CMCOpen Access

Computers, Materials & Continua

ISSN:1546-2218(print)
ISSN:1546-2226(online)
Publication Frequency:Monthly

  • Online
    Articles

    4186

  • on board
    editors

    111

Table of Content


About the Journal

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.

Indexing and Abstracting

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.

  • Open Access

    ARTICLE

    Deep Learning Based Face Detection and Identification of Criminal Suspects

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2331-2343, 2023, DOI:10.32604/cmc.2023.032715
    Abstract Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person’s face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network (DNN) model which employs a… More >

  • Open Access

    ARTICLE

    Numerical Investigation of Malaria Disease Dynamics in Fuzzy Environment

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2345-2361, 2023, DOI:10.32604/cmc.2023.033261
    Abstract The application of fuzzy theory is vital in all scientific disciplines. The construction of mathematical models with fuzziness is little studied in the literature. With this in mind and for a better understanding of the disease, an SEIR model of malaria transmission with fuzziness is examined in this study by extending a classical model of malaria transmission. The parameters and , being function of the malaria virus load, are considered fuzzy numbers. Three steady states and the reproduction number of the model are analyzed in fuzzy senses. A numerical technique is developed in a fuzzy environment to solve the studied… More >

  • Open Access

    ARTICLE

    Central Aggregator Intrusion Detection System for Denial of Service Attacks

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2363-2377, 2023, DOI:10.32604/cmc.2023.032694
    Abstract Vehicle-to-grid technology is an emerging field that allows unused power from Electric Vehicles (EVs) to be used by the smart grid through the central aggregator. Since the central aggregator is connected to the smart grid through a wireless network, it is prone to cyber-attacks that can be detected and mitigated using an intrusion detection system. However, existing intrusion detection systems cannot be used in the vehicle-to-grid network because of the special requirements and characteristics of the vehicle-to-grid network. In this paper, the effect of denial-of-service attacks of malicious electric vehicles on the central aggregator of the vehicle-to-grid network is investigated… More >

  • Open Access

    ARTICLE

    Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2379-2395, 2023, DOI:10.32604/cmc.2023.032886
    Abstract Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were… More >

  • Open Access

    ARTICLE

    Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2397-2413, 2023, DOI:10.32604/cmc.2023.030924
    Abstract This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the… More >

  • Open Access

    ARTICLE

    Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2415-2430, 2023, DOI:10.32604/cmc.2023.031352
    Abstract This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using the normal and dysregulated bone remodeling of the myeloma bone disease (MBD). For the more precise performance of the model, fractional-order derivatives have been used to solve the disease model numerically. The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts (OC) and bone formation or osteoblasts (OB). The connections of OC and OB are represented by a nonlinear differential system based on the cellular components, which depict stable fluctuation in the usual bone case and unstable fluctuation through the MBD.… More >

  • Open Access

    ARTICLE

    Building 3-D Human Data Based on Handed Measurement and CNN

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2431-2441, 2023, DOI:10.32604/cmc.2023.029618
    Abstract 3-dimension (3-D) printing technology is growing strongly with many applications, one of which is the garment industry. The application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee aesthetics. This paper proposes a method to construct 3-D human models by applying deep learning. We calculate the location of the main slices of the human body, including the neck, chest, belly, buttocks, and the rings of the extremities, using pre-existing information. Then, on the positioning frame, we find the key points (fixed and unaltered) of these key slices and update… More >

  • Open Access

    ARTICLE

    Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2443-2459, 2023, DOI:10.32604/cmc.2023.026379
    Abstract Melanoma remains a serious illness which is a common form of skin cancer. Since the earlier detection of melanoma reduces the mortality rate, it is essential to design reliable and automated disease diagnosis model using dermoscopic images. The recent advances in deep learning (DL) models find useful to examine the medical image and make proper decisions. In this study, an automated deep learning based melanoma detection and classification (ADL-MDC) model is presented. The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma. The ADL-MDC technique performs contrast enhancement and data augmentation at… More >

  • Open Access

    ARTICLE

    Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2461-2478, 2023, DOI:10.32604/cmc.2023.031614
    Abstract Well organized datacentres with interconnected servers constitute the cloud computing infrastructure. User requests are submitted through an interface to these servers that provide service to them in an on-demand basis. The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category. Task scheduling in cloud poses numerous challenges impacting the cloud performance. If not handled properly, user satisfaction becomes questionable. More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment.… More >

  • Open Access

    ARTICLE

    Classification of Adversarial Attacks Using Ensemble Clustering Approach

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2479-2498, 2023, DOI:10.32604/cmc.2023.024858
    Abstract As more business transactions and information services have been implemented via communication networks, both personal and organization assets encounter a higher risk of attacks. To safeguard these, a perimeter defence like NIDS (network-based intrusion detection system) can be effective for known intrusions. There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks, where obfuscation techniques are applied to disguise patterns of intrusive traffics. The current research focuses on non-payload connections at the TCP (transmission control protocol) stack level that… More >

  • Open Access

    ARTICLE

    Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2499-2516, 2023, DOI:10.32604/cmc.2023.029914
    Abstract Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases. Traditionally, this process is done manually by human expert. However, the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness. To improve the recognition speed and consistency, researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network (CNN). CNN requires many training images to obtain high percentage of recognition accuracy. Unfortunately, it is very difficult to get large number of samples… More >

  • Open Access

    ARTICLE

    Th-Shaped Tunable Multi-Band Antenna for Modern Wireless Applications

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2517-2530, 2023, DOI:10.32604/cmc.2023.031833
    Abstract A compact, reconfigurable antenna supporting multiple wireless services with a minimum number of switches is found lacking in literature and the same became the focus and outcome of this work. It was achieved by designing a Th-Shaped frequency reconfigurable multi-band microstrip planar antenna, based on use of a single switch within the radiating structure of the antenna. Three frequency bands (i.e., 2007–2501 MHz, 3660–3983 MHz and 9341–1046 MHz) can be operated with the switch in the ON switch state. In the OFF state of the switch, the antenna operates within the 2577–3280 MHz and 9379–1033 MHz Bands. The proposed antenna shows an acceptable input impedance… More >

  • Open Access

    ARTICLE

    Pixel’s Quantum Image Enhancement Using Quantum Calculus

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2531-2539, 2023, DOI:10.32604/cmc.2023.033282
    Abstract The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values. The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone. The brain Magnetic Resonance Imaging (MRI) scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer. Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease. To solve this issue, this research offers a quantum calculus-based MRI… More >

  • Open Access

    ARTICLE

    Chained Dual-Generative Adversarial Network: A Generalized Defense Against Adversarial Attacks

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2541-2555, 2023, DOI:10.32604/cmc.2023.032795
    Abstract Neural networks play a significant role in the field of image classification. When an input image is modified by adversarial attacks, the changes are imperceptible to the human eye, but it still leads to misclassification of the images. Researchers have demonstrated these attacks to make production self-driving cars misclassify Stop Road signs as 45 Miles Per Hour (MPH) road signs and a turtle being misclassified as AK47. Three primary types of defense approaches exist which can safeguard against such attacks i.e., Gradient Masking, Robust Optimization, and Adversarial Example Detection. Very few approaches use Generative Adversarial Networks (GAN) for Defense against… More >

  • Open Access

    ARTICLE

    A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2557-2573, 2023, DOI:10.32604/cmc.2023.026316
    Abstract Phasor Measurement Units (PMUs) provide Global Positioning System (GPS) time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system. Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition. A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view. However, such ongoing development and improvement to PMUs’ principal work are essential to the network operators to enhance the grid… More >

  • Open Access

    ARTICLE

    Numerical Procedure for Fractional HBV Infection with Impact of Antibody Immune

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2575-2588, 2023, DOI:10.32604/cmc.2023.029046
    Abstract The current investigations are presented to solve the fractional order HBV differential infection system (FO-HBV-DIS) with the response of antibody immune using the optimization based stochastic schemes of the Levenberg-Marquardt backpropagation (LMB) neural networks (NNs), i.e., LMBNNs. The FO-HBV-DIS with the response of antibody immune is categorized into five dynamics, healthy hepatocytes (H), capsids (D), infected hepatocytes (I), free virus (V) and antibodies (W). The investigations for three different FO variants have been tested numerically to solve the nonlinear FO-HBV-DIS. The data magnitudes are implemented 75% for training, 10% for certification and 15% for testing to solve the FO-HBV-DIS with… More >

  • Open Access

    ARTICLE

    Aspect Level Songs Rating Based Upon Reviews in English

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2589-2605, 2023, DOI:10.32604/cmc.2023.032173
    Abstract With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision… More >

  • Open Access

    ARTICLE

    A Fused Machine Learning Approach for Intrusion Detection System

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2607-2623, 2023, DOI:10.32604/cmc.2023.032617
    Abstract The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet. The interconnectivity of networks has brought various complexities in maintaining network availability, consistency, and discretion. Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities. An intrusion detection system controls the flow of network traffic with the help of computer systems. Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic. For this purpose, when the network traffic encounters known or unknown… More >

