Heart failure and atrial fibrillation: updates and perspectives by V. N. Larina, I. K. Skiba, A. S. Skiba, V. G. Larin, E. A. Vartanyan

“…Epidemiological data, algorithms for managing patients with HF and atrial fibrillation, prevention of thromboembolic events, results of studies comparing the effectiveness of different strategies for atrial fibrillation treatment, catheter ablation compared with drug therapy in patients with HF are presented.…”
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People counting using IR-UWB radar sensors and machine learning techniques by Ange Joel Nounga Njanda, Jocelyn Edinio Zacko Gbadoubissa, Emanuel Radoi, Ado Adamou Abba Ari, Roua Youssef, Aminou Halidou

“…Next, we create features based on statistical and entropic properties of the signal and apply several classification algorithms, including ANN, Random Forest, KNN, XGBOOST, and multiple linear regression, to predict the number of people present. …”
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A Comparative study of the Envisaged and Definite Stock Prices of BSE SMEs Using RNN during the COVID-19 Pandemic by S. Kaur, A. Munde, A. K. Goyal

“…The stock market is unstable, but the use of machine learning algorithms allows to predict its future dynamics before spending. …”
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Hyperspectral Reconstruction Method Based on Global Gradient Information and Local Low-Rank Priors by Chipeng Cao, Jie Li, Pan Wang, Weiqiang Jin, Runrun Zou, Chun Qi

“…It combines reconstruction algorithms to recover hyperspectral data from low-dimensional measurement images. …”
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Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities by Renzhong Zhang, Haorui Li, Yunxiao Shen, Jiayi Yang, Wang Li, Dongsheng Zhao, Andong Hu

“…Looking ahead, research will focus on developing hybrid models that integrate physical modeling with deep learning, exploring adaptive learning algorithms and multi-modal data fusion techniques to enhance long-term predictive capabilities, particularly in addressing the impact of climate change on the ionosphere. …”
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Machine Learning Application for Improving Customer and Postal Logistics Operator Satisfaction in Urban Areas – A Review by Adisa MEDIĆ, Amel KOSOVAC, Ermin MUHAREMOVIĆ, Muhamed BEGOVIĆ

“…Machine learning (ML) is a crucial component of artificial intelligence that has recently attracted attention for its application in logistics. ML algorithms are used on large datasets. They create logic correlations among given data and provide predictions of specific values. …”
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Identification and validation of parthanatos-related genes in end-stage renal disease by Xuan Dai, Lianfang Yuan

“…Two machine learning algorithms identified candidate genes, refined through ROC analysis. …”
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MolNexTR: a generalized deep learning model for molecular image recognition by Yufan Chen, Ching Ting Leung, Yong Huang, Jianwei Sun, Hao Chen, Hanyu Gao

“…In addition, it employs a series of novel augmentation algorithms to significantly enhance the robustness and performance of the model.…”
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Personalizing the Marketing with Artificial Intelligence (AI) by Rozhko Viktor I., Pletnova Yelyzaveta S.

“…Another major challenge is the technical limitations of AI. While AI algorithms are capable of analyzing vast amounts of data, they cannot always properly adapt to a changing market environment or predict consumer behavior in non-standard situations. …”
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Ensemble deep learning-enabled single-shot composite structured illumination microscopy (eDL-cSIM) by Jiaming Qian, Chunyao Wang, Hongjun Wu, Qian Chen, Chao Zuo

“…Experimental results demonstrate that eDL-cSIM integrates the advantages of various state-of-the-art neural networks, enabling robust super-resolution image predictions across different specimen types or structures of interest, and outperforms classical physics-driven methods in terms of imaging speed, reconstruction quality and environmental robustness, while avoiding intricate and specialized algorithmic procedures. …”
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Machine Learning (ML) Based Repair-Count and Periodic Maintenance Policy for Multipurpose CNC Machinery by Alifin Fakhri Ikhwanul, Winarno, Fasa Nadia, Darajatun Rizki Achmad, Kusnadi, Safariyani Eva

“…Several censored data were utilized to predict failure times using machine learning algorithms, such as process temperature, air temperature, rotational speed, and torque. …”
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Synthetic Data Generation for AI-Informed End-of-Line Testing for Lithium-Ion Battery Production by Tessa Krause, Daniel Nusko, Johannes Rittmann, Luciana Pitta Bauermann, Moritz Kroll, Carlo Holly

“…To make these predictions, we implement artificial intelligence algorithms to extract information from the measurements. …”
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Regenerative Braking Systems in Electric Vehicles: A Comprehensive Review of Design, Control Strategies, and Efficiency Challenges by Emilia M. Szumska

“…Based on a systematic analysis of 89 peer-reviewed articles from Scopus, it highlights a shift from basic PID controllers to advanced predictive algorithms like Model Predictive Control (MPC) and machine learning approaches. …”
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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning by Jan Li, Tim Coopmans, Patrick Emonts, Kenneth Goodenough, Jordi Tura, Evert van Nieuwenburg

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Graph convolution network for fraud detection in bitcoin transactions by Ahmad Asiri, K. Somasundaram

“…We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). …”
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Explainable Artificial Intelligence to Unveil Intrinsic Characteristics of Conditioning Factors Governing Forest Fire Susceptibility by A. Teke, T. Kavzoglu

“…The results showed that the NGBoost had the highest predictive performance with an overall accuracy of 81.42%, and outperformed the other algorithms by approximately 5% to 8%. …”
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Exploring replay by Georgy Antonov, Peter Dayan

“…They therefore need to explore. However, the algorithmic structure of exploratory choices in the brain still remains largely elusive. …”
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Interpretable Machine Learning for the German residential rental market – shedding light into model mechanics by Severin Bachmann

“…We compare the drivers in Machine learning models and give insights into their strengths and weaknesses predicting rental prices. The study employs SHAP values to measure feature importance. …”
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Litter accumulation and fire risks show direct and indirect climate-dependence at continental scale by Mark A. Adams, Mathias Neumann

“…Results provide guidance for future decomposition studies. Algorithms reported here can significantly improve accuracy and reliability of predictions of carbon and nutrient dynamics and fire risk.…”
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How Re-Infections and Newborns Can Impact Visible and Hidden Epidemic Dynamics? by Igor Nesteruk

“…With the use of effective algorithms for parameter identification, the proposed approach can ensure effective predictions of visible and hidden numbers of cases and infectious and removed patients.…”
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