Purchasing Prediction Using Machine Learning Algorithms for Optimizing Inventory Management by Reza Hamdi Prayetno, Rani Destika Purba, Kyrene Wirawan, Kelvin Sweet, Evta Indra

“…The model successfully captured seasonal patterns and trends in sales data, proving its ability to forecast stock requirements. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. …”
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Predictive Analytics in Agriculture: Machine Learning Models for Coconut Tree Health by Goswami Anjali, Kirit Dhablia Dharmesh

“…Several ML algorithms are analyzed in the study for data from several sources like satellite imagery, drone based sensors, and field data, including Convolutional Neural Networks (CNNs), Random Forest and Support Vector Machines (SVMs). With integration of these data sources, ML models can find patterns, anomalies in health problems. …”
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An explainable feature selection framework for web phishing detection with machine learning by Sakib Shahriar Shafin

“…Specifically, we employ SHapley Additive exPlanations (SHAP) for global perspective and aggregated local interpretable model-agnostic explanations (LIME) to determine specific localized patterns. The proposed SHAP and LIME-aggregated FS (SLA-FS) framework pinpoints the most informative features, enabling more precise, swift, and adaptable phishing detection. …”
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Unlocking biological complexity: the role of machine learning in integrative multi-omics by Ravindra Kumar, Rajrani Ruhel, Andre J. van Wijnen

“…It offers sophisticated algorithms that can identify and discover hidden patterns and provide insights into complex biological networks. …”
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Application of machine learning algorithm to predict the behavior of stocks marketed in Brazil by Gabriel Donadio Costa, Rogério João Lunkes

“…In order to assist investors in the decision-making process, artificial intelligence tools aim at finding patterns hidden in data and providing useful, timely and accurate information. …”
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Machine learning applied to the design and optimization of polymeric materials: A review by Sudarsan M. Pai, Karim A. Shah, Sruthi Sunder, Rodrigo Q. Albuquerque, Christian Brütting, Holger Ruckdäschel

“…ML approaches can analyze vast amounts of data, uncover hidden patterns, and generate predictive models that significantly reduce the time needed to develop materials with desired properties. …”
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Machine learning tools for deciphering the regulatory logic of enhancers in health and disease by Spyros Foutadakis, Vasiliki Bourika, Ioanna Styliara, Panagiotis Koufargyris, Asimina Safarika, Eleni Karakike

“…Transcriptional enhancers are DNA regulatory elements that control the levels and spatiotemporal patterns of gene expression during development, homeostasis, and pathophysiological processes. …”
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Specifics of predicting the profitability of individual bank products based on machine learning by Inna Strelchenko, Dmytro Stognii, Anatolii Strelchenko

“…It explores the use of machine learning to build adaptive predictive models that can identify hidden patterns in financial data and provide more accurate estimates of the future profitability of banking products. …”
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A Novel Hybrid Machine Learning Framework for Wind Speed Prediction by Rhafes Mohamed Yassine, Moussaoui Omar, Raboaca Maria Simona, Mihaltan Traian Candin

“…However, using wind power is challenging due to the variability and unpredictability of wind patterns. Consequently, the ability to predict wind power in advance is crucial. …”
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Devising a Breast Cancer Diagnosis Protocol through Machine Learning by Tooba Mujtaba, Saif Ullah Hashmi, Usama Bin Imtiaz, Sheikh Jameel Fathima Nusra

“…The support vector machine (SVM) and decision tree models of machine learning were used to evaluate the prognostic and diagnostic significance. …”
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Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics by Pankaj Lal Sahu, Sukumaran Sandeep, Hariprasad Kodamana

“…MLWP models realistically captured the absolute vorticity patterns and their advection, demonstrating their ability to represent the dynamics underlying TC translation. …”
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Machine Learning Advancements in Urban Traffic Simulation: A Comprehensive Survey by Harshit Maheshwari, Li Yang, Richard W. Pazzi

“…Traditional simulation models often struggle to capture the intricacies of urban traffic patterns, leading to unrealistic simulations, which negatively affect traffic management and urban planning. …”
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Research on Machine Vision–Based Intelligent Tracking System for Maintenance Personnel by Yinglin Ma, Hongmei Shi, Yao Wang, Baofeng Li

“…Our intelligent monitoring approach involves three key steps: train maintenance personnel identification, tracking of maintenance activities to generate movement trajectories, and analysis of movement patterns to detect anomalous behavior. This study primarily addresses the challenges of personnel identification and process tracking. …”
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Alloys innovation through machine learning: a statistical literature review by Alireza Valizadeh, Ryoji Sahara, Maaouia Souissi

“…The critical analysis of the literature not only reveals prevailing trends and patterns but also shines a light on the inherent limitations within the traditional trial-and-error paradigm.…”
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On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction by Yanwen Wang, Mahdi Khodadadzadeh, Raúl Zurita-Milla

“…Our results showed the evaluations follow similar patterns in all datasets and predictions: when dissimilarity is low (usually lower than 30%), RDM-CV provides the most accurate evaluation results. …”
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Healthcare Prediction Using Novel Machine Learning Methods and Metaheuristic Algorithm by Yazdan Ashgevari, Behrouz Alefy, Faranak Kazemi

“…ML models are trained on historical healthcare data to learn patterns and relationships between different variables. …”
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Machine Learning Applications in Use-Wear Analysis: A Critical Review by Anastasia Eleftheriadou, Shannon P. McPherron, João Marreiros

“…Use-wear analysis examines the macroscopic and microscopic patterns of traces left on tool surfaces as a result of use. …”
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TBM shield mud cake prediction model based on machine learning by Qi Zhang, Peng Xu, Jing Zhang, Zhao Yang, Yu Li, Xintong Kong, Xiao Yuan

“…IntroductionDuring tunnel boring machine (TBM) shield tunneling in clayey strata, the excavated soil consolidates on the cutter head or cutting tools, forming mud cakes that significantly impact the efficiency of shield tunneling.MethodsTo predict mud cakes during shield tunneling, four distinct supervised machine learning models, including logistic regression, support vector machine, random forest, and BP neural network were employed. …”
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Predictive athlete performance modeling with machine learning and biometric data integration by Qin Jianjun, Haytham F. Isleem, Walaa J. K. Almoghayer, Mohammad Khishe

“…By merging physiological signals i.e., Heart rate variability, oxygen consumption, muscle activation patterns, with psychological signals i.e., mental toughness, athlete engagement, group cohesion along with contextual training data, we create a hybrid model that performs superiorly as compared to traditional unidimensional models. …”
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