Analysis of State-of-Charge Estimation Methods for Li-Ion Batteries Considering Wide Temperature Range by Yu Miao, Yang Gao, Xinyue Liu, Yuan Liang, Lin Liu

“…Future research should focus on developing high-precision, temperature-adaptive models and lightweight real-time algorithms. Additionally, exploring the deep coupling of physical models and data-driven methods with multi-source heterogeneous data fusion technology can further improve the accuracy and robustness of SOC estimation. …”
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The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications by Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga, Alessandra Bertoldo, Manfredo Atzori

“…However, deep learning models can underperform if trained with bad processed data. …”
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Dynamic Workload Management System in the Public Sector: A Comparative Analysis by Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Gerasimos Vonitsanos, Dimitris Papadopoulos, Ioanna Giannoukou, Spyros Sioutas

“…The results demonstrate ANFIS as the superior model, consistently outperforming other algorithms across all metrics. …”
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Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer by Junyi Li, Shixin Li, Dongpo Zhang, Yibing Zhu, Yue Wang, Xiaoxiao Xing, Juefei Mo, Yong Zhang, Daixiang Liao, Jun Li

“…To address these limitations, this study systematically analyzed RNA-seq high-throughput data and combined 10 machine learning algorithms to construct 117 models. The optimal algorithm combination, StepCox[both] and ridge regression, was identified, and an immune-related gene signature (IRGS) composed of 12 genes was developed. …”
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Semantic Segmentation with Multispectral Satellite Images of Waterfowl Habitat by Mateo Gannod, Nicholas Masto, Collins Owusu, Cory Highway, Katherine Brown, Abigail Blake-Bradshaw, Jamie Feddersen, Heath Hagy, Douglas Talbert, Bradley Cohen

“…We performed several experiments using individual band combination composites and/or vegetation indices to identify optimal bands using MRUNET architectures. We subsequently used 3 ensemble models of important individual networks. …”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

“…We also used five-fold cross-validation with 20 cycles to obtain the optimal parameters for each model, in order to improve the accuracy of predictions. …”
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Identifying Influential Nodes in Complex Networks via Transformer with Multi-Scale Feature Fusion by Tingshuai Jiang, Yirun Ruan, Tianyuan Yu, Liang Bai, Yifei Yuan

“…Through the transformer module, node information is effectively aggregated, thereby improving the model’s ability to recognize key nodes. …”
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Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review by Guido Marchi, Mattia Mercier, Jacopo Cefalo, Carmine Salerni, Martina Ferioli, Piero Candoli, Leonardo Gori, Federico Cucchiara, Giovanni Cenerini, Giacomo Guglielmi, Michele Mondoni

“…Finally, the role of deep-learning models in early complication detection and automated analysis of follow-up imaging studies is examined. …”
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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

“…The ML predictions closely matched experimental outcomes, demonstrating the potential of data-driven modeling in optimizing asphalt mix design. This dual approach—experimental validation and ML-based prediction—confirms that SCBA can effectively serve as a sustainable filler in SMA, offering performance gains while reducing environmental impact. …”
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Transformative applications in biology education: A case study on the efficacy of adaptive learning with numerical insights by Shin I. Aleksandrovich, Tyan Ramazan, Raissa Utegaliyeva, Balzat Sarimbayeva, Gaukhar Keubassova, Rakhima Bissalyyeva, Kuanysh Syman, Gaukhar Abdikarimova

“…Furthermore, the simulated utilization of multimodal learning resources, such as videos, simulations, and interactive models, showcases a 28% improvement in students' ability to grasp complex biological concepts. …”
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Machine learning based adaptive traffic prediction and control using edge impulse platform by Manoj Tolani, G. E. Saathwik, Ayush Roy, L. A. Ameeth, Dhanush Bharadwaj Rao, Ambar Bajpai, Arun Balodi

“…Using machine learning algorithms, the system can forecast future traffic conditions and optimize real-time traffic control by significantly reducing congestion and delays. …”
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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 model is empirically tested using healthcare data of 31 provinces of China for a period from 2013 to 2018. …”
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Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach by Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi

“…Following hyperparameter tuning to improve the model's performance, the selected model was deployed in a real-world network environment to classify QoS into "Above Average," "Average," and "Below Average," categories. …”
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Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study by Emanuele Frassini, Teddy S. Vijfvinkel, Rick M. Butler, Maarten van der Elst, Benno H. W. Hendriks, John J. van den Dobbelsteen

“…Future research should validate these findings across different procedural contexts and explore ways to optimize training times without losing accuracy. Integrating these models into clinical scheduling systems could improve efficiency in cath labs. …”
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Carbon Sequestration Strategies in Regenerative Agricultural Systems by Leveraging Wireless Sensor Networks for Precision Carbon Management by Al-Jawahry Hassan M., AI_Sadi Hafidh l., Rajasekhar Boddu

“…In this study, a total approach towards optimizing carbon sequestration strategies using advanced technologies like Wireless Sensor Network (WSN), Digital Twin model, and predictive algorithms like Random Forest Regression and gradient boosting are presented. …”
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Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States. by Mukhtar Ijaiya, Erica Troncoso, Marang Mutloatse, Duruanyanwu Ifeanyi, Benjamin Obasa, Franklin Emerenini, Lucien De Voux, Thobeka Mnguni, Shantelle Parrott, Ejike Okwor, Babafemi Dare, Oluwayemisi Ogundare, Emmanuel Atuma, Molly Strachan, Ruby Fayorsey, Kelly Curran

“…Machine learning (ML) models can help HIV programs implement targeted interventions to improve the quality of care, develop effective early interventions, and provide insights into optimal resource allocation and program sustainability. …”
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Study on the Trajectory Planning Method for High Bearing Capacity of Joint Manipulator by Tang Runzhi, Li Tiejuni, Yang Dongi

“…The torque peak of the manipulator is suppressed to lowest by using genetic algorithms to optimize the motion parameters of the curve. …”
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Benchmarking Federated Few-Shot Learning for Video-Based Action Recognition by Nguyen Anh Tu, Nartay Aikyn, Nursultan Makhanov, Assanali Abu, Kok-Seng Wong, Min-Ho Lee

“…Additionally, we explore three meta-learning paradigms and three FL algorithms to investigate their effectiveness and suggest the optimal choices for performance improvement. …”
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Enhancing Dongba Pictograph Recognition Using Convolutional Neural Networks and Data Augmentation Techniques by Shihui Li, Lan Thi Nguyen, Wirapong Chansanam, Natthakan Iam-On, Tossapon Boongoen

“…Experimental results demonstrate that the proposed model achieves a classification accuracy of 99.43% and consistently outperforms other conventional methods, with its performance peaking at 99.84% under optimized training conditions—specifically, with 75 training epochs and a batch size of 512. …”
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An Intelligent LoRaWAN-Based IoT Device for Monitoring and Control Solutions in Smart Farming Through Anomaly Detection Integrated With Unsupervised Machine Learning by Maram Fahaad Almufareh, Mamoona Humayun, Zulfiqar Ahmad, Asfandyar Khan

“…Predominantly, the study also indicates precision in the temperature variation prediction model through the use of the predictive model based on the linear regression and random forest algorithms. …”
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