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REVIEW

Weiqiang Jin^1,2, Ningwei Wang¹, Lei Zhang³, Xingwu Tian¹, Bohang Shi¹, Biao Zhao^1,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 3961-4018, 2025, DOI:10.32604/cmc.2025.067857 - 30 July 2025
（This article belongs to the Special Issue: Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles)
Abstract With the growing adoption of Artifical Intelligence (AI), AI-driven autonomous techniques and automation systems have seen widespread applications, become pivotal in enhancing operational efficiency and task automation across various aspects of human living. Over the past decade, AI-driven automation has advanced from simple rule-based systems to sophisticated multi-agent hybrid architectures. These technologies not only increase productivity but also enable more scalable and adaptable solutions, proving particularly beneficial in industries such as healthcare, finance, and customer service. However, the absence of a unified review for categorization, benchmarking, and ethical risk assessment hinders the AI-driven automation progress.… More >

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REVIEW

Abdelhadi Zineddine^1,#, Yousra Belfaik^2,#, Abdeslam Rehaimi¹, Yassine Sadqi^3,*, Said Safi¹
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4019-4054, 2025, DOI:10.32604/cmc.2025.066139 - 30 July 2025
Abstract With the proliferation of online services and applications, adopting Single Sign-On (SSO) mechanisms has become increasingly prevalent. SSO enables users to authenticate once and gain access to multiple services, eliminating the need to provide their credentials repeatedly. However, this convenience raises concerns about user security and privacy. The increasing reliance on SSO and its potential risks make it imperative to comprehensively review the various SSO security and privacy threats, identify gaps in existing systems, and explore effective mitigation solutions. This need motivated the first systematic literature review (SLR) of SSO security and privacy, conducted in… More >

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866

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Tony Thomas^*, Ravi Prakash, Soumya Pal
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4055-4104, 2025, DOI:10.32604/cmc.2025.064903 - 30 July 2025
Abstract The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time data collection, analysis, and personalized treatment through interconnected devices such as sensors and wearables. The integration of Digital Twins (DTs), the virtual replicas of physical components and processes, has also been found to be a game changer for the ever-evolving IoMT. However, these advancements in the healthcare domain come with significant cybersecurity challenges, exposing it to malicious attacks and several security threats. Intrusion Detection Systems (IDSs) serve as a critical defense mechanism, yet traditional IDS approaches often struggle with the complexity and scale… More >

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REVIEW

Jungpil Shin^1,*, Wahidur Rahman², Tanvir Ahmed², Bakhtiar Mazrur², Md. Mohsin Mia², Romana Idress Ekfa², Md. Sajib Rana², Pankoo Kim^3,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4105-4153, 2025, DOI:10.32604/cmc.2025.066910 - 30 July 2025
Abstract Sentiment Analysis, a significant domain within Natural Language Processing (NLP), focuses on extracting and interpreting subjective information—such as emotions, opinions, and attitudes—from textual data. With the increasing volume of user-generated content on social media and digital platforms, sentiment analysis has become essential for deriving actionable insights across various sectors. This study presents a systematic literature review of sentiment analysis methodologies, encompassing traditional machine learning algorithms, lexicon-based approaches, and recent advancements in deep learning techniques. The review follows a structured protocol comprising three phases: planning, execution, and analysis/reporting. During the execution phase, 67 peer-reviewed articles were More >

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751

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REVIEW

Aya M. Al-Zoghby^1,2, Ahmed Ismail Ebada^1,*, Aya S. Saleh¹, Mohammed Abdelhay³, Wael A. Awad¹
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4155-4193, 2025, DOI:10.32604/cmc.2025.065571 - 30 July 2025
（This article belongs to the Special Issue: Multi-Modal Deep Learning for Advanced Medical Diagnostics)
Abstract Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics, advancing precision medicine by enabling integration and learning from diverse data sources. The exponential growth of high-dimensional healthcare data, encompassing genomic, transcriptomic, and other omics profiles, as well as radiological imaging and histopathological slides, makes this approach increasingly important because, when examined separately, these data sources only offer a fragmented picture of intricate disease processes. Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling, more robust disease characterization, and improved treatment decision-making. This review… More >

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REVIEW

Saurabh Agarwal¹, Deepak Sharma², Nancy Girdhar³, Cheonshik Kim⁴, Ki-Hyun Jung^5,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4195-4221, 2025, DOI:10.32604/cmc.2025.066202 - 30 July 2025
Abstract In today’s digital era, the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation. Unfortunately, this progress has also given rise to the misuse of manipulated images across various domains. One of the pressing challenges stemming from this advancement is the increasing difficulty in discerning between unaltered and manipulated images. This paper offers a comprehensive survey of existing methodologies for detecting image tampering, shedding light on the diverse approaches employed in the field of contemporary image forensics. The methods used to identify image forgery can be broadly classified into… More >