  • Open Access

    ARTICLE

    Two Layer Symmetric Cryptography Algorithm for Protecting Data from Attacks

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2625-2640, 2023, DOI:10.32604/cmc.2023.030899
    Abstract Many organizations have insisted on protecting the cloud server from the outside, although the risks of attacking the cloud server are mostly from the inside. There are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server attacks. Still, the attackers have designed even better mechanisms to break these security algorithms. Cloud cryptography is the best data protection algorithm that exchanges data between authentic users. In this article, one symmetric cryptography algorithm will be designed to secure cloud server data, used to send and receive cloud server data securely. A double… More >

  • Open Access

    ARTICLE

    Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2641-2659, 2023, DOI:10.32604/cmc.2023.028796
    Abstract Cloud computing is one of the most attractive and cost-saving models, which provides online services to end-users. Cloud computing allows the user to access data directly from any node. But nowadays, cloud security is one of the biggest issues that arise. Different types of malware are wreaking havoc on the clouds. Attacks on the cloud server are happening from both internal and external sides. This paper has developed a tool to prevent the cloud server from spamming attacks. When an attacker attempts to use different spamming techniques on a cloud server, the attacker will be intercepted through two effective techniques:… More >

  • Open Access

    ARTICLE

    An Improved Text-Based and Image-Based CAPTCHA Based on Solving and Response Time

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2661-2675, 2023, DOI:10.32604/cmc.2023.031245
    Abstract CAPTCHA is an acronym that stands for Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA), it is a good example of an authentication system that can be used to determine the true identity of any user. It serves as a security measure to prevent an attack caused by web bots (automatic programs) during an online transaction. It can come as text-based or image-based depending on the project and the programmer. The usability and robustness, as well as level of security, provided each of the varies and call for the development of an improved system. Hence, this… More >

  • Open Access

    ARTICLE

    Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2677-2693, 2023, DOI:10.32604/cmc.2023.033273
    Abstract Applications of internet-of-things (IoT) are increasingly being used in many facets of our daily life, which results in an enormous volume of data. Cloud computing and fog computing, two of the most common technologies used in IoT applications, have led to major security concerns. Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient. Several artificial intelligence (AI) based security solutions, such as intrusion detection systems (IDS), have been proposed in recent years. Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection… More >

  • Open Access

    ARTICLE

    Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2695-2709, 2023, DOI:10.32604/cmc.2023.033153
    Abstract The Internet of Things (IoT) is a modern approach that enables connection with a wide variety of devices remotely. Due to the resource constraints and open nature of IoT nodes, the routing protocol for low power and lossy (RPL) networks may be vulnerable to several routing attacks. That’s why a network intrusion detection system (NIDS) is needed to guard against routing assaults on RPL-based IoT networks. The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks. Therefore, we propose in this paper a novel approach to balance… More >

  • Open Access

    ARTICLE

    An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2711-2724, 2023, DOI:10.32604/cmc.2023.033233
    Abstract The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure. The stochastic procedures mainly depend on the combination of the artificial neural network (ANNs) along with the Levenberg-Marquardt Backpropagation (LMB) i.e., ANNs-LMB technique. The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional order α. The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1. The data… More >

  • Open Access

    ARTICLE

    Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2725-2738, 2023, DOI:10.32604/cmc.2023.029163
    Abstract Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In addition, deep belief network (DBN)… More >

  • Open Access

    ARTICLE

    Big Data Testing Techniques: Taxonomy, Challenges and Future Trends

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2739-2770, 2023, DOI:10.32604/cmc.2023.030266
    Abstract Big Data is reforming many industrial domains by providing decision support through analyzing large data volumes. Big Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and quality of data. However, because of the diversity and complexity of data, testing Big Data is challenging. Though numerous research efforts deal with Big Data testing, a comprehensive review to address testing techniques and challenges of Big Data is not available as yet. Therefore, we have systematically reviewed the Big Data testing techniques’ evidence occurring in the period 2010–2021. This paper discusses testing data processing… More >

  • Open Access

    ARTICLE

    Non-Negative Adaptive Mechanism-Based Sliding Mode Control for Parallel Manipulators with Uncertainties

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2771-2787, 2023, DOI:10.32604/cmc.2023.033460
    Abstract In this paper, a non-negative adaptive mechanism based on an adaptive nonsingular fast terminal sliding mode control strategy is proposed to have finite time and high-speed trajectory tracking for parallel manipulators with the existence of unknown bounded complex uncertainties and external disturbances. The proposed approach is a hybrid scheme of the online non-negative adaptive mechanism, tracking differentiator, and nonsingular fast terminal sliding mode control (NFTSMC). Based on the online non-negative adaptive mechanism, the proposed control can remove the assumption that the uncertainties and disturbances must be bounded for the NFTSMC controllers. The proposed controller has several advantages such as simple… More >

  • Open Access

    ARTICLE

    An Improved BPNN Prediction Method Based on Multi-Strategy Sparrow Search Algorithm

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2789-2802, 2023, DOI:10.32604/cmc.2023.031304
    Abstract Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law trends. Back propagation (BP) neural network is a widely used prediction method. To reduce its probability of falling into local optimum and improve the prediction accuracy, we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm (MSSA). The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm (SSA). Three strategies are designed to improve the SSA to enhance its optimization-seeking ability, leading to the MSSA-BP prediction model.… More >

  • Open Access

    ARTICLE

    Neural Machine Translation by Fusing Key Information of Text

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2803-2815, 2023, DOI:10.32604/cmc.2023.032732
    Abstract When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation… More >

  • Open Access

    ARTICLE

    A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2817-2838, 2023, DOI:10.32604/cmc.2023.033632
    Abstract Epigenetics is the study of phenotypic variations that do not alter DNA sequences. Cancer epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human cancers. One of these alterations is DNA methylation; an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in cancer. Therefore, studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA sequences. Currently, microarray technologies; such as Illumina Infinium BeadChip assays; are used to study DNA methylation at an extremely large number… More >

  • Open Access

    ARTICLE

    Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2839-2852, 2023, DOI:10.32604/cmc.2023.032760
    Abstract A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism. The second-order neighborhood shows the connection between the current pixel and the surroundings of this pixel. This connection is for each RGB component color of the input image. Once the image edges are detected for the three primary colors: red, green, and blue, these colors are merged using the combination rule. Then, the final decision is applied to obtain the segmentation. This process allows different data sources to be combined, which is essential to improve the image information… More >

  • Open Access

    ARTICLE

    Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation Operator

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2853-2870, 2023, DOI:10.32604/cmc.2023.032654
    Abstract The effectiveness of the logic mining approach is strongly correlated to the quality of the induced logical representation that represent the behaviour of the data. Specifically, the optimum induced logical representation indicates the capability of the logic mining approach in generalizing the real datasets of different variants and dimensions. The main issues with the logic extracted by the standard logic mining techniques are lack of interpretability and the weakness in terms of the structural and arrangement of the 2 Satisfiability logic causing lower accuracy. To address the issues, the logical permutation serves as an alternative mechanism that can enhance the… More >

  • Open Access

    ARTICLE

    An Efficient Hybrid Model for Arabic Text Recognition

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2871-2888, 2023, DOI:10.32604/cmc.2023.032550
    Abstract In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a… More >

  • Open Access

    ARTICLE

    Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2889-2903, 2023, DOI:10.32604/cmc.2023.032192
    Abstract With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an… More >

  • Open Access

    ARTICLE

    An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2905-2925, 2023, DOI:10.32604/cmc.2023.031936
    Abstract Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the… More >

  • Open Access

    ARTICLE

    Proposed Framework for Detection of Breast Tumors

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2927-2944, 2023, DOI:10.32604/cmc.2023.033111
    Abstract Computer vision is one of the significant trends in computer science. It plays as a vital role in many applications, especially in the medical field. Early detection and segmentation of different tumors is a big challenge in the medical world. The proposed framework uses ultrasound images from Kaggle, applying five diverse models to denoise the images, using the best possible noise-free image as input to the U-Net model for segmentation of the tumor, and then using the Convolution Neural Network (CNN) model to classify whether the tumor is benign, malignant, or normal. The main challenge faced by the framework in… More >

  • Open Access

    ARTICLE

    Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2945-2966, 2023, DOI:10.32604/cmc.2023.033417
    Abstract Recently, machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductor manufacturing. The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features. This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns. First, the number of defects during the actual process may be limited. Therefore, insufficient data are generated using convolutional auto-encoder (CAE), and the expanded data are verified using the evaluation technique… More >

  • Open Access

    ARTICLE

    Information Extraction Based on Multi-turn Question Answering for Analyzing Korean Research Trends

