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

“…This study presents an assessment of familial hypercholesterolemia (FH) probability using different algorithms (CatBoost, XGBoost, Random Forest, SVM) and its ensembles, leveraging electronic health record data. …”
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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

“…The problem is further converted to a quantum optimization problem and solved with Quantum Approximation Optimization Algorithm (QAOA). As a matter of fact, current quantum computers are still limited in accuracy and scalability. …”
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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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MiR-155 has a protective role in the development of non-alcoholic hepatosteatosis in mice. by Ashley M Miller, Derek S Gilchrist, Jagtar Nijjar, Elisa Araldi, Cristina M Ramirez, Christopher A Lavery, Carlos Fernández-Hernando, Iain B McInnes, Mariola Kurowska-Stolarska

“…Using miRNA target prediction algorithms and the microarray transcriptomic profile of miR-155(-/-) livers, we identified and validated that Nr1h3 (LXRα) as a direct miR-155 target gene that is potentially responsible for the liver phenotype of miR-155(-/-) mice. …”
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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

“…Hierarchical structures describing a syntax of harmony have long been studied and proposed by music theorists, but algorithms that model these structures either require costly expert annotations for training or are based on music theorists' predispositions about harmonic syntax. …”
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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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The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models by Zulfikar Ali Ansari, Manish Madhava Tripathi, Rafeeq Ahmed

“…Although artificial intelligence (AI) has showed amazing promise in breast cancer prediction mainly machine learning (ML) algorithms as well as deep learning (DL), practical use of these models is greatly hampered by their lack of interpretability and transparency. …”
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Optimal micro-grid battery scheduling within a comprehensive smart pricing scheme by Mohammed Ashraf Ali, Ahmad H. Besheer, Hassan M. Emara, Ahmed Bahgat

“…The algorithm utilizes day-ahead forecasts for MG load profiles and photovoltaic output power, enabling the prediction of BESS’s optimal power profile a day in advance. …”
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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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