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767

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REVIEW

Fatima Tanveer¹, Faisal Iradat^1,*, Waseem Iqbal^2,*, Awais Ahmad³
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4223-4257, 2025, DOI:10.32604/cmc.2025.067536 - 30 July 2025
Abstract RESTful APIs have been adopted as the standard way of developing web services, allowing for smooth communication between clients and servers. Their simplicity, scalability, and compatibility have made them crucial to modern web environments. However, the increased adoption of RESTful APIs has simultaneously exposed these interfaces to significant security threats that jeopardize the availability, confidentiality, and integrity of web services. This survey focuses exclusively on RESTful APIs, providing an in-depth perspective distinct from studies addressing other API types such as GraphQL or SOAP. We highlight concrete threats—such as injection attacks and insecure direct object references… More >

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1051

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REVIEW

Sameera V Mohd Sagheer^1,*, Meghana K H², P M Ameer³, Muneer Parayangat⁴, Mohamed Abbas⁴
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4259-4297, 2025, DOI:10.32604/cmc.2025.063726 - 30 July 2025
Abstract Integrating multiple medical imaging techniques, including Magnetic Resonance Imaging (MRI), Computed Tomography, Positron Emission Tomography (PET), and ultrasound, provides a comprehensive view of the patient health status. Each of these methods contributes unique diagnostic insights, enhancing the overall assessment of patient condition. Nevertheless, the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution, data collection methods, and noise levels. While traditional models like Convolutional Neural Networks (CNNs) excel in single-modality tasks, they struggle to handle multi-modal complexities, lacking the capacity to model global relationships. This research presents a novel approach for… More >

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761

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ARTICLE

Jize Zhang¹, T. P. C. Klaver², Songge Yang¹, Brajendra Mishra¹, Yu Zhong^1,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4299-4311, 2025, DOI:10.32604/cmc.2025.067793 - 30 July 2025
Abstract Designing high-performance high-entropy alloys (HEAs) with transformation-induced plasticity (TRIP) or twinning-induced plasticity (TWIP) effects requires precise control over stacking fault energy (SFE) and phase stability. However, the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods. A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy (SFE), FCC phase stability, and FCC-to-HCP transition temperatures (T₀). The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of… More >

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678

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ARTICLE

Changsheng Zhu^1,2,*, Jintao Miao¹, Zihao Gao^3,*, Shuo Liu¹, Jingjie Li¹
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4313-4340, 2025, DOI:10.32604/cmc.2025.067157 - 30 July 2025
Abstract As the demand for advanced material design and performance prediction continues to grow, traditional phase-field models are increasingly challenged by limitations in computational efficiency and predictive accuracy, particularly when addressing high-dimensional and complex data in multicomponent systems. To overcome these challenges, this study proposes an innovative model, LSGWO-BP, which integrates an improved Grey Wolf Optimizer (GWO) with a backpropagation neural network (BP) to enhance the accuracy and efficiency of quasi-phase equilibrium predictions within the KKS phase-field framework. Three mapping enhancement strategies were investigated–Circle-Root, Tent-Cosine, and Logistic-Sine mappings–with the Logistic mapping further improved via Sine perturbation… More >

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ARTICLE

Sandra Gajević¹, Slavica Miladinović¹, Jelena Jovanović¹, Onur Güler², Serdar Özkaya², Blaža Stojanović^1,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4341-4361, 2025, DOI:10.32604/cmc.2025.065645 - 30 July 2025
（This article belongs to the Special Issue: Computing Technology in the Design and Manufacturing of Advanced Materials)
Abstract This paper presents an investigation of the tribological performance of AA2024–B₄C composites, with a specific focus on the influence of reinforcement and processing parameters. In this study three input parameters were varied: B₄C weight percentage, milling time, and normal load, to evaluate their effects on two output parameters: wear loss and the coefficient of friction. AA2024 alloy was used as the matrix alloy, while B₄C particles were used as reinforcement. Due to the high hardness and wear resistance of B₄C, the optimized composite shows strong potential for use in aerospace structural elements and automotive brake components. The… More >

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628

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ARTICLE

Muhammad Adil^1,*, Mona M. Jamjoom², Zahid Ullah³
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4363-4380, 2025, DOI:10.32604/cmc.2025.066551 - 30 July 2025
Abstract In today’s digital world, the Internet of Things (IoT) plays an important role in both local and global economies due to its widespread adoption in different applications. This technology has the potential to offer several advantages over conventional technologies in the near future. However, the potential growth of this technology also attracts attention from hackers, which introduces new challenges for the research community that range from hardware and software security to user privacy and authentication. Therefore, we focus on a particular security concern that is associated with malware detection. The literature presents many countermeasures, but… More >

