Deep Learning for Anomaly Detection in CNC Machine Vibration Data: A RoughLSTM-Based Approach by Rasım Çekik, Abdullah Turan

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Leveraging machine learning to predict residential location choice: A comparative analysis by Vahid Noferesti, Hamid Mirzahossein

“…A comparative analysis of various machine learning algorithms reveals that XGBoost and gradient boosting models significantly outperform traditional methods, achieving a 42 % accuracy rate in predicting residential location choices on the 33 % validation data of household travel survey data from MWCOG. …”
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Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification by Md Takbir Alam, Md Ashibul Islam Khan, Nahian Nakiba Dola, Tahia Tazin, Mohammad Monirujjaman Khan, Amani Abdulrahman Albraikan, Faris A. Almalki

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The Application of Machine Learning Algorithms for Text Mining based on Sentiment Analysis Approach by Reza Samizade, Elnaz Mahmoudi Saeid Abad

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Earthquake induced liquefaction hazard analysis for Chittagong City using machine learning by Md. Mehedi Hasan, Md. Zillur Rahman, Abul Kashem Faruki Fahim

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Comparative analysis of machine learning algorithms for predicting depression among individuals with diabetes by Hind Bourkhime, Noura Qarmiche, Soumaya Benmaamar, Nada Lazar, Mohammed Omari, Mohamed Berraho, Nabil Tachfouti, Samira El Fakir, Hanan El Ouahabi, Nada Otmani

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Internet of Things-Based Smart Infant-Incubators Using Machine Learning Analysis by Mahmoud Gamal, Ibrahim Radi, Amr Yousef, and Ali Gaber Mohamed Ali

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Inspection Data-Driven Machine Learning Models for Predicting the Remaining Service Life of Deteriorating Bridge Decks by Gitae Roh, Changsu Shim, Hyunhye Song

“…Environmental zoning was applied based on regional conditions, while structural zoning was performed according to load characteristics, thereby allowing the classification of deck regions into moment zones and cantilever sections. Machine learning models were employed to identify dominant deterioration mechanisms, with the validity of the zoning classification being evaluated via model accuracy and SHAP value analysis. …”
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Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning by Jayasree R, Sheela Selvakumari

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Part B: Innovative Data Augmentation Approach to Boost Machine Learning for Hydrodynamic Purposes—Computational Efficiency by Hamed Majidiyan, Hossein Enshaei, Damon Howe, Eric Gubesch

“…We previously highlighted the sensitivity of trained models to noise, the importance of computational efficiency, and the need for feature engineering/compactness in hydrodynamic models due to the stochastic nature of waves. A novel data analysis framework was introduced with two purposes to augment data for machine learning (ML) models: transferring features from high-fidelity to low-fidelity surrogates and enhancing simulation data and increasing computational efficiency. …”
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A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data by Zhou Hao Leong, Shaun Ray Han Loh, Leong Chai Leow, Thun How Ong, Song Tar Toh

“…More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. …”
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Land Use Analysis Using Machine-Learning Based on Cloud Computing Platform by Syukur Toha Prasetyo, Fahmi Arief Rahman, Sinar Suryawati, Slamet Supriyadi, Eko Setiawan

“…The application of machine-learning on a cloud computing platform (Google Earth Engine, GEE) in land use analysis enables efficient and rapid processing of spatial data on a wide scale. …”
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Methodological Integration of Machine Learning and Geospatial Analysis for PM10 Pollution Mapping by Kalid Hassen Yasin, Muaz Ismael Yasin, Anteneh Derribew Iguala, Tadele Bedo Gelete, Erana Kebede

“…Traditional approaches inadequately capture complex predictor-pollutant interactions, whereas machine learning (ML) offers a superior capacity for modelling nonlinear relationships. …”
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Analysis Of Backscatter To Extraction Of Shoreline Using Machine Learning Methods In The Bangkalan Regency by Arifin Fahmi, Wicaksono Ashari

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Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data by Munib Mesinovic, Peter Watkinson, Tingting Zhu

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Estimating Nitrogen Dioxide Levels Using Open Data and Machine Learning: A Comparative Modeling Study by D. Varam, R. Mitra, F. Kamran, D. A. Abuhani, H. Sulieman, I. Zualkernan

“…Specifically, predictions for urban, rural, and mixed cities demonstrated that urban areas exhibited higher NO₂ concentrations, while rural regions showed comparatively lower levels. The analysis underscores the importance of tailoring models to regional and temporal contexts, affirming that open-source data, combined with machine learning techniques, can effectively estimate NO₂ pollution levels across diverse environments.…”
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Integrating Multi-Source Urban Data with Interpretable Machine Learning for Uncovering the Multidimensional Drivers of Urban Vitality by Yuchen Xie, Jiaxin Zhang, Yunqin Li, Zehong Zhu, Junye Deng, Zhixiu Li

“…This study introduces an interpretable machine learning framework, using Nanchang, China as a case study. …”
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A Systematic Literature Review of Machine Learning-Based Personality Trait Detection Using Electroencephalographic Data by Celina Rieck, Pascal Penava, Ricardo Buettner

“…Deep learning models, particularly hybrid architectures, outperform traditional machine learning classifiers, highlighting the advantage of deep feature extraction in EEG data processing. …”
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A secure and efficient encryption system based on adaptive and machine learning for securing data in fog computing by Priyanka Rajan Kumar, Sonia Goel

“…This research introduces a novel adaptive encryption framework powered by machine learning to address these security concerns. …”
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Stacking ensemble machine learning for predicting land surface temperature hotspots using landsat 9 data by Momohjimoh Abdulsalami, Dahiru Dahuwa, Saratu Muhammad Hussaini, Yahaya Jibrin Danjuma, Michael Adewale Ibitomi, Danga Onimisi Abdulmalik, Bunmi Oyekola Isaac, Zainab Usman, Joseph Omeiza Alao, Aliyu Abdullateef

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