Anomaly Detection in Data Streams Using Machine Learning and Deep Learning by Muhammad Amin Daneshwar

“…The goal of this anomaly identification in the data streams is to find patterns that deviate substantially from the way things usually work. …”
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Ecological Effects of Local Materials in Landscape Design based on Machine Learning by Jianjian Li

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Improving medical machine learning models with generative balancing for equity and excellence by Brandon Theodorou, Benjamin Danek, Venkat Tummala, Shivam Pankaj Kumar, Bradley Malin, Jimeng Sun

“…Abstract Applying machine learning to clinical outcome prediction is challenging due to imbalanced datasets and sensitive tasks that contain rare yet critical outcomes and where equitable treatment across diverse patient groups is essential. …”
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Power sequence definition under woodworking milling on contour-milling machines by Alexander N. Chukarin, Sergey V. Golosnoy

“…As a result of the conducted research, the mechanism of the force generation under milling; rules of the forces distribution over the projections; and patterns of variation in the cut-off allowance under milling are determined and specified. …”
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Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir by Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya

“…ML classification models such as Random Forest, Decision Tree, Naïve Bayes, KNN, and SVM have demonstrated their potential in uncovering hidden patterns from data, including a person's educational history, work experience and interests. …”
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Assessing distortion in carbon fiber woven fabrics based on machine vision by Shiyue Li, Quanzhou Yao, Lin Ye

“…This work proposes a machine vision method to locate defective areas, identify defects, and describe fiber tow distribution patterns. …”
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Enhancing Registration Offices’ Communication Through Interpretable Machine-Learning Techniques by Danilo Augusto Sarti, Tommaso Bardelli, Pier Giacomo Bianchi, Anna Pia Maria Giulini

“…This study presents a protocol for applying Interpretable Machine Learning (IML) to enhance communication within Variety Registration Offices (VROs). …”
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Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms by Silviu Gresoi, Grigore Stamatescu, Ioana Făgărășan

“…By analyzing historical consumption patterns, anomaly detection techniques, and geospatial data, the proposed system enhances fraud detection capabilities across both smart and non-smart grids. …”
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Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection by Tengzi Liu, Muhammad Zohaib Hassan Shah, Xucun Yan, Dongping Yang

“…The Electroencephalogram (EEG) pattern of seizure activities is highly individual-dependent and requires experienced specialists to annotate seizure events. …”
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Analysis and prediction of infectious diseases based on spatial visualization and machine learning by Yunyun Cheng, Yanping Bai, Jing Yang, Xiuhui Tan, Ting Xu, Rong Cheng

“…Firstly, we used ArcGIS software to analyze the spatial agglomeration pattern of the number of patients in various regions of China through global spatial autocorrelation analysis, local spatial autocorrelation analysis, center of gravity trajectory migration algorithm and other statistical tools; In addition, the areas with serious COVID-19 epidemic and requiring special attention were screened out. …”
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Machine learning-assisted development of polypyrrole-grafted yarns for e-textiles by Matteo Iannacchero, Joakim Löfgren, Mithila Mohan, Patrick Rinke, Jaana Vapaavuori

“…The optimized yarns are woven into prototype fabrics with different patterns, and we demonstrate their applicability as flexible conductive wearable and heaters.…”
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Application of Machine Learning Models in Social Sciences: Managing Nonlinear Relationships by Theodoros Kyriazos, Mary Poga

“…Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. …”
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Machine learning-based single-sample molecular classifier for cancer grading by Zoia Antysheva, Nikita Kotlov, Mariia V. Guryleva, Ivan Valiev, Viktor Svekolkin, Anna Belozerova, Sheila T. Yong, Dmitry Tabakov, Alexander Bagaev, Vladimir Kushnarev

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Stability Prediction in Sustainable Energy Systems Using Machine Learning Models by Md Sarowar Hossain, Mohammad A. Abido

“…By analyzing diverse datasets covering factors like demand, supply, environmental variables, and grid dynamics, machine learning models can capture complex patterns in power system behavior. …”
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Machine Learning and Deep Learning for Wildfire Spread Prediction: A Review by Henintsoa S. Andrianarivony, Moulay A. Akhloufi

“…ML models, such as support vector machines and ensemble models, use tabular data points to identify patterns and predict fire behavior. …”
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Anomaly detection in virtual machine logs against irrelevant attribute interference. by Hao Zhang, Yun Zhou, Huahu Xu, Jiangang Shi, Xinhua Lin, Yiqin Gao

“…The LADSVM approach excels at detecting anomalies in virtual machine logs characterized by strong sequential patterns and noise. …”
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Enhancing Urban Parking Efficiency Through Machine Learning Model Integration by Sai Sneha Channamallu, Sharareh Kermanshachi, Jay Michael Rosenberger, Apurva Pamidimukkala

“…An analysis of the importance of various features clearly highlighted the influence of the day of the week on parking demand and patterns; the impact of seasonality on the volume of parking usage; and the time of day, which plays a crucial role in determining parking behavior. …”
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Predicting agricultural drought in central Europe by using machine learning algorithms by Endre Harsányi

“…Thus, this research evaluates the patterns and magnitude of agriculture droughts using Standardized Precipitation Evapotranspiration Index (SPEI) from 1926 to 2020 in eastern Hungary, and assess the performance of six machine learning models (Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), Extreme Gradient Boost (XGB), Support Vector Machines (SVM), and Multi-Layer Perceptron (ANN-MLP)) in predicting agriculture droughts. …”
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Examining peptide–gold nanoparticle interactions through explainable machine learning by Malak Gamal Abdelmeguid, Jose Isagani B. Janairo, Nishanth G. Chemmangattuvalappil

“…Specifically, the model identified sequence patterns important for peptide binding to gold, such as the residues categorized as tiny, small, and aromatic are greater than 15% in terms of composition. …”
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A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines by Yurim Choi, Inwhee Joe

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