037054
Abstract In Information Centric Networking (ICN) where content is the object of exchange, in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks. Setting up cache space at any node enables users to access data nearby, thus relieving the processing pressure on the servers. However, the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents. To address the issues mentioned, a cooperative caching strategy (CSTL) for remote sensing satellite… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3923-3939, 2023, DOI:10.32604/cmc.2023.036278
Abstract In crowded cities, searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’ time, increases air pollution, and traffic congestion. Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement. But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number, personal identity, and desired destination. This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’ security breaches, single points of failure,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3941-3956, 2023, DOI:10.32604/cmc.2023.037796
Abstract Data security and user privacy are two main security concerns in the cold-chain logistics system (CCLS). Many security issues exist in traditional CCLS, destroying data security and user privacy. The digital signature can provide data verification and identity authentication based on the mathematical difficulty problem for logistics data sharing in CCLS. This paper first established a blockchain-enabled cold-chain logistics system (BCCLS) based on union blockchain technology, which can provide secure data sharing among different logistics nodes and guarantee logistics data security with the untampered blockchain ledger. Meanwhile, a lattice-based undeniable signature scheme is designed to strengthen the security of logistics… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3957-3970, 2023, DOI:10.32604/cmc.2023.037254
Abstract In the traditional fringe projection profilometry system, the projector and the camera light center are both spatially virtual points. The spatial position relationships specified in the model are not easy to obtain, leading to inaccurate system parameters and affecting measurement accuracy. This paper proposes a method for solving the system parameters of the fringe projection profilometry system, and the spatial position of the camera and projector can be adjusted in accordance with the obtained calibration parameters. The steps are as follows: First, in accordance with the conversion relationship of the coordinate system in the calibration process, the calculation formula of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3971-3991, 2023, DOI:10.32604/cmc.2023.037431
Abstract Proteins are essential for many biological functions. For example, folding amino acid chains reveals their functionalities by maintaining tissue structure, physiology, and homeostasis. Note that quantifiable protein characteristics are vital for improving therapies and precision medicine. The automatic inference of a protein’s properties from its amino acid sequence is called “basic structure”. Nevertheless, it remains a critical unsolved challenge in bioinformatics, although with recent technological advances and the investigation of protein sequence data. Inferring protein function from amino acid sequences is crucial in biology. This study considers using raw sequencing to explain biological facts using a large corpus of protein… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3993-4008, 2023, DOI:10.32604/cmc.2023.037464
Abstract Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. At the initial stage, the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4009-4024, 2023, DOI:10.32604/cmc.2023.037433
Abstract Traditional indoor human activity recognition (HAR) is a time-series data classification problem and needs feature extraction. Presently, considerable attention has been given to the domain of HAR due to the enormous amount of its real-time uses in real-time applications, namely surveillance by authorities, biometric user identification, and health monitoring of older people. The extensive usage of the Internet of Things (IoT) and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing. The more commonly utilized inference and problem-solving technique in the HAR system have recently been deep learning (DL). The study develops… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4025-4045, 2023, DOI:10.32604/cmc.2023.036688
Abstract Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns. Despite the considerable progress in existing methods, we note that: Previous works overlooked fine-grain forgery cues with high transferability. Such cues positively impact the model’s accuracy and generalizability. Moreover, single-modality often causes overfitting of the model, and Red-Green-Blue (RGB) modal-only is not conducive to extracting the more detailed forgery traces. We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues. First, we propose two functional modules to reveal and locate the deeper forged features. Our method locates… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4047-4063, 2023, DOI:10.32604/cmc.2023.036945
Abstract With the rapid development of the Internet globally since the 21st century, the amount of data information has increased exponentially. Data helps improve people’s livelihood and working conditions, as well as learning efficiency. Therefore, data extraction, analysis, and processing have become a hot issue for people from all walks of life. Traditional recommendation algorithm still has some problems, such as inaccuracy, less diversity, and low performance. To solve these problems and improve the accuracy and variety of the recommendation algorithms, the research combines the convolutional neural networks (CNN) and the attention model to design a recommendation algorithm based on the… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4065-4084, 2023, DOI:10.32604/cmc.2023.036444
Abstract A computer network can be defined as many computing devices connected via a communication medium like the internet. Computer network development has proposed how humans and devices communicate today. These networks have improved, facilitated, and made conventional forms of communication easier. However, it has also led to uptick in-network threats and assaults. In 2022, the global market for information technology is expected to reach $170.4 billion. However, in contrast, 95% of cyber security threats globally are caused by human action. These networks may be utilized in several control systems, such as home-automation, chemical and physical assault detection, intrusion detection, and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4085-4100, 2023, DOI:10.32604/cmc.2023.036797