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660

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ARTICLE

Hyeong-Gyeong Kim¹, Sang-Min Choi², Hyeon Seo², Suwon Lee^2,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4381-4397, 2025, DOI:10.32604/cmc.2025.067024 - 30 July 2025
Abstract Adversarial attacks pose a significant threat to artificial intelligence systems by exposing them to vulnerabilities in deep learning models. Existing defense mechanisms often suffer drawbacks, such as the need for model retraining, significant inference time overhead, and limited effectiveness against specific attack types. Achieving perfect defense against adversarial attacks remains elusive, emphasizing the importance of mitigation strategies. In this study, we propose a defense mechanism that applies random cropping and Gaussian filtering to input images to mitigate the impact of adversarial attacks. First, the image was randomly cropped to vary its dimensions and then placed… More >

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1238

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695

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ARTICLE

Habib Hadj-Mabrouk^*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4399-4430, 2025, DOI:10.32604/cmc.2025.067143 - 30 July 2025
（This article belongs to the Special Issue: Artificial Intelligence and Advanced Computation Technology in Railways)
Abstract A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational, maintenance, and feedback phases following railway incidents or accidents. These approaches exploit railway safety data once the transport system has received authorization for commissioning. However, railway standards and regulations require the development of a safety management system (SMS) from the specification and design phases of the railway system. This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to… More >

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646

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ARTICLE

Yuan Feng¹, Yajie Si², Jianwei Zhang^3,4,*, Zengyu Cai^5,*, Hongying Zhao⁵
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4431-4449, 2025, DOI:10.32604/cmc.2025.064696 - 30 July 2025
Abstract With the rapid development of the industrial Internet, the network security environment has become increasingly complex and variable. Intrusion detection, a core technology for ensuring the security of industrial control systems, faces the challenge of unbalanced data samples, particularly the low detection rates for minority class attack samples. Therefore, this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network (SA-WGAN) to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios. The proposed method integrates a self-attention mechanism… More >

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1054

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652

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ARTICLE

Bandar Alotaibi^1,*, Aljawhara Almutarie², Shuaa Alotaibi³, Munif Alotaibi⁴
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4451-4467, 2025, DOI:10.32604/cmc.2025.066050 - 30 July 2025
Abstract X (formerly known as Twitter) is one of the most prominent social media platforms, enabling users to share short messages (tweets) with the public or their followers. It serves various purposes, from real-time news dissemination and political discourse to trend spotting and consumer engagement. X has emerged as a key space for understanding shifting brand perceptions, consumer preferences, and product-related sentiment in the fashion industry. However, the platform’s informal, dynamic, and context-dependent language poses substantial challenges for sentiment analysis, mainly when attempting to detect sarcasm, slang, and nuanced emotional tones. This study introduces a hybrid… More >

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652

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ARTICLE

Haolang Feng^1,2, Yuling Chen^1,2,*, Yang Huang^1,2, Xuewei Wang³, Haiwei Sang⁴
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4469-4490, 2025, DOI:10.32604/cmc.2025.064896 - 30 July 2025
Abstract Deep neural networks remain susceptible to adversarial examples, where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily detected. Although many adversarial attack methods produce adversarial examples that have achieved great results in the white-box setting, they exhibit low transferability in the black-box setting. In order to improve the transferability along the baseline of the gradient-based attack technique, we present a novel Stochastic Gradient Accumulation Momentum Iterative Attack (SAMI-FGSM) in this study. In particular, during each iteration, the gradient information More >

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ARTICLE

Naif Al Mudawi^1,*, Muhammad Hanzla², Abdulwahab Alazeb¹, Mohammed Alshehri¹, Haifa F. Alhasson³, Dina Abdulaziz AlHammadi⁴, Ahmad Jalal^2,5
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4491-4509, 2025, DOI:10.32604/cmc.2025.065490 - 30 July 2025
（This article belongs to the Special Issue: Advanced Algorithms for Feature Selection in Machine Learning)
Abstract Unmanned Aerial Vehicles (UAVs) are increasingly employed in traffic surveillance, urban planning, and infrastructure monitoring due to their cost-effectiveness, flexibility, and high-resolution imaging. However, vehicle detection and classification in aerial imagery remain challenging due to scale variations from fluctuating UAV altitudes, frequent occlusions in dense traffic, and environmental noise, such as shadows and lighting inconsistencies. Traditional methods, including sliding-window searches and shallow learning techniques, struggle with computational inefficiency and robustness under dynamic conditions. To address these limitations, this study proposes a six-stage hierarchical framework integrating radiometric calibration, deep learning, and classical feature engineering. The workflow… More >

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767

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ARTICLE

Evren Daglarli^*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4511-4537, 2025, DOI:10.32604/cmc.2025.065572 - 30 July 2025
（This article belongs to the Special Issue: Quantum Machine Learning/Deep Learning based Future Generation Computing System)
Abstract This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors. Therefore, we present a brain-inspired cognitive architecture for autonomous agents that integrates a prefrontal cortex–inspired model with modern deep learning (a transformer-based reinforcement learning module) and quantum algorithms. In particular, our framework incorporates quantum computational routines (Deutsch–Jozsa, Bernstein–Vazirani, and Grover’s search) to enhance decision-making efficiency. As a novelty of this research, this comprehensive computational structure is empowered by quantum computing operations so that superiority in speed and robustness of learning compared to classical methods can be demonstrated.… More >

