Nonintrusive Load Disaggregation Based on Attention Neural Networks by Shunfu Lin, Jiayu Yang, Yi Li, Yunwei Shen, Fangxing Li, Xiaoyan Bian, Dongdong Li

“…Specifically, the proposed model shows a 13.85% improvement in mean absolute error (MAE), a 21.27% improvement in signal aggregate error (SAE), and a 26.15% improvement in F1 score over existing algorithms. …”
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USING ARTIFICIAL INTELLIGENCE IN OBSTETRICS TO DIAGNOSE FETAL MALFORMATIONS AND PREVENT DISEASES by Елена Валерьевна Литвинова, Оксана Владимировна Носкова

“…Significant advances in the application of artificial intelligence in obstetrics and gynecology are shown, identifying the need for further research and improvements with respect to achieving universality and improving the effectiveness of many of the models developed. …”
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Fuzzy logic-controlled three-phase dynamic voltage restorer for enhancing voltage stabilization and power quality by Abu Shufian, Sowrov Komar Shib, Durjoy Roy Dipto, Md Tanvir Rahman, Nasif Hannan, Shaikh Anowarul Fattah

“…The FLC-based DVR system offers robust voltage regulation, improved fault resilience, and enhanced grid stability by addressing critical PQ challenges exacerbated by the transition to RES and increased dependency on sensitive loads. …”
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The effect of canine lingual attachments during maxillary arch distalization with clear aligner: a 4D finite element analysis and in vitro simulator study by Bochun Mao, Yajing Tian, Hanzhang Zhou, Yan Gu

“…Method A dual-methodological approach was employed: 1) A four-dimensional finite element model (4D FEM) incorporating automated staging simulation was developed, utilizing iterative computations for long-term tooth movement prediction and thermal expansion algorithms for CA morphology adaptation; 2) An electromechanical orthodontic simulator (OSIM) was implemented for in vitro validation. …”
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Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence by Sahar Rezaei, Farzan Asadirad, Alireza Motamedi, Mohammadsadegh Kamran, Farzaneh Parsa, Haniyeh Samimi, Parna Ghannadikhosh, Mahdi Zahmatyar, Seyed Ali Hosseinzadeh, Hossein Arabi

“…These findings suggest that AI-enhanced qEEG analysis could revolutionize AD diagnosis, enabling earlier intervention and improved patient outcomes.…”
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A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…This model improves coefficient of determination (R2) and ratio of performance to interquartile distance (RPIQ) by 0.09 and 0.28, respectively, and reduces root mean square error (RMSE) by 0.52 g kg−1 compared to Model (ii). …”
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Review of applications of deep learning in veterinary diagnostics and animal health by Sam Xiao, Navneet K. Dhand, Zhiyong Wang, Kun Hu, Kun Hu, Peter C. Thomson, John K. House, Mehar S. Khatkar, Mehar S. Khatkar

“…Deep learning (DL), a subfield of artificial intelligence (AI), involves the development of algorithms and models that simulate the problem-solving capabilities of the human mind. …”
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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“…Overall, 24 AI technologies were identified, covering traditional algorithms such as random forest, support vector machine, and k-nearest neighbor, as well as deep learning models such as convolutional neural networks, Bidirectional Encoder Representations From Transformers, and ChatGPT. …”
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Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review by Joana Seringa, João Abreu, Teresa Magalhaes

“…When the prediction timeframe was unspecified, Categorical Boosting achieved the highest performance with a mean AUC of 0.88, followed by Generalised Linear Model Net and XGBoost (both 0.79).Electronic health records were the primary data source across studies; however, few models included patient-reported outcomes or socioeconomic variables.None of the studies conducted an economic evaluation to assess the cost-effectiveness of these models.Conclusions ML holds substantial potential for improving HF care. …”
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Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data by Yisa Li, Dengsheng Lu, Yagang Lu, Guiying Li

“…Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. …”
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Remote sensing reveals the role of forage quality and quantity for summer habitat use in red deer by Thomas Rempfler, Christian Rossi, Jan Schweizer, Wibke Peters, Claudio Signer, Flurin Filli, Hannes Jenny, Klaus Hackländer, Sven Buchmann, Pia Anderwald

“…Results The combination of vegetation indices and optical traits greatly improved predictive power in both the biomass (R2 = 0.60, Root mean square error (RMSE) = 88.55 g/m2) and relative nitrogen models (R2 = 0.34, RMSE = 0.28%). …”
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Machine Learning Applications in Use-Wear Analysis: A Critical Review by Anastasia Eleftheriadou, Shannon P. McPherron, João Marreiros

