Improved Intelligent Condition Monitoring with Diagnostic Indicator Selection by Urszula Jachymczyk, Paweł Knap, Krzysztof Lalik

“…By applying the proposed method, it was possible to successfully filter out redundant features, enabling simpler machine learning (ML) models to match or even surpass the performance of more complex deep learning (DL) architectures. …”
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Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligence by Mohammed Salman C K, Muskan Beura, Archana Singh, Anil Dahuja, Vinayak B. Kamble, Rajendra P. Shukla, Sijo Joseph Thandapilly, Veda Krishnan

“…Non-enzymatic sensors offer superior stability and repeatability in complex matrices, enabling real-time glucose quantification across multiple timepoints without enzyme degradation constraints. Machine learning algorithms, both supervised and unsupervised, enhance predictive accuracy by elucidating complex relationships within digestion data. …”
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Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review by Vishal Gaurav, Chander Grover, Mehul Tyagi, Suman Saurabh

“…In healthcare, AI encompasses various subfields, including machine learning, deep learning, natural language processing, and expert systems. …”
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Balancing Explainability and Privacy in Bank Failure Prediction: A Differentially Private Glass-Box Approach by Junyoung Byun, Jaewook Lee, Hyeongyeong Lee, Bumho Son

“…Predicting bank failures is a critical task requiring balancing the need for model explainability with the necessity of preserving data privacy. Traditional machine learning models often lack transparency, which poses challenges for stakeholders who need to understand the factors leading to predictions. …”
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Heuristic Forest Fire Detection Using the Deep Learning Model with Optimized Cluster Head Selection Technique by Sengottaiyan N., Ananthi J., Rajesh Sharma R., Hamsanandhini S., Sungheetha Akey, Chinnaiyan R., Ketema Adare Gemeda

“…The major goal is to augment the accuracy and efficiency of forest fire prediction, leveraging the capabilities of advanced machine learning algorithms and optimized sensor network management. …”
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Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis by Haoyu Wang, Xihe Qiu, Bin Li, Xiaoyu Tan, Jingjing Huang

“…It demonstrated superior diagnostic performance compared to conventional machine learning models and existing deep learning approaches, effectively integrating domain knowledge with data-driven learning to produce explainable representations and robust generalization capabilities, which can potentially be utilized for clinical use. …”
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Monitoring of ball bearings via vibration analysis and envelope technique for predictive maintenance purposes by Adiel Pessoa, Paulo Cezar Büchner

“…The next step we will intend to explore the new technologies like machine learning and artificial intelligence, to also analyze all variants of defects in a bearing. …”
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Toward improving precision and complexity of transformer-based cost-sensitive learning models for plant disease detection by Manh-Tuan Do, Manh-Hung Ha, Duc-Chinh Nguyen, Oscal Tzyh-Chiang Chen, Oscal Tzyh-Chiang Chen

“…This study introduces an automated system for early disease detection in plants that enhanced a lightweight model based on the robust machine learning algorithm. In particular, we introduced a transformer module, a fusion of the SPP and C3TR modules, to synthesize features in various sizes and handle uneven input image sizes. …”
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An efficient convolution neural network method for copy-move video forgery detection by Mohamed Meselhy Eltoukhy, Faisal S. Alsubaei, Akram M. Mortda, Khalid M. Hosny

“…Consequently, specialists can employ the suggested method as a machine-learning instrument for detecting fake videos in real-time.…”
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Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete by Liyun Cui, Peiyuan Chen, Liang Wang, Jin Li, Hao Ling

“…The prediction of concrete strength is an interesting point of investigation and could be realized well, especially for the concrete with the complex system, with the development of machine learning and artificial intelligence. Therefore, an excellent algorithm should put emphasis to receiving increased attention from researchers. …”
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A Study on the Differences in Optimized Inputs of Various Data-Driven Methods for Battery Capacity Prediction by Kuo Xin, Fu Jia, Byoungik Choi, Geesoo Lee

