Machine learning to predict killer whale (Orcinus orca) behaviors using partially labeled vocalization data by Sophia Sandholm

“…In a call, an orca typically varies the frequencies, varies the length, varies the temporal patterns, varies their volumes, and can use multiple frequencies simultaneously. …”
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Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data by Pei-Chun Lin, Kuan-Yen Chen, Jenhung Wang

“…This study aims to develop a machine learning-based framework for predicting the severity of highway traffic accidents by leveraging high-resolution data from Taiwan’s Electronic Toll Collection (ETC) system. …”
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Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm by Noemí DeCastro-García, Ángel Luis Muñoz Castañeda, David Escudero García, Miguel V. Carriegos

“…Selecting the best configuration of hyperparameter values for a Machine Learning model yields directly in the performance of the model on the dataset. …”
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Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors by Boryana Todorova, David Steyrl, Matthew J. Hornsey, Samuel Pearson, Cameron Brick, Florian Lange, Jay J. Van Bavel, Madalina Vlasceanu, Claus Lamm, Kimberly C. Doell

“…However, most of the predictors show divergent patterns, predicting some but not all outcomes or even having opposite effects. …”
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Optimizing Bearing Fault Diagnosis in Rotating Electrical Machines Using Deep Learning and Frequency Domain Features by Eduardo Quiles-Cucarella, Alejandro García-Bádenas, Ignacio Agustí-Mercader, Guillermo Escrivá-Escrivá

“…Scalograms proved particularly effective in identifying distinct vibration patterns for faults in bearings’ inner and outer races. …”
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Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model by Jintak Choi, Zuobin Xiong, Kyungtae Kang

“…When rolling pins are machining with CNC equipment, a sensor system is installed to collect acoustic data, analyze failure patterns, and apply RUL prediction algorithms. …”
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DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data by Ella Rannon, Sagi Shaashua, David Burstein

“…Results We introduce DRAMMA, a machine learning method for predicting new ARGs with no sequence similarity to known ARGs or any annotated gene. …”
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Dynamic Pricing Models in E-Commerce: Exploring Machine Learning Techniques to Balance Profitability and Customer Satisfaction by Xiaochen Guo, Lei Zhang

“…This study provides valuable insights into the application of advanced machine learning techniques in e-commerce pricing. …”
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Appraisal of Industrial Pollutants in Sewage and Biogas Production Using Multivariate Analysis and Unsupervised Machine Learning Clustering by Wiktor Halecki, Anna Młyńska, Krzysztof Chmielowski

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Machine Learning Classification of 3D Intracellular Trafficking Using Custom and Imaris-Derived Motion Features by Oleg Kovtun

“…The approach employs an extended gradient-boosted decision trees classifier trained on an array of synthetic trajectories designed to simulate diffusion behaviors typical of intracellular environments. <b>Results:</b> The machine learning classifier demonstrated a classification accuracy of over 90% on synthetic datasets, effectively capturing and distinguishing complex diffusion patterns. …”
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Investigating the Effects of Ground-Transmitted Vibrations from Vehicles on Buildings and Their Occupants, with an Idea for Applying Machine Learning by Marta Mikielewicz, Anna Jakubczyk-Gałczyńska, Robert Jankowski

“…Occupants inside affected buildings may experience discomfort, disrupted sleep patterns, and increased stress levels due to the pervasive nature of vibrations. …”
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Non-parametric feature selection and machine learning based seasonal GHI prediction for SAPV systems in India by Aadyasha Patel, Gnana Swathika O․V․

“…For Global Horizontal Irradiance (GHI) prediction of Stand-Alone Photovoltaic (SAPV) system, this necessitates focusing on variables that demonstrate substantial seasonal patterns. Intensity of solar radiation fluctuates all through the year owing to the Earth’s tilt and meteorological conditions. …”
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Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data by Shehu Mohammed, Neha Malhotra

“…In response to the formulated research problem, this study articulates a new multimodal machine-learning framework for early AD diagnosis. …”
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Enhancing DDoS Attacks Mitigation Using Machine Learning and Blockchain-Based Mobile Edge Computing in IoT by Mahmoud Chaira, Abdelkader Belhenniche, Roman Chertovskih

“…We evaluate multiple machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Transformer architectures, and LightGBM, using the CICDDoS2019 dataset. …”
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Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach by Rahem Rahmati, Fatemeh Zarimeidani, Farnaz Ahmadi, Hannaneh Yousefi-Koma, Abdolreza Mohammadnia, Maryam Hajimoradi, Shadi Shafaghi, Elham Nazari

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Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home by Yan Hu, Bingce Wang, Yuyan Sun, Jing An, Zhiliang Wang

“…This technology aims to recognize the activity patterns of users from a series of observations on the user’ actions and the environmental conditions, so as to avoid distress situations as much as possible. …”
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Revolutionizing Education: Harnessing Graph Machine Learning for Enhanced Problem-Solving in Environmental Science and Pollution Technology by R. Krishna Kumari

“…Its application extends far beyond mere analysis towards the profound ability to forecast ecological patterns, prescribe sustainable interventions, and tailor pollution mitigation strategies with precision and efficacy. …”
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Spatiotemporal evaluation of irrigation groundwater quality in Hungarian agricultural sites using hydrochemical and machine learning approaches by Musaab A. A. Mohammed, Norbert P. Szabó, Viktória Mikita, Péter Szűcs

“…The research integrates hydrochemical assessment with machine learning to enhance the spatiotemporal evaluation of irrigation water quality. …”
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Machine Learning Classification Strategy to Improve Streamflow Estimates in Diverse River Basins in the Colorado River Basin by Sarah Maebius, K. E. Bennett, J. Schwenk

“…Overall, land use cover data at a low temporal resolution was not sufficient to capture the irregular patterns of reservoir releases, demonstrating the importance of having high‐resolution reservoir release data sets at a global scale. …”
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Machine learning-based hydrograph modeling with LSTM: A case study in the Jatigede Reservoir Catchment, Indonesia by Neil Andika, Piter Wongso, Faizal Immaddudin Wira Rohmat, Siska Wulandari, Ammar Fadhil, Riswanto Rosi, Nabila Siti Burnama

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