Population immunity to SARS-CoV-2 virus in residents of the Irkutsk Region in the dynamics of the epidemic by S. V. Balakhonov, V. I. Dubrovina, M. V. Chesnokova, D. D. Bryukhova, N. O. Kiseleva, A. B. Pyatidesyatnikova, K. M. Korytov, V. V. Voitkova, A. N. Perezhogin, T. A. Gavrilova, A. A. Seledtsov

“…In the fight against this viral disease, an important role is assigned to the study of the development of population immunity to the SARSCoV-2 virus, which will make it possible to assess the dynamics of seroprevalence and the formation of post-infectious humoral immunity, forecasting the development of the epidemiological situation, elucidating the characteristics of the epidemic process, and will also contribute to planning activities for specific and non-specific prevention of the disease.The aim: to determine the dynamics of population immunity to SARS-CoV-2 among the population of the Irkutsk region during the COVID-19 pandemic.Materials and methods. …”
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Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction by He Chen, Rongchang Fan

“…The system achieves superior posture recognition performance with 78.6% mAP and 91.5% PCK@0.5, outperforming state-of-the-art methods while maintaining real-time inference capabilities (27.3 FPS). …”
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A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines by Mathew Habyarimana, Abayomi A. Adebiyi

“…The operational efficiency of many industrial processes is greatly affected by condition monitoring, which has become more and more important in the detection and forecast of electrical machine failures. …”
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Traffic Status Evolution Trend Prediction Based on Congestion Propagation Effects under Rainy Weather by Yongjie Xue, Rui Feng, Shaohua Cui, Bin Yu

“…In rainy weather, the accurate prediction of traffic status not only helps road traffic managers to formulate traffic management methods but also helps travelers design travel routes and even adjust travel time. …”
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Analysis of the height difference of the zero isotherm according to two temperature profilers by E. A. Bolelov, O. V. Vasiliev, K. I. Galaeva, S. A. Ziabkin

“…One of the main indicators characterizing the quality of meteorological support of flights is the justifiability of aviation weather forecasts and forecasts of dangerous weather phenomena. …”
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Intelligent Optimization of OSPF Path Selection Using Machine Learning Models for Adaptive Network Routing by Rebeen Rebwar Hama Amin

“…Four important ML functions namely traffic forecast, anomaly detection, failure prediction, and dynamic cost optimization—have been used to improve OSPF performance. …”
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Socio-demographic disparities in global trends of lip and oral cavity neoplasms from 1990 to 2021 by Amr Sayed Ghanem, Ágnes Tóth, Attila Csaba Nagy

“…We analyzed annual incidence, mortality, and DALYs across 204 countries, using age-standardized rates and the Socio-demographic Index (SDI) to assess development-related impacts. Statistical methods included Kruskal–Wallis tests, linear regression, joinpoint regression for trends, and Exponential Smoothing for forecasts (2022–2030), with analyses conducted in STATA and Python, and p < 0.05 as significant. …”
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A Monte Carlo simulation technique to determine the optimal portfolio by Hassan Ghodrati, Zahra Zahiri

“…One of the primary concerns on any stock market is to detect the risk associated with various assets. One of the recognized methods in order to measure, to forecast, and to manage the existing risk is associated with Value at Risk (VaR), which draws much attention by financial institutions in recent years. …”
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Research on Mount Wilson Magnetic Classification Based on Deep Learning by Yuanbo He, Yunfei Yang, Xianyong Bai, Song Feng, Bo Liang, Wei Dai

“…The Mount Wilson magnetic classification of sunspot groups is thought to be meaningful to forecast flares’ eruptions. In this paper, we adopt a deep learning method, CornerNet-Saccade, to perform the Mount Wilson magnetic classification of sunspot groups. …”
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AI-Driven Belt Failure Prediction and Prescriptive Maintenance with Motor Current Signature Analysis by João Paulo Costa, José Torres Farinha, Mateus Mendes, Jorge O. Estima

“…The results obtained from the testing phase reveal a high level of accuracy in predicting belt failures, with the developed models consistently outperforming traditional methods. The incorporation of LSTM networks and swarm intelligence algorithms led to a significant improvement in predictive capabilities, allowing for the early detection of degradation patterns and timely intervention. …”
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Exploring the Limitations of Federated Learning: A Novel Wasserstein Metric-Based Poisoning Attack on Traffic Sign Classification by Suzan Almutairi, Ahmed Barnawi