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2967-2980, 2023, DOI:10.32604/cmc.2023.031983
    Abstract Analyzing Research and Development (R&D) trends is important because it can influence future decisions regarding R&D direction. In typical trend analysis, topic or technology taxonomies are employed to compute the popularities of the topics or codes over time. Although it is simple and effective, the taxonomies are difficult to manage because new technologies are introduced rapidly. Therefore, recent studies exploit deep learning to extract pre-defined targets such as problems and solutions. Based on the recent advances in question answering (QA) using deep learning, we adopt a multi-turn QA model to extract problems and solutions from Korean R&D reports. With the… More >

  • Open Access

    ARTICLE

    Analysis on D2D Heterogeneous Networks with State-Dependent Priority Traffic

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2981-2998, 2023, DOI:10.32604/cmc.2023.028597
    Abstract In this work, we consider the performance analysis of state dependent priority traffic and scheduling in device to device (D2D) heterogeneous networks. There are two priority transmission types of data in wireless communication, such as video or telephone, which always meet the requirements of high priority (HP) data transmission first. If there is a large amount of low priority (LP) data, there will be a large amount of LP data that cannot be sent. This situation will cause excessive delay of LP data and packet dropping probability. In order to solve this problem, the data transmission process of high priority… More >

  • Open Access

    ARTICLE

    SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2999-3014, 2023, DOI:10.32604/cmc.2023.032326
    Abstract Due to highly underdetermined nature of Single Image Super-Resolution (SISR) problem, deep learning neural networks are required to be more deeper to solve the problem effectively. One of deep neural networks successful in the Super-Resolution (SR) problem is ResNet which can render the capability of deeper networks with the help of skip connections. However, zero padding (ZP) scheme in the network restricts benefits of skip connections in SRResNet and its performance as the ratio of the number of pure input data to that of zero padded data increases. In this paper. we consider the ResNet with Partial Convolution based Padding… More >

  • Open Access

    ARTICLE

    Load Balancing Based on Multi-Agent Framework to Enhance Cloud Environment

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3015-3028, 2023, DOI:10.32604/cmc.2023.033212
    Abstract According to the advances in users’ service requirements, physical hardware accessibility, and speed of resource delivery, Cloud Computing (CC) is an essential technology to be used in many fields. Moreover, the Internet of Things (IoT) is employed for more communication flexibility and richness that are required to obtain fruitful services. A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents. This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number… More >

  • Open Access

    ARTICLE

    Implications of Onshore Development on Global Software Engineering

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3029-3044, 2023, DOI:10.32604/cmc.2023.032831
    Abstract Recently software industry has paid significant attention to customizing software products across distributed boundaries. Communicating the requirements of multiple clients across distributed borders is a crucial challenge for the software customization process. Local decision-making and local development at the client site are considered methods for reducing difficulties in communicating the requirements of multiple clients across distributed boundaries. This paper introduces a new model called the onshore development model (ODM) for accomplishing the customization requests in the distributed development process of software. This model presents a scenario for enhancing the onsite development of specific requirements to reduce delays and misunderstandings between… More >

  • Open Access

    ARTICLE

    Age and Gender Classification Using Backpropagation and Bagging Algorithms

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3045-3062, 2023, DOI:10.32604/cmc.2023.030567
    Abstract Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems. The main aim of the research is to develop a system able to predicate and classify gender, age, and accent. So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed. Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories. It has high precision compared to other algorithms used in this problem, as the adaptive… More >

  • Open Access

    ARTICLE

    Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3063-3083, 2023, DOI:10.32604/cmc.2023.032363
    Abstract Currently, breast cancer has been a major cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this. The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease. Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms. Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues. To… More >

  • Open Access

    ARTICLE

    IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3085-3100, 2023, DOI:10.32604/cmc.2023.032972
    Abstract Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning (ML) and Deep Learning (DL)… More >

  • Open Access

    ARTICLE

    Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3101-3115, 2023, DOI:10.32604/cmc.2023.032969
    Abstract Cloud Computing (CC) is the most promising and advanced technology to store data and offer online services in an effective manner. When such fast evolving technologies are used in the protection of computer-based systems from cyberattacks, it brings several advantages compared to conventional data protection methods. Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems (CPS), Internet of Things (IoT), mobile devices, desktop and laptop computer, and critical systems. Malicious software (malware) is nothing but a type of software that targets the computer-based systems so as to launch cyber-attacks and threaten the integrity, secrecy, and accessibility… More >

  • Open Access

    ARTICLE

    Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3117-3131, 2023, DOI:10.32604/cmc.2023.033038
    Abstract Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on the identification of vehicles accurately… More >

  • Open Access

    ARTICLE

    Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3133-3149, 2023, DOI:10.32604/cmc.2023.032740
    Abstract Nowadays, security plays an important role in Internet of Things (IoT) environment especially in medical services’ domains like disease prediction and medical data storage. In healthcare sector, huge volumes of data are generated on a daily basis, owing to the involvement of advanced health care devices. In general terms, health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis. At the same time, it is also significant to maintain the delicate contents of health care images during reconstruction stage. Therefore, an encryption system is required in order to raise… More >

  • Open Access

    ARTICLE

    Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3151-3166, 2023, DOI:10.32604/cmc.2023.032765
    Abstract With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Bio-medical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals. The current study… More >

  • Open Access

    ARTICLE

    Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3167-3181, 2023, DOI:10.32604/cmc.2023.033054
    Abstract Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they… More >

  • Open Access

    ARTICLE

    Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3183-3198, 2023, DOI:10.32604/cmc.2023.033513
    Abstract Managing physical objects in the network’s periphery is made possible by the Internet of Things (IoT), revolutionizing human life. Open attacks and unauthorized access are possible with these IoT devices, which exchange data to enable remote access. These attacks are often detected using intrusion detection methodologies, although these systems’ effectiveness and accuracy are subpar. This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization. The employed metaheuristic optimizer is a new version of the whale optimization algorithm (WOA), which is guided by the dipper throated optimizer (DTO) to improve… More >

  • Open Access

    ARTICLE

    Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3199-3219, 2023, DOI:10.32604/cmc.2023.032595
    Abstract Time series forecasting and analysis are widely used in many fields and application scenarios. Time series historical data reflects the change pattern and trend, which can serve the application and decision in each application scenario to a certain extent. In this paper, we select the time series prediction problem in the atmospheric environment scenario to start the application research. In terms of data support, we obtain the data of nearly 3500 vehicles in some cities in China from Runwoda Research Institute, focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,… More >

  • Open Access

    ARTICLE

    (α, γ)-Anti-Multi-Fuzzy Subgroups and Some of Its Properties

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3221-3229, 2023, DOI:10.32604/cmc.2023.033006
    Abstract Recently, fuzzy multi-sets have come to the forefront of scientists’ interest and have been used in algebraic structures such as multi-groups, multi-rings, anti-fuzzy multigroup and (α, γ)-anti-fuzzy subgroups. In this paper, we first summarize the knowledge about the algebraic structure of fuzzy multi-sets such as (α, γ)-anti-multi-fuzzy subgroups. In a way, the notion of anti-fuzzy multigroup is an application of anti-fuzzy multi sets to the theory of group. The concept of anti-fuzzy multigroup is a complement of an algebraic structure of a fuzzy multi set that generalizes both the theories of classical group and fuzzy group. The aim of this… More >

  • Open Access

    ARTICLE

    A Grey Simulation-Based Fuzzy Hierarchical Approach for Diagnosing Healthcare Service Quality

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3231-3248, 2023, DOI:10.32604/cmc.2023.031428
    Abstract This study aims to assess and rank the service quality of the healthcare system utilizing a Fuzzy Analytical Hierarchical Process (Fuzzy AHP) and Grey Relational Analysis (Fuzzy GRA) technique. In this study, the six primary characteristics of healthcare service quality, comprising Tangibles (A), Healthcare Staff (B), Responsiveness (C), Relationships (D), Support Service (E), and Accessibility (F), are examined through a case study of 20 private and public hospitals in Hanoi, Vietnam. The weighting results of Fuzzy AHP technique indicated that Responsiveness (C) has the highest ranking, followed by Relationships (D) and Healthcare Staff (B). Meanwhile, Tangibility has finally comprised the… More >

  • Open Access

    ARTICLE

    Optimal Resource Allocation for NOMA Wireless Networks

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3249-3261, 2023, DOI:10.32604/cmc.2023.031673
    Abstract The non-orthogonal multiple access (NOMA) method is a novel multiple access technique that aims to increase spectral efficiency (SE) and accommodate enormous user accesses. Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information, and multi-user detection algorithms, such as successive interference cancellation (SIC), are performed at the receiving end to demodulate the necessary user signals. Although its basic signal waveform, like LTE baseline, could be based on orthogonal frequency division multiple access (OFDMA) or discrete Fourier transform (DFT)-spread OFDM, NOMA superimposes numerous users in the power domain. In contrast… More >

  • Open Access

    ARTICLE

    Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3263-3278, 2023, DOI:10.32604/cmc.2023.032864
    Abstract File labeling techniques have a long history in analyzing the anthological trends in computational linguistics. The situation becomes worse in the case of files downloaded into systems from the Internet. Currently, most users either have to change file names manually or leave a meaningless name of the files, which increases the time to search required files and results in redundancy and duplications of user files. Currently, no significant work is done on automated file labeling during the organization of heterogeneous user files. A few attempts have been made in topic modeling. However, one major drawback of current topic modeling approaches… More >

