Recent Tools of Software-Defined Networking Traffic Generation and Data Collection by Tabarak Khudhair, Omar Athab

“…Although many studies have focused on SDN management, monitoring, control and improving quality of service, only a few are focused on presenting what is used to generate traffic and collect data. …”
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Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care. by Youli Jiang, Ao Li, Zhihuan Li, Yanfeng Li, Rong Li, Qingshi Zhao, Guisu Li

“…Predictive models were developed using machine learning algorithms, including Gradient Boosting Machine (GBM), Random Forest (RF), and Logistic Regression (LR). …”
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Optimizing Stroke Classification with Pre-Trained Deep Learning Models by Serra Aksoy, Pinar Demircioglu, Ismail Bogrekci

“…Conclusions: ConvNeXt Base reveals promise in improving stroke prediction accuracy, enabling earlier diagnosis and personalized treatment, which could lead to faster, more effective medical interventions.…”
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Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What’s the Best Approach for Crime Forecasting? by Eugenio Cesario, Paolo Lindia, Andrea Vinci

“…Compared to traditional police district partitioning, these data-driven methods offer significant advantages in improving crime forecasting accuracy across urban environments.…”
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Use of satellite data for detecting icebergs and evaluating the iceberg threats by I. A. Bychkova, V. G. Smirnov

“…Te approaches proposed to detect icebergs from satellite data allow improving the quality and efciency of service for a wide number of users with ensuring the efciency and safety of Arctic navigation and activities on the Arctic shelf.…”
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A redundant weight removal method inspired by specific immunotherapy for boosting transfer learning security by Qing Huang, Hongli Deng, Junxiang Wang, Tao Yang, Bochuan Zheng

“…Currently, redundant weights are removed mainly through pruning algorithms designed by correlating student data with teacher model nodes. …”
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ADF-SL: An Adaptive and Fair Scheme for Smart Learning Task Distribution by Ahmed A. Al-Saedi, Veselka Boeva

“…In healthcare, SL enables distributed training across a variety of medical devices, hospitals, and organizations, improving model robustness while maintaining patient confidentiality. …”
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Incorporating Contextual Factors into a Comprehensive Analysis of Operational Efficiency and Service Quality in Healthcare Sector by Utsav Pandey, Sanjeet Singh

“…The proposed two-phased integrated technique enhances the performance of healthcare services and provides a roadmap for improvement for inefficient regions.…”
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Development of a Smart Waste Management System for Route Optimization and Adaptive Demand Management in Dubai by H. Hassan, T. Ali, A. Tamimi

“…Preliminary results indicate that route optimization alone has achieved a reduction of 19.1% in CO₂ emissions, and further improvement is expected with full implementation of the adaptive demand management module. …”
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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. This shows that domain-specific pre-trained models have a good effect on the performance of deep learning models. …”
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Multi-source 3D point clouds fusion for potential rock mass hazard evaluation in high-steep rock slopes by W. Yuan, C. Liu, C. Liu, T. Wang, B. Adriano, H. Bao, R. Shibasaki, S. Koshimura

“…The results demonstrate that the fusion of TLS-UAV multi-source data enhances spatial coverage and point cloud density by 19%, enabling comprehensive slope modeling and improving multi-angle structural characterization of target rock masses. …”
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CMDMamba: dual-layer Mamba architecture with dual convolutional feed-forward networks for efficient financial time series forecasting by Zhenkai Qin, Zhenkai Qin, Zhenkai Qin, Baozhong Wei, Baozhong Wei, Yujia Zhai, Ziqian Lin, Xiaochuan Yu, Xiaochuan Yu, Jingxuan Jiang

“…By doing so, it provides more accurate time series modeling, optimizes algorithmic trading strategies, and facilitates investment portfolio risk warnings.ResultsExperiments conducted on four real-world financial datasets demonstrate that CMDMamba achieves a 10.4% improvement in prediction accuracy for multivariate forecasting tasks compared to state-of-the-art models.DiscussionMoreover, CMDMamba excels in both predictive accuracy and computational efficiency, setting a new benchmark in the field of financial time series forecasting.…”
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Enhanced Detection of Intrusion Detection System in Cloud Networks Using Time-Aware and Deep Learning Techniques by Nima Terawi, Huthaifa I. Ashqar, Omar Darwish, Anas Alsobeh, Plamen Zahariev, Yahya Tashtoush

