Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication by Chetan Rakshe, Christy Bobby Thomas, Mohanavelu Kalathe, Vanteemar S. Sreeraj, Ganesan Venkatasubramanian, Deepesh Kumar, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…In this study, we propose a novel pipeline to evaluate electroencephalography (EEG)-based biometric system across different emotional states and optimize critical brain regions using machine learning algorithms. …”
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LC-TMNet: learned lossless medical image compression with tunable multi-scale network by Hengrui Liao, Yue Li

“…Neural networks, with their powerful probabilistic estimation capabilities, seamlessly integrate with entropy encoders to achieve lossless compression. …”
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Towards an Efficient Remote Sensing Image Compression Network with Visual State Space Model by Yongqiang Wang, Feng Liang, Shang Wang, Hang Chen, Qi Cao, Haisheng Fu, Zhenjiao Chen

“…We extend it to an omni-selective scan for the global-spatial correlations within our Channel and Global Context Entropy Model (CGCM), enabling the integration of spatial and channel priors to minimize redundancy across slices. …”
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A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach by Vasileios Alevizos, Zongliang Yue, Sabrina Edralin, Clark Xu, Nikitas Gerolimos, George A. Papakostas

“…While conventional dictionary-based algorithms rely on repeated sequences, LPPIE translates numeric data sequences into highly compact logarithmic representations. …”
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A Systematic Literature Review of Risk Assessment Methodologies for Battery Electric Vehicles by Ayudhia Pangestu Gusti, Dwitya Harits Waskito, Sunarto Kaleg, Ludfi Pratiwi Bowo, Angjuang Pratama, Defi Rizki Maulani, Ayumi Putri Varadita, Sinung Nugroho, I Kadek Candra Parmana Wiguna

“…The findings highlight the growing reliance on real-time diagnostics and advanced algorithms for enhancing BEV safety and reliability. …”
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AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring by Venkatraman Manikandan, Suresh Neethirajan

“…Future research directions emphasize multi-modal sensor integration, standardized evaluation frameworks, and domain-adaptive models capable of generalizing across diverse poultry breeds, housing conditions, and environmental contexts while maintaining interpretability for practical farm deployment.…”
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A double-layer ensemble framework for rubber plantation mapping using multi-source data in the google earth engine: a case study of the southwestern border region of China by Hui Wang, Jie Li, Jinliang Wang, Yuncheng Deng, Shupeng Gao, Jing Zou, An Chen, Haichao Xu

“…This layer utilizes five machine learning algorithms, namely Random Forest, Maximum Entropy Model, Gradient Tree Boosting, Support Vector Machine, and Classification and Regression Tree, to construct the corresponding PFT-EMs. …”
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Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning by Mehdi Rashidi, Serena Arima, Andrea Claudio Stetco, Chiara Coppola, Debora Musarò, Marco Greco, Marina Damato, Filomena My, Angela Lupo, Marta Lorenzo, Antonio Danieli, Giuseppe Maruccio, Alberto Argentiero, Andrea Buccoliero, Marcello Dorian Donzella, Michele Maffia

“…<b>Result:</b> Among all the algorithms used in this research, random forest (RF) was the best-performing model, achieving an accuracy of 82.72% with a ROC-AUC score of 89.65%. …”
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Assessing current and future areas of ecological suitability for Lutzomyia shannoni in North America by Sydney DeWinter, Grace K. Nichol, Christopher Fernandez-Prada, Amy L. Greer, J. Scott Weese, Katie M. Clow

“…The ecological niche model was created using maximum entropy (MaxEnt) algorithms to identify regions which currently are or may become ecologically suitable for Lu. shannoni. …”
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Pediatric sepsis phenotypes for enhanced therapeutics: An application of clustering to electronic health records by Ioannis Koutroulis, Tom Velez, Tony Wang, Seife Yohannes, Jessica E. Galarraga, Joseph A. Morales, Robert J. Freishtat, James M. Chamberlain

“…All 4 segmentations identified at least 1 high severity phenotype, but LCA‐identified phenotypes reflected superior stratification, high entropy approaching 1 (eg, 0.994) indicating excellent separation between estimated phenotypes, and differential treatment/treatment response, and outcomes that were non‐randomly distributed across phenotypes (P < 0.001). …”
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