  • Open Access

    ARTICLE

    Optimal Deep Transfer Learning Based Colorectal Cancer Detection and Classification Model

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3279-3295, 2023, DOI:10.32604/cmc.2023.031037
    Abstract Colorectal carcinoma (CRC) is one such dispersed cancer globally and also prominent one in causing cancer-based death. Conventionally, pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples, stained and fixed through Haematoxylin and Eosin (H&E). The advancement of graphical processing systems has resulted in high potentiality for deep learning (DL) techniques in interpretating visual anatomy from high resolution medical images. This study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification (SMADTL-CCDC) algorithm. The presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal cancer. To accomplish this,… More >

  • Open Access

    ARTICLE

    Blockchain Merkle-Tree Ethereum Approach in Enterprise Multitenant Cloud Environment

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3297-3313, 2023, DOI:10.32604/cmc.2023.030558
    Abstract This research paper puts emphasis on using cloud computing with Blockchain (BC) to improve the security and privacy in a cloud. The security of data is not guaranteed as there is always a risk of leakage of users’ data. Blockchain can be used in a multi-tenant cloud environment (MTCE) to improve the security of data, as it is a decentralized approach. Data is saved in unaltered form. Also, Blockchain is not owned by a single organization. The encryption process can be done using a Homomorphic encryption (HE) algorithm along with hashing technique, hereby allowing computations on encrypted data without the… More >

  • Open Access

    ARTICLE

    An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3315-3332, 2023, DOI:10.32604/cmc.2023.033250
    Abstract Proper waste management models using recent technologies like computer vision, machine learning (ML), and deep learning (DL) are needed to effectively handle the massive quantity of increasing waste. Therefore, waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process. This study concentrates on the design of hazardous waste detection and classification using ensemble learning (HWDC-EL) technique to reduce toxicity and improve human health. The goal of the HWDC-EL technique is to detect the multiple classes of wastes, particularly hazardous and non-hazardous wastes.… More >

  • Open Access

    ARTICLE

    Residual Attention Deep SVDD for COVID-19 Diagnosis Using CT Scans

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3333-3350, 2023, DOI:10.32604/cmc.2023.033413
    Abstract COVID-19 is the common name of the disease caused by the novel coronavirus (2019-nCoV) that appeared in Wuhan, China in 2019. Discovering the infected people is the most important factor in the fight against the disease. The gold-standard test to diagnose COVID-19 is polymerase chain reaction (PCR), but it takes 5–6 h and, in the early stages of infection, may produce false-negative results. Examining Computed Tomography (CT) images to diagnose patients infected with COVID-19 has become an urgent necessity. In this study, we propose a residual attention deep support vector data description SVDD (RADSVDD) approach to diagnose COVID-19. It is… More >

  • Open Access

    ARTICLE

    FPGA Implementation of Extended Kalman Filter for Parameters Estimation of Railway Wheelset

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3351-3370, 2023, DOI:10.32604/cmc.2023.032940
    Abstract It is necessary to know the status of adhesion conditions between wheel and rail for efficient accelerating and decelerating of railroad vehicle. The proper estimation of adhesion conditions and their real-time implementation is considered a challenge for scholars. In this paper, the development of simulation model of extended Kalman filter (EKF) in MATLAB/Simulink is presented to estimate various railway wheelset parameters in different contact conditions of track. Due to concurrent in nature, the Xilinx® System-on-Chip Zynq Field Programmable Gate Array (FPGA) device is chosen to check the onboard estimation of wheel-rail interaction parameters by using the National Instruments (NI) myRIO®More >

  • Open Access

    ARTICLE

    Pixel-Level Feature Extraction Model for Breast Cancer Detection

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3371-3389, 2023, DOI:10.32604/cmc.2023.031949
    Abstract Breast cancer is the most prevalent cancer among women, and diagnosing it early is vital for successful treatment. The examination of images captured during biopsies plays an important role in determining whether a patient has cancer or not. However, the stochastic patterns, varying intensities of colors, and the large sizes of these images make it challenging to identify and mark malignant regions in them. Against this backdrop, this study proposes an approach to the pixel categorization based on the genetic algorithm (GA) and principal component analysis (PCA). The spatial features of the images were extracted using various filters, and the… More >

  • Open Access

    ARTICLE

    Double Update Intelligent Strategy for Permanent Magnet Synchronous Motor Parameter Identification

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3391-3404, 2023, DOI:10.32604/cmc.2023.033397
    Abstract The parameters of permanent magnet synchronous motor (PMSM) affect the performance of vector control servo system. Because of the complexity of nonlinear model of PMSM, it is very difficult to identify the parameters of PMSM. Aiming at the problems of large amount of data calculation, low identification accuracy and poor robustness in the process of multi parameter identification of permanent magnet synchronous motor, this paper proposes a weighted differential evolutionary particle swarm optimization algorithm based on double update strategy. By introducing adaptive judgment factor to control the proportion of weighted difference evolution (WDE) algorithm and particle swarm optimization (PSO) algorithm… More >

  • Open Access

    ARTICLE

    DSAFF-Net: A Backbone Network Based on Mask R-CNN for Small Object Detection

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3405-3419, 2023, DOI:10.32604/cmc.2023.027627
    Abstract Recently, object detection based on convolutional neural networks (CNNs) has developed rapidly. The backbone networks for basic feature extraction are an important component of the whole detection task. Therefore, we present a new feature extraction strategy in this paper, which name is DSAFF-Net. In this strategy, we design: 1) a sandwich attention feature fusion module (SAFF module). Its purpose is to enhance the semantic information of shallow features and resolution of deep features, which is beneficial to small object detection after feature fusion. 2) to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when… More >

  • Open Access

    ARTICLE

    An Artificial Approach for the Fractional Order Rape and Its Control Model

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3421-3438, 2023, DOI:10.32604/cmc.2023.030996
    Abstract The current investigations provide the solutions of the nonlinear fractional order mathematical rape and its control model using the strength of artificial neural networks (ANNs) along with the Levenberg-Marquardt backpropagation approach (LMBA), i.e., artificial neural networks-Levenberg-Marquardt backpropagation approach (ANNs-LMBA). The fractional order investigations have been presented to find more realistic results of the mathematical form of the rape and its control model. The differential mathematical form of the nonlinear fractional order mathematical rape and its control model has six classes: susceptible native girls, infected immature girls, susceptible knowledgeable girls, infected knowledgeable girls, susceptible rapist population and infective rapist population. The… More >

  • Open Access

    ARTICLE

    Few-Shot Object Detection Based on the Transformer and High-Resolution Network

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3439-3454, 2023, DOI:10.32604/cmc.2023.027267
    Abstract Now object detection based on deep learning tries different strategies. It uses fewer data training networks to achieve the effect of large dataset training. However, the existing methods usually do not achieve the balance between network parameters and training data. It makes the information provided by a small amount of picture data insufficient to optimize model parameters, resulting in unsatisfactory detection results. To improve the accuracy of few shot object detection, this paper proposes a network based on the transformer and high-resolution feature extraction (THR). High-resolution feature extraction maintains the resolution representation of the image. Channels and spatial attention are… More >

  • Open Access

    ARTICLE

    Stochastic Computational Heuristic for the Fractional Biological Model Based on Leptospirosis

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3455-3470, 2023, DOI:10.32604/cmc.2023.033352
    Abstract The purpose of these investigations is to find the numerical outcomes of the fractional kind of biological system based on Leptospirosis by exploiting the strength of artificial neural networks aided by scale conjugate gradient, called ANNs-SCG. The fractional derivatives have been applied to get more reliable performances of the system. The mathematical form of the biological Leptospirosis system is divided into five categories, and the numerical performances of each model class will be provided by using the ANNs-SCG. The exactness of the ANNs-SCG is performed using the comparison of the reference and obtained results. The reference solutions have been obtained… More >

  • Open Access

    ARTICLE

    An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3471-3487, 2023, DOI:10.32604/cmc.2023.033457
    Abstract The attention-based encoder-decoder technique, known as the trans-former, is used to enhance the performance of end-to-end automatic speech recognition (ASR). This research focuses on applying ASR end-to-end transformer-based models for the Arabic language, as the researchers’ community pays little attention to it. The Muslims Holy Qur’an book is written using Arabic diacritized text. In this paper, an end-to-end transformer model to building a robust Qur’an vs. recognition is proposed. The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework. A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic… More >

  • Open Access

    ARTICLE

    Novel Framework of Segmentation 3D MRI of Brain Tumors

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3489-3502, 2023, DOI:10.32604/cmc.2023.033356
    Abstract Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image. The proposed techniques consider the… More >