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Fangju Zhou¹, Hanbo Zhang², Na Ye¹, Jing Huang¹, Zhu Ren^1,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4539-4561, 2025, DOI:10.32604/cmc.2025.065946 - 30 July 2025
Abstract This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and a detector. When measurements are transmitted via wireless networks to a remote estimator, the innovation sequence becomes susceptible to interception and manipulation by adversaries. We consider a class of linear deception attacks, wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector. Given the inherent volatility of the detection function based on the detector, we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution… More >

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997

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590

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Yi-Hao Tu, Yi-Wei Ma^*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4563-4582, 2025, DOI:10.32604/cmc.2025.066899 - 30 July 2025
Abstract This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network (SETL-DDQN) and an extended Gumbel distribution method, designed to optimize the Contention Window (CW) in IEEE 802.11 networks. Unlike conventional Deep Reinforcement Learning (DRL)-based approaches for CW size adjustment, which often suffer from overestimation bias and limited exploration diversity, leading to suboptimal throughput and collision performance. Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions. First, SETL adopts a DDQN architecture (SETL-DDQN) to improve Q-value estimation accuracy and enhance training stability. Second, we incorporate a… More >

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Bing Liu^*, Ting Nie, Yansong Liu, Weibo Hu
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4583-4599, 2025, DOI:10.32604/cmc.2025.065296 - 30 July 2025
Abstract Among the four candidate algorithms in the fourth round of NIST standardization, the BIKE (Bit Flipping Key Encapsulation) scheme has a small key size and high efficiency, showing good prospects for application. However, the BIKE scheme based on QC-MDPC (Quasi Cyclic Medium Density Parity Check) codes still faces challenges such as the GJS attack and weak key attacks targeting the decoding failure rate (DFR). This paper analyzes the BGF decoding algorithm of the BIKE scheme, revealing two deep factors that lead to DFR, and proposes a weak key optimization attack method for the BGF decoding More >

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802

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498

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Xi Peng, Peng Jia^*, Ximing Fan, Jiayong Liu^*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4601-4625, 2025, DOI:10.32604/cmc.2025.065162 - 30 July 2025
Abstract Memory leak is a common software vulnerability that can decrease the reliability of an application and, in severe cases, even cause program crashes. If there are intentionally triggerable memory leak vulnerabilities in a program, attackers can exploit these bugs to launch denial-of-service attacks or induce the program to exhibit unexpected behaviors due to low memory conditions. Existing fuzzing techniques primarily focus on improving code coverage, and specialized fuzzing techniques for individual memory-related defects like uncontrolled memory allocation do not address memory leak vulnerabilities. MemLock is the first fuzzing technique to address memory consumption vulnerabilities including… More >

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Ching-Lung Fan^1,*, Yu-Jen Chung², Shan-Min Yen^1,3
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4627-4653, 2025, DOI:10.32604/cmc.2025.066856 - 30 July 2025
Abstract Efficient banana crop detection is crucial for precision agriculture; however, traditional remote sensing methods often lack the spatial resolution required for accurate identification. This study utilizes low-altitude Unmanned Aerial Vehicle (UAV) images and deep learning-based object detection models to enhance banana plant detection. A comparative analysis of Faster Region-Based Convolutional Neural Network (Faster R-CNN), You Only Look Once Version 3 (YOLOv3), Retina Network (RetinaNet), and Single Shot MultiBox Detector (SSD) was conducted to evaluate their effectiveness. Results show that RetinaNet achieved the highest detection accuracy, with a precision of 96.67%, a recall of 71.67%, and… More >

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ARTICLE

Siqi Zhang, Shengda Pan^*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4655-4676, 2025, DOI:10.32604/cmc.2025.064864 - 30 July 2025
（This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
Abstract Infrared images typically exhibit diverse backgrounds, each potentially containing noise and target-like interference elements. In complex backgrounds, infrared small targets are prone to be submerged by background noise due to their low pixel proportion and limited available features, leading to detection failure. To address this problem, this paper proposes an Attention Shift-Invariant Cross-Evolutionary Feature Fusion Network (ASCFNet) tailored for the detection of infrared weak and small targets. The network architecture first designs a Multidimensional Lightweight Pixel-level Attention Module (MLPA), which alleviates the issue of small-target feature suppression during deep network propagation by combining channel reshaping,… More >

