DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data by Ella Rannon, Sagi Shaashua, David Burstein

“…DRAMMA utilizes various features, including protein properties, genomic context, and evolutionary patterns. The model demonstrated robust predictive performance both in cross-validation and an external validation set annotated by an empirical ARG database. …”
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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

“…The primary objective is to develop a pricing model that effectively balances profitability with customer satisfaction by leveraging sequential data, such as time-series and customer behavior patterns. The approach utilizes LSTM’s ability to capture long-term dependencies in sequential data, while optimization methods like Stochastic Gradient Descent (SGD) enhance model convergence and performance. …”
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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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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

“…Also, a level of gain is assigned to each partition allowing us to study patterns and allocate whose samples are more profitable. …”
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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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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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A Comprehensive Review of Transformer Winding Diagnostics: Integrating Frequency Response Analysis with Machine Learning Approaches by Meysam Beheshti Asl, Issouf Fofana, Fethi Meghnefi, Youssouf Brahami, Joao Pedro Da Costa Souza

“…However, traditional FRA interpretation relies heavily on visual and subjective comparison of frequency response curves, which can introduce human bias and lead to inconsistent results. Integrating Machine Learning (ML) with FRA can significantly enhance fault diagnosis by automatically identifying complex patterns within the data that are difficult to detect using through human analysis. …”
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Analysis of Combined Strength Training with Small-Sided Games in Football Education Using Machine Learning Methods by Huseyin Guneralp, Hasan Ulas Yavuz, Boran Sekeroglu, Musa Oytun, Cevdet Tinazci

“…Eighteen physical measurements of the players were obtained using sensitive devices before and after they were completed. Four tree-based machine learning models, decision tree, random forest, gradient boosting, and extreme gradient boosting, were applied to solve the complex pattern of training strategies using the measurements. …”
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ML‐UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper by Warren G. Hill, Bryce MacIver, Gary A. Churchill, Mariana G. DeOliveira, Mark L. Zeidel, Marcelo Cicconet

“…Abstract The void spot assay has gained popularity as a way of assessing functional bladder voiding parameters in mice, but analyzing the size and distribution of urine spot patterns on filter paper with software remains problematic due to inter‐laboratory differences in image contrast and resolution quality and non‐void artifacts. …”
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Machine Learning Insights into the Last 400 Years of Etna Lateral Eruptions from Historical Volcanological Data by Arianna Beatrice Malaguti, Claudia Corradino, Alessandro La Spina, Stefano Branca, Ciro Del Negro

“…Here, we applied a machine learning technique to automate the analysis of these datasets, handling intricate patterns that are not easily captured by explicit commands. …”
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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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Investigation of brain response to acquisition and learning the second languages based on EEG signals and machine learning techniques by Talal A. Aldhaheri, Sonali B. Kulkarni, Pratibha R. Bhise, Baraq Ghaleb

“…By comparing brain activity patterns between Arabic and Hindi, this study contributes to neurolinguistics and offers data that can be used to develop more effective language learning strategies and interventions.…”
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