Two Decades of Coastal Development across the Arabian Gulf: Insights from Remote Sensing and Machine Learning by B. Dahy, R. Sousa, M. Sultan, J. A. Burt

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Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning by Sangwon Lee, Yongha Hwang, Yan Jin, Sihyeong Ahn, Jaewan Park

“…Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. …”
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Computational analysis of reproducibility and image quality of 2011 and 2017 models of Heidelberg Spectralis SD-OCT machines by Kyoung A Viola Lee, Corey Tesdahl, Keith Zimmerman, Kimberly Jun, Sabrina Khalil, Alexander Shahin, Abdullah Abou-Samra, Ramesh Ayyala, Radouil Tzekov

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Safeguarding against Cyber Threats: Machine Learning-Based Approaches for Real-Time Fraud Detection and Prevention by Vikas R. Shetty, Pooja R., Rashmi Laxmikant Malghan

“…These findings provide valuable guidance for companies on choosing effective anti-fraud strategies and shed light on the adaptability of algorithms to real financial contexts, contributing to machine learning-based fraud detection.…”
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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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Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data by Shehu Mohammed, Neha Malhotra

“…The importance of this work is in the opportunity to shift diagnostic paradigms by employing deep learning algorithms, including CNNs, LSTM networks, and GNNs to analyze spatial, temporal, and relational patterns across multi-modal data. …”
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Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS by Ruizhi Zhang, Dayong Zhang, Bo Shu, Yang Chen

“…A dataset comprising 2700 known geological hazard locations in Yibin City was analyzed to extract key environmental and topographic features influencing hazard susceptibility. Several machine learning models were evaluated, including random forest, XGBoost, and CatBoost, with model optimization performed using the Sparrow Search Algorithm (SSA) to enhance prediction accuracy. …”
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Leveraging Artificial Intelligence for Smart Healthcare Management: Predicting and Reducing Patient Waiting Times with Machine Learning by Kristijan CINCAR, Todor IVAŞCU

“…The proposed system is built on a multitude of machine-learning algorithms such as Random Forest Regression, XGBoost, Support Vector Regression (SVR), and Artificial Neural Networks (ANNs) to render accurate estimations of patient waiting times. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…Furthermore, these workflows lay the foundation for implementing long-term learning algorithms, a pivotal increment for monitoring initiatives. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…Furthermore, these workflows lay the foundation for implementing long-term learning algorithms, a pivotal increment for monitoring initiatives. …”
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Monitoring the dynamics of coastal wetlands ecosystems in Brittany (France) using LANDSAT time series and machine learning by Adrien Le Guillou, Simona Niculescu

“…The study exploits the potential of satellite image time series (SITS), machine learning (ML), and Random Forest (RF) algorithms.These algorithms enable the software to learn autonomously from multiple datasets, including Landsat 4/5 and 8 SITS archive images. …”
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Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas by Yue Hu, Xin Cao, Hongyi Chen, Daoying Geng, Kun Lv

“…The logical regression, random forest, support vector machine (SVM) and adaptive boosting algorithms were used to establish models. …”
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Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review by Daniel H. de la Iglesia, Carlos Chinchilla Corbacho, Jorge Zakour Dib, Vidal Alonso-Secades, Alfonso J. López Rivero

“…This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. …”
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Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data by Jember Azanaw, Mihret Melese, Eshetu Abera Worede

“…Geographic differences in access to better water sources were found through spatial analysis, with rural areas being the most impacted. Using machine-learning algorithms, specifically Random Forest, has yielded significant insights into the factors influencing Ethiopia’s unimproved water supply. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Second, we trained ensemble machine learning (ML) algorithms that incorporated these factors, with Shapley Additive exPlanations (SHAP) value analysis quantifying predictor importance. …”
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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…Abstract In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. …”
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Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski, Izabela Rojek

“…Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. …”
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Machine learning-based prediction and classification of seawater intrusion in the hyper-arid coastal aquifer of Fujairah, UAE by Assaad Kassem, Ahmed Sefelnasr, Abdel Azim Ebraheem, Luqman Ali, Faisal Baig, Mohsen Sherif

“…Study focus: Fifteen machine learning (ML) algorithms were evaluated to predict and classify total dissolved solids (TDS) as an indicator of SWI. …”
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The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches by Lu Zhang, Lu Zhang, YuJing Lv, Mengqing Ma, Jile Lv, Jie Chen, Shang Lei, Yi Man, Guimei Xing, Yu Wang

“…Three hub genes (JDP2, ALOX5, and KPNB1) were gained by two machine learning algorithms. The nomogram constructed based on these three hub genes showed high predictive accuracy. …”
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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach by Yang Chen, Xiaojie Su, Fei Yao, Yushan Liu, Hua Xing, Yubin Ju, Zhiran Kang, Wuquan Sun, Lijun Yao, Li Gong

“…Then both datasets were randomly assigned to a training and validating dataset and a test dataset in a ratio of 7:3. Six machine learning algorithms were selected and trained by our dataset. …”
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