Ecological Systems Classification: Integrating Machine Learning, Ancillary Modeling, and Sentinel-2 Satellite Imagery by Michael Sunde, David Diamond, Lee Elliott

“…Since widely available land cover and vegetation datasets are generally lacking in either thematic resolution or spatial coverage, there is a need to integrate modeling approaches and ancillary data with traditional satellite image classifications to produce more detailed ecosystem maps for large areas. In this study, we present a comprehensive approach using satellite imagery, machine learning, and ancillary modeling approaches to develop high-resolution ecological system type maps statewide for Arkansas, USA. …”
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Application of machine learning in the analysis of seismic data to identify tectonic faults in various seismogeological conditions by Nikolaenko, Sergey Viktorovich, Kovalenko, Andrey A., Nateganov, Andrey E., Kruk, Pavel N. K, Deryushev, Aleksandr B.

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Lessons Learned from a Human Rabies Breakthrough Infection: A Rare Case Report by Jun Tang, Haiyun Dong, Min Xu, Wenfang He, Jinxiu Li, Guyi Wang

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Deep Learning for Early Earthquake Detection: Application of Convolutional Neural Networks for P-Wave Detection by Dauren Zhexebay, Alisher Skabylov, Margulan Ibraimov, Serik Khokhlov, Aldiyar Agishev, Gulnur Kudaibergenova, Aibala Orazakova, Almansur Agishev

“…Recent advancements in deep learning, particularly convolutional neural networks (CNNs), provide a promising alternative for analyzing seismic waves. …”
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Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation by Srishti Gaur, Guler Aslan-Sungur (Rojda), Andy VanLoocke, Darren T. Drewry

“…These models require careful choice of predictors to avoid redundancy and allow robust cross-validation. In this study we present a systematic and comprehensive evaluation of machine learning (ML) models to assess the capability of meteorological and proximal sensing data for predicting LE at a half-hourly temporal resolution across multiple growing seasons for an agricultural system. …”
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Evaluating the impact of common clinical confounders on performance of deep-learning-based sepsis risk assessment by Shikha Chaganti, Vivek Singh, Alasdair Edward Gent, Rishikesan Kamaleswaran, Ali Kamen

“…IntroductionEarly identification of sepsis in the emergency department using machine learning remains a challenging problem, primarily due to the lack of a gold standard for sepsis diagnosis, the heterogeneity in clinical presentations, and the impact of confounding conditions.MethodsIn this work, we present a deep-learning-based predictive model designed to enable early detection of patients at risk of developing sepsis, using data from the first 24 h of admission. …”
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Recent Trends in Proxy Model Development for Well Placement Optimization Employing Machine Learning Techniques by Sameer Salasakar, Sabyasachi Prakash, Ganesh Thakur

“…The data-driven models include statistical- and machine learning (ML)-based approximations of nonlinear problems. …”
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A Bio-Inspired Learning Dendritic Motion Detection Framework with Direction-Selective Horizontal Cells by Tianqi Chen, Yuki Todo, Zhiyu Qiu, Yuxiao Hua, Hiroki Sugiura, Zheng Tang

“…Inspired by the biological theory of the human visual system, we proposed a learnable horizontal-cell-based dendritic neuron model (HCdM) that captures motion direction with high efficiency while remaining highly robust. Unlike present deep learning models, which rely on extension of computation and extraction of global features, the HCdM mimics the localized processing of dendritic neurons, enabling efficient motion feature integration. …”
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Research on the influence of Siberian fir polyprenols on learning and memory of mice with an experimental model of Alzheimer's disease by Nikolay I. Suslov, Yulia S. Fedorova, Maxim L. Korobov

“…The effect of the sum of polyisoprenoids on learning and memory was studied in a dose range of 5, 20, 50, 100, 200, and 500 mg/kg. …”
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Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study by Cristina Tavares, Nathalia Nascimento, Paulo Alencar, Donald Cowan

“…The applicability of the approach is illustrated by an experimental case study based on the Scikit-Learn heuristics, in which existing model selections presented in the literature are compared with selections suggested by the approach. …”
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Effects of Rhythmic and Simple Auditory Stimulations on Learning the Timing of Sequential Motor Task in Children With DCD by Ahmad Dehnavi, Alireza Saberi Kakhaki, Hamidreza Taheri Torbati, Mohammadreza Shahabi Kaeb

