Kemahyanto Exaudi^1,2, Deris Stiawan^3,*, Bhakti Yudho Suprapto¹, Hanif Fakhrurroja⁴, Mohd. Yazid Idris⁵, Tami A. Alghamdi⁶, Rahmat Budiarto⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.069377 - 10 November 2025

Abstract Sudden wildfires cause significant global ecological damage. While satellite imagery has advanced early fire detection and mitigation, image-based systems face limitations including high false alarm rates, visual obstructions, and substantial computational demands, especially in complex forest terrains. To address these challenges, this study proposes a novel forest fire detection model utilizing audio classification and machine learning. We developed an audio-based pipeline using real-world environmental sound recordings. Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network (CNN), enabling the capture of distinctive fire acoustic signatures (e.g., crackling, roaring) that are minimally impacted by… More >

Moneerah Alotaibi^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069195 - 10 November 2025

Abstract Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods, which often demand extensive computational resources and struggle with diverse data acquisition techniques. This research presents a novel approach for vehicle classification and recognition in aerial image sequences, integrating multiple advanced techniques to enhance detection accuracy. The proposed model begins with preprocessing using Multiscale Retinex (MSR) to enhance image quality, followed by Expectation-Maximization (EM) Segmentation for precise foreground object identification. Vehicle detection is performed using the state-of-the-art YOLOv10 framework, while feature extraction incorporates Maximally Stable Extremal… More >

Amjad Rehman^1,*, Tanzila Saba¹, Mona M. Jamjoom², Shaha Al-Otaibi³, Muhammad I. Khan¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068958 - 10 November 2025

Abstract Modern intrusion detection systems (MIDS) face persistent challenges in coping with the rapid evolution of cyber threats, high-volume network traffic, and imbalanced datasets. Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively. This study introduces an advanced, explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets, which reflects real-world network behavior through a blend of normal and diverse attack classes. The methodology begins with sophisticated data preprocessing, incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions, ensuring standardized and model-ready inputs.… More >

Yağmur Yılan, Ahad Beykent^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.068440 - 10 November 2025

Abstract Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies, hedge risk and plan generation schedules. By leveraging advanced data analytics and machine learning methods, accurate and reliable price forecasts can be achieved. This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting (XGBoost). We benchmark XGBoost against four alternatives—Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul (EXIST). All models were trained on an identical chronological 80/20 train–test split, with hyperparameters More >

Omar Darwish¹, Shorouq Al-Eidi², Abdallah Al-Shorman¹, Majdi Maabreh³, Anas Alsobeh⁴, Plamen Zahariev⁵, Yahya Tashtoush^6,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.068266 - 10 November 2025

Abstract Covert timing channels (CTC) exploit network resources to establish hidden communication pathways, posing significant risks to data security and policy compliance. Therefore, detecting such hidden and dangerous threats remains one of the security challenges. This paper proposes LinguTimeX, a new framework that combines natural language processing with artificial intelligence, along with explainable Artificial Intelligence (AI) not only to detect CTC but also to provide insights into the decision process. LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely. LinguTimeX demonstrates strong effectiveness in detecting CTC across… More >

Noreen Fuentes¹, Janeth Ugang¹, Narcisan Galamiton¹, Suzette Bacus¹, Samantha Shane Evangelista², Fatima Maturan², Lanndon Ocampo^2,3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.068104 - 10 November 2025

Abstract This study demonstrates a novel integration of large language models, machine learning, and multi-criteria decision-making to investigate self-moderation in small online communities, a topic under-explored compared to user behavior and platform-driven moderation on social media. The proposed methodological framework (1) utilizes large language models for social media post analysis and categorization, (2) employs k-means clustering for content characterization, and (3) incorporates the TODIM (Tomada de Decisão Interativa Multicritério) method to determine moderation strategies based on expert judgments. In general, the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation… More >

Chirag Devendrakumar Parikh^*

Journal on Artificial Intelligence, Vol.7, pp. 571-584, 2025, DOI:10.32604/jai.2025.073765 - 01 December 2025

Abstract With the increasing complexity and scale of hyperscale data centers, the requirement for intelligent, real-time power delivery has never been more critical to ensure uptime, energy efficiency, and sustainability. Those techniques are typically static, reactive (since CPU and workload scaling is applied to performance events that occur after a request has been submitted, and is thus can be classified as a reactive response.), and require manual operation, and cannot cope with the dynamic nature of the workloads, the distributed architectures as well as the non-uniform energy sources in today’s data centers. In this paper, we… More >

Miah A. Robinson, Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Khalid H. Abed^*

Journal on Artificial Intelligence, Vol.7, pp. 615-632, 2025, DOI:10.32604/jai.2025.073535 - 01 December 2025

Abstract Ensuring the safety of Artificial Intelligence (AI) is essential for providing dependable services, especially in various sectors such as the military, education, healthcare, and automotive industries. A highly effective method to boost the precision and performance of an AI agent involves multi-configuration training, followed by thorough evaluation in a specific setting to gauge performance outcomes. This research thoroughly investigates the design of three AI agents, each configured with a different number of hidden units. The first agent is equipped with 128 hidden units, the second with 256, and the third with 512, all utilizing the… More >

Zhe He¹, Yaning Zhang^2,*

International Journal of Mental Health Promotion, Vol.27, No.11, pp. 1747-1768, 2025, DOI:10.32604/ijmhp.2025.070491 - 28 November 2025

Abstract Background: Depression represents a significant global mental health burden, particularly among middle-aged and older Chinese with chronic diseases in high-altitude regions, where harsh environmental conditions and limited social support exacerbate mental health disparities. This paper aims to develop an interpretable machine learning prediction framework to identify the key factors of depression in this vulnerable population, thereby proposing targeted intervention measures. Methods: Utilizing data from the China Health and Retirement Longitudinal Study in 2020, this paper screened out and analyzed 2431 samples. Subsequently, Recursive Feature Elimination and Least Absolute Shrinkage and Selection Operator were applied to screen… More >

Kinzah Noor¹, Agbotiname Lucky Imoize^2,*, Michael Adedosu Adelabu³, Cheng-Chi Lee^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1575-1664, 2025, DOI:10.32604/cmes.2025.073200 - 26 November 2025

Abstract The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern… More > Graphic Abstract