A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings by Hafiz Muhammad Shakeel, Shamaila Iram, Richard Hill, Hafiz Muhammad Athar Farid, Akbar Sheikh-Akbari, Farrukh Saleem

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Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications by Ashfakul Karim Kausik, Adib Bin Rashid, Ramisha Fariha Baki, Md Mifthahul Jannat Maktum

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Unlocking the Potential of Remanufacturing Through Machine Learning and Data-Driven Models—A Survey by Yong Han Kim, Wei Ye, Ritbik Kumar, Finn Bail, Julia Dvorak, Yanchao Tan, Marvin Carl May, Qing Chang, Ragu Athinarayanan, Gisela Lanza, John W. Sutherland, Xingyu Li, Chandra Nath

“…Recently, the integration of cutting-edge technologies, such as sensor-based product data acquisition and storage, data analytics, machine health management, artificial intelligence (AI)-driven scheduling, and human–robot collaboration (HRC), in remanufacturing procedures has received significant attention from remanufacturers and the circular economy community. …”
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Analysis and training of a traffic sign recognition neural network model by A. U. Mentsiev, T. G. Aigumov, E. M. Abdulmukminova

Subjects: “…data analysis…”
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Self-supervised feature learning for acoustic data analysis by Ahmet Pala, Anna Oleynik, Ketil Malde, Nils Olav Handegard

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Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application by Ingrid Cintura, Antonio Arenas

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Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results by Andy Victor Pakpahan, Fahmi Reza Ferdiansyah, Robby Gustian, Muhammad Nur Faiz, Sukma Aji

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Lithological Classification Using ZY1-02D Hyperspectral Data by Means of Machine Learning and Deep Learning Methods in the Kohat–Pothohar Plateau, Khyber Pakhtunkhwa, Pakistan by Waqar Ahmad, Lei Liu, Zhenhua Guo, Yasir Shaheen Khalil, Nazir Ul Islam, Fakhrul Islam

“…In this study, ZY1-02D hyperspectral image (HSI) data with moderate spectral and very high spatial resolution were employed for lithological mapping using spectral indices along with support vector machine (SVM) machine learning and spatial–spectral transformer (SSTF) deep learning methods in the Kohat–Pothohar Plateau at the eastern edge of the Main Boundary Thrust (MBT) in Pakistan. …”
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Application of Machine Learning Methods for Gravity Anomaly Prediction by Katima Zhanakulova, Bakhberde Adebiyet, Elmira Orynbassarova, Ainur Yerzhankyzy, Khaini-Kamal Kassymkanova, Roza Abdykalykova, Maksat Zakariya

“…This study evaluates machine learning (ML) methods—Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Ensemble of Trees—for predicting gravity anomalies in southeastern Kazakhstan and compares their effectiveness with traditional Kriging interpolation. …”
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Data harmonization for the analysis of personalized treatment of psychosis with metacognitive training by Caroline König, Pedro Copado, Maria Lamarca, Wafaa Guendouz, Rabea Fischer, Merle Schlechte, Vanessa Acuña, Fabrice Berna, Łucasz Gawęda, Alfredo Vellido, Àngela Nebot, Cecilio Angulo, Susana Ochoa

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Color-Based Lifetime Estimation of LEDs Using Spectral Power Distribution Prediction Through Analytical and Machine Learning Models by J. Lokesh, Savitha G. Kini, M. G. Mahesha, Anjan N. Padmasali

“…The study also compares chromaticity coordinate projections with the TM-35-19 projection method and explores lifetime predictions using CCT binning, as outlined in ANSI C78.377, to assess long-term stability. Both analytical and machine learning (ML) models are employed for SPD prediction, with the support vector machine demonstrating superior performance. …”
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Machine Learning for Accurate Office Room Occupancy Detection Using Multi-Sensor Data by Yusuf Ibrahim, Umar Yusuf Bagaye, Abubakar Ibrahim Muhammad

“…In this paper, we present a comparative study of several machine learning (ML) approaches for accurate office room occupancy detection through the analysis of multi-sensor data. …”
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Towards a Conceptual Modeling of Trustworthiness in AI-Based Big Data Analysis by Sebastian Bruchhaus, Alexander Kreibich, Thoralf Reis, Marco X. Bornschlegl, Matthias L. Hemmje

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Activity recognition in motor-manual cross-cutting operations by machine learning on multimodal data by Stelian Alexandru Borz, Tomi Kaakkurivaara, Gabriel Osei Forkuo, Nopparat Kaakkurivaara

“…In forest operations, established time-study methods, such as the use of a stopwatch and video recording, have dominated for several years. Advancements in machine learning and innovative data loggers present opportunities to reconsider and enhance these methods. …”
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Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

“…Consequently, there is no unified approach for predicting wildfire occurrences using remote sensing data and AI techniques. The goal of this study is to explore the potential of predicting wildfire occurrences using various available environmental parameters - meteorological, geo-spatial, and anthropogenic - and machine learning (ML) algorithms. …”
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Predicting the infecting dengue serotype from antibody titre data using machine learning. by Bethan Cracknell Daniels, Darunee Buddhari, Taweewun Hunsawong, Sopon Iamsirithaworn, Aaron R Farmer, Derek A T Cummings, Kathryn B Anderson, Ilaria Dorigatti

“…We applied four machine learning classifiers and multinomial logistic regression to the titre data to predict the infecting serotype. …”
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Collaborative Data Cleaning Framework: a Pilot Case Study for Machine Learning Development by Nikolaus Parulian, Bertram Ludäscher

“… This study experiments with collaborative data cleaning, a pivotal phase in data preparation for both analysis and machine learning. …”
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A synthetic data-driven machine learning approach for athlete performance attenuation prediction by Mauricio C. Cordeiro, Ciaran O. Cathain, Ciaran O. Cathain, Lorcan Daly, Lorcan Daly, David T. Kelly, David T. Kelly, Thiago B. Rodrigues

“…IntroductionAthlete performance monitoring is effective for optimizing training strategies and preventing injuries. However, applying machine learning (ML) frameworks to this domain remains challenging due to data scarcity limitations. …”
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Thermographic Data Processing and Feature Extraction Approaches for Machine Learning-Based Defect Detection by Alexey Moskovchenko, Michal Svantner

“…Infrared thermography is a non-destructive testing method used to detect defects in materials and structures. Machine learning algorithms have been applied to thermographic data to automate the defect detection process. …”
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Machine learning enables legal risk assessment in internet healthcare using HIPAA data by Shixian Liu, Hailing Liu, Siyu Fan, Leming Song, Zeyu Wang

“…Abstract This study explores how artificial intelligence technologies can enhance the regulatory capacity for legal risks in internet healthcare based on a machine learning (ML) analytical framework and utilizes data from the health insurance portability and accountability act (HIPAA) database. …”
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