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  • Open Access

    REVIEW

    An Overview and Comparative Study of Traditional, Chaos-Based and Machine Learning Approaches in Pseudorandom Number Generation

    Issah Zabsonre Alhassan1,2,*, Gaddafi Abdul-Salaam1, Michael Asante1, Yaw Marfo Missah1, Alimatu Sadia Shirazu1

    Journal of Cyber Security, Vol.7, pp. 165-196, 2025, DOI:10.32604/jcs.2025.063529 - 07 July 2025

    Abstract Pseudorandom number generators (PRNGs) are foundational to modern cryptography, yet existing approaches face critical trade-offs between cryptographic security, computational efficiency, and adaptability to emerging threats. Traditional PRNGs (e.g., Mersenne Twister, LCG) remain widely used in low-security applications despite vulnerabilities to predictability attacks, while machine learning (ML)-driven and chaos-based alternatives struggle to balance statistical robustness with practical deployability. This study systematically evaluates traditional, chaos-based, and ML-driven PRNGs to identify design principles for next-generation systems capable of meeting the demands of high-security environment like blockchain and IoT. Using a framework that quantifies cryptographic robustness (via NIST SP… More >

  • Open Access

    ARTICLE

    Machine Learning and Explainable AI-Guided Design and Optimization of High-Entropy Alloys as Binder Phases for WC-Based Cemented Carbides

    Jianping Li, Wan Xiong, Tenghang Zhang, Hao Cheng, Kun Shen, Miaojin He, Yu Zhang, Junxin Song, Ying Deng*, Qiaowang Chen*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2189-2216, 2025, DOI:10.32604/cmc.2025.066128 - 03 July 2025

    Abstract Tungsten carbide-based (WC-based) cemented carbides are widely recognized as high-performance tool materials. Traditionally, single metals such as cobalt (Co) or nickel (Ni) serve as the binder phase, providing toughness and structural integrity. Replacing this phase with high-entropy alloys (HEAs) offers a promising approach to enhancing mechanical properties and addressing sustainability challenges. However, the complex multi-element composition of HEAs complicates conventional experimental design, making it difficult to explore the vast compositional space efficiently. Traditional trial-and-error methods are time-consuming, resource-intensive, and often ineffective in identifying optimal compositions. In contrast, artificial intelligence (AI)-driven approaches enable rapid screening and… More >

  • Open Access

    ARTICLE

    QHF-CS: Quantum-Enhanced Heart Failure Prediction Using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data

    Prasanna Kottapalle1,*, Tan Kuan Tak2, Pravin Ramdas Kshirsagar3, Gopichand Ginnela4, Vijaya Krishna Akula5

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3857-3892, 2025, DOI:10.32604/cmc.2025.065287 - 03 July 2025

    Abstract Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide, exacerbated by the COVID-19 pandemic. Age, cholesterol, and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators. These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult, and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles. Modern medical datasets’ complexity and high dimensionality challenge traditional prediction models like Support Vector Machines and Decision Trees. Quantum approaches include QSVM, QkNN, QDT, and others.… More >

  • Open Access

    ARTICLE

    Research on Adaptive Reward Optimization Method for Robot Navigation in Complex Dynamic Environment

    Jie He, Dongmei Zhao, Tao Liu*, Qingfeng Zou, Jian’an Xie

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2733-2749, 2025, DOI:10.32604/cmc.2025.065205 - 03 July 2025

    Abstract Robot navigation in complex crowd service scenarios, such as medical logistics and commercial guidance, requires a dynamic balance between safety and efficiency, while the traditional fixed reward mechanism lacks environmental adaptability and struggles to adapt to the variability of crowd density and pedestrian motion patterns. This paper proposes a navigation method that integrates spatiotemporal risk field modeling and adaptive reward optimization, aiming to improve the robot’s decision-making ability in diverse crowd scenarios through dynamic risk assessment and nonlinear weight adjustment. We construct a spatiotemporal risk field model based on a Gaussian kernel function by combining… More >

  • Open Access

    REVIEW

    Research Trends and Networks in Self-Explaining Autonomous Systems: A Bibliometric Study

    Oscar Peña-Cáceres1,2,*, Elvis Garay-Silupu3, Darwin Aguilar-Chuquizuta4, Henry Silva-Marchan4

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2151-2188, 2025, DOI:10.32604/cmc.2025.065149 - 03 July 2025

    Abstract Self-Explaining Autonomous Systems (SEAS) have emerged as a strategic frontier within Artificial Intelligence (AI), responding to growing demands for transparency and interpretability in autonomous decision-making. This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025, drawing upon 1380 documents indexed in Scopus. The analysis applies co-citation mapping, keyword co-occurrence, and author collaboration networks using VOSviewer, MASHA, and Python to examine scientific production, intellectual structure, and global collaboration patterns. The results indicate a sustained annual growth rate of 41.38%, with an h-index of 57 and an average of 21.97 citations… More >