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Abdulwahab Alazeb¹, Muhammad Hanzla², Naif Al Mudawi^1,*, Mohammed Alshehri¹, Haifa F. Alhasson³, Dina Abdulaziz AlHammadi⁴, Ahmad Jalal^2,5
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4677-4697, 2025, DOI:10.32604/cmc.2025.065899 - 30 July 2025
Abstract Unmanned Aerial Vehicles (UAVs) have become indispensable for intelligent traffic monitoring, particularly in low-light conditions, where traditional surveillance systems struggle. This study presents a novel deep learning-based framework for nighttime aerial vehicle detection and classification that addresses critical challenges of poor illumination, noise, and occlusions. Our pipeline integrates MSRCR enhancement with OPTICS segmentation to overcome low-light challenges, while YOLOv10 enables accurate vehicle localization. The framework employs GLOH and Dense-SIFT for discriminative feature extraction, optimized using the Whale Optimization Algorithm to enhance classification performance. A Swin Transformer-based classifier provides the final categorization, leveraging hierarchical attention mechanisms More >

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Hafiz Muhammad Waqas¹, Tahir Mahmood^1,2, Walid Emam³, Ubaid ur Rehman⁴, Dragan Pamucar^5,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4699-4727, 2025, DOI:10.32604/cmc.2025.065011 - 30 July 2025
Abstract Cyber threat detection is a crucial aspect of contemporary cybersecurity due to the depth and complexity of cyberattacks. It is the identification of malicious activity, unauthorized access, and possible intrusions in networks and systems. Modern detection methods employ artificial intelligence and machine learning to study vast amounts of data, learn patterns, and anticipate potential threats. Real-time monitoring and anomaly detection improve the capacity to react to changing threats more rapidly. Cyber threat detection systems aim to reduce false positives and provide complete coverage against the broadest possible attacks. This research advocates for proactive measures and… More >

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Jingrui Liu^1,2,*, Zhiwen Hou^1,2, Boyu Wang^1,2, Tianxiang Yin^3,4
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4729-4754, 2025, DOI:10.32604/cmc.2025.066138 - 30 July 2025
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract In response to the increasing global energy demand and environmental pollution, microgrids have emerged as an innovative solution by integrating distributed energy resources (DERs), energy storage systems, and loads to improve energy efficiency and reliability. This study proposes a novel hybrid optimization algorithm, DE-HHO, combining differential evolution (DE) and Harris Hawks optimization (HHO) to address microgrid scheduling issues. The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts. The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind, solar, micro-gas turbine, More >

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Brij B. Gupta^1,2,3,4,*, Akshat Gaurav⁵, Wadee Alhalabi⁶, Varsha Arya^7,8, Shavi Bansal^9,10, Ching-Hsien Hsu¹
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4755-4772, 2025, DOI:10.32604/cmc.2025.064053 - 30 July 2025
Abstract Detecting cyber attacks in networks connected to the Internet of Things (IoT) is of utmost importance because of the growing vulnerabilities in the smart environment. Conventional models, such as Naive Bayes and support vector machine (SVM), as well as ensemble methods, such as Gradient Boosting and eXtreme gradient boosting (XGBoost), are often plagued by high computational costs, which makes it challenging for them to perform real-time detection. In this regard, we suggested an attack detection approach that integrates Visual Geometry Group 16 (VGG16), Artificial Rabbits Optimizer (ARO), and Random Forest Model to increase detection accuracy… More >

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Bing Wei, Yubin Li, Yi Wu^*, Ming Zhong, Ning Luo
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4773-4804, 2025, DOI:10.32604/cmc.2025.065090 - 30 July 2025
Abstract Metadata prefetching and data placement play a critical role in enhancing access performance for file systems operating over wide-area networks. However, developing effective strategies for metadata prefetching in environments with concurrent workloads and for data placement across distributed networks remains a significant challenge. This study introduces novel and efficient methodologies for metadata prefetching and data placement, leveraging fine-grained control of prefetching strategies and variable-sized data fragment writing to optimize the I/O bandwidth of distributed file systems. The proposed metadata prefetching technique employs dynamic workload analysis to identify dominant workload patterns and adaptively refines prefetching policies, More >

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963

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602

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Kun Lan¹, Feiyang Gao¹, Xiaoliang Jiang^1,*, Jianzhen Cheng^2,*, Simon Fong³
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4805-4824, 2025, DOI:10.32604/cmc.2025.065864 - 30 July 2025
（This article belongs to the Special Issue: Multi-Modal Deep Learning for Advanced Medical Diagnostics)
Abstract With the continuous development of artificial intelligence and machine learning techniques, there have been effective methods supporting the work of dermatologist in the field of skin cancer detection. However, object significant challenges have been presented in accurately segmenting melanomas in dermoscopic images due to the objects that could interfere human observations, such as bubbles and scales. To address these challenges, we propose a dual U-Net network framework for skin melanoma segmentation. In our proposed architecture, we introduce several innovative components that aim to enhance the performance and capabilities of the traditional U-Net. First, we establish… More >

