WU Hongchun1, ZHENG Youqi1, CAO Liangzhi1, DU Xianan1, WANG Xuesong2, ZU Tiejun1, LIU Zhouyu1, HE Qingming1, CHEN Ronghua1, GE Li1, YANG Rui2, GAO Xinzhao2, WANG Shixi2... by WU Hongchun1, ZHENG Youqi1, CAO Liangzhi1, DU Xianan1, WANG Xuesong2, ZU Tiejun1, LIU Zhouyu1, HE Qingming1, CHEN Ronghua1, GE Li1, YANG Rui2, GAO Xinzhao2, WANG Shixi2, A Reai2

“…The findings suggest that employing advanced numerical simulation algorithms can circumvent the inherent shortcomings of existing methods and software in terms of theoretical models, addressing the issue of computational accuracy that fails to meet engineering requirements due to model defects. …”
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An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles by Prot Piyakawanich, Pattarapong Phasukkit

“…By synergistically combining K-Means Clustering with a multiple-input deep learning regression-based model (DL-KMA), we have achieved substantial improvements in altitude estimation accuracy. …”
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An advanced real-time crash prediction framework for combined hard shoulder running and variable speed limits system using transformer by Mohamed Abdel-Aty, Tarek Hasan, B M Tazbiul Hassan Anik

“…Additionally, the results indicate substantial improvements in model prediction performance (i.e., an increment of sensitivity values by 7.04% and 8.33% in the AM and PM models, respectively) after incorporating the general safety condition of a freeway segment as an input feature while estimating real-time crash prediction models. …”
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Flood extent mapping in SAR images using semi-supervised approach by Girisha S, Savitha G, Sughosh P

“…The findings highlight the method's effectiveness in improving flood-zone segmentation accuracy, leading to more efficient disaster management.…”
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Anchor Dragging Risk Estimation Strategy from Supervised Cost-Sensitive Learning by Sang-Lok Yoo, Shem Otoi Onyango, Joo-Sung Kim, Kwang-Il Kim

“…This study also demonstrated potential applications of the model, discussed its limitations, and suggested possible improvements for the ML approach. …”
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A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence by Abdullah Alabdulatif

“…The proposed model combines an Artificial Neural Network and Support Vector Machine as base classifiers and employs a Random Forest as a meta-classifier to fuse predictions, improving detection performance. …”
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A Study on Staging Cystic Echinococcosis Using Machine Learning Methods by Tuvshinsaikhan Tegshee, Temuulen Dorjsuren, Sungju Lee, Dolgorsuren Batjargal

“…This research underscores the potential of hybrid and pre-trained models in advancing medical image classification, providing a promising approach to improving the differential diagnosis of CE disease.…”
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Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. by Sadia Nazim, Muhammad Mansoor Alam, Syed Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Suud

“…There has been relatively little study on explainability, especially when dealing with malware imagery data, irrespective of the fact that DL/ML algorithms have revolutionized malware detection. Explainability techniques such as SHAP, LIME, and Grad-CAM approaches are employed to present a complete comprehension of feature significance and local or global predictive behavior of the model over various malware categories. …”
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COSMIC-Based Early Software Size Estimation Using Deep Learning and Domain-Specific BERT by Yohannes Sefane Molla, Esubalew Alemneh, Samuel Temesgen Yimer

“…On average, RE-BERT-based regressor models provided 1.23% to 3.19% improvement over BASE-BERT regressor models. …”
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Analysis of Energy Sustainability and Problems of Technological Process of Primary Aluminum Production by Yury Valeryevich Ilyushin, Egor Andreevich Boronko

“…This paper is devoted to the problem of magnetohydrodynamic stability (MHDS) in the energy-intensive process of primary aluminum production by electrolysis. Improving MHDS control is important because of the high costs and reduced efficiency caused by the instability of magnetic and current fields. …”
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Leveraging advanced technologies for early detection and diagnosis of oral cancer: Warning alarm by Saantosh Saravanan, N. Aravindha Babu, Lakshmi T, Mukesh Kumar Dharmalingam Jothinathan

“…Specialized algorithms such as the Recombination-Based Improved Population Optimization Parallel Covariance Matrix Adaptation Evolution Strategy (RB-IPOP CMA-ES) allow for better accuracy of deep learning models, as this enhances the performance of developing models for early diagnosis of oral cancer. …”
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On the effect of sampling frequency on the electricity theft detection performance by Fatemeh Soleimani Nasab, Foad Ghaderi

“…We also proposed a bagging network that could improve the overall detection performance at different sampling frequencies.…”
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ODSEI Chip: An Open 3D Microfluidic Platform for Studying Tumor Spheroid‐Endothelial Interactions by Jooyoung Ro, Junyoung Kim, Juhee Park, Yongjun Choi, Yoon‐Kyoung Cho

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An Optimal Sizing Methodology for a Wind/PV Hybrid Energy Production System for Agricultural Irrigation in Skikda, Algeria by Nadhir Abderrahmane, Allaoua Brahmia, Adlen Kerboua, Ridha Kelaiaia

“…This innovative design not only enhances sustainability, but also improves the system’s energy efficiency. To ensure precise and customized sizing of the system for the irrigation area, a detailed mathematical modeling of the key system components (solar panels, wind turbines, and reservoir) was conducted. …”
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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…To further enhance performance, XGB was optimized using Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO). Among these, XGB-GWO demonstrated the highest accuracy, with R² improving to 0.991 and MSE decreasing significantly from 83.95 to 14.42, resulting in an 82.82% error reduction. …”
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MonoAMP: Adaptive Multi-Order Perceptual Aggregation for Monocular 3D Vehicle Detection by Xiaoxi Hu, Tao Chen, Wentao Zhang, Guangyi Ji, Hongxia Jia

“…Additionally, the ablation study shows a 3.78% improvement in object detection accuracy over the baseline method. …”
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Design and predict the potential of imidazole-based organic dyes in dye-sensitized solar cells using fingerprint machine learning and supported by a web application by Mohamed M. Elsenety

“…Among of these, Deep Neural Network models of MLPRegressor algorithm based on the daylight fingerprint shows a significant coefficient of determination combined with the lowest errors. …”
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Hybrid CFD and Monte Carlo-Driven Optimization Approach for Heat Sink Design by Raquel Busqué, Matias Bossio, Raimon Fabregat, Francesc Bonada, Héctor Maicas, Jordi Pijuan, Albert Brigido

“…The method integrates CFD simulations in Ansys Fluent with a Monte Carlo-driven optimization algorithm, modeling the design of a heat sink domain as a porous medium. …”
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Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey by Zhengxin Wang, Longlong Zhao, Jintao Meng, Yu Han, Xiaoli Li, Ruixia Jiang, Jinsong Chen, Hongzhong Li

“…The rapid development of deep learning technology, especially the introduction of self-attention Transformer models, has greatly improved the accuracy of cloud detection tasks while achieving efficient processing of large-scale remote sensing images. …”
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XSShield: Defending Against Stored XSS Attacks Using LLM-Based Semantic Understanding by Yuan Zhou, Enze Wang, Wantong Yang, Wenlin Ge, Siyi Yang, Yibo Zhang, Wei Qu, Wei Xie

“…Experimental evaluation shows that XSShield achieves 93% accuracy and an F1 score of 0.9266 on the GPT-4 model, improving accuracy by an average of 88.8% compared to existing solutions. …”
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