Home / Journals / CMC / Vol.74, No.2, 2023
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  • Open AccessOpen Access

    ARTICLE

    Deep Learning Based Face Detection and Identification of Criminal Suspects

    S. Sandhya1, A. Balasundaram2,*, Ayesha Shaik1
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Numerical Investigation of Malaria Disease Dynamics in Fuzzy Environment

    Fazal Dayan1,*, Dumitru Baleanu2,3,4, Nauman Ahmed5, Jan Awrejcewicz6, Muhammad Rafiq7, Ali Raza8, Muhammad Ozair Ahmad5
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Central Aggregator Intrusion Detection System for Denial of Service Attacks

    Sajjad Ahmad1, Imran Raza1, M. Hasan Jamal1, Sirojiddin Djuraev2, Soojung Hur3, Imran Ashraf3,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2,3, Faten Khalid Karim1,*, Sameer Alshetewi4, Abdelhameed Ibrahim5, Abdelaziz A. Abdelhamid6,7, D. L. Elsheweikh8
    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 More >

  • Open AccessOpen Access

    ARTICLE

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

    Ahmed Abdu Alattab1,*, Mohammed Eid Ibrahim1, Reyazur Rashid Irshad1, Anwar Ali Yahya2, Amin A. Al-Awady3
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

    Narongsak Yotha1, Qusain Hiader2, Zulqurnain Sabir3, Muhammad Asif Zahoor Raja4, Salem Ben Said5, Qasem Al-Mdallal5, Thongchai Botmart6, Wajaree Weera6,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Bich Nguyen1, Binh Nguyen1, Hai Tran2, Vuong Pham1, Le Nhi Lam Thuy1, Pham The Bao1,*
    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 More >

  • Open AccessOpen Access

    ARTICLE

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

    Amani Abdulrahman Albraikan1, Nadhem NEMRI2, Mimouna Abdullah Alkhonaini3, Anwer Mustafa Hilal4,*, Ishfaq Yaseen4, Abdelwahed Motwakel4
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

    Pradeep Krishnadoss*, Vijayakumar Kedalu Poornachary, Parkavi Krishnamoorthy, Leninisha Shanmugam
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Classification of Adversarial Attacks Using Ensemble Clustering Approach

    Pongsakorn Tatongjai1, Tossapon Boongoen2,*, Natthakan Iam-On2, Nitin Naik3, Longzhi Yang4
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Sultan Alkaabi1,*, Salman Yussof1, Sameera Al-Mulla2
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Wasi Ur Rehman Khan1, Muhammad Fawad Khan1, Muhammad Irfan2, Sadiq Ullah1, Naveed Mufti1, Usman Ali1, Rizwan Ullah1, Fazal Muhammad3,*, Saifur Rahman2, Faisal Althobiani4, Mohammed Alshareef5, Mohammad E. Gommosani6
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Pixel’s Quantum Image Enhancement Using Quantum Calculus

    Husam Yahya1, Dumitru Baleanu2,3,4, Rabha W. Ibrahim5,*, Nadia M.G. Al-Saidi6
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Amitoj Bir Singh1, Lalit Kumar Awasthi1, Urvashi1, Mohammad Shorfuzzaman2, Abdulmajeed Alsufyani2, Mueen Uddin3,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

    A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

    Nayef Alqahtani1,2,*, Ali Alqahtani3
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Numerical Procedure for Fractional HBV Infection with Impact of Antibody Immune

    Sakda Noinang1, Zulqurnain Sabir2, Muhammad Asif Zahoor Raja3, Soheil Salahshour4, Wajaree Weera5,*, Thongchai Botmart5
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Aspect Level Songs Rating Based Upon Reviews in English

    Muhammad Aasim Qureshi1, Muhammad Asif2, Saira Anwar3, Umar Shaukat1, Atta-ur-Rahman4, Muhammad Adnan Khan5,*, Amir Mosavi6,7,8
    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 More >

  • Open AccessOpen Access

    ARTICLE

    A Fused Machine Learning Approach for Intrusion Detection System

    Muhammad Sajid Farooq1, Sagheer Abbas1, Atta-ur-Rahman2, Kiran Sultan3, Muhammad Adnan Khan4,*, Amir Mosavi5,6,7
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Two Layer Symmetric Cryptography Algorithm for Protecting Data from Attacks

    Muhammad Nadeem1, Ali Arshad2, Saman Riaz2, Syeda Wajiha Zahra1, Shahab S. Band3, Amir Mosavi4,5,6,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

    Muhammad Nadeem1, Ali Arshad2, Saman Riaz2, SyedaWajiha Zahra1, Muhammad Rashid2, Shahab S. Band3,*, Amir Mosavi4,5,6
    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 More >

  • Open AccessOpen Access

    ARTICLE

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

    Ademola Olusola Adesina1, Patrick Seun Ayobioloja2, Ibidun Christiana Obagbuwa3, Tola John Odule1, Adenrele A. Afolorunso2, Sunday Adeola Ajagbe4,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

    Reem Alkanhel1, El-Sayed M. El-kenawy2, Abdelaziz A. Abdelhamid3,4, Abdelhameed Ibrahim5, Manal Abdullah Alohali6, Mostafa Abotaleb7, Doaa Sami Khafaga8,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Reem Alkanhel1,*, Doaa Sami Khafaga2, El-Sayed M. El-kenawy3, Abdelaziz A. Abdelhamid4,5, Abdelhameed Ibrahim6, Rashid Amin7, Mostafa Abotaleb8, B. M. El-den6
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Wajaree Weera1, Thongchai Botmart1,*, Charuwat Chantawat1, Zulqurnain Sabir2,3, Waleed Adel4,5, Muhammad Asif Zahoor Raja6, Muhammad Kristiawan7
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

