Fahd N. Al-Wesabi^1,*, Hamad Almansour², Huda G. Iskandar^3,4, Ishfaq Yaseen⁵

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5765-5787, 2025, DOI:10.32604/cmc.2025.067571 - 23 October 2025

Abstract Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired (BVI) individuals in a limited timeframe in the event of emergencies, particularly in enclosed areas. Fire detection becomes crucial as it directly impacts human safety and the environment. While modern technology requires precise techniques for early detection to prevent damage and loss, few research has focused on artificial intelligence (AI)-based early fire alert systems for BVI individuals in indoor settings. To prevent such fire incidents, it is crucial to identify fires accurately and promptly, and alert BVI personnel… More >

Hongle Li¹, SeongKi Kim^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5571-5594, 2025, DOI:10.32604/cmc.2025.067424 - 23 October 2025

Abstract Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles, as well as the realism and immersion of virtual environments. However, traditional algorithms are often limited to single-objective optimization and lack real-time adaptability to dynamic environments. This study addresses these limitations through a proposed real-time dynamic multiobjective (RDMO) path-planning algorithm based on an enhanced A* framework. The proposed algorithm employs a queue-based structure and composite multiheuristic functions to dynamically manage game tasks and compute optimal paths under changing-map-connectivity conditions in real… More >

Kadriye Simsek Alan^*, Ayca Durgut, Helin Doga Demirel

Journal on Artificial Intelligence, Vol.7, pp. 397-415, 2025, DOI:10.32604/jai.2025.071133 - 20 October 2025

Abstract Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures. While earlier research has emphasized CPU utilization forecasting, joint prediction of CPU and memory usage under real workload conditions remains underexplored. This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset. The framework combines Extreme Gradient Boosting (XGBoost) with a MultiOutputRegressor (MOR) to capture nonlinear interactions across multiple resource dimensions, supported by a leakage-safe imputation strategy that prevents bias from missing values. Nested… More >

Bo Yang^1,*, Guo Zhou¹, Shuai Zhou², Yaxing Ren³

Energy Engineering, Vol.122, No.10, pp. 3905-3915, 2025, DOI:10.32604/ee.2025.069600 - 30 September 2025

Abstract This article has no abstract. More >

Wenfeng Zheng¹, Chao Liu², Lirong Yin^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2705-2707, 2025, DOI:10.32604/cmes.2025.072137 - 30 September 2025

Abstract This article has no abstract. More >

Chih-Yu Liu^1,2, Cheng-Yu Ku^1,2,*, Ming-Han Tsai¹, Jia-Yi You³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2969-2990, 2025, DOI:10.32604/cmes.2025.070663 - 30 September 2025

Abstract Amid growing typhoon risks driven by climate change with projected shifts in precipitation intensity and temperature patterns, Taiwan faces increasing challenges in flood risk. In response, this study proposes a geographic information system (GIS)-based artificial intelligence (AI) model to assess flood susceptibility in Keelung City, integrating geospatial and hydrometeorological data collected during Typhoon Krathon (2024). The model employs the random forest (RF) algorithm, using seven environmental variables excluding average elevation, slope, topographic wetness index (TWI), frequency of cumulative rainfall threshold exceedance, normalized difference vegetation index (NDVI), flow accumulation, and drainage density, with the number of… More >

Nezir Aydin^1,2,*, Melike Cari³, Betul Kara³, Ertugrul Ayyildiz^1,3

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2625-2650, 2025, DOI:10.32604/cmc.2025.067290 - 23 September 2025

Abstract Transportation systems are rapidly transforming in response to urbanization, sustainability challenges, and advances in digital technologies. This review synthesizes the intersection of artificial intelligence (AI), fuzzy logic, and multi-criteria decision-making (MCDM) in transportation research. A comprehensive literature search was conducted in the Scopus database, utilizing carefully selected AI, fuzzy, and MCDM keywords. Studies were rigorously screened according to explicit inclusion and exclusion criteria, resulting in 73 eligible publications spanning 2006–2025. The review protocol included transparent data extraction on methodological approaches, application domains, and geographic distribution. Key findings highlight the prevalence of hybrid fuzzy AHP and… More >

Omar Sabri¹, Bassam Al-Shargabi^2,*, Abdelrahman Abuarqoub²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2443-2486, 2025, DOI:10.32604/cmc.2025.066987 - 23 September 2025

Abstract This review comprehensively analyzes advancements in artificial intelligence, particularly machine learning and deep learning, in medical imaging, focusing on their transformative role in enhancing diagnostic accuracy. Our in-depth analysis of 138 selected studies reveals that artificial intelligence (AI) algorithms frequently achieve diagnostic performance comparable to, and often surpassing, that of human experts, excelling in complex pattern recognition. Key findings include earlier detection of conditions like skin cancer and diabetic retinopathy, alongside radiologist-level performance for pneumonia detection on chest X-rays. These technologies profoundly transform imaging by significantly improving processes in classification, segmentation, and sequential analysis across… More >

Batyrkhan Omarov^1,2,3,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2195-2253, 2025, DOI:10.32604/cmc.2025.064799 - 23 September 2025

Abstract Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing, enabling automated lesion detection, physiological monitoring, and therapy planning with accuracy that rivals expert performance. This survey reviews the principal model families as convolutional, recurrent, generative, reinforcement, autoencoder, and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation, classification, reconstruction, and anomaly detection. A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust, context-aware predictions. To support clinical adoption, we outline post-hoc explainability More >

Ragava Raja R^1,2,*, Girish Khanna R³

Journal on Artificial Intelligence, Vol.7, pp. 301-327, 2025, DOI:10.32604/jai.2025.069693 - 22 September 2025

Abstract Packaging materials are indispensable in modern industries but also significantly contribute to environmental degradation, resource consumption, and waste generation. This systematic review critically assesses the integration of artificial intelligence (AI), life cycle sustainability assessment (LCSA) following ISO 14040 standards, and circularity mapping to overcome sustainability barriers in packaging. The study identifies environmental, economic, and social hotspots across the life cycle stages of packaging materials by examining real-world case studies such as Coca-Cola’s adoption of recycled PET bottles and Unilever’s commitment to 100% recyclable plastic. AI technologies highlight transformative tools for optimising resource allocation, enhancing waste… More >