Modern Methods for Diagnosing Faults in Rotor Systems: A Comprehensive Review and Prospects for AI-Based Expert Systems by Oleksandr Roshchupkin, Ivan Pavlenko

“…Some techniques like the vibration signal analysis method, spectral analysis, thermography, ultrasound diagnosis, and machine learning algorithms for predicting failure are of particular interest among them. …”
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Analyzing the compressive performance of lightweight foamcrete and parameter interdependencies using machine intelligence strategies by Wang Guoyuan, Fan Wenbo, Shi Qingbin, Luo Yingqi

“…A sensitivity analysis was conducted to determine how important certain aspects were. For predicting foamcrete’s compressive strength, MEP was better than GEP. …”
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An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data by Amena Mahmoud, Eiko Takaoka

“…The selected features were then used to train a stacking ensemble model, which combined multiple base learners, including Multi-Layer Perceptron (MLP), Random Forest (RF) model, K-nearest neighbor (KNN) model, and Support vector machine (SVM), with a meta-learner Extreme Gradient Boosting (Xgboost) model to make final predictions. The stacking ensemble achieved an accuracy of (97%), outperforming individual machine learning algorithms and traditional diagnostic methods. …”
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…The accuracy of the DLCN score in predicting FH was first evaluated by examining the proportion of patients with positive DNA tests relative to those with a DLCN score of 6 and above, the threshold for genetic testing. …”
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Water Quality Monitoring Using Landsat 8 OLI in Pleasant Bay, Massachusetts, USA by Haley E. Synan, Brian L. Howes, Sara Sampieri, Steven E. Lohrenz

“…Satellite-derived estimates of chlorophyll-a and Secchi depth were acquired using various algorithms including the “Case-2 Regional/Coast Color” (C2RCC), “Case-2 Extreme” (C2X), l2gen processor, and a random forest machine learning algorithm. …”
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Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization. by Weihao Wang, Wei Zhu, Janos Hajagos, Laura Fochtmann, Farrukh M Koraishy

“…Estimated glomerular filtration rate (eGFR) decline is associated with negative health outcomes, but the use of decision tree algorithms to predict eGFR decline is underreported. …”
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Paint shop vehicle sequencing based on quantum computing considering color changeover and painting quality by Jing Huang, Qing Chang, Hua-Tzu Fan

“…We use a machine learning model pretrained on historical data to predict the probability of painting defect. The problem is formulated as a combinational optimization problem with two cost components, i.e., color changeover cost and repair cost. …”
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Long-Short Term Memory Networks and Synthetic Data for Heavy Vehicle Rollover Prevention by Guido Perboli, Antonio Tota, Filippo Velardocchia

“…Considering the same and other connected implications, the necessity for techniques able to estimate and predict overturning eventualities appears evident. …”
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Medication Adherence: does Patient Participation in Randomized Clinical Trials Affect on it? by N. O. Vasyukova, Yu. V. Lukina, N. P. Kutishenko, S. Yu. Martsevich, O. I. Zvonareva

“…., the absence of a “gold standard” for assessing adherence in clinical practice makes it difficult to predict and significantly improve it among patients. …”
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An exploration of machine learning approaches for early Autism Spectrum Disorder detection by Nawshin Haque, Tania Islam, Md Erfan

“…This study explores the application of Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree, and Random Forest for predicting Autism in children and toddlers by leveraging advancements in machine learning. …”
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The Potential of Unsupervised Induction of Harmonic Syntax for Jazz by Ruben Cartuyvels, John Koslovsky, Marie-Francine Moens

“…Experiments show that our methods can model harmony in datasets of jazz pieces, often resulting in realistic parse trees that overlap with expert annotations, without access to these annotations during training at all. Code, models, and predictions are publicly available.1…”
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A Review of Embodied Grasping by Jianghao Sun, Pengjun Mao, Lingju Kong, Jun Wang

“…Then, the embodied algorithms are introduced, starting from pre-trained models, with three main research goals: (1) embodied perception, using data captured by visual sensors to perform point cloud extraction or 3D reconstruction, combined with pre-trained models, to understand the target object and external environment and directly predict the execution of actions; (2) embodied strategy: In imitation learning, the pre-trained model is used to enhance data or as a feature extractor to enhance the generalization ability of the model. …”
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Artificial Intelligence in Glioblastoma—Transforming Diagnosis and Treatment by Alen Rončević, Nenad Koruga, Anamarija Soldo Koruga, Robert Rončević

“…They can also enhance prognostication by predicting survival, recurrence, and treatment responses, helping clinicians to make more informed decisions. …”
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Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications by Revathy Aruchsamy, Inthumathi Velusamy, Prasantha Bharathi Dhandapani, Suleman Nasiru, Christophe Chesneau

“…Machine learning algorithm utilizes pattern recognition as an instrument for identifying patterns and also similarity measure (SM) is a beneficial pattern recognition tool used to classify items, discover variations, and make future predictions for decision making. …”
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Global miniaturization of broadband antennas by prescreening and machine learning by Slawomir Koziel, Anna Pietrenko-Dabrowska, Ubaid Ullah

“…Our technique includes parameter space pre-screening and the iterative refinement of kriging surrogate models using the predicted merit function minimization as an infill criterion. …”
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Comprehensive multi-omics analysis of histone acetylation modulators identifies ASH1L as a novel aggressive marker for osteosarcoma by Chenlie Ni, Qiwen Sun, Haibo Yin

“…The effectiveness of HAMRPs in predicting the immune landscape of osteosarcoma was confirmed using CIBERSORT, ssGSEA, and ESTIMATE algorithms. …”
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IMMUNOPATHOGENESIS OF MYASTHENIA GRAVIS (REVIEW) by E. D. Gasymly

“…High clinical heterogeneity of autoimmune myasthenia, initiating the flow, increases the urgency of studying its pathogenesis, searching for specific methods of marker diagnostics, developing algorithms for predicting the features of the development of the disease. …”
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ULOTrack: Underwater Long-Term Object Tracker for Marine Organism Capture by Ju He, Yang Yu, Hongyu Wei, Hu Xu

“…Therefore, existing tracking algorithms face difficulty in direct application to underwater object tracking. …”
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Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data. by Daphne Ezer, Victoria Moignard, Berthold Göttgens, Boris Adryan

“…In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. …”
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Fusion of Masked Autoencoder for Adaptive Augmentation Sequential Recommendation by SUN Xiujuan, SUN Fuzhen, LI Pengcheng, WANG Aofei, WANG Shaoqing

“…Finally, the sequential recommender module integrates position information, global context, and the personalized user interaction sequence to obtain the final item representation and predict the user's future possible interaction items based on the representation, thereby providing more reliable recommendation results for users. …”
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