    Mohammad Alamgeer1, Amal Al-Rasheed2, Ahmad Alhindi3, Manar Ahmed Hamza4,*, Abdelwahed Motwakel4, Mohamed I. Eldesouki5
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Big Data Testing Techniques: Taxonomy, Challenges and Future Trends

    Iram Arshad1,*, Saeed Hamood Alsamhi1, Wasif Afzal2
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Van-Truong Nguyen*
    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 More >

  • Open AccessOpen Access

    ARTICLE

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

    Xiangyan Tang1,2, Dengfang Feng2,*, KeQiu Li1, Jingxin Liu2, Jinyang Song3, Victor S. Sheng4
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Neural Machine Translation by Fusing Key Information of Text

    Shijie Hu1, Xiaoyu Li1,*, Jiayu Bai1, Hang Lei1, Weizhong Qian1, Sunqiang Hu1, Cong Zhang2, Akpatsa Samuel Kofi1, Qian Qiu2,3, Yong Zhou4, Shan Yang5
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Noureldin Eissa1,2,*, Uswah Khairuddin1,3, Rubiyah Yusof1, Ahmed Madani2
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

    Slim Ben Chaabane1,2,*, Anas Bushnag1
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation Operator

    Siti Zulaikha Mohd Jamaludin1, Mohd. Asyraf Mansor2, Aslina Baharum3,*, Mohd Shareduwan Mohd Kasihmuddin1, Habibah A. Wahab4, Muhammad Fadhil Marsani1
    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… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Hybrid Model for Arabic Text Recognition

    Hicham Lamtougui1,*, Hicham El Moubtahij2, Hassan Fouadi1, Khalid Satori1
    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 More >

  • Open AccessOpen Access

    ARTICLE

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

    Mahmoud Ragab1,2,3,*, Mohammed W. Al-Rabia4,5, Sami Saeed Binyamin6, Ahmed A. Aldarmahi7,8
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Walid El-Shafai1,2, Noha A. El-Hag3, Ahmed Sedik4, Ghada Elbanby5, Fathi E. Abd El-Samie1, Naglaa F. Soliman6, Hussah Nasser AlEisa7,*, Mohammed E. Abdel Samea8
    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. More >

  • Open AccessOpen Access

    ARTICLE

    Proposed Framework for Detection of Breast Tumors

    Mostafa Elbaz1,2,*, Haitham Elwahsh1, Ibrahim Mahmoud El-Henawy2
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

    Jeonghoon Choi1, Dongjun Suh1,*, Marc-Oliver Otto2
    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 More >

  • Open AccessOpen Access

    ARTICLE

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

    Seongung Jo1, Heung-Seon Oh1,*, Sanghun Im1, Gibaeg Kim1, Seonho Kim2
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Analysis on D2D Heterogeneous Networks with State-Dependent Priority Traffic

    Guangjun Liang1,2, Jianfang Xin3,*, Linging Xia1, Xueli Ni1,4, Yi Cao5
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Safi Ullah1,2, Seong-Ho Song1,*
    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 More >

  • Open AccessOpen Access

    ARTICLE

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

    Shrouk H. Hessen1,2,*, Hatem M. Abdul-kader1, Ayman E. Khedr3, Rashed K. Salem1
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Implications of Onshore Development on Global Software Engineering

    Abdulrahman M. Qahtani1, Ahmed S. Ghiduk1,2,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Age and Gender Classification Using Backpropagation and Bagging Algorithms

    Ammar Almomani1,2,*, Mohammed Alweshah3, Waleed Alomoush4, Mohammad Alauthman5, Aseel Jabai2, Anwar Abbass2, Ghufran Hamad2, Meral Abdalla2, Brij B. Gupta1,6,7
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

    Sukhendra Singh1, Sur Singh Rawat, Manoj Gupta3, B. K. Tripathi4, Faisal Alanazi5, Arnab Majumdar6, Pattaraporn Khuwuthyakorn7, Orawit Thinnukool7,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Saeed Masoud Alshahrani1, Fatma S. Alrayes2, Hamed Alqahtani3, Jaber S. Alzahrani4, Mohammed Maray5, Sana Alazwari6, Mohamed A. Shamseldin7, Mesfer Al Duhayyim8,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Mohammed Maray1, Hamed Alqahtani2, Saud S. Alotaibi3, Fatma S. Alrayes4, Nuha Alshuqayran5, Mrim M. Alnfiai6, Amal S. Mehanna7, Mesfer Al Duhayyim8,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Saeed Masoud Alshahrani1, Saud S. Alotaibi2, Shaha Al-Otaibi3, Mohamed Mousa4, Anwer Mustafa Hilal5,*, Amgad Atta Abdelmageed5, Abdelwahed Motwakel5, Mohamed I. Eldesouki6
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Mesfer Al Duhayyim1,*, Mohammed Maray2, Ayman Qahmash2, Fatma S. Alrayes3, Nuha Alshuqayran4, Jaber S. Alzahrani5, Mohammed Alghamdi2,6, Abdullah Mohamed7
    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… More >

  • Open AccessOpen Access

    ARTICLE

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

    Marwa Obayya1, Nadhem NEMRI2, Lubna A. Alharbi3, Mohamed K. Nour4, Mrim M. Alnfiai5, Mohammed Abdullah Al-Hagery6, Nermin M. Salem7, Mesfer Al Duhayyim8,*
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

    Mesfer Al Duhayyim1,*, Hadeel Alsolai2, Siwar Ben Haj Hassine3, Jaber S. Alzahrani4, Ahmed S. Salama5, Abdelwahed Motwakel6, Ishfaq Yaseen6, Abu Sarwar Zamani6
    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… More >

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