Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning by Adrienn VARGA-BALOGH, Ádám LEELŐSSY, Róbert MÉSZÁROS

“…Precipitation type prediction is crucial for various sectors, including aviation, agriculture, and public safety. …”
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Identification and validation of biomarkers associated with glycolysis in polycystic ovarian syndrome by Rongyan Zhu, Xiao Yu, Yulan Li

“…Utilizing publicly available datasets, biomarkers were identified via differential analysis, various PPI algorithms, and validation of expression patterns. Subsequent analyses included functional enrichment, tissue and cell-specific expression profiling, m6A modification site prediction, compound screening, molecular network construction, and molecular docking. …”
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Ion channel classification through machine learning and protein language model embeddings by Ghazikhani Hamed, Butler Gregory

“…This study extends our previous work on protein language models for ion channel prediction, significantly advancing the methodology and performance. …”
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Melanoma Skin Lesion Classification Using Neural Networks: A systematic review by ahmed hammo, Mohammed Younis

“…Based on the decision fusion, theoretical and applied contributions were studied using traditional classification algorithms and multiple neural networks. The period 2018-2021 has been focused on new trends. …”
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Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos by Lisa Boucret, Floris Chabrun, Magalie Boguenet, Pascal Reynier, Pierre-Emmanuel Bouet, Pascale May-Panloup

“…Abstract Time-lapse imaging and deep-learning algorithms are promising tools to assess the most viable embryos and improve embryo selection in IVF laboratories. …”
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Dead Sea Stromatolite Reefs: Testing Ground for Remote Sensing Automated Detection of Life Forms and Their Traces in Harsh Environments by Nuphar Gedulter, Amotz Agnon, Noam Levin

“…We collected and measured 82 field samples with an ASD spectrometer and used our spectral dataset to train three machine learning algorithms (linear regression, K-Nearest Neighbor, XGBoost). …”
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From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AI by Gizem Izmir Tunahan, Goksu Tuysuzoglu, Hector Altamirano

“…Drawing on over 1.3 million ten-minute passive infrared (PIR) sensor observations collected throughout 2023 at the UCL Bartlett Library, we modeled seat-level occupancy using 24 spatial, environmental, and temporal features through advanced machine learning algorithms. Among the models tested, Categorical Boosting (CatBoost) demonstrated the highest predictive performance, achieving a classification accuracy of 72.5%, with interpretability enhanced through SHAP (Shapley Additive exPlanations) analysis. …”
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The evolutionary analysis of emerging low frequency HIV-1 CXCR4 using variants through time--an ultra-deep approach. by John Archer, Andrew Rambaut, Bruce E Taillon, P Richard Harrigan, Marilyn Lewis, David L Robertson

“…Then, in conjunction with coreceptor prediction algorithms that infer HIV tropism, our software was used to quantify the viral population structure pre- and post-treatment. …”
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Anomalous Behavior in Weather Forecast Uncertainty: Implications for Ship Weather Routing by Marijana Marjanović, Jasna Prpić-Oršić, Anton Turk, Marko Valčić

“…This allows uncertainty to be quantified not as a static estimate, but as a function sensitive to both variable type and prediction horizon. When integrated into routing algorithms, such representations allow for route planning strategies that are not only more reflective of real-world meteorological limitations but also more robust to evolving weather conditions, demonstrated by a 3–7% increase in travel time in exchange for improved safety margins across eight test cases.…”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…In this study, a comprehensive dataset consisting of 30,660 independent data points was utilized to develop machine learning (ML) models for Sw prediction. Nine well log parameters—Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature—were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. …”
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Overview of Startups Developing Artificial Intelligence for the Energy Sector by Naiyer Mohammadi Lanbaran, Darius Naujokaitis, Gediminas Kairaitis, Gabrielė Jenciūtė, Neringa Radziukynienė

“…Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. …”
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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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Behavior Modeling and Bio-Hybrid Systems: Using Reinforcement Learning to Enhance Cyborg Cockroach in Bio-Inspired Swarm Robotics by Le Minh Triet, Nguyen Truong Thinh

“…Future work will explore scaling the swarm system and integrating advanced sensors and AI algorithms.…”
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Novel integrated computational AMP discovery approaches highlight diversity in the helminth AMP repertoire. by Allister Irvine, Darrin McKenzie, Ciaran J McCoy, Robert L J Graham, Ciaren Graham, Sharon A Huws, Louise E Atkinson, Angela Mousley

“…This study highlights limitations in the homology-based approaches, used to identify putative nematode AMPs, for the characterisation of flatworm AMPs, and reveals that innovative algorithmic AMP prediction approaches provide an alternative strategy for novel helminth AMP discovery. …”
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Recent Advances in Creep Modelling Using the <i>θ</i> Projection Method by William Harrison

“…Using a power law approach along with optimisation algorithms, the residual error between predicted and experimentally observed creep curves is reduced. …”
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Mir-494-3p enhances aggressive phenotype of non-small cell lung cancer cells by regulating SET/I2PP2A by Yuwen Du, Taisuke Kajino, Yukako Shimada, Takashi Takahashi, Ayumu Taguchi

“…Integration of RNA sequencing analysis of NSCLC cells with miR-494-3p inhibition and a bioinformatic search of miRNA target prediction algorithms resulted in identification of SET/I2PP2A as a direct target of miR-494-3p. …”
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Myoelectric Control in Rehabilitative and Assistive Soft Exoskeletons: A Comprehensive Review of Trends, Challenges, and Integration with Soft Robotic Devices by Alejandro Toro-Ossaba, Juan C. Tejada, Daniel Sanin-Villa

“…Key contributions include critically evaluating machine learning-based motion prediction, model-free adaptive control methods, and real-time validation strategies to enhance rehabilitation outcomes. …”
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Human-in-the-loop control strategy for IoT-based smart thermostats with Deep Reinforcement Learning by Payam Fatehi Karjou, Fabian Stupperich, Phillip Stoffel, Drk Müller

“…They use sensors and algorithms to learn user behavior and optimize heating schedules accordingly. …”
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A systematic review on artificial intelligence approaches for smart health devices by Lerina Aversano, Martina Iammarino, Ilaria Mancino, Debora Montano

“…We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. …”
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Machine-learning of medical cannabis chemical profiles reveals analgesia beyond placebo expectations by Adi Hatav, Yelena Vysotski, Anna Shapira, Shiri Procaccia, David Meiri, Dvir Aran

“…Model robustness was evaluated using six additional machine learning algorithms. Results Here we show that incorporating chemical composition markedly improves the prediction of pain relief (AUC = 0.63 ± 0.10) compared to models using only demographic and clinical features (AUC = 0.52 ± 0.09; p < 0.001). …”
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