“…This paper extracts 11 types of lithium battery-related health features, and experiments are conducted on two traditional machine learning networks and three advanced deep learning networks in three aspects of input differences. …”
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Analysis and prediction of condylar resorption following orthognathic surgery by Pieter-Jan Verhelst, Sigrid Janssens, Harold Matthews, Giacomo Begnoni, Peter Claes, Eman Shaheen, Hilde Peeters, Constantinus Politis, Reinhilde Jacobs

“…Univariable analysis on a condylar level also identified compressive movements of the ramus and a higher mandibular plane angle as risk factors. Using machine learning for the multivariable analysis, the amount of mandibular advancement was the most important predictor for condylar resorption. …”
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Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks by Alice Nureen Adhiambo Omondi, Yashon Ouma, Simon Njoroge Mburu, Cleophas Mecha Achisa

“…The use of earth observations and machine learning methods has not been done extensively in developing countries, specifically, in water quality monitoring and management. …”
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Smart Contracts and Shared Platforms in Sustainable Health Care: Systematic Review by Carlos Antonio Marino, Claudia Diaz Paz

“…A quantitative assessment of the studies based on machine learning and data reduction methodologies was complemented with a qualitative, in-depth, detailed review of the frameworks propounded in the literature. …”
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A Specific Time Lag Regulation of Soil Moisture Across Layers on Soil Salinization in the Northeast Tibetan Plateau Agroecosystem by Di Wei, Ziqi Zhang, Lin Yan, Jia Yu, Yun Zhang, Bo Wang

“…Based on Landsat 8 satellite imagery and ERA5-Land reanalysis datasets, this study explored the variation characteristics of soil water and salt in the northeast Tibetan Plateau from 2013 to 2023, inferred by geostatistical methods like ridge regression, windowed cross correlation, and machine learning algorithms. The results show that the negative correlation effect between deep soil moisture (100–289 cm) and soil salinization is stronger. …”
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Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis by Sunil L. Bangare, Deepali Virmani, Girija Rani Karetla, Pankaj Chaudhary, Harveen Kaur, Syed Nisar Hussain Bukhari, Shahajan Miah

“…The application of new technologies like machine learning, deep learning, and other models support doctors, nurses, and radiologists to predict heart disease effectively. …”
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Artificial intelligence artificial muscle of dielectric elastomers by Dongyang Huang, Jiaxuan Ma, Yubing Han, Chang Xue, Mengying Zhang, Weijia Wen, Sheng Sun, Jinbo Wu

“…Establishing an AM material database is highly valuable, as seemingly minor material data can be correlated with descriptors and target values via machine learning. Through material data mining integrating materials science and data science, we can predict potential breakthroughs in AM materials. …”
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Differential expression of plasma proteins and pathway enrichments in pediatric diabetic ketoacidosis by Paolo Spagnolo, Enis Cela, Maitray A. Patel, David Tweddell, Mark Daley, Cheril Clarson, Saverio Stranges, Gediminas Cepinskas, Douglas D. Fraser

“…Data analysis was performed using multivariate statistics, machine learning, and bioinformatics. Results This study identified 214 differentially expressed proteins (162 upregulated, 52 downregulated; adj P < 0.05 and a fold change > 2), reflecting cellular dysfunction and metabolic stress in severe DKA. …”
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A feasibility study on using soft insoles for estimating 3D ground reaction forces with incorporated 3D-printed foam-like sensors by Nick Willemstein, Saivimal Sridar, Herman van der Kooij, Ali Sadeghi

“…These results were comparable to or outperformed other works that used commercial force-sensing resistors with machine learning. Four participants participated in three trials over a week, which showed a decrease in estimation performance over time but stayed on average 11.35% RMS and 8.6% MAE after a week with the performance seeming consistent between days two and seven. …”
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Applying model-based recursive partitioning to improve pedestrian exposure models to support transportation safety analyses by Jakob Wiegand, Vikash Gayah

“…NB regression) with recursive data partitioning techniques commonly found in tree-based machine learning methods. This innovative approach yields a collection of exposure models stratified according to selected input variables with unique relationships between explanatory variables and exposure. …”
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