“…In contrast, Deep Neural Network (DNN) models showed significant improvements with optimization, with DNN combined with GS (DNN-GS) reaching 89% accuracy. …”
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Comparative Effectiveness of Risankizumab vs Other Biologics in Crohn’s Disease: Long-Term and Quality-of-Life Outcomes after Inadequate Response to Prior Therapy by Monika Babczyńska, Agnieszka Buliszak, Piotr Marcjasz, Anna Bioły, Agnieszka Borończyk, Piotr Zając

“…Further direct comparisons are warranted to clarify its role in treatment algorithms. …”
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Dual-Path Large Kernel Learning and Its Applications in Single-Image Super-Resolution by Zhen Su, Mang Sun, He Jiang, Xiang Ma, Rui Zhang, Chen Lv, Qiqi Kou, Deqiang Cheng

“…When compared with other prevalent algorithms, DLKL exhibits remarkable proficiency in generating images with sharper textures and structures that are more akin to natural ones. …”
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Optimization of PV parameters under varied environmental conditions: A hybrid secant–Newton-Raphson method by Brahim El Fahmi, Driss Saadaoui, Imade Choulli, Khalid Assalaou, Ismail Abazine, Mustapha Elyaqouti, El hanafi Arjdal, Mohammed Agdam, Yassine El aidi Idrissi

“…This hybrid approach provides significant improvement over purely numerical techniques and metaheuristic algorithms because it provides rapid convergence, reduced computational load, and high accuracy in PV parameter extraction.…”
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An FPGA-accelerated multi-level AI-integrated simulation framework for multi-time domain power systems with high penetration of power converters by Chen Liu, Peng Su, Hao Bai, Xizheng Guo, Alber Filbà Martínez, Jose Luis Dominguez Garcia

“…The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports, ensuring compatibility with artificial intelligence algorithms. By integrating AI techniques, such as back propagation neural networks, the framework predicts variables with high computational complexity, improving accuracy and simulation efficiency. …”
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Resampling-driven machine learning models for enhanced high streamflow forecasting by Nureehan Salaeh, Sirimon Pinthong, Warit Wipulanusat, Uruya Weesakul, Jakkarin Weekaew, Quoc Bao Pham, Pakorn Ditthakit

“…KNN also exhibited notable improvements under several Org-Resampling strategies. …”
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A Comprehensive Review of Quality of Aquaculture Services in Integrated Multi-Trophic Systems by Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Marco A. Márquez Vera, Alejandro Fuentes-Penna, Ricardo A. Barrera-Cámara, Miguel A. Ruiz-Jaimes, Yadira Toledo-Navarro, María Beatríz Bernábe-Loranca, Eric Simancas-Acevedo, Francisco R. Trejo-Macotela, Marco A. Vera-Jiménez

“…IMTA has emerged as a sustainable approach to meet the growing global demand for aquatic food products by combining species from different trophic levels in a single system, optimising resource use, improving environmental performance, and diversifying production. …”
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Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems by Abubakar Unguwanrimi Yakubu, Liu Qingsheng, Meng Kai, Chen Jinwei, Omer Abbaker Ahmed Mohammed, Jiahao Zhao, Qi Jiang, Xuanhong Ye, Junyi Liu, Qinglong Yu, Muhammad Aurangzeb, Shusheng Xiong

“…This review synthesizes over one hundred studies published between 2015 and 2025, highlighting advancements in steady-state and dynamic modeling, AI-driven multi-objective optimization, and integrated thermal management. Optimization algorithms such as PSO, WOA, MIGA, and NSGA-II have shown promising results, including up to 15 % reduction in hydrogen consumption and 20 to 30 % improvement in thermal uniformity. …”
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