Weiping Zeng¹, Xiangping Bryce Zhai^1,2,3,*, Cheng Sun¹, Liusha Jiang^1,2, Yicong Du³, Xuefeng Yan^1,3

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070777 - 09 December 2025

Abstract With the expanding applications of unmanned aerial vehicles (UAVs), precise flight evaluation has emerged as a critical enabler for efficient path planning, directly impacting operational performance and safety. Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints. However, these methods often overlook the correlation between pilot control quality and UAV flight dynamics, limiting their adaptability in complex scenarios. In this paper, we propose an intelligent flight evaluation model specifically designed to enhance multi-waypoint trajectory optimization algorithms. Our model leverages a decision tree to integrate attitude More >

Khadija Bouzaachane^1,*, 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 >

Dain Lee^1,#, Hojune Shin^1,#, Jihyeon Choi¹, Younho Lee^1,2,*

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 >

Amal Al-Rasheed¹, Rahim Khan^2,*, Fahad Alturise³, Salem Alkhalaf⁴

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 957-973, 2025, DOI:10.32604/cmc.2025.064102 - 09 June 2025

Abstract The Internet of Things (IoT) is a smart infrastructure where devices share captured data with the respective server or edge modules. However, secure and reliable communication is among the challenging tasks in these networks, as shared channels are used to transmit packets. In this paper, a decision tree is integrated with other metrics to form a secure distributed communication strategy for IoT. Initially, every device works collaboratively to form a distributed network. In this model, if a device is deployed outside the coverage area of the nearest server, it communicates indirectly through the neighboring devices.… More >

Duc-Nghia Tran¹, Viet-Manh Do^1,2, Manh-Tuyen Vi^3,*, Duc-Tan Tran^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2525-2553, 2025, DOI:10.32604/cmc.2025.062590 - 16 April 2025

Abstract This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors. Data collection and behavioral analysis are achieved using machine learning (ML) algorithms through accelerometer sensors. However, behavioral analysis poses challenges due to the complexity of cow activities. The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints. Shorter windows may lack sufficient information, reducing algorithm performance. Additionally, the sensor’s position on the cows may shift during practical use, altering the collected accelerometer… More >

Faisal Ghazi Beshaw¹, Thamir Hassan Atyia², Mohd Fadzli Mohd Salleh¹, Mohamad Khairi Ishak³, Abdul Sattar Din^1,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3553-3583, 2025, DOI:10.32604/cmc.2025.061400 - 16 April 2025

Abstract The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries. In order to improve the precision and openness of energy consumption projections, this study investigates the combination of machine learning (ML) methods with Shapley additive explanations (SHAP) values. The study evaluates three distinct models: the first is a Linear Regressor, the second is a Support Vector Regressor, and the third is a Decision Tree Regressor, which was scaled up to a Random Forest Regressor/Additions made were the third one which was… More >

Sondes Baccouri^1,2,#,*, Takoua Abdellatif ^3,#

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1071-1093, 2025, DOI:10.32604/cmes.2025.062902 - 11 April 2025

Abstract Managing sensitive data in dynamic and high-stakes environments, such as healthcare, requires access control frameworks that offer real-time adaptability, scalability, and regulatory compliance. BIG-ABAC introduces a transformative approach to Attribute-Based Access Control (ABAC) by integrating real-time policy evaluation and contextual adaptation. Unlike traditional ABAC systems that rely on static policies, BIG-ABAC dynamically updates policies in response to evolving rules and real-time contextual attributes, ensuring precise and efficient access control. Leveraging decision trees evaluated in real-time, BIG-ABAC overcomes the limitations of conventional access control models, enabling seamless adaptation to complex, high-demand scenarios. The framework adheres to the… More >

Kai Liu¹, Meizhao Liu¹, Ming Tang¹, Chen Zhang^2,*, Junwu Zhu^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2893-2908, 2025, DOI:10.32604/cmc.2024.057074 - 17 February 2025

Abstract The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key features and accurately predict fault types due to the complexity of meteorological factors and their nonlinear relationships. In response to these challenges, we propose the Feature-Enhanced XGBoost power grid fault prediction method (FE-XGBoost). Specifically, we first combine the gradient boosting decision tree and recursive feature elimination method to extract essential features from meteorological data. Then, we incorporate a piecewise linear chaotic map to More >

Erblin Halabaku, Eliot Bytyçi^*

Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 987-1006, 2024, DOI:10.32604/iasc.2024.059429 - 30 December 2024

Abstract Machine learning has emerged as a pivotal tool in deciphering and managing this excess of information in an era of abundant data. This paper presents a comprehensive analysis of machine learning algorithms, focusing on the structure and efficacy of random forests in mitigating overfitting—a prevalent issue in decision tree models. It also introduces a novel approach to enhancing decision tree performance through an optimized pruning method called Adaptive Cross-Validated Alpha CCP (ACV-CCP). This method refines traditional cost complexity pruning by streamlining the selection of the alpha parameter, leveraging cross-validation within the pruning process to achieve More >

Islam Zada^1,*, Mohammed Naif Alatawi², Syed Muhammad Saqlain¹, Abdullah Alshahrani³, Adel Alshamran⁴, Kanwal Imran⁵, Hessa Alfraihi⁶

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835 - 15 August 2024

Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >