Enhancing Wind Turbine Power Output Estimation Using Causal Inference and Adaptive Neuro-Fuzzy Inference System ANFIS by Ahmed A. Mostfa, Nawfal A. Zakar, Rasha Raad Al-Mola, Abdel-Nasser Sharkawy

“…To meet the demand for renewable energy at the lowest cost, wind energy became the target of machine learning algorithms and was employed to predict the output power of wind turbines. …”
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On the principles of building a model of a specialist – a graduate of a pedagogical university by K. S. Kаtаеv, S. G. Kаtаеv, I. V. Kаmenskaya

“…The reasonableness of the obtained results of the study, the availability of a variety of algorithms for data interpretation indicate the real possibility of using in practice the proposed structure of the model, including the way of organising data as a monitoring system.…”
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Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry by Xue Wu, Qi Yang, Shanshan Liu, Peng Yang, Zhengqi Liu, Zhitu Zhu

“…Methods A total of 533 participants, including 382 healthy individuals and 151 RA patients, were recruited to explore altered metabolic profiles through the analysis of dried blood spot samples by mass spectrometry. Multiple algorithms were applied to identify potential biomarkers. …”
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Enhancing Stroke Prediction with Logistic Regression and Support Vector Machine Using Oversampling Techniques by Syamsul Risal, Fajar Apriyadi, A. Sumardin, Andini Dani Achmad, Annisa Nurul Puteri

“…This study compares the performance of Logistic Regression (LR) and Support Vector Machine (SVM) algorithms combined with different oversampling methods—SMOTE, Borderline-SMOTE, ADASYN, Random Over Sampling (ROS), and Random Under Sampling (RUS)—on a stroke prediction dataset. …”
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Transfer and deep learning models for daily reference evapotranspiration estimation and forecasting in Spain from local to national scale by Yu Ye, Aurora González-Vidal, Miguel A. Zamora-Izquierdo, Antonio F. Skarmeta

“…This study compares standard ML and Deep Learning (DL) algorithms for estimating and forecasting daily ET0 at different spatial scales in Spain. …”
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Assessment of road-cut slope stability using empirical, numerical, and machine learning methodologies by Virat Singh Chauhan, Md. Rehan Sadique, Mohd. Masroor Alam, Mohd. Ahmadullah Farooqi

“…The outcomes of the machine learning algorithms are compared against those obtained from Finite Element Analysis (FEA), with observed discrepancies ranging from 2 to 10% across the different ML models.…”
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Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments by Abhishek Sharma, Timothy N Fazio, Long Song, Samantha Plumb, Uwe Aickelin, Mojgan Kouhounestani, Mark John Putland

“…This framework was designed to be easily adapted by other institutions for the development of their own ML models.Methods We analysed data from 297 392 ED visits of patients aged 18 and above at a quaternary hospital between 30 June 2019 and 31 December 2022. Eight ML algorithms were evaluated, and ultimately, twelve lasso models built from 21 features were trained to predict four outcomes of LOS and DD at three time points post-triage. …”
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Deep learning radiomics based on MRI for differentiating tongue cancer T - staging by Zhaoyi Lu, Bowen Zhu, Hang Ling, Xi Chen

“…Performance was evaluated via AUC, DCA, IDI, and NRI in different sets. …”
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A Comprehensive Review on Sensor-Based Electronic Nose for Food Quality and Safety by Teodora Sanislav, George D. Mois, Sherali Zeadally, Silviu Folea, Tudor C. Radoni, Ebtesam A. Al-Suhaimi

“…Our review found that most of the efforts use portable, low-cost electronic noses, coupled with pattern recognition algorithms, for evaluating the quality levels in certain well-defined food classes, reaching accuracies exceeding 90% in most cases. …”
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A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients... by Syed Waseem Abbas Sherazi, Jang-Whan Bae, Jong Yun Lee

“…Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms.<h4>Methods</h4>We used the Korea Acute Myocardial Infarction Registry dataset and selected 11,189 subjects among 13,104 with the 2-year follow-up. …”
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Non-Invasive Continuous Respiratory Monitoring on General Hospital Wards: A Systematic Review. by Kim van Loon, Bas van Zaane, Els J Bosch, Cor J Kalkman, Linda M Peelen

“…<h4>Data synthesis</h4>Six intervention and five diagnostic studies were included, evaluating the use of eight different devices for continuous respiratory monitoring. …”
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Electronic catalog as an object of quality assessment by G. A. Skaruk

“…The composition of EC components as quality assessment objects and main evaluating indicators for each component are determined: bibliographical databases (including fullness, data identity and relevance, efficiency, completeness of documents’ identifying characteristics, compliance with the bibliographic description rules, entire and depth of document content reflection, stability of indexing); lexicographic databases (including the information search language (ISL) vocabulary completeness and detail, data identity and relevance, correct wording and alignment of the access points, the compliance with requirements of data presentation formats, ISL grammar tools composition and functionality); factographic data (information completeness and accuracy on the availability and location of publication copies, their movement); database management system and information processing s y st em (functional suitability, accessibility, ease of use, application reliability and security, efficiency, mobility); search system (ISL functionality, ease of navigation in the catalog, efficiency of search techniques and algorithms, relevance criteria, completeness and ease of understanding of search instructions, maintaining the system to help users); user interface (completeness of tasks solved by the system, compliance with human capabilities, visibility and functionality of formats, user-friendly modes of operation with the interface, user feedback, comfort, compliance with aesthetic requirements); system for ensuring the reliability of information storage .…”
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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…A pre-processed and augmented dataset is used for training of the convolutional neural network having an architecture with hierarchical structure and residual block usage. The model is evaluated based on the confusion matrix and the heat maps of different convolutional layers are analyzed. …”
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Specific features of pregnancy in patients with type 1 diabetes in the prepubertal period. Results of a 15-year long clinical observation by Lyubov' Leonidovna Bolotskaya, Natalya Vladimirovna Efremova, Yury Ivanovich Suntsov

“…Aim. To evaluate effect of different factors on pregnancy course in patients with type 1 diabetes mellitus developing in the prepubertal period. …”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…Different types of sensors were installed at multiple depths in the ET covers, and resistivity tests were conducted periodically at the same location. …”
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Analysis of potential molecular targets and mechanisms of brominated flame retardants in causing osteoarthritis using network toxicology, machine learning, SHAP analysis, and molec... by Yu Liu, Guohang Shen, Zidong Xia, Ruoyan Wang, Yupei Dai

“…Subsequently, a comprehensive machine learning workflow involving 113 different algorithms was used to identify 10 core target genes as potential mediators of BDE-47-induced osteoarthritis. …”
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MRI quantified enlarged perivascular space volumes as imaging biomarkers correlating with severity of anxiety depression in young adults with long-time mobile phone use by Li Li, Yalan Wu, Jiaojiao Wu, Bin Li, Rui Hua, Feng Shi, Lizhou Chen, Yeke Wu

“…For classifying depression status, eight features were selected to construct a K nearest neighbors model with an AUC value of 0.931 (95%CI 0.814-1.000) in the testing dataset.DiscussionThe utilization of MRI-quantified EPVS metrics combined with machine-learning algorithms presents a promising method for evaluating severity of anxiety and depression symptoms in patients with LTMPU, which might introduce a non-invasive, objective, and quantitative approach to enhance diagnostic efficiency and guide personalized treatment strategies.…”
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Galectin-9 as a biomarker for disease activity in systemic lupus erythematosus. by Naoki Matsuoka, Yuya Fujita, Jumpei Temmoku, Makiko Yashiro Furuya, Tomoyuki Asano, Shuzo Sato, Haruki Matsumoto, Hiroko Kobayashi, Hiroshi Watanabe, Eiji Suzuki, Hideko Kozuru, Hiroshi Yastuhashi, Kiyoshi Migita

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Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery by Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn

“…Though the models performed less effectively for follow-ups, the addition of public data enhanced their performance, providing a viable solution to improve their efficacy for the follow-ups. These algorithms hold promise as a valuable tool for clinicians for automated segmentation of planning and follow-up MRI scans during stereotactic radiosurgery treatment planning and response evaluations, respectively. …”
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Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies by Denesh Sooriamoorthy, Aaruththiran Manoharan, Siva Kumar Sivanesan, Soon Kian Lun, Alexander Chee Hon Cheong, Sathish Kumar Selva Perumal

“…Using a 4 × 5 PV array model simulated in MATLAB/Simulink, different adaptive PSO variants i.e. linear, exponential, and performance-based adaptive w along with linear and trigonometric-based adaptive c1 and c2 are evaluated across diverse irradiance scenarios. …”
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