  • Open Access

    ARTICLE

    A Big Data Based Dynamic Weight Approach for RFM Segmentation

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3503-3513, 2023, DOI:10.32604/cmc.2023.023596
    Abstract Using the RFM (Recency, Frequency, Monetary value) model can provide valuable insights about customer clusters which is the core of customer relationship management. Due to accurate customer segment coming from dynamic weighted applications, in-depth targeted marketing may also use type of dynamic weight of R, F and M as factors. In this paper, we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights. Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data… More >

  • Open Access

    ARTICLE

    Proposed Biometric Security System Based on Deep Learning and Chaos Algorithms

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3515-3537, 2023, DOI:10.32604/cmc.2023.033765
    Abstract Nowadays, there is tremendous growth in biometric authentication and cybersecurity applications. Thus, the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors. Therefore, designing and implementing robust security algorithms for users’ biometrics is still a hot research area to be investigated. This work presents a powerful biometric security system (BSS) to protect different biometric modalities such as faces, iris, and fingerprints. The proposed BSS model is based on hybridizing auto-encoder (AE) network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their secrecy. The employed… More >

  • Open Access

    ARTICLE

    Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3539-3560, 2023, DOI:10.32604/cmc.2023.032976
    Abstract This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers (VPLN) in challenging image datasets. Since binarization of the input image is the most important and difficult step in the detection of VPLN, a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects (CCO) and hence enriches the solution space with more solution candidates. Due to the combination of the outputs of the three binarization techniques, many CCOs are produced into the output pool from which one or… More >

  • Open Access

    ARTICLE

    Robust Vehicle Detection Based on Improved You Look Only Once

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3561-3577, 2023, DOI:10.32604/cmc.2023.029999
    Abstract Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance. The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance. Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms. This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy. The proposed method works in three phases; in the first phase, the K-means algorithm performs data clustering on datasets… More >

  • Open Access

    ARTICLE

    Data De-identification Framework

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3579-3606, 2023, DOI:10.32604/cmc.2023.031491
    Abstract As technology develops, the amount of information being used has increased a lot. Every company learns big data to provide customized services with its customers. Accordingly, collecting and analyzing data of the data subject has become one of the core competencies of the companies. However, when collecting and using it, the authority of the data subject may be violated. The data often identifies its subject by itself, and even if it is not a personal information that infringes on an individual’s authority, the moment it is connected, it becomes important and sensitive personal information that we have never thought of.… More >

  • Open Access

    ARTICLE

    Optimizing Optical Attocells Positioning of Indoor Visible Light Communication System

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3607-3625, 2023, DOI:10.32604/cmc.2023.031192
    Abstract Visible light communication (VLC), which is a prominent emerging solution that complements the radio frequency (RF) technology, exhibits the potential to meet the demands of fifth-generation (5G) and beyond technologies. The random movement of mobile terminals in the indoor environment is a challenge in the VLC system. The model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio (SNR). As such, the optical attocells positions were optimized in this study with a developed try and error (TE) algorithm. The optimized optical attocells were examined… More >

  • Open Access

    ARTICLE

    Fusion Strategy for Improving Medical Image Segmentation

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3627-3646, 2023, DOI:10.32604/cmc.2023.027606
    Abstract In this paper, we combine decision fusion methods with four meta-heuristic algorithms (Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, modification of Cuckoo Search (CS McCulloch) algorithm and Genetic algorithm) in order to improve the image segmentation. The proposed technique based on fusing the data from Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms are obtained for improving magnetic resonance images (MRIs) segmentation. Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods. In order to obtain parts of the points that determine similar… More >

  • Open Access

    ARTICLE

    Vehicle Detection in Challenging Scenes Using CenterNet Based Approach

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3647-3661, 2023, DOI:10.32604/cmc.2023.020916
    Abstract Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System (ITS) and reduces road accidents. The major obstacles in automatic detection of tiny vehicles are due to occlusion, environmental conditions, illumination, view angles and variation in size of objects. This research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed area. In this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks (DNN) namely CenterNet. Substantially DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in… More >

  • Open Access

    ARTICLE

    Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3663-3677, 2023, DOI:10.32604/cmc.2023.030064
    Abstract The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio (SNR). The proposed method utilizes the robust measures of location i.e., the “Median” as well as the measures of variation i.e., “Median absolute deviation (MAD) and Interquartile range (IQR)” in the SNR. By this way, two independent robust signal-to-noise ratios have been proposed. The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio (RSNR). The results obtained via… More >

  • Open Access

    ARTICLE

    Ontological Model for Cohesive Smart Health Services Management

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3679-3695, 2023, DOI:10.32604/cmc.2023.030340
    Abstract Health care has become an essential social-economic concern for all stakeholders (e.g., patients, doctors, hospitals etc.), health needs, private care and the elderly class of society. The massive increase in the usage of health care Internet of things (IoT) applications has great technological evolvement in human life. There are various smart health care services like remote patient monitoring, diagnostic, disease-specific remote treatments and telemedicine. These applications are available in a split fashion and provide solutions for variant diseases, medical resources and remote service management. The main objective of this research is to provide a management platform where all these services… More >

  • Open Access

    ARTICLE

    A Dual Attention Encoder-Decoder Text Summarization Model

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3697-3710, 2023, DOI:10.32604/cmc.2023.031525
    Abstract A worthy text summarization should represent the fundamental content of the document. Recent studies on computerized text summarization tried to present solutions to this challenging problem. Attention models are employed extensively in text summarization process. Classical attention techniques are utilized to acquire the context data in the decoding phase. Nevertheless, without real and efficient feature extraction, the produced summary may diverge from the core topic. In this article, we present an encoder-decoder attention system employing dual attention mechanism. In the dual attention mechanism, the attention algorithm gathers main data from the encoder side. In the dual attention model, the system… More >

  • Open Access

    ARTICLE

    A Secure Multi-factor Authentication Protocol for Healthcare Services Using Cloud-based SDN

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3711-3726, 2023, DOI:10.32604/cmc.2023.027992
    Abstract Cloud-based SDN (Software Defined Network) integration offers new kinds of agility, flexibility, automation, and speed in the network. Enterprises and Cloud providers both leverage the benefits as networks can be configured and optimized based on the application requirement. The integration of cloud and SDN paradigms has played an indispensable role in improving ubiquitous health care services. It has improved the real-time monitoring of patients by medical practitioners. Patients’ data get stored at the central server on the cloud from where it is available to medical practitioners in no time. The centralisation of data on the server makes it more vulnerable… More >

  • Open Access

    ARTICLE

    Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3727-3741, 2023, DOI:10.32604/cmc.2023.032794
    Abstract Cervical Cancer (CC) is a rapidly growing disease among women throughout the world, especially in developed and developing countries. For this many women have died. Fortunately, it is curable if it can be diagnosed and detected at an early stage and taken proper treatment. But the high cost, awareness, highly equipped diagnosis environment, and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage. To solve this issue, the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix… More >

  • Open Access

    ARTICLE

    Detection of Omicron Caused Pneumonia from Radiology Images Using Convolution Neural Network (CNN)

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3743-3761, 2023, DOI:10.32604/cmc.2023.033924
    Abstract COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across the world. The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world. It is essential to detect COVID-19 infection caused by different variants to take preventive measures accordingly. The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming. The impacts of the COVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic. Pneumonia is the major symptom of COVID-19 infection.… More >

  • Open Access

    ARTICLE

    Improved Symbiotic Organism Search with Deep Learning for Industrial Fault Diagnosis

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3763-3780, 2023, DOI:10.32604/cmc.2023.033448
    Abstract Developments in data storage and sensor technologies have allowed the cumulation of a large volume of data from industrial systems. Both structural and non-structural data of industrial systems are collected, which covers data formats of time-series, text, images, sound, etc. Several researchers discussed above were mostly qualitative, and ceratin techniques need expert guidance to conclude on the condition of gearboxes. But, in this study, an improved symbiotic organism search with deep learning enabled fault diagnosis (ISOSDL-FD) model for gearbox fault detection in industrial systems. The proposed ISOSDL-FD technique majorly concentrates on the identification and classification of faults in the gearbox… More >

  • Open Access

    ARTICLE

    Adapted Speed System in a Road Bend Situation in VANET Environment

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3781-3794, 2023, DOI:10.32604/cmc.2023.033119
    Abstract Today, road safety remains a serious concern for governments around the world. In fact, approximately 1.35 million people die and 2–50 million are injured on public roads worldwide each year. Straight bends in road traffic are the main cause of many road accidents, and excessive and inappropriate speed in this very critical area can cause drivers to lose their vehicle stability. For these reasons, new solutions must be considered to stop this disaster and save lives. Therefore, it is necessary to study this topic very carefully and use new technologies such as Vehicle Ad Hoc Networks (VANET), Internet of Things… More >

  • Open Access

    ARTICLE

    Topological Evaluation of Certain Computer Networks by Contraharmonic-Quadratic Indices

