An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks by Thanh Han-Trong, Hinh Nguyen Van, Huong Nguyen Thi Thanh, Vu Tran Anh, Dung Nguyen Tuan, Luu Vu Dang

“…Those results of the evaluated algorithms through the coefficient F1-score are greater than 94% and the highest value is 97.65%.…”
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Machine learning insights on activities of daily living disorders in Chinese older adults by Huanting Zhang, Wenhao Zhou, Jianan He, Xingyou Liu, Jie Shen

“…Nine machine learning algorithms, including neural networks and an ensemble model, were employed with a 2/3 training and 1/3 testing split. …”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Due to their limits, machine learning algorithms outperform traditional methods in real-time fault diagnosis, predictive maintenance, and multi-fault categorization. …”
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Consumer Happiness in the Purchase of Electric Vehicles: a Fuzzy Logic Model by Fernando Lámbarry-Vilchis, Aboud Barsekh Onji, Leticia Refugio Chavarría López, Paola Judith Maldonado Colín

“…This research was conducted using a fuzzy Delphi method survey targeting a specific consumer group and two fuzzy inference systems: a multi-input single-output FIS model and an FIS Tree employing a hierarchical fuzzy inference structure, which leverages the survey's training data to optimize the models using different machine learning algorithms. The FIS tree model demonstrated superior efficacy in predicting the consumer satisfaction index, achieving an average forecast error of 0.65%. …”
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Comprehensive protein datasets and benchmarking for liquid–liquid phase separation studies by Carlos Pintado-Grima, Oriol Bárcenas, Eva Arribas-Ruiz, Valentín Iglesias, Michał Burdukiewicz, Salvador Ventura

“…Moreover, we describe limitations in classical and state-of-the-art predictive algorithms by providing the most comprehensive benchmark to date. …”
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Construction and Comprehensive Prognostic Analysis of a Novel Immune-Related lncRNA Signature and Immune Landscape in Gastric Cancer by Xiaolong Liang, Lang Zha, Gangfeng Yu, Xiong Guo, Chuan Qin, Anqi Cheng, Ziwei Wang

“…Therefore, effective predictive biomarkers are urgently needed for GC patients to realize the benefits of immunotherapy. …”
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Extending the forecasting horizon of daily new COVID-19 cases using non-pharmaceutical measures and the effective reproduction number (Rt): A deep learning-based framework by Tuga Mauritsius

“…The inclusion of additional variables was found to diminish the predictive accuracy of DL algorithms.…”
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Unbalance Responses of Rotor/Stator Systems with Nonlinear Bearings by the Time Finite Element Method by D. Demailly, F. Thouverez, L. Jézéquel

“…The current trend is to take into account many kinds of non-linearities in order to obtain more realistic predictions. The use of algorithms based on nonlinear methods is therefore needed. …”
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Construction of the miRNA/Pyroptosis-Related Molecular Regulatory Axis in Abdominal Aortic Aneurysm: Evidence From Transcriptome Data Combined With Multiple Machine Learning Approa... by Yongchao Su, Chuangang Lu, Shuchen Chen

“…Conclusion: A predictive model (PRG classifier) incorporating eight PRGs through multiple machine learning algorithms was developed and validated. …”
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Authorship identification methods in student plagiarism detection by A. I. Paramonov, I. A. Trukhanovich

“…In the modern educational context the problem of plagiarism is urgent and requires the development of effective methods of detection and prevention of this phenomenon. …”
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From LMS to adaptive training systems by Y. B. Popova

“…The use of information technology and, in particular, learning management systems, increases the ability of both the teacher and the learner to achieve their goals in the educational process. Such systems provide educational content, help organize and monitor training, collect progress statistics, and can also take into account the individual characteristics of each user of the system. …”
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AI-driven healthcare: Fairness in AI healthcare: A survey. by Sribala Vidyadhari Chinta, Zichong Wang, Avash Palikhe, Xingyu Zhang, Ayesha Kashif, Monique Antoinette Smith, Jun Liu, Wenbin Zhang

“…We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. …”
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The Effect of Derived Features on Art Genre Classification with Machine Learning by Didem Abidin

“…Although this process was used to be done by art experts before, now artificial intelligence techniques may help people manage this classification task. The algorithms used for classification are already improved, and now they can make classifications and predictions for any kind of genre classification. …”
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Digital Methods to Study (and Reduce) the Impact of Disinformation by Miriam Di Lisio, Domenico Trezza

“…The second is that algorithms could be sources of bias. Social media companies need to be very careful about relying on automated classification. …”
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Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Frimpong Twum, Gaddafi Abdul-Salaam

“…The focus on contextual nonbiomedical measurements using a supervised machine learning modeling technique is aimed at creating an understanding of the reasons why treatment default occurs, including identifying important contextual parameters that contribute to treatment default. The predicted accuracy scores of four supervised machine learning algorithms, namely, gradient boosting, logistic regression, random forest, and support vector machine were 0.87, 0.90, 0.81, and 0.77, respectively. …”
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Early identification of children with Attention-Deficit/Hyperactivity Disorder (ADHD). by Yang S Liu, Fernanda Talarico, Dan Metes, Yipeng Song, Mengzhe Wang, Lawrence Kiyang, Dori Wearmouth, Shelly Vik, Yifeng Wei, Yanbo Zhang, Jake Hayward, Ghalib Ahmed, Ashley Gaskin, Russell Greiner, Andrew Greenshaw, Alex Alexander, Magdalena Janus, Bo Cao

“…Key predictive factors included EDI subdomain scores, sex, and socioeconomic status. …”
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Combining CFD and AI/ML Modeling to Improve the Performance of Polypropylene Fluidized Bed Reactors by Nayef Ghasem

“…It also combines CFD with artificial intelligence and machine learning (AI/ML) algorithms, like artificial neural networks (ANN), resulting in a powerful predictive tool for accurately predicting reactor metrics based on operating conditions. …”
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Optimising 3D-printed carbon fibre composites using machine learning: Balancing strength and efficiency by José Humberto S. Almeida, Jr., Guilherme Ferreira Gomes

“…This study introduces a data-driven framework that integrates machine learning (ML) and genetic algorithms (GA) to optimise interlaminar strength and minimise printing time simultaneously. …”
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Exploring Protein Conformational Changes Using a Large‐Scale Biophysical Sampling Augmented Deep Learning Strategy by Yao Hu, Hao Yang, Mingwei Li, Zhicheng Zhong, Yongqi Zhou, Fang Bai, Qian Wang

“…Abstract Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. …”
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NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil by Gaia Meoni, Leonardo Tenori, Francesca Di Cesare, Stefano Brizzolara, Pietro Tonutti, Chiara Cherubini, Laura Mazzanti, Claudio Luchinat

“…By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. …”
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