Abstract Automatic Speaker Identification (ASI) involves the process of distinguishing an audio stream associated with numerous speakers’ utterances. Some common aspects, such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording, make the ASI task much more complicated and complex. This research proposes a deep learning model to improve the accuracy of the ASI system and reduce the model training time under limited computation resources. In this research, the performance of the transformer model is investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz, Mel-Frequency Cepstral Coefficients (MFCCs), delta MFCCs, delta-delta MFCCs and spectral… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4101-4118, 2023, DOI:10.32604/cmc.2023.036207
Abstract The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early identification of incoming seizures significantly impacts the lives of people with Epilepsy. Automated detection of epileptic seizures (ES) has dramatically improved the life quality of the patients. Recent Electroencephalogram (EEG) related seizure detection mechanisms encountered several difficulties in real-time. The EEGs are the non-stationary signal, and seizure patterns would change with patients and recording sessions. Further, EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs. Artificial intelligence (AI) methods in the domain of ES analysis use traditional deep… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4119-4136, 2023, DOI:10.32604/cmc.2023.033916
Abstract Cloud computing makes dynamic resource provisioning more accessible. Monitoring a functioning service is crucial, and changes are made when particular criteria are surpassed. This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service (QoS), estimating the required resources, and modifying allotted resources depending on workload and parallelism due to resources. Resource allocation is a complex challenge due to the versatile service providers and resource providers. The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service. The objective of a coherent and rational resource allocation is to… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4137-4154, 2023, DOI:10.32604/cmc.2023.035237
Abstract Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions. The number of social media users has been increasing over the last few years, which have allured researchers’ interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a better way. Irony and sarcasm detection is a complex task in Natural Language Processing (NLP). Irony detection has inferences in advertising, sentiment analysis (SA), and opinion mining. For the last few years, irony-aware SA has gained significant computational… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4155-4173, 2023, DOI:10.32604/cmc.2023.036956
Abstract There are quintillions of data on deoxyribonucleic acid (DNA) and protein in publicly accessible data banks, and that number is expanding at an exponential rate. Many scientific fields, such as bioinformatics and drug discovery, rely on such data; nevertheless, gathering and extracting data from these resources is a tough undertaking. This data should go through several processes, including mining, data processing, analysis, and classification. This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention. This software simulates the extraction of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4175-4189, 2023, DOI:10.32604/cmc.2023.034868
Abstract The first major outbreak of the severely complicated hand, foot and mouth disease (HFMD), primarily caused by enterovirus 71, was reported in Taiwan in 1998. HFMD surveillance is needed to assess the spread of HFMD. The parameters we use in mathematical models are usually classical mathematical parameters, called crisp parameters, which are taken for granted. But any biological or physical phenomenon is best explained by uncertainty. To represent a realistic situation in any mathematical model, fuzzy parameters can be very useful. Many articles have been published on how to control and prevent HFMD from the perspective of public health and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4191-4205, 2023, DOI:10.32604/cmc.2023.027050
Abstract Recently, urbanization becomes a major concern for developing as well as developed countries. Owing to the increased urbanization, one of the important challenging issues in smart cities is waste management. So, automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management. Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials. This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWO-RWOD) in Smart Cities. The goal of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4207-4216, 2023, DOI:10.32604/cmc.2023.034439
Abstract The power grid operation process is complex, and many operation process data involve national security, business secrets, and user privacy. Meanwhile, labeled datasets may exist in many different operation platforms, but they cannot be directly shared since power grid data is highly privacy-sensitive. How to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart grid. Therefore, this paper proposes federated learning named entity recognition method for the power grid field, aiming to solve the problem of building a… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4217-4229, 2023, DOI:10.32604/cmc.2023.034960
Abstract At present Bayesian Networks (BN) are being used widely for demonstrating uncertain knowledge in many disciplines, including biology, computer science, risk analysis, service quality analysis, and business. But they suffer from the problem that when the nodes and edges increase, the structure learning difficulty increases and algorithms become inefficient. To solve this problem, heuristic optimization algorithms are used, which tend to find a near-optimal answer rather than an exact one, with particle swarm optimization (PSO) being one of them. PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds (how they search for food). PSO is employed… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4231-4253, 2023, DOI:10.32604/cmc.2023.034287
