Rolling window for detecting multiple Chan signatures to diagnose excessive water production by Ahmed MohamedSalih Musa Hamdoon, A. P. Mohammed Abdalla Ayoub Mohammed, A. P. Khaled Abdalla Elraies

“…Eight algorithms were evaluated, with Support Vector Machines (SVM) and Random Forest (RF) achieving a remarkable 94% F1 score while the remaining algorithms averaged 93%.…”
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EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments by Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, Shujaat Ali, Muhammad Tahir

“…We will also assess its applicability by integrating other ML models, which could provide enhanced insights for optimizing scheduling algorithms across diverse fog-cloud settings.…”
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Efficient Orchestration of Distributed Workloads in Multi-Region Kubernetes Cluster by Radoslav Furnadzhiev, Mitko Shopov, Nikolay Kakanakov

“…Experiments cover multiple cluster configurations, varying in node count, region count, and inter-region latencies, with performance metrics recorded for scheduler efficiency, inter-zone communication impact, and execution time across different optimization algorithms. The obtained results indicate that network-aware scheduling approaches significantly improve latency-aware placement decisions, achieving lower inter-region communication delays while maintaining resource efficiency.…”
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Experimental and machine learning based analysis of pervious concrete enhanced with fly ash and silica fume by Siva Shanmukha Anjaneya Babu Padavala, Siva Avudaiappan, Venkatesh Noolu

“…SVM achieved the highest predictive accuracy (R2 = 0.98), while KNN and ANN showed lower performance (R2 = 0.69 and 0.71, respectively). …”
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Enhancing stone matrix asphalt performance with sugarcane bagasse ash: Mechanical properties and machine learning-based predictions using XGBoost and random forest by Hamed Khani Sanij, Rezvan Babagoli, Reza Mohammadi Elyasi

“…Among the models, XGBoost achieved the best performance (R² > 0.93), followed closely by RF, while SVR showed relatively lower accuracy. The ML predictions closely matched experimental outcomes, demonstrating the potential of data-driven modeling in optimizing asphalt mix design. …”
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Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations by Khaled Sarieddine, Mohammad Ali Sayed, Sadegh Torabi, Ribal Atallah, Chadi Assi

“…The edge-based detection relies on the behavioral characteristics of the power grid in the presence of interconnected EVCS while combining cyber and physical layer features to implement deep learning algorithms for the effective detection of oscillatory load attacks at the EVCS. …”
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Intelligent System for Reducing Waste and Enhancing Efficiency in Copper Production Using Machine Learning by Bagdaulet Kenzhaliyev, Timur Imankulov, Aksultan Mukhanbet, Sergey Kvyatkovskiy, Maral Dyussebekova, Nurdaulet Tasmurzayev

“…The growing environmental impact of copper production necessitates innovative approaches for optimizing metallurgical processes and minimizing waste. …”
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Battery Cell Charging Behavior Analysis Using Constant Current and Constant Voltage Methods by Anna Cyntia, Muhammad Imran Hamid, Ayu Elsa Afriyanti

“…While CC/CV offers optimal management for lithium-ion battery charging, future research can focus on investigating the long-term effects of CC/CV on battery life under various environmental conditions, considering the findings and methodologies of similar studies.…”
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A Modified Differential Evolution for Source Localization Using RSS Measurements by Yunjie Tao, Lincan Li, Shengming Chang

“…While differential evolution (DE) has demonstrated notable efficacy in optimizing multimodal cost functions, conventional implementations often grapple with suboptimal convergence rates and susceptibility to local optima. …”
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Semantic Segmentation-Driven Knowledge Distillation-Based Infrared Visible Image Fusion Framework by Xingshuo Wang

“…However, many existing fusion algorithms overly emphasize visual quality and traditional statistical evaluation metrics while neglecting the requirements of real-world applications, especially in high-level vision tasks. …”
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YOLOv8-POS: a lightweight model for coal-rock image recognition by Yanqin Zhao, Wenyu Wang

“…This reduces unnecessary computational overhead while preserving the integrity of critical feature information, thus significantly cutting down on the model’s parameters and computational demands. …”
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Applications, Challenges, and Future Perspectives of Artificial Intelligence in Psychopharmacology, Psychological Disorders and Physiological Psychology: A Comprehensive Review by Mohammad Hossein Salemi, Elham Foroozandeh, Molouk Khademi Ashkzari

“…However, challenges persist, including data privacy, algorithmic bias, ethical dilemmas, and regulatory hurdles, emphasizing the need for robust governance. …”
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Enhanced YOLOv8 with lightweight and efficient detection head for for detecting rice leaf diseases by Bo Gan, Guolin Pu, Weiyin Xing, Lianfang Wang, Shu Liang

“…However, the diversity of these diseases, their uneven distribution, and complex field environments create challenges for precise, multi-scale detection. While YOLO object detection algorithms show strong performance in automated detection, their feature extraction capabilities remain limited in complex agricultural settings. …”
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Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete by Mohamed Abdellatief, G. Murali, Saurav Dixit

“…The impact of 12 influential features on CS was evaluated to optimize the performance of the proposed models. Among the algorithms, XGB outperformed the others with an R² of 90.1 % and a lower RMSE of 11.52 MPa, surpassing RF (88.7 %), GB (89.2 %), and GPR (86.1 %). …”
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The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations—a narrative review by Prasanthi Samathoti, Rajasekhar Komarla Kumarachari, Sarad Pawar Naik Bukke, Eashwar Sai Komarla Rajasekhar, Ashish Ashokkumar Jaiswal, Zohre Eftekhari

“…Nanomedicine leverages nanoparticles for targeted drug delivery, enhancing therapeutic efficacy while minimizing adverse effects. Concurrently, AI algorithms facilitate early cancer detection, personalized treatment planning, and predictive analytics, thereby optimizing clinical outcomes. …”
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Techno-economic modeling and analysis of a PV EV charged with battery energy storage system (BESS) on Kalimantan Island by Aripriharta, Satria Adiguna, Arif N. Afandi, Muhammad Cahyo Bagaskoro

“…Results show Tarakan as the most optimal location, generating 215,804.88 kWh for IDR 916.9/kWh and lowering emissions by 435,884.29 kgCO2e, while Samarinda is the least optimal location. …”
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Implementation and Performance of Lightweight Authentication Encryption ASCON on IoT Devices by Gabriela Cagua, Valerie Gauthier-Umana, Carlos Lozano-Garzon

“…Lightweight cryptographic algorithms have emerged to address these challenges, with ASCON gaining recognition for its robustness and efficiency. …”
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AI enhancing prefabricated aesthetics and low carbon coupled with 3D printing in chain hotel buildings from multidimensional neural networks by Gangwei Cai, Yin Lou, Feidong Lu

“…The results indicate that AI can not only optimize energy consumption and structural efficiency in the renovation process but also achieve low-carbon goals while maintaining high-quality aesthetic designs. …”
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A Systematic Review of CNN Architectures, Databases, Performance Metrics, and Applications in Face Recognition by Andisani Nemavhola, Colin Chibaya, Serestina Viriri

“…Overall, the findings emphasize the need for advanced algorithms capable of handling large-scale, real-world challenges while optimizing accuracy and computational efficiency in face recognition systems.…”
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Artificial intelligence-augmented smart grid architecture for cyber intrusion detection and mitigation in electric vehicle charging infrastructure by Ankita Sharma, Shalli Rani, Mohammad Shabaz

“…It incorporates AI-driven security models and anomaly detection algorithms to enhance grid resilience and optimize energy utilization. …”
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