“…WMPA leverages historical information from the FL process to forecast the next round&#x2019;s global model as a reference. …”
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Methodology for assessing the business activity index of an enterprise based on the analysis of the business environment by Glukhov Vladimir, Kolomysova Mariia

“…An overview of common business activity indices is presented, methods for their calculation are given, a generalized method for calculating the consolidated business activity index is presented, the role of business activity as an indicator of economic system development is shown; indicators for assessing business activity are systematized in relation to the specifics of entrepreneurial activity. …”
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Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation by Ahmad J. Obaidullah, Wael A. Mahdi

“…The results of this study show the robustness of GPR in generating reliable and precise forecasts, thus providing a strong method for intricate regression tasks in pharmaceutical and other scientific fields. …”
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An innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using Grey Wolf Optimization algorithm by Muhannad J. Alshareef

“…Swarm optimization strategies have been employed to detect the GMPP; however, these methods are associated with an unacceptable amount of time to reach convergence. …”
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Online Pre-Diagnosis of Multiple Faults in Proton Exchange Membrane Fuel Cells by Convolutional Neural Network Based Bi-Directional Long Short-Term Memory Parallel Model with Atten... by Junyi Chen, Huijun Ran, Ziyang Chen, Trevor Hocksun Kwan, Qinghe Yao

“…To address these gaps, this study proposes an online multi-fault prediction framework integrating three novel contributions: (1) a sensor fusion strategy leveraging existing thermal/electrochemical measurements (voltage, current, temperature, humidity, and pressure) without requiring embedded stack sensors; (2) a real-time sliding window mechanism enabling dynamic prediction updates every 1 s under variable load conditions; and (3) a modified CNN-based Bi-LSTM parallel model with attention mechanism (ConvBLSTM-PMwA) architecture featuring multi-input multi-output (MIMO) capability for simultaneous flooding/air-starvation detection. Through comparative analysis of different neural architectures using experimental datasets, the optimized ConvBLSTM-PMwA achieved 96.49% accuracy in predicting dual faults 64.63 s pre-occurrence, outperforming conventional LSTM models in both temporal resolution and long-term forecast reliability.…”
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Predictive Analytics in Maternal Health: A Machine Learning Approach for Classification of Preeclampsia by Pakiza Amin, Saima Gulzar Ahmad, Hikmat Ullah Khan, Ehsan Ullah Munir, Naeem Ramzan

“…The performance of our proposed models in the public datasets was an AUC-ROC of more than 95% and in the clinical dataset an even higher 96%. These ensemble methods accurately show that they have effective results in improving the precision and reliability of pre-eclampsia forecasts. …”
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Benchmarking and improving algorithms for attributing satellite-observed contrails to flights by A. Sarna, V. Meijer, R. Chevallier, A. Duncan, K. McConnaughay, S. Geraedts, K. McCloskey

“…In this work, we present a method for producing synthetic contrail detections with predetermined contrail-to-flight attributions that can be used to evaluate – or “benchmark” – and improve such attribution algorithms. …”
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Predicting weather-related power outages in large scale distribution grids with deep learning ensembles by L. Prieto-Godino, C. Peláez-Rodríguez, J. Pérez-Aracil, J. Pastor-Soriano, S. Salcedo-Sanz

“…Additionally, it effectively reduces the inherent overfitting of these methods, removing the necessity for a complex hyperparameter selection process.…”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…Leveraging deep learning models, it becomes feasible to detect and forecast the early stages of numerous diseases based on individual health conditions. …”
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Lessons Learned from the COVID-19 Pandemic: Simulation of the Tuberculosis Epidemic as a Function of Population Coverage with Screening by A. A. Starshinova, I. F. Dovgalyuk, N. N. Osipov, D. A. Kudlay

“…The COVID-19 pandemic has led to the discontinuation of many support programs for tuberculosis patients worldwide, and lower coverage of population with screening for tuberculosis.The objective: To build a model describing the spread of tuberculosis depending on the population coverage with preventive screening, and to obtain a long-term forecast of the infection spread using this model.Subjects and Methods. …”
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