  • Open Access

    ARTICLE

    E-GlauNet: A CNN-Based Ensemble Deep Learning Model for Glaucoma Detection and Staging Using Retinal Fundus Images

    Maheen Anwar1, Saima Farhan1, Yasin Ul Haq2, Waqar Azeem3, Muhammad Ilyas4, Razvan Cristian Voicu5,*, Muhammad Hassan Tanveer5

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3477-3502, 2025, DOI:10.32604/cmc.2025.065141 - 03 July 2025

    Abstract Glaucoma, a chronic eye disease affecting millions worldwide, poses a substantial threat to eyesight and can result in permanent vision loss if left untreated. Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective. To address these challenges, this research proposes a computer-aided diagnosis (CAD) approach using Artificial Intelligence (AI) techniques for binary and multiclass classification of glaucoma stages. An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network (ConvNet) models–ResNet-50, VGG-16, and InceptionV3 is utilized in this paper. This fusion technique enhances… More >

  • Open Access

    ARTICLE

    Addressing Modern Cybersecurity Challenges: A Hybrid Machine Learning and Deep Learning Approach for Network Intrusion Detection

    Khadija Bouzaachane1,*, El Mahdi El Guarmah2, Abdullah M. Alnajim3, Sheroz Khan4

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2391-2410, 2025, DOI:10.32604/cmc.2025.065031 - 03 July 2025

    Abstract The rapid increase in the number of Internet of Things (IoT) devices, coupled with a rise in sophisticated cyberattacks, demands robust intrusion detection systems. This study presents a holistic, intelligent intrusion detection system. It uses a combined method that integrates machine learning (ML) and deep learning (DL) techniques to improve the protection of contemporary information technology (IT) systems. Unlike traditional signature-based or single-model methods, this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification. This combination provides a more nuanced and adaptable defense. The research utilizes the NF-UQ-NIDS-v2… More >

  • Open Access

    ARTICLE

    Graph-Embedded Neural Architecture Search: A Variational Approach for Optimized Model Design

    Kazuki Hemmi1,2,*, Yuki Tanigaki3, Kaisei Hara4, Masaki Onishi1,2

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2245-2271, 2025, DOI:10.32604/cmc.2025.064969 - 03 July 2025

    Abstract Neural architecture search (NAS) optimizes neural network architectures to align with specific data and objectives, thereby enabling the design of high-performance models without specialized expertise. However, a significant limitation of NAS is that it requires extensive computational resources and time. Consequently, performing a comprehensive architectural search for each new dataset is inefficient. Given the continuous expansion of available datasets, there is an urgent need to predict the optimal architecture for the previously unknown datasets. This study proposes a novel framework that generates architectures tailored to unknown datasets by mapping architectures that have demonstrated effectiveness on… More >

  • Open Access

    ARTICLE

    HEaaN-ID3: Fully Homomorphic Privacy-Preserving ID3-Decision Trees Using CKKS

    Dain Lee1,#, Hojune Shin1,#, Jihyeon Choi1, Younho Lee1,2,*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3673-3705, 2025, DOI:10.32604/cmc.2025.064161 - 03 July 2025

    Abstract In this study, we investigated privacy-preserving ID3 Decision Tree (PPID3) training and inference based on fully homomorphic encryption (FHE), which has not been actively explored due to the high computational cost associated with managing numerous child nodes in an ID3 tree. We propose HEaaN-ID3, a novel approach to realize PPID3 using the Cheon-Kim-Kim-Song (CKKS) scheme. HEaaN-ID3 is the first FHE-based ID3 framework that completes both training and inference without any intermediate decryption, which is especially valuable when decryption keys are inaccessible or a single-cloud security domain is assumed. To enhance computational efficiency, we adopt a… More >

  • Open Access

    ARTICLE

    Behavior of Spikes in Spiking Neural Network (SNN) Model with Bernoulli for Plant Disease on Leaves

    Urfa Gul#, M. Junaid Gul#, Gyu Sang Choi, Chang-Hyeon Park*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3811-3834, 2025, DOI:10.32604/cmc.2025.063789 - 03 July 2025

    Abstract Spiking Neural Network (SNN) inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection, offering enhanced performance and efficiency in contrast to Artificial Neural Networks (ANN). Unlike conventional ANNs, which process static images without fully capturing the inherent temporal dynamics, our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification, integrating an encoding method to convert static RGB plant images into temporally encoded spike trains. Additionally, while Bernoulli trials and standard deep learning architectures like Convolutional Neural Networks (CNNs) and Fully Connected Neural Networks… More >

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