Non-Invasive Biomarkers in the Era of Big Data and Machine Learning by Konstantinos Lazaros, Styliani Adam, Marios G. Krokidis, Themis Exarchos, Panagiotis Vlamos, Aristidis G. Vrahatis

“…Computational advancements, particularly in artificial intelligence and machine learning, are addressing these limitations by uncovering complex patterns in multi-modal datasets, enhancing diagnostic accuracy and facilitating personalized medicine. …”
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BLE Signal Processing and Machine Learning for Indoor Behavior Classification by Yi-Shiun Lee, Yong-Yi Fanjiang, Chi-Huang Hung, Yung-Shiang Huang

“…This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. …”
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Phyloreferences: Tree-Native, Reproducible, and Machine-Interpretable Taxon Concepts by Hilmar Lapp, Nico Cellinese, Stijn Conix

“…To fully take advantage of this wealth of data to discover and understand large-scale evolutionary and ecological patterns, computational data integration, i.e., the use of machines to link data at large scale, is crucial. …”
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From Words to Ratings: Machine Learning and NLP for Wine Reviews by Iliana Ilieva, Margarita Terziyska, Teofana Dimitrova

“…The present study aims to show how natural language processing (NLP) and machine learning methods can be applied to analyze expert-written Bulgarian wine descriptions and to extract patterns related to wine quality and style. …”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

“…Machine learning (ML) and Internet of Things (IoT) are bolting down the agriculture in the area of plant disease management, especially. …”
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Task relevant autoencoding enhances machine learning for human neuroscience by Seyedmehdi Orouji, Vincent Taschereau-Dumouchel, Aurelio Cortese, Brian Odegaard, Cody Cushing, Mouslim Cherkaoui, Mitsuo Kawato, Hakwan Lau, Megan A. K. Peters

“…We thus developed a Task-Relevant Autoencoder via Classifier Enhancement (TRACE) designed to identify behaviorally-relevant target neural patterns. We benchmarked TRACE against a standard autoencoder and other models for two severely truncated machine learning datasets (to match the data typically available in functional magnetic resonance imaging [fMRI] data for an individual subject), then evaluated all models on fMRI data from 59 subjects who observed animals and objects. …”
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Neural signals, machine learning, and the future of inner speech recognition by Adiba Tabassum Chowdhury, Ahmed Hassanein, Aous N. Al Shibli, Youssuf Khanafer, Mohannad Natheef AbuHaweeleh, Shona Pedersen, Muhammad E. H. Chowdhury

“…We analyze both traditional methods such as support vector machines (SVMs) and random forests, as well as advanced deep learning approaches like convolutional neural networks (CNNs), which are particularly effective at capturing the dynamic and non-linear patterns of inner speech-related brain activity. …”
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Prediction of Airline Ticket Price Using Machine Learning Method by Hüseyin Korkmaz

“…Traditional methods often fall short of capturing the intricate and rapidly changing patterns of airfare pricing. With the advent of machine learning algorithms, there is a growing potential to enhance the accuracy and reliability of ticket price predictions. …”
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Integration of geospatial techniques and machine learning in land parcel prediction by Nekkanti Haripavan, Subhashish Dey, Chimakurthi Harika Mani Chandana

“…By harnessing the temporal and spectral information captured by Earth observation satellites, we aim to identify patterns and trends in land parcel changes. These insights are used to train and fine-tune our machine learning models, which will subsequently forecast future land parcel developments. …”
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AI, machine learning and deep learning in cyber risk management by Ishrag Hamid, M. M. Hafizur Rahman

“…Abstract The integration of Artificial Intelligence (AI) and Machine Learning (ML) in both climate change and cyber risk management has significantly bolstered their effectiveness. …”
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Genomic prediction of plant traits by popular machine learning methods by K. N. Kozlov, M. P. Bankin, E. A. Semenova, M. G. Samsonova

“…In many cases, to identify new relationships between phenotypes and genotypes, it is preferable to use machine learning, deep learning and artificial intelligence, especially explainable artificial intelligence, capable of recognizing complex patterns. 80 sources were manually selected; while there were no restrictions on the release date, the main attention was paid to the originality of the proposed approach for use in genomic prediction. …”
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Enhancing Education with Machine Learning: Predicting Student Readability Scores by Claire Bell

“…This study explores the use of machine learning techniques to predict students' reading scores, with a particular focus on Random Forest Classification (RFC) as a reliable baseline model. …”
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Protocols for Water and Environmental Modeling Using Machine Learning in California by Minxue He, Prabhjot Sandhu, Peyman Namadi, Erik Reyes, Kamyar Guivetchi, Francis Chung

“…In contrast, ML models learn patterns from data and require different development methodologies, which existing protocols do not address. …”
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Censoring Sensitivity Analysis for Benchmarking Survival Machine Learning Methods by János Báskay, Tamás Mezei, Péter Banczerowski, Anna Horváth, Tamás Joó, Péter Pollner

“…While established methodologies exist for validating standard machine learning models, current benchmarking approaches rarely assess model robustness under varying censoring conditions. …”
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Application of machine learning techniques for churn prediction in the telecom business by Raji Krishna, D. Jayanthi, D.S. Shylu Sam, K. Kavitha, Naveen Kumar Maurya, T. Benil

“…Telecom managers and analysts investigate why customers cancel their subscriptions and analyze the behavior patterns of customers who have stopped using the services. …”
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Multi-Spectral Optimization for Tissue Probing Using Machine Learning by Yarden Tzabari Kelman, Hadas Lupa Yitzhak, Nadav Shabairou, Shahaf Finder, Zeev Zalevsky

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Predicting future brand value: The role of machine learning monitoring by Ahmad Al Adwan, Ghaiath Altrjman, Lu’ay Al-Mu’ani

“…These findings demonstrate the effectiveness of machine learning in identifying predictive patterns relevant to brand performance and provide a practical framework for leveraging digital tools to enhance brand valuation strategies. …”
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Recent advances in ultra-precision machining of lithium niobate crystals by Yebing TIAN, Chengwei WEI, Xiaomei SONG, Cheng QIAN

“…Such advancements are essential for enhancing machining efficiency, improving surface quality, and minimizing damage. …”
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Traffic travel pattern recognition based on sparse Global Positioning System trajectory data by Juan Chen, Kepei Qi, Shiyu Zhu

“…Then, support-vector machine, decision tree, and convolutional neural network are used for traffic travel pattern recognition. …”
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PATTERN FORECASTING OF PERFORMANCE INDICES FOR AIR-AND-SCREEN CLEANER FROM CHAFFING EFFICIENCY RISE by Yury Ivanovich Yermolyev, Andrey Vladimirovich Butovchenko, Artem Alexandrovich Doroshenko

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