The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases by Artur Świerczek, Dominika Batko, Elżbieta Wyska

“…Its integration into DDD and translational science, in combination with AI and ML algorithms, holds promise for advancing therapeutic strategies and improving autoimmune patients’ outcomes.…”
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Machine learning model for prediction of palliative care phases in patients with advanced cancer: a retrospective study by Junchen Guo, Yunyun Dai, Sishan Jiang, Junqingzhao Liu, Xianghua Xu, Yongyi Chen

“…Conclusions The prediction model developed in this study based on the machine learning algorithm showed good performance, offering significant potential for facilitating timely interventions, enhancing symptom management, and optimizing palliative care resource allocation in advanced cancer patients in mainland China.…”
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Machine Learning-Driven Prediction of One-Year Readmission in HFrEF Patients: The Key Role of Inflammation by Ma F, Hu Y, Han P, Qiu Y, Liu Y, Ren J

“…Integrating such models into clinical practice could improve risk stratification, reduce readmissions, and enhancing patient outcomes.Keywords: HFrEF, readmission, prediction model, machine learning…”
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A bayesian network model for neurocognitive disorders digital screening in Chinese population: development and validation study by Yifan Yu, Shuaijie Zhang, Hongkai Li, Fuzhong Xue

“…Gender and the top 30 variables with the highest coefficient of determination () in explaining the variance in NCD status were retained for model construction. Subsequently, the optimal network structure was identified using the Tabu search algorithm guided by Bayesian Information Criterion, with parameters estimated by maximum likelihood estimation. …”
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Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality by Miguel A. Becerra, Diego H. Peluffo-Ordoñez, Johana Vela, Cristian Mejía, Juan P. Ugarte, Catalina Tobón

“…Finally, the IQ criteria were optimized through a particle swarm optimization algorithm. …”
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Predicting Optimum Moisture Content by the individual and hybrid approach of machine learning by Yinghui Yang, Yahui Dai, Qunting Yang

“…To further enhance the predictive accuracy of these models, two meta-heuristic optimization techniques—Atom Search Optimization (ASO) and Reptile Search Algorithm (RSA)—are employed. …”
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Convolutional Neural Decoder for Surface Codes by Hyunwoo Jung, Inayat Ali, Jeongseok Ha

“…The numerical results show that the proposed decoding algorithm effectively improves the decoding performance in terms of logical error rate as compared to the existing algorithms on various quantum error models.…”
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Integrating Data Mining, Deep Learning, and Gene Ontology Analysis for Gene Expression-Based Disease Diagnosis Systems by Sergii Babichev, Igor Liakh, Jiri Skvor

“…Bayesian optimization method was employed to determine the optimal hyperparameters for all models. …”
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Using Artificial Intelligence in Employment: Problems and Prospects of Legal Regulation by D. A. Novikov

“…Objective: to identify the legal problems of using artificial intelligence in hiring employees and the main directions of solving them.Methods: formal-legal analysis, comparative-legal analysis, legal forecasting, legal modeling, synthesis, induction, deduction.Results: a number of legal problems arising from the use of artificial intelligence in hiring were identified, among which are: protection of the applicant’s personal data, obtained with the use of artificial intelligence; discrimination and unjustified refusal to hire due to the bias of artificial intelligence algorithms; legal responsibility for the decision made by a generative algorithm during hiring. …”
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Study