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Saeed Alzahrani¹, Nazar Hussain², Farah Mohammad^3,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4825-4849, 2025, DOI:10.32604/cmc.2025.066322 - 30 July 2025
Abstract Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients. This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling. By integrating real-time images, electronic health records, and genomic information, the system enables personalized simulations for disease progression modeling, treatment response prediction, and preventive care strategies. In dysarthric speech, which is characterized by articulation imprecision, temporal misalignments, and phoneme distortions, existing models struggle to capture these irregularities. Traditional approaches, often relying solely on audio features, fail to address… More >

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996

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564

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Gang Liu¹, Guosheng Xu^1,*, Chenyu Wang¹, Guoai Xu²
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4851-4868, 2025, DOI:10.32604/cmc.2025.066331 - 30 July 2025
Abstract In wireless communication transmission, image encryption plays a key role in protecting data privacy against unauthorized access. However, conventional encryption methods often face challenges in key space security, particularly when relying on chaotic sequences, which may exhibit vulnerabilities to brute-force and predictability-based attacks. To address the limitations, this paper presents a robust and efficient encryption scheme that combines iterative hyper-chaotic systems and Convolutional Neural Networks (CNNs). Firstly, a novel two-dimensional iterative hyper-chaotic system is proposed because of its complex dynamic behavior and expanded parameter space, which can enhance the key space complexity and randomness, ensuring… More >

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Roseline Oluwaseun Ogundokun^*, Pius Adewale Owolawi, Chunling Tu
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4869-4885, 2025, DOI:10.32604/cmc.2025.064944 - 30 July 2025
Abstract Breast cancer is among the leading causes of cancer mortality globally, and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification. Existing machine learning (ML) methods struggle with intra-class heterogeneity and inter-class similarity, necessitating more robust classification models. This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning (DL) and Bat Swarm Optimization (BSO) hyperparameter optimization to improve breast cancer histopathology (BCH) image classification. A dataset of 804 Hematoxylin and Eosin (H&E) stained images classified as Benign, in situ, Invasive, and Normal categories (ICIAR2018_BACH_Challenge) has… More >

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Xintao Duan^1,2,*, Yinhang Wu¹, Zhao Wang¹, Chuan Qin³
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4887-4906, 2025, DOI:10.32604/cmc.2025.064620 - 30 July 2025
Abstract Deep neural network (DNN) models have achieved remarkable performance across diverse tasks, leading to widespread commercial adoption. However, training high-accuracy models demands extensive data, substantial computational resources, and significant time investment, making them valuable assets vulnerable to unauthorized exploitation. To address this issue, this paper proposes an intellectual property (IP) protection framework for DNN models based on feature layer selection and hyper-chaotic mapping. Firstly, a sensitivity-based importance evaluation algorithm is used to identify the key feature layers for encryption, effectively protecting the core components of the model. Next, the L1 regularization criterion is applied to More >

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558

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Yu-Hsien Lin^*, Po-Cheng Chuang, Joyce Yi-Tzu Huang
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4907-4948, 2025, DOI:10.32604/cmc.2025.065995 - 30 July 2025
（This article belongs to the Special Issue: Reinforcement Learning: Algorithms, Challenges, and Applications)
Abstract This study proposes an automatic control system for Autonomous Underwater Vehicle (AUV) docking, utilizing a digital twin (DT) environment based on the HoloOcean platform, which integrates six-degree-of-freedom (6-DOF) motion equations and hydrodynamic coefficients to create a realistic simulation. Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements, deep reinforcement learning (DRL) offers a promising alternative. In the positioning stage, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is employed for synchronized depth and heading control, which offers stable training, reduced overestimation… More >

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660

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Sujie Shao, Zhengpu Wang^*, Siya Xu, Shaoyong Guo, Xuesong Qiu
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4949-4972, 2025, DOI:10.32604/cmc.2025.065053 - 30 July 2025
Abstract With the exponential growth of mobile terminals and the widespread adoption of Internet of Things (IoT) technologies, an increasing number of devices rely on wireless local area networks (WLAN) for data transmission. To address this demand, deploying more access points (APs) has become an inevitable trend. While this approach enhances network coverage and capacity, it also exacerbates co-channel interference (CCI). The multi-AP cooperation introduced in IEEE 802.11be (Wi-Fi 7) represents a paradigm shift from conventional single-AP architectures, offering a novel solution to CCI through joint resource scheduling across APs. However, designing efficient cooperation mechanisms and… More >

