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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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

“…Pervious concrete (PC) has quickly gained attention as an eco-friendly solution to urban stormwater management, offering improved drainage performance while decreasing environmental impact. This study explores the use of fly ash (FA) and silica fume (SF) as supplementary cementitious materials (SCMs) to enhance both mechanical and environmental performance of PC concrete. …”
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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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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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Burden of chronic spontaneous urticaria in Italy through healthcare resource utilization and direct costs: a retrospective analysis of real-world using administrative healthcare da... by Giulia Ronconi, Letizia Dondi, Silvia Calabria, Leonardo Dondi, Irene Dell’Anno, Lucia Casoli, Diletta Valsecchi, Ornella Bonavita, Eustachio Nettis, Nello Martini, Carlo Piccinni

“…Conclusions During the first year following the new CSU diagnosis, a lower than recommended antihistamines dispensation while an elevated use of OCS and a low and delayed omalizumab initiation after CSU exacerbation were observed, suggesting the urgent need to optimize treatment management to limit the burden on patients and healthcare systems.…”
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Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study by Ju-Seung Kwun, Houng-Beom Ahn, Si-Hyuck Kang, Sooyoung Yoo, Seok Kim, Wongeun Song, Junho Hyun, Ji Seon Oh, Gakyoung Baek, Jung-Won Suh

“…ObjectiveThis study aimed to use the Observational Medical Outcomes Partnership Common Data Model to develop a predictive model by applying machine learning algorithms that can effectively predict major adverse cardiac and cerebrovascular events (MACCE) in patients undergoing noncardiac surgery. …”
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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

“…This research analyses the optimal combination of solar panels and battery capacity in 11 cities in Kalimantan using particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms to maximize energy output, reduce levelised energy costs, and maximally reduce carbon emissions. …”
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Implementation and Performance of Lightweight Authentication Encryption ASCON on IoT Devices by Gabriela Cagua, Valerie Gauthier-Umana, Carlos Lozano-Garzon

“…The Internet of Things (IoT) is growing rapidly, enabling interconnected devices to communicate and share data seamlessly. While this expansion drives significant advancements across industries, it also introduces critical challenges in ensuring secure device communication. …”
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Identifying low-risk breast cancer patients for axillary biopsy exemption: a multimodal preoperative predictive model by Jiaqi Zhang, Jianing Zhang, Zhihao Liu, Yudong Zhou, Xiaoni Zhao, Yalong Wang, Danni Li, Jinsui Du, Chenglong Duan, Yi Pan, Qi Tian, Feiqian Wang, Ke Wang, Lizhe Zhu, Bin Wang

“…Abstract Background As the most prevalent female malignancy worldwide, breast cancer frequently involves axillary lymph node metastasis (ALNM), which critically affects therapeutic algorithms. Current guidelines mandate preoperative ultrasound-guided axillary biopsy for suspicious lymph nodes, potentially exposing some low-risk patients with negative results to invasive risks. …”
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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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Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization by Miguel García-Ortegón, Srijit Seal, Carl Rasmussen, Andreas Bender, Sergio Bacallado

“…So far, most studies of NPs have focused on low-dimensional datasets of highly-correlated tasks. While these homogeneous datasets are useful for benchmarking, they may not be representative of realistic transfer learning. …”
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Research on auditory and olfactory regulation methods for abnormal driver emotions based on EEG signals by Bangbei Tang, Bangbei Tang, Yan Li, Yingzhang Wu, Yilun Li, Qizong Yue

“…For anger, fragrance regulation proved more beneficial for mild and moderate anger (reducing anger by 66.67 and 73.75%, respectively), while music regulation was most effective in mitigating high anger levels, resulting in a 58% reduction. …”
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Novel channel attention-based filter pruning methods for low-complexity semantic segmentation models by Md. Bipul Hossain, Na Gong, Mohamed Shaban

“…Examples of widely used semantic segmentation models are the U-Net and DeeplabV3+ models. While the aforementioned models have been deemed very successful in segmenting medical targets including organs and diseases in high resolution images, the computational complexity represents a burden for the real-time application of the algorithms or the deployment of the models on resource-constrained platforms. …”
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