“…Introduction: Children and adolescents with Developmental Coordination Disorder (DCD) usually fail to understand spatial awareness and motor timing. The present study assessed Rhythmic Auditory Stimulations (RAS) and Simple Auditory Stimulations (SAS) to facilitate the learning of timing in sequential motor task and recorded the results of their relative and absolute timing errors. …”
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Separating the albedo-reducing effect of different light-absorbing particles on snow using deep learning by L.-A. Chevrollier, A. Wehrlé, J. M. Cook, N. Pirk, L. G. Benning, A. M. Anesio, M. Tranter

“…This method includes a deep-learning emulator of a radiative transfer model (RTM) and an inversion algorithm. …”
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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm by Jing Yang, Touseef Sadiq, Jiale Xiong, Muhammad Awais, Uzair Aslam Bhatti, Roohallah Alizadehsani, Juan Manuel Gorriz

“…To overcome these challenges, the approach proposed incorporates advanced techniques such as convolutional neural networks (CNNs), an improved differential evolution (DE) algorithm for pre‐training, and a reinforcement learning (RL)‐based model for training. Developing this method presented a significant challenge due to the imbalanced classification of the Z‐Alizadeh Sani myocarditis dataset from Omid Hospital in Tehran. …”
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Reigniting work engagement through coping for burned-out academics: An open distance learning context by Bronwyn Wright, Linda Steyn, Annelize van Niekerk

“…Contribution/value-add: This research offers insights into burnout’s impact on academics and presents a framework to assist academics in managing burnout stressors while enhancing work engagement and overall well-being.…”
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Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software by Amr Abd-Elrahman, Katie Britt, Tao Liu

“… Deep learning classification of invasive species using widely-used ArcGIS Pro software and increasingly common drone imagery can aid in identification and management of natural areas. …”
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Power management in isolated microgrids using machine learning-based robust model predictive control by Chou-Yi Hsu, Amit Ved, Hannah Jessie Rani R, Zayd Ajsan Balsem, Nora Rashid Najem, Abhayveer Singh, P․Sasi Kiran, Ankita Aggarwal, Satish Kumar Samal, Alireza Kamranfar

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Topological Data Analysis and Multiple Kernel Learning for Species Identification of Modern and Archaeological Small Ruminants by Manon Vuillien, Davide Adamo, Emmanuelle Vila, Amane Agraw, Thierry Argant, Daniel Helmer, Marjan Mashkour, Abdelkader Moussous, Olivier Notter, Elena Rossoni-Notter, Isabelle Théry, Marco Corneli

“…This paper presents a case study to test the potential of topological data analysis (TDA) and multiple kernel learning (MKL) for inter-specific identification of 150 3D astragali belonging to modern and archaeological specimens. …”
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Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector by A. Gavrikov, V. Cerrone, A. Serafini, R. Brugnera, A. Garfagnini, M. Grassi, B. Jelmini, L. Lastrucci, S. Aiello, G. Andronico, V. Antonelli, A. Barresi, D. Basilico, M. Beretta, A. Bergnoli, M. Borghesi, A. Brigatti, R. Bruno, A. Budano, B. Caccianiga, A. Cammi, R. Caruso, D. Chiesa, C. Clementi, S. Dusini, A. Fabbri, G. Felici, F. Ferraro, M.G. Giammarchi, N. Giudice, R.M. Guizzetti, N. Guardone, C. Landini, I. Lippi, S. Loffredo, L. Loi, P. Lombardi, C. Lombardo, F. Mantovani, S.M. Mari, A. Martini, L. Miramonti, M. Montuschi, M. Nastasi, D. Orestano, F. Ortica, A. Paoloni, E. Percalli, F. Petrucci, E. Previtali, G. Ranucci, A.C. Re, M. Redchuck, B. Ricci, A. Romani, P. Saggese, G. Sava, C. Sirignano, M. Sisti, L. Stanco, E. Stanescu Farilla, V. Strati, M.D.C. Torri, A. Triossi, C. Tuvè, C. Venettacci, G. Verde, L. Votano

“…In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. …”
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Comparative neuropsychological analysis of audio-verbal memory features in first-formers with different learning success by G. F. Khodakovskaya

“…The article presents the results of comparative neuropsychological analysis of audio-verbal memory features in first- formers with different learning success: successful in learning or with partial and complex learning disabilities. …”
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A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments by Abdullah AlHayan, Jalal Al-Muhtadi

“…The rapid proliferation of Internet of Things (IoT) devices presents significant security challenges due to inherent vulnerabilities and increasing cyberattacks. …”
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