Abstract Recently, with the normalization of non-face-to-face online environments in response to the COVID-19 pandemic, the possibility of cyberattacks through endpoints has increased. Numerous endpoint devices are managed meticulously to prevent cyberattacks and ensure timely responses to potential security threats. In particular, because telecommuting, telemedicine, and tele-education are implemented in uncontrolled environments, attackers typically target vulnerable endpoints to acquire administrator rights or steal authentication information, and reports of endpoint attacks have been increasing considerably. Advanced persistent threats (APTs) using various novel variant malicious codes are a form of a sophisticated attack. However, conventional commercial antivirus and anti-malware systems that use signature-based… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4255-4272, 2023, DOI:10.32604/cmc.2023.034821
Abstract Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4273-4291, 2023, DOI:10.32604/cmc.2023.036975
Abstract The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image. Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image. However, existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset. Therefore, this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation. To perform more accurate emotional region prediction, this study designs a multilayer attentional… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4293-4315, 2023, DOI:10.32604/cmc.2023.036710
Abstract Machine Learning (ML)-based prediction and classification systems employ data and learning algorithms to forecast target values. However, improving predictive accuracy is a crucial step for informed decision-making. In the healthcare domain, data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis. Among ML algorithms, Artificial Neural Networks (ANNs) are considered the most suitable framework for many classification tasks. The network weights and the activation functions are the two crucial elements in the learning process of an ANN. These weights affect the prediction ability and the convergence… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4317-4340, 2023, DOI:10.32604/cmc.2023.036249
Abstract The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4341-4355, 2023, DOI:10.32604/cmc.2023.037154
Abstract Due to their adaptability, Unmanned Aerial Vehicles (UAVs) play an essential role in the Internet of Things (IoT). Using wireless power transfer (WPT) techniques, an UAV can be supplied with energy while in flight, thereby extending the lifetime of this energy-constrained device. This paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed simultaneously. In this paper, we propose an optimization strategy for the resource allocation of UAVs in sensor communication networks. It is a practical solution to the problem of marine sensor networks that are located far from… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4357-4375, 2023, DOI:10.32604/cmc.2023.031372
Abstract As deep learning techniques such as Convolutional Neural Networks (CNNs) are widely adopted, the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip (SoC). Although conventional CNN accelerators can reduce the computational time of learning and inference tasks, they tend to occupy large chip areas due to many multiply-and-accumulate (MAC) operators when implemented in complex digital circuits, incurring excessive power consumption. To overcome these drawbacks, this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter (ADC). This paper introduces the architecture of an analog convolutional… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4377-4393, 2023, DOI:10.32604/cmc.2023.037625
Abstract According to Cisco’s Internet Report 2020 white paper, there will be 29.3 billion connected devices worldwide by 2023, up from 18.4 billion in 2018. 5G connections will generate nearly three times more traffic than 4G connections. While bringing a boom to the network, it also presents unprecedented challenges in terms of flow forwarding decisions. The path assignment mechanism used in traditional traffic scheduling methods tends to cause local network congestion caused by the concentration of elephant flows, resulting in unbalanced network load and degraded quality of service. Using the centralized control of software-defined networks, this study proposes a data center… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4395-4418, 2023, DOI:10.32604/cmc.2023.036094
Abstract A well-managed financial market of stocks, commodities, derivatives, and bonds is crucial to a country’s economic growth. It provides confidence to investors, which encourages the inflow of cash to ensure good market liquidity. However, there will always be a group of traders that aims to manipulate market pricing to negatively influence stock values in their favor. These illegal trading activities are surely prohibited according to the rules and regulations of every country’s stock market. It is the role of regulators to detect and prevent any manipulation cases in order to provide a trading platform that is fair and efficient. However,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4419-4444, 2023, DOI:10.32604/cmc.2023.037112
Abstract Aspect-based sentiment analysis (ABSA) is a fine-grained process. Its fundamental subtasks are aspect term extraction (ATE) and aspect polarity classification (APC), and these subtasks are dependent and closely related. However, most existing works on Arabic ABSA content separately address them, assume that aspect terms are preidentified, or use a pipeline model. Pipeline solutions design different models for each task, and the output from the ATE model is used as the input to the APC model, which may result in error propagation among different steps because APC is affected by ATE error. These methods are impractical for real-world scenarios where the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4445-4465, 2023, DOI:10.32604/cmc.2023.038639
Abstract The Corona Virus Disease 2019 (COVID-19) effect has made
telecommuting and remote learning the norm. The growing number of
Internet-connected devices provides cyber attackers with more attack vectors.
The development of malware by criminals also incorporates a number of
sophisticated obfuscation techniques, making it difficult to classify and detect
malware using conventional approaches. Therefore, this paper proposes a
novel visualization-based malware classification system using transfer and
ensemble learning (VMCTE). VMCTE has a strong anti-interference ability.