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Venkata Satya Suresh kumar Kondeti¹, Raghavendra Kulkarni¹, Binu Sudhakaran Pillai², Surendran Rajendran^3,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4973-4995, 2025, DOI:10.32604/cmc.2025.066428 - 30 July 2025
（This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
Abstract Next-generation 6G networks seek to provide ultra-reliable and low-latency communications, necessitating network designs that are intelligent and adaptable. Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures. Nonetheless, sustaining elevated Quality of Service (QoS) in dynamic, resource-limited systems poses significant hurdles. This study introduces an innovative packet-based proactive end-to-end (ETE) resource management system that facilitates network slicing with improved resilience and proactivity. To get around the drawbacks of conventional reactive systems, we develop a cost-efficient slice provisioning architecture that takes into account limits on radio, processing, and… More >

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Yanan Liu¹, Lei Cao^1,*, Zheng Zhang^1,*, Ge Li², Shuo Qiu¹, Suhao Wang¹
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4997-5019, 2025, DOI:10.32604/cmc.2025.066836 - 30 July 2025
Abstract As the adoption of Vehicular Ad-hoc Networks (VANETs) grows, ensuring secure communication between smart vehicles and remote application servers (APPs) has become a critical challenge. While existing solutions focus on various aspects of security, gaps remain in addressing both high security requirements and the resource-constrained nature of VANET environments. This paper proposes an extended-Kerberos protocol that integrates Physical Unclonable Function (PUF) for authentication and key agreement, offering a comprehensive solution to the security challenges in VANETs. The protocol facilitates mutual authentication and secure key agreement between vehicles and APPs, ensuring the confidentiality and integrity of vehicle-to-network… More >

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570

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Fatima Abu Siryeh^*, Hussein Alrammahi, Abdullahi Abdu İbrahim
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5021-5046, 2025, DOI:10.32604/cmc.2025.065206 - 30 July 2025
（This article belongs to the Special Issue: Challenges and Innovations in Multimedia Encryption and Information Security)
Abstract Biometric template protection is essential for finger-based authentication systems, as template tampering and adversarial attacks threaten the security. This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication. The system was tested against NIST SD4 and Anguli fingerprint datasets, wherein 10,000 watermarked fingerprints were employed for training. The designed approach recorded a tamper detection rate of 98.3%, performing 3–6% better than current DCT, SVD, and DWT-based watermarking approaches. The false positive rate (≤1.2%) and false negative rate (≤1.5%) were much lower compared to previous… More >

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Jian Peng¹, Hui He², Dengyong Zhang^2,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5047-5061, 2025, DOI:10.32604/cmc.2025.066368 - 30 July 2025
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Object detection plays a critical role in drone imagery analysis, especially in remote sensing applications where accurate and efficient detection of small objects is essential. Despite significant advancements in drone imagery detection, most models still struggle with small object detection due to challenges such as object size, complex backgrounds. To address these issues, we propose a robust detection model based on You Only Look Once (YOLO) that balances accuracy and efficiency. The model mainly contains several major innovation: feature selection pyramid network, Inner-Shape Intersection over Union (ISIoU) loss function and small object detection head. To… More >

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Thi My Binh Nguyen^#, Thi Hoa Hue Nguyen^#, Thi Ngoc Huyen Do^*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5063-5076, 2025, DOI:10.32604/cmc.2025.064610 - 30 July 2025
（This article belongs to the Special Issue: Particle Swarm Optimization: Advances and Applications)
Abstract Efficient warehouse management is critical for modern supply chain systems, particularly in the era of e-commerce and automation. The Multi-Picker Robot Routing Problem (MPRRP) presents a complex challenge involving the optimization of routes for multiple robots assigned to retrieve items from distinct locations within a warehouse. This study introduces optimized metaheuristic strategies to address MPRRP, with the aim of minimizing travel distances, energy consumption, and order fulfillment time while ensuring operational efficiency. Advanced algorithms, including an enhanced Particle Swarm Optimization (PSO-MPRRP) and a tailored Genetic Algorithm (GA-MPRRP), are specifically designed with customized evolutionary operators to More >

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Shui Liu^1,2, Ke Xiong^1,2,*, Yeshen Li^1,2, Zhifei Zhang^1,2,*, Yu Zhang³, Pingyi Fan⁴
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5077-5093, 2025, DOI:10.32604/cmc.2025.064880 - 30 July 2025
（This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)
Abstract Accurate prediction of cloud resource utilization is critical. It helps improve service quality while avoiding resource waste and shortages. However, the time series of resource usage in cloud computing systems often exhibit multidimensionality, nonlinearity, and high volatility, making the high-precision prediction of resource utilization a complex and challenging task. At present, cloud computing resource prediction methods include traditional statistical models, hybrid approaches combining machine learning and classical models, and deep learning techniques. Traditional statistical methods struggle with nonlinear predictions, hybrid methods face challenges in feature extraction and long-term dependencies, and deep learning methods incur high… More >