“…By addressing these factors, this paper facilitates the effective use of machine learning, enabling use-wear analysts and archaeologists to develop robust models that automate, accelerate, and improve their research.…”
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ORGANIZATIONAL ISSUES OF CARDIOREHABILITATION SERVICE IN RUSSIA. THE RESULTS OF PILOT PROJECT “DEVELOPMENT OF REHABILITATION SYSTEM OF CARDIOVASCULAR PATIENTS IN MEDICAL INSTITUTIO... by D. M. Aronov, M. G. Bubnova, S. A. Boytsov, G. Е. Ivanova, A. G. Andreev, О. L. Barbarash, V. V. Belova, V. N. Belov, B. V. Borisov, Е. V. Ivanov, I. М. Karamova, A. V. Karpukhin, V. B. Krasnitskiy, Е. S. Kyblanova, P. А. Lebedev, Е. А. Lisnyak, N. P. Lyamina, Т. N. Mizurova, О. F. Misyura, I. E. Mishina, О. V. Mischenko, S. Yu. Nikulina, N. I. Ostroushko, S. А. Poteshkina, А. S. Sidorov, А. А. Sprikut, I. S. Sukhinina, А. G. Tkacheva, S. А. Ustyugov, G. А. Chumakova

“…To analyze,  how is, under  the  framework of the  Pilot Project, organized and being conducted  the improvement of work of the known departments in exact medical institutions (MI) of RF for rehabilitation of cardiovascular  diseases (CVD) patients  at the  three  steps  under  the current “Order of medical care and medical rehabilitation”, and how do the scientifically proven and types and methods  of cardiorehabilitation (CR) being implemented,  and to try to elaborate  on an exact algorithm for patients course.Material and methods. …”
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Machine learning for detection of diffusion abnormalities-related respiratory changes among normal, overweight, and obese individuals based on BMI and pulmonary ventilation paramet... by Xin-Yue Song, Xin-Peng Xie, Wen-Jing Xu, Yu-Jia Cao, Bin-Miao Liang

“…We applied several supervised ML algorithms and feature selection strategies to distinguish between DN and DA, including Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Naive Bayes (BAYES), K-Nearest Neighbors (KNN), SelectKBest, Recursive Feature Elimination with Cross-Validation (RFECV), and SelectFromModel. …”
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A Two-Stage Location-Allocation Optimization Method for Fixed UAV Nests in Power Inspection Considering Node Failure Scenarios by Zheng Huang, Hongxing Wang, Yiming Tang, Feng Gao, Biao Du, Jia Wang

“…The problem is divided into two subproblems, each modeled as an integer linear programming (ILP) problem. …”
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A Novel Framework for Co-Expansion Planning of Transmission Lines and Energy Storage Devices Considering Unit Commitment by Edimar José de Oliveira, Lucas Santiago Nepomuceno, Leonardo Willer de Oliveira, Arthur Neves de Paula

“…The problem is formulated as a mixed-integer nonlinear program and solved using a decomposition-based approach that combines a genetic algorithm with mixed-integer linear programming. Uncertainties are modeled through representative day scenarios obtained via clustering. …”
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Machine learning for prediction of childhood mental health problems in social care by Ryan Crowley, Katherine Parkin, Emma Rocheteau, Efthalia Massou, Yasmin Friedmann, Ann John, Rachel Sippy, Pietro Liò, Anna Moore

“…Assessments of algorithmic fairness showed potential biases within these models. …”
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Insulin resistance quantified by estimated glucose disposal rate predicts cardiovascular disease incidence: a nationwide prospective cohort study by Shiyi Tao, Lintong Yu, Jun Li, Ji Wu, Xuanchun Huang, Zicong Xie, Tiantian Xue, Yonghao Li, Lilan Su

“…NRI and DCA analysis also suggested the improvement from eGDR to identify prevalent CVD and the favorable clinical efficacy of the multivariate model. …”
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Multiple Description Coding Using Data Hiding and Regions of Interest for Broadcasting Applications by M. Verza, N. Conci, G. Boato, F. G. B. De Natale

“…Using data hiding, reconstruction parameters are embedded in the transform coefficients, thus allowing an improvement in PSNR of the single descriptions at the cost of a negligible overhead. …”
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A Fair Group Recommendation System Based on Members and Leader Influences by Mostafa Sabzekar, Bentolhoda Moazeni

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