Even if malware uses obfuscation, fuzzing, encryption, and other techniques
to evade detection, it can be accurately classified into its corresponding
malware family. Unlike traditional… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4467-4483, 2023, DOI:10.32604/cmc.2023.036822
Abstract As the Internet of Things (IoT) endures to develop, a huge count of data has been created. An IoT platform is rather sensitive to security challenges as individual data can be leaked, or sensor data could be used to cause accidents. As typical intrusion detection system (IDS) studies can be frequently designed for working well on databases, it can be unknown if they intend to work well in altering network environments. Machine learning (ML) techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy. This article introduces… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4485-4499, 2023, DOI:10.32604/cmc.2023.036118
Abstract Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4501-4517, 2023, DOI:10.32604/cmc.2023.038446
Abstract Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield. To improve the classification accuracy of rice diseases, this paper proposed a classification and identification method based on an improved ShuffleNet V2 (GE-ShuffleNet) model. Firstly, the Ghost module is used to replace the convolution in the two basic unit modules of ShuffleNet V2, and the unimportant convolution is deleted from the two basic unit modules of ShuffleNet V2. The Hardswish activation function is applied to replace the ReLU activation function to improve the identification accuracy of the model. Secondly, an effective channel attention (ECA) module is… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4519-4534, 2023, DOI:10.32604/cmc.2023.037035
Abstract In view of the various adverse effects, fake news detection has become an extremely important task. So far, many detection methods have been proposed, but these methods still have some limitations. For example, only two independently encoded unimodal information are concatenated together, but not integrated with multimodal information to complete the complementary information, and to obtain the correlated information in the news content. This simple fusion approach may lead to the omission of some information and bring some interference to the model. To solve the above problems, this paper proposes the Fake News Detection model based on BLIP (FNDB). First,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4535-4552, 2023, DOI:10.32604/cmc.2023.037087
Abstract Computed Tomography (CT) images have been extensively employed in disease diagnosis and treatment, causing a huge concern over the dose of radiation to which patients are exposed. Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients; on the other hand, decreasing it by using a Low-Dose CT (LDCT) image may cause more noise and increased artifacts, which can compromise the diagnosis. So, image reconstruction from LDCT image data is necessary to improve radiologists’ judgment and confidence. This study proposed three novel models for denoising LDCT images based… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4553-4571, 2023, DOI:10.32604/cmc.2023.032118
Abstract Artificial Intelligence (AI) is finding increasing application in healthcare monitoring. Machine learning systems are utilized for monitoring patient health through the use of IoT sensor, which keep track of the physiological state by way of various health data. Thus, early detection of any disease or derangement can aid doctors in saving patients’ lives. However, there are some challenges associated with predicting health status using the common algorithms, such as time requirements, chances of errors, and improper classification. We propose an Artificial Krill Herd based on the Random Forest (AKHRF) technique for monitoring patients’ health and eliciting an optimal prescription based… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4573-4592, 2023, DOI:10.32604/cmc.2023.037954
Abstract The imbalance of dissolved gas analysis (DGA) data will lead to over-fitting, weak generalization and poor recognition performance for fault diagnosis models based on deep learning. To handle this problem, a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network (ACGAN) under imbalanced data is proposed in this paper, which meets both the requirements of balancing DGA data and supplying accurate diagnosis results. The generator combines one-dimensional convolutional neural networks (1D-CNN) and long short-term memories (LSTM), which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4593-4609, 2023, DOI:10.32604/cmc.2023.037514
Abstract Recently, human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems. Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs, physical human action recognition (HAR) through body sensors gives useful information about the lifestyle and functionality of an individual. This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based Behavior Recognition (ITSOML-BR) technique using body sensor data. The presented ITSOML-BR technique collects data from… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4611-4631, 2023, DOI:10.32604/cmc.2023.033598
Abstract 5G use cases, for example enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and an ultra-reliable low latency communication (URLLC), need a network architecture capable of sustaining stringent latency and bandwidth requirements; thus, it should be extremely flexible and dynamic. Slicing enables service providers to develop various network slice architectures. As users travel from one coverage region to another area, the call must be routed to a slice that meets the same or different expectations. This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks. Rules of thumb which indicates the accuracy… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4633-4648, 2023, DOI:10.32604/cmc.2023.035779
Abstract Coronary artery disease (CAD) is one of the most authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue. The breakdown of coronary cardiovascular disease is one of the principal sources of death all over the world. Cardiovascular deterioration is a challenge, especially in youthful and rural countries where there is an absence of human-trained professionals. Since heart diseases happen without apparent signs, high-level detection is desirable. This paper proposed a robust and tuned random forest model using the randomized grid search technique to predict CAD. The proposed framework increases the ability of CAD predictions by tracking down risk… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4649-4668, 2023, DOI:10.32604/cmc.2023.035987
Abstract Clustering high dimensional data is challenging as data dimensionality increases the distance between data points, resulting in sparse regions that degrade clustering performance. Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space. Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams. Data streams are not only high-dimensional, but also unbounded and evolving. This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams. Although many articles have contributed to the literature… More >
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