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622

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Bingcai Wei¹, Hui Liu^1,*, Chuang Qian², Haoliang Shen³, Yibiao Chen³, Yixin Wang³
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5095-5109, 2025, DOI:10.32604/cmc.2025.065812 - 30 July 2025
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Within the domain of low-level vision, enhancing low-light images and removing sand-dust from single images are both critical tasks. These challenges are particularly pronounced in real-world applications such as autonomous driving, surveillance systems, and remote sensing, where adverse lighting and environmental conditions often degrade image quality. Various neural network models, including MLPs, CNNs, GANs, and Transformers, have been proposed to tackle these challenges, with the Vision KAN models showing particular promise. However, existing models, including the Vision KAN models use deterministic neural networks that do not address the uncertainties inherent in these processes. To overcome… More >

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576

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Vivek Kumar Tiwary^1,*, Arunkumar P.¹, Vinayak R. Malik^1,2
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5111-5131, 2025, DOI:10.32604/cmc.2025.066129 - 30 July 2025
（This article belongs to the Special Issue: Design, Optimisation and Applications of Additive Manufacturing Technologies)
Abstract Additive manufacturing (AM), a key technology in the evolution of Industry 4.0, has revolutionized production processes by enabling the precise, layer-by-layer fabrication of complex and customized components, enhancing efficiency and flexibility in smart manufacturing systems. However, one significant challenge hindering the acceptance of this technology is the limited print size, constrained by the machine’s small bed. To address this issue, a suitable polymer joining technique could be applied as a post-fabrication step. The present article examines findings on the Ultrasonic Welding (UW) of Material Extrusion (MEX)-3D printed parts made from commonly used thermoplastics, Acrylonitrile Butadiene… More >

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598

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P. William^1,2, Ved Prakash Mishra¹, Osamah Ibrahim Khalaf^3,*, Arvind Mukundan⁴, Yogeesh N⁵, Riya Karmakar⁶
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5133-5156, 2025, DOI:10.32604/cmc.2025.065840 - 30 July 2025
Abstract Cloud computing has become an essential technology for the management and processing of large datasets, offering scalability, high availability, and fault tolerance. However, optimizing data replication across multiple data centers poses a significant challenge, especially when balancing opposing goals such as latency, storage costs, energy consumption, and network efficiency. This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization (DMGO), designed to enhance data replication efficiency in cloud environments. Unlike traditional static replication systems, DMGO adapts dynamically to variations in network conditions, system demand, and resource availability. The approach utilizes multi-objective optimization More >

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610

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Chongyang Wang¹, Qiongyan Li¹, Shu Liu², Pengle Cheng^1,*, Ying Huang³
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5157-5176, 2025, DOI:10.32604/cmc.2025.067367 - 30 July 2025
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract With rapid urbanization, fires pose significant challenges in urban governance. Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles. This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model. By capturing both thermal and visual cues, our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes. We first established a dual-view dataset comprising infrared and visible light videos, implemented an innovative image feature fusion strategy, and More >

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635

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Muhammad Sibtain¹, Mehdi Hussain^1,*, Qaiser Riaz¹, Sana Qadir¹, Naveed Riaz¹, Ki-Hyun Jung^2,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5177-5199, 2025, DOI:10.32604/cmc.2025.066198 - 30 July 2025
Abstract Ransomware is malware that encrypts data without permission, demanding payment for access. Detecting ransomware on Android platforms is challenging due to evolving malicious techniques and diverse application behaviors. Traditional methods, such as static and dynamic analysis, suffer from polymorphism, code obfuscation, and high resource demands. This paper introduces a multi-stage approach to enhance behavioral analysis for Android ransomware detection, focusing on a reduced set of distinguishing features. The approach includes ransomware app collection, behavioral profile generation, dataset creation, feature identification, reduction, and classification. Experiments were conducted on ∼3300 Android-based ransomware samples, despite the challenges posed… More >

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Baishun Zhou^1,2, Haijiao Zhao³, Yuxin Wen², Gangyi Ding¹, Ying Xing^3,*, Xinyang Lin⁴, Lei Xiao⁵
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5201-5221, 2025, DOI:10.32604/cmc.2025.065726 - 30 July 2025
Abstract In recent years, with the rapid development of software systems, the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics. Defect prediction methods based on software metric elements highly rely on software metric data. However, redundant software metric data is not conducive to efficient defect prediction, posing severe challenges to current software defect prediction tasks. To address these issues, this paper focuses on the rational clustering of software metric data. Firstly, multiple software projects are evaluated to determine the preset number… More >

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597

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Sarmad K. Ibrahim^1,*, Saif A. Abdulhussien², Hazim M. ALkargole¹, Hassan H. Qasim¹
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5223-5238, 2025, DOI:10.32604/cmc.2025.066548 - 30 July 2025
Abstract The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and Massive Machine Type Communication (mMTC)—present tremendous challenges to conventional methods of bandwidth allocation. A new deep reinforcement learning-based (DRL-based) bandwidth allocation system for real-time, dynamic management of 5G radio access networks is proposed in this paper. Unlike rule-based and static strategies, the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput, fairness, and compliance with QoS requirements. By using… More >

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