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3795-3810, 2023, DOI:10.32604/cmc.2023.033976
    Abstract In various fields, different networks are used, most of the time not of a single kind; but rather a mix of at least two networks. These kinds of networks are called bridge networks which are utilized in interconnection networks of PC, portable networks, spine of internet, networks engaged with advanced mechanics, power generation interconnection, bio-informatics and substance intensify structures. Any number that can be entirely calculated by a graph is called graph invariants. Countless mathematical graph invariants have been portrayed and utilized for connection investigation during the latest twenty years. Nevertheless, no trustworthy evaluation has been embraced to pick, how… More >

  • Open Access

    ARTICLE

    Forecasting Future Trajectories with an Improved Transformer Network

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3811-3828, 2023, DOI:10.32604/cmc.2023.029787
    Abstract An increase in car ownership brings convenience to people’s life. However, it also leads to frequent traffic accidents. Precisely forecasting surrounding agents’ future trajectories could effectively decrease vehicle-vehicle and vehicle-pedestrian collisions. Long-short-term memory (LSTM) network is often used for vehicle trajectory prediction, but it has some shortages such as gradient explosion and low efficiency. A trajectory prediction method based on an improved Transformer network is proposed to forecast agents’ future trajectories in a complex traffic environment. It realizes the transformation from sequential step processing of LSTM to parallel processing of Transformer based on attention mechanism. To perform trajectory prediction more… More >

  • Open Access

    ARTICLE

    Fault Tolerant Optical Mark Recognition

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3829-3847, 2023, DOI:10.32604/cmc.2023.026422
    Abstract Optical Mark Recognition (OMR) systems have been studied since 1970. It is widely accepted as a data entry technique. OMR technology is used for surveys and multiple-choice questionnaires. Due to its ease of use, OMR technology has grown in popularity over the past two decades and is widely used in universities and colleges to automatically grade and grade student responses to questionnaires. The accuracy of OMR systems is very important due to the environment in which they are used. The OMR algorithm relies on pixel projection or Hough transform to determine the exact answer in the document. These techniques rely… More >

  • Open Access

    ARTICLE

    ETL Maturity Model for Data Warehouse Systems: A CMMI Compliant Framework

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3849-3863, 2023, DOI:10.32604/cmc.2023.027387
    Abstract The effectiveness of the Business Intelligence (BI) system mainly depends on the quality of knowledge it produces. The decision-making process is hindered, and the user’s trust is lost, if the knowledge offered is undesired or of poor quality. A Data Warehouse (DW) is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions. The Extract, Transform, and Load (ETL) process is the backbone of a DW system, and it is responsible for moving data from source systems into the DW system. The more mature the ETL… More >

  • Open Access

    ARTICLE

    Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3865-3880, 2023, DOI:10.32604/cmc.2023.029038
    Abstract Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies, including big data, the cloud, and the Internet of Things (IoT). Many researchers try to integrate IoT-based smart farming on cloud platforms effectively. They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes. Since IoT-cloud systems involve massive structured and unstructured data, data optimization comes into the picture. Hence, this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization (ICISF-DDO), which enhances the performance of the smart farming infrastructure… More >

  • Open Access

    ARTICLE

    Improved Video Steganography with Dual Cover Medium, DNA and Complex Frames

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3881-3898, 2023, DOI:10.32604/cmc.2023.030197
    Abstract The most valuable resource on the planet is no longer oil, but data. The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value. In order to transmit sensitive information securely, researchers are combining robust cryptography and steganographic approaches. The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid (DNA) for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility. In the previous approach, DNA was used only for frame selection. If this DNA is compromised, then our frames with… More >

  • Open Access

    ARTICLE

    Translation of English Language into Urdu Language Using LSTM Model

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3899-3912, 2023, DOI:10.32604/cmc.2023.032290
    Abstract English to Urdu machine translation is still in its beginning and lacks simple translation methods to provide motivating and adequate English to Urdu translation. In order to make knowledge available to the masses, there should be mechanisms and tools in place to make things understandable by translating from source language to target language in an automated fashion. Machine translation has achieved this goal with encouraging results. When decoding the source text into the target language, the translator checks all the characteristics of the text. To achieve machine translation, rule-based, computational, hybrid and neural machine translation approaches have been proposed to… More >

  • Open Access

    ARTICLE

    Ontology-Based News Linking for Semantic Temporal Queries

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3913-3929, 2023, DOI:10.32604/cmc.2023.033001
    Abstract Daily newspapers publish a tremendous amount of information disseminated through the Internet. Freely available and easily accessible large online repositories are not indexed and are in an un-processable format. The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed. There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora, especially for South Asian languages. The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language.… More >

  • Open Access

    ARTICLE

    Identity-Based Edge Computing Anonymous Authentication Protocol

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3931-3943, 2023, DOI:10.32604/cmc.2023.029711
    Abstract With the development of sensor technology and wireless communication technology, edge computing has a wider range of applications. The privacy protection of edge computing is of great significance. In the edge computing system, in order to ensure the credibility of the source of terminal data, mobile edge computing (MEC) needs to verify the signature of the terminal node on the data. During the signature process, the computing power of edge devices such as wireless terminals can easily become the bottleneck of system performance. Therefore, it is very necessary to improve efficiency through computational offloading. Therefore, this paper proposes an identity-based… More >

  • Open Access

    ARTICLE

    GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3945-3976, 2023, DOI:10.32604/cmc.2023.031194
    Abstract As a result of the increased number of COVID-19 cases, Ensemble Machine Learning (EML) would be an effective tool for combatting this pandemic outbreak. An ensemble of classifiers can improve the performance of single machine learning (ML) classifiers, especially stacking-based ensemble learning. Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results. However, building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected. Therefore, the goal of this paper is to develop and evaluate a generic, data-independent… More >

  • Open Access

    ARTICLE

    Rooted Tree Optimization for Wind Turbine Optimum Control Based on Energy Storage System

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3977-3996, 2023, DOI:10.32604/cmc.2023.029838
    Abstract The integration of wind turbines (WTs) in variable speed drive systems belongs to the main factors causing low stability in electrical networks. Therefore, in order to avoid this issue, WTs hybridization with a storage system is a mandatory. This paper investigates WT system operating at variable speed. The system contains of a permanent magnet synchronous generator (PMSG) supported by a battery storage system (BSS). To enhance the quality of active and reactive power injected into the network, direct power control (DPC) scheme utilizing space-vector modulation (SVM) technique based on proportional-integral (PI) control is proposed. Meanwhile, to improve the rendition of… More >

  • Open Access

    ARTICLE

    Android Malware Detection Using ResNet-50 Stacking

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3997-4014, 2023, DOI:10.32604/cmc.2023.028316
    Abstract There has been an increase in attacks on mobile devices, such as smartphones and tablets, due to their growing popularity. Mobile malware is one of the most dangerous threats, causing both security breaches and financial losses. Mobile malware is likely to continue to evolve and proliferate to carry out a variety of cybercrimes on mobile devices. Mobile malware specifically targets Android operating system as it has grown in popularity. The rapid proliferation of Android malware apps poses a significant security risk to users, making static and manual analysis of malicious files difficult. Therefore, efficient identification and classification of Android malicious… More >

  • Open Access

    ARTICLE

    LoRa Backscatter Network Efficient Data Transmission Using RF Source Range Control

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4015-4025, 2023, DOI:10.32604/cmc.2023.027078
    Abstract Networks based on backscatter communication provide wireless data transmission in the absence of a power source. A backscatter device receives a radio frequency (RF) source and creates a backscattered signal that delivers data; this enables new services in battery-less domains with massive Internet-of-Things (IoT) devices. Connectivity is highly energy-efficient in the context of massive IoT applications. Outdoors, long-range (LoRa) backscattering facilitates large IoT services. A backscatter network guarantees timeslot-and contention-based transmission. Timeslot-based transmission ensures data transmission, but is not scalable to different numbers of transmission devices. If contention-based transmission is used, collisions are unavoidable. To reduce collisions and increase transmission… More >

  • Open Access

    ARTICLE

    Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4027-4041, 2023, DOI:10.32604/cmc.2023.033039
    Abstract In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2n potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead to instability. Because of… More >

  • Open Access

    ARTICLE

    Federation Boosting Tree for Originator Rights Protection

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4043-4058, 2023, DOI:10.32604/cmc.2023.031684
    Abstract The problem of data island hinders the application of big data in artificial intelligence model training, so researchers propose a federated learning framework. It enables model training without having to centralize all data in a central storage point. In the current horizontal federated learning scheme, each participant gets the final jointly trained model. No solution is proposed for scenarios where participants only provide training data in exchange for benefits, but do not care about the final jointly trained model. Therefore, this paper proposes a new boosted tree algorithm, called RPBT (the originator Rights Protected federated Boosted Tree algorithm). Compared with… More >

  • Open Access

    ARTICLE

    An Efficient Technique to Prevent Data Misuse with Matrix Cipher Encryption Algorithms

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4059-4079, 2023, DOI:10.32604/cmc.2023.032882
    Abstract Many symmetric and asymmetric encryption algorithms have been developed in cloud computing to transmit data in a secure form. Cloud cryptography is a data encryption mechanism that consists of different steps and prevents the attacker from misusing the data. This paper has developed an efficient algorithm to protect the data from invaders and secure the data from misuse. If this algorithm is applied to the cloud network, the attacker will not be able to access the data. To encrypt the data, the values of the bytes have been obtained by converting the plain text to ASCII. A key has been… More >

  • Open Access

    ARTICLE

    Numerical Comparison of Shapeless Radial Basis Function Networks in Pattern Recognition

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4081-4098, 2023, DOI:10.32604/cmc.2023.032329
    Abstract This work focuses on radial basis functions containing no parameters with the main objective being to comparatively explore more of their effectiveness. For this, a total of sixteen forms of shapeless radial basis functions are gathered and investigated under the context of the pattern recognition problem through the structure of radial basis function neural networks, with the use of the Representational Capability (RC) algorithm. Different sizes of datasets are disturbed with noise before being imported into the algorithm as ‘training/testing’ datasets. Each shapeless radial basis function is monitored carefully with effectiveness criteria including accuracy, condition number (of the interpolation matrix),… More >

  • Open Access

    ARTICLE

    Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4099-4118, 2023, DOI:10.32604/cmc.2023.033124
    Abstract System logs are essential for detecting anomalies, querying faults, and tracing attacks. Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection, it cannot meet the actual needs. The implementation of automated log anomaly detection is a topic that demands urgent research. However, the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data. Meanwhile, there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection. This paper proposes a novel and practical detection model… More >

  • Open Access

    ARTICLE

    An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4119-4133, 2023, DOI:10.32604/cmc.2023.033182
    Abstract It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative… More >

  • Open Access

    ARTICLE

    Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4135-4147, 2023, DOI:10.32604/cmc.2023.032315
    Abstract This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition (SVD). It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size. At each step, the SVD is determined. First, the diagonal matrix’s maximum value (norm) is selected (representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating the matrix or scaled). Then, the similar norms are grouped, and each leading group is separated into many subgroups (elements of each subgroup… More >

  • Open Access

    ARTICLE

    Control of Distributed Generation Using Non-Sinusoidal Pulse Width Modulation

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4149-4164, 2023, DOI:10.32604/cmc.2023.033405
    Abstract The islanded mode is one of the connection modes of the grid distributed generation resources. In this study, a distributed generation resource is connected to linear and nonlinear loads via a three-phase inverter where a control method needing no current sensors or compensator elements is applied to the distribute generation system in the islanded mode. This control method has two main loops in each phase. The first loop controls the voltage control loops that adjust the three-phase point of common coupling, the amplitude of the non-sinusoidal reference waveform and the near-state pulse width modulation (NSPWM) method. The next loop compensatesthe… More >

  • Open Access

    ARTICLE

    Identification and Visualization of Spatial and Temporal Trends in Textile Industry

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4165-4181, 2023, DOI:10.32604/cmc.2023.026607
    Abstract The research volume increases at the study rate, causing massive text corpora. Due to these enormous text corpora, we are drowning in data and starving for information. Therefore, recent research employed different text mining approaches to extract information from this text corpus. These proposed approaches extract meaningful and precise phrases that effectively describe the text's information. These extracted phrases are commonly termed keyphrases. Further, these key phrases are employed to determine the different fields of study trends. Moreover, these key phrases can also be used to determine the spatiotemporal trends in the various research fields. In this research, the progress… More >

  • Open Access

    ARTICLE

    Developing a Secure Framework Using Feature Selection and Attack Detection Technique

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4183-4201, 2023, DOI:10.32604/cmc.2023.032430
    Abstract Intrusion detection is critical to guaranteeing the safety of the data in the network. Even though, since Internet commerce has grown at a breakneck pace, network traffic kinds are rising daily, and network behavior characteristics are becoming increasingly complicated, posing significant hurdles to intrusion detection. The challenges in terms of false positives, false negatives, low detection accuracy, high running time, adversarial attacks, uncertain attacks, etc. lead to insecure Intrusion Detection System (IDS). To offset the existing challenge, the work has developed a secure Data Mining Intrusion detection system (DataMIDS) framework using Functional Perturbation (FP) feature selection and Bengio Nesterov Momentum-based… More >

  • Open Access

    ARTICLE

    A Multi-Level Circulant Cross-Modal Transformer for Multimodal Speech Emotion Recognition

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4203-4220, 2023, DOI:10.32604/cmc.2023.028291
    Abstract Speech emotion recognition, as an important component of human-computer interaction technology, has received increasing attention. Recent studies have treated emotion recognition of speech signals as a multimodal task, due to its inclusion of the semantic features of two different modalities, i.e., audio and text. However, existing methods often fail in effectively represent features and capture correlations. This paper presents a multi-level circulant cross-modal Transformer (MLCCT) for multimodal speech emotion recognition. The proposed model can be divided into three steps, feature extraction, interaction and fusion. Self-supervised embedding models are introduced for feature extraction, which give a more powerful representation of the… More >

  • Open Access

    ARTICLE

    Intelligent Systems and Photovoltaic Cells Empowered Topologically by Sudoku Networks

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4221-4238, 2023, DOI:10.32604/cmc.2023.034320
    Abstract A graph invariant is a number that can be easily and uniquely calculated through a graph. Recently, part of mathematical graph invariants has been portrayed and utilized for relationship examination. Nevertheless, no reliable appraisal has been embraced to pick, how much these invariants are associated with a network graph in interconnection networks of various fields of computer science, physics, and chemistry. In this paper, the study talks about sudoku networks will be networks of fractal nature having some applications in computer science like sudoku puzzle game, intelligent systems, Local area network (LAN) development and parallel processors interconnections, music composition creation,… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4239-4259, 2023, DOI:10.32604/cmc.2023.031969
    Abstract Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal… More >

  • Open Access

    ARTICLE

    Reducing Dataset Specificity for Deepfakes Using Ensemble Learning

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4261-4276, 2023, DOI:10.32604/cmc.2023.034482
    Abstract The emergence of deep fake videos in recent years has made image falsification a real danger. A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content. Because of how clever these videos are frequently, Manipulation is challenging to spot. Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human. In earlier times, it was not so easy to alter the videos, which required expertise in the domain… More >

  • Open Access

    ARTICLE

    Partially Deep-Learning Encryption Technique

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4277-4291, 2023, DOI:10.32604/cmc.2023.034593
    Abstract The biggest problem facing the world is information security in the digital era. Information protection and integrity are hot topics at all times, so many techniques have been introduced to transmit and store data securely. The increase in computing power is increasing the number of security breaches and attacks at a higher rate than before on average. Thus, a number of existing security systems are at risk of hacking. This paper proposes an encryption technique called Partial Deep-Learning Encryption Technique (PD-LET) to achieve data security. PD-LET includes several stages for encoding and decoding digital data. Data preprocessing, convolution layer of… More >

  • Open Access

    ARTICLE

    Light-Weighted Decision Support Framework for Selecting Cloud Service Providers

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4293-4317, 2023, DOI:10.32604/cmc.2023.033893
    Abstract Multi-criteria decision making (MCDM) is a technique used to achieve better outcomes for some complex business-related problems, whereby the selection of the best alternative can be made in as many cases as possible. This paper proposes a model, the multi-criteria decision support method, that allows both service providers and consumers to maximize their profits while preserving the best matching process for resource allocation and task scheduling. The increasing number of service providers with different service provision capabilities creates an issue for consumers seeking to select the best service provider. Each consumer seeks a service provider based on various preferences, such… More >

  • Open Access

    ARTICLE

    Enhanced Coyote Optimization with Deep Learning Based Cloud-Intrusion Detection System

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4319-4336, 2023, DOI:10.32604/cmc.2023.033497
    Abstract Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. Intrusion Detection System (IDS) refers to one of the commonly utilized system for detecting attacks on cloud. IDS proves to be an effective and promising technique, that identifies malicious activities and known threats by observing traffic data in computers, and warnings are given when such threats were identified. The current mainstream IDS are assisted… More >

  • Open Access

    ARTICLE

    Vibration of a Two-Layer “Metal+PZT” Plate Contacting with Viscous Fluid

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4337-4362, 2023, DOI:10.32604/cmc.2023.033446
    Abstract The present work investigates the mechanically forced vibration of the hydro-elasto-piezoelectric system consisting of a two-layer plate “elastic+PZT”, a compressible viscous fluid, and a rigid wall. It is assumed that the PZT (piezoelectric) layer of the plate is in contact with the fluid and time-harmonic linear forces act on the free surface of the elastic-metallic layer. This study is valuable because it considers for the first time the mechanical vibration of the metal+piezoelectric bilayer plate in contact with a fluid. It is also the first time that the influence of the volumetric concentration of the constituents on the vibration of… More >

  • Open Access

    ARTICLE

    View Types and Visual Communication Cues for Remote Collaboration

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4363-4379, 2023, DOI:10.32604/cmc.2023.034209
    Abstract Over the last several years, remote collaboration has been getting more attention in the research community because of the COVID-19 pandemic. In previous studies, researchers have investigated the effect of adding visual communication cues or shared views in collaboration, but there has not been any previous study exploring the influence between them. In this paper, we investigate the influence of view types on the use of visual communication cues. We compared the use of the three visual cues (hand gesture, a pointer with hand gesture, and sketches with hand gesture) across two view types (dependent and independent views), respectively. We… More >

  • Open Access

    ARTICLE

    TRUSED: A Trust-Based Security Evaluation Scheme for A Distributed Control System

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4381-4398, 2023, DOI:10.32604/cmc.2023.031472
    Abstract Distributed control systems (DCS) have revolutionized the communication process and attracted more interest due to their pervasive computing nature (cyber/physical), their monitoring capabilities and the benefits they offer. However, due to distributed communication, flexible network topologies and lack of central control, the traditional security strategies are inadequate for meeting the unique characteristics of DCS. Moreover, malicious and untrustworthy nodes pose a significant threat during the formation of a DCS network. Trust-based secure systems not only monitor and track the behavior of the nodes but also enhance the security by identifying and isolating the malicious node, which reduces the risk and… More >

  • Open Access

    ARTICLE

    Malware Detection in Android IoT Systems Using Deep Learning

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4399-4415, 2023, DOI:10.32604/cmc.2023.032984
    Abstract The Android Operating System (AOS) has been evolving since its inception and it has become one of the most widely used operating system for the Internet of Things (IoT). Due to the high popularity and reliability of AOS for IoT, it is a target of many cyber-attacks which can cause compromise of privacy, financial loss, data integrity, unauthorized access, denial of services and so on. The Android-based IoT (AIoT) devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the market. Recently, several detection preventive malwares are developed to conceal their malicious… More >

  • Open Access

    ARTICLE

    Type-2 Neutrosophic Set and Their Applications in Medical Databases Deadlock Resolution

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4417-4434, 2023, DOI:10.32604/cmc.2023.033175
    Abstract Electronic patient data gives many advantages, but also new difficulties. Deadlocks may delay procedures like acquiring patient information. Distributed deadlock resolution solutions introduce uncertainty due to inaccurate transaction properties. Soft computing-based solutions have been developed to solve this challenge. In a single framework, ambiguous, vague, incomplete, and inconsistent transaction attribute information has received minimal attention. The work presented in this paper employed type-2 neutrosophic logic, an extension of type-1 neutrosophic logic, to handle uncertainty in real-time deadlock-resolving systems. The proposed method is structured to reflect multiple types of knowledge and relations among transactions’ features that include validation factor degree, slackness… More >

  • Open Access

    ARTICLE

    Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4435-4451, 2023, DOI:10.32604/cmc.2023.033091
    Abstract The recent developments in Multimedia Internet of Things (MIoT) devices, empowered with Natural Language Processing (NLP) model, seem to be a promising future of smart devices. It plays an important role in industrial models such as speech understanding, emotion detection, home automation, and so on. If an image needs to be captioned, then the objects in that image, its actions and connections, and any silent feature that remains under-projected or missing from the images should be identified. The aim of the image captioning process is to generate a caption for image. In next step, the image should be provided with… More >

  • Open Access

    ARTICLE

    A Neural Study of the Fractional Heroin Epidemic Model

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4453-4467, 2023, DOI:10.32604/cmc.2023.033232
    Abstract This works intends to provide numerical solutions based on the nonlinear fractional order derivatives of the classical White and Comiskey model (NFD-WCM). The fractional order derivatives have provided authentic and accurate solutions for the NDF-WCM. The solutions of the fractional NFD-WCM are provided using the stochastic computing supervised algorithm named Levenberg-Marquard Backpropagation (LMB) based on neural networks (NNs). This regression approach combines gradient descent and Gauss-Newton iterative methods, which means finding a solution through the sequences of different calculations. WCM is used to demonstrate the heroin epidemics. Heroin has been on-growth world wide, mainly in Asia, Europe, and the USA.… More >

  • Open Access

    ARTICLE

    Multi Criteria Decision Making for Evaluation and Ranking of Cancer Information

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4469-4481, 2023, DOI:10.32604/cmc.2023.030728
    Abstract Cancer is a disease that is rapidly expanding in prevalence all over the world. Cancer cells can metastasize, or spread, across the body and impact several different cell types. Additionally, the incidence rates of several subtypes of cancer have been on the rise in India. The countermeasures for the cancer disease can be taken by determining the specific expansion rate of each type. To rank the various forms of cancer’s rate of progression, we used some of the available data. Numerous studies are available in the literature which show the growth rate of cancer by different techniques. The accuracy of… More >

  • Open Access

    ARTICLE

    Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4483-4500, 2023, DOI:10.32604/cmc.2023.032588
    Abstract In recent times, cities are getting smart and can be managed effectively through diverse architectures and services. Smart cities have the ability to support smart medical systems that can infiltrate distinct events (i.e., smart hospitals, smart homes, and community health centres) and scenarios (e.g., rehabilitation, abnormal behavior monitoring, clinical decision-making, disease prevention and diagnosis postmarking surveillance and prescription recommendation). The integration of Artificial Intelligence (AI) with recent technologies, for instance medical screening gadgets, are significant enough to deliver maximum performance and improved management services to handle chronic diseases. With latest developments in digital data collection, AI techniques can be employed… More >

  • Open Access

    ARTICLE

    A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4501-4530, 2023, DOI:10.32604/cmc.2023.033928
    Abstract Recently, researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems (HPCs) to achieve exascale by applying parallelism at multiple levels. Combining different programming paradigms, such as Message Passing Interface (MPI), Open Multiple Processing (OpenMP), and Open Accelerators (OpenACC), can increase computation speed and improve performance. During the integration of multiple models, the probability of runtime errors increases, making their detection difficult, especially in the absence of testing techniques that can detect these errors. Numerous studies have been conducted to identify these errors, but no technique exists for detecting errors… More >

  • Open Access

    ARTICLE

    Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4531-4545, 2023, DOI:10.32604/cmc.2023.033042
    Abstract Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning. Each feature in a dataset has 2n possible subsets, making it challenging to select the optimum collection of features using typical methods. As a result, a new metaheuristics-based feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research. Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results. Thus, we adopted hybrid optimization in our method of optimizing, which allowed us… More >

  • Open Access

    ARTICLE

    Modeling and Simulation of DVR and D-STATCOM in Presence of Wind Energy System

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4547-4570, 2023, DOI:10.32604/cmc.2023.034082
    Abstract The present study suggests that series voltage injection is more effective than parallel current injection to improve voltage quality on the load side. The line voltage can be accurately symmetrized at the connection point by creating and controlling a series voltage component in each phase. This is more reliable and effective than parallel current injection. A dynamic voltage restorer (DVR) and a distribution static synchronous compensator (DSTATCOM) were utilized to provide the required power. The DVR is an effective and modern device utilized in parallel within the grid and can protect sensitive loads from voltage problems in the grid by… More >

  • Open Access

    ARTICLE

    Industrial Recycling Process of Batteries for EVs

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4571-4586, 2023, DOI:10.32604/cmc.2023.032995
    Abstract The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars. Renewable energy sources have the ability to bring the fossil fuel age to an end. Electrochemical storage devices, particularly lithium-ion batteries, are critical for this transition’s success. This is owing to a combination of favorable characteristics such as high energy density and minimal self-discharge. Given the environmental degradation caused by hazardous wastes and the scarcity of some resources, recycling used lithium-ion batteries has significant economic and practical importance. Many efforts have been undertaken in recent years to recover cathode materials… More >

  • Open Access

    ARTICLE

    A Hyperparameter Optimization for Galaxy Classification

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4587-4600, 2023, DOI:10.32604/cmc.2023.033155
    Abstract In this study, the morphological galaxy classification process was carried out with a hybrid approach. Since the Galaxy classification process may contain detailed information about the universe’s formation, it remains the current research topic. Researchers divided more than 100 billion galaxies into ten different classes. It is not always possible to understand which class the galaxy types belong. However, Artificial Intelligence (AI) can be used for successful classification. There are studies on the automatic classification of galaxies into a small number of classes. As the number of classes increases, the success of the used methods decreases. Based on the literature,… More >

  • Open Access

    ARTICLE

    Crime Prediction Methods Based on Machine Learning: A Survey

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4601-4629, 2023, DOI:10.32604/cmc.2023.034190
    Abstract The objective of crime prediction, one of the most important technologies in social computing, is to extract useful information from many existing criminal records to predict the next process-related crime. It can aid the police in obtaining criminal information and warn the public to be vigilant in certain areas. With the rapid growth of big data, the Internet of Things, and other technologies, as well as the increasing use of artificial intelligence in forecasting models, crime prediction models based on deep learning techniques are accelerating. Therefore, it is necessary to classify the existing crime prediction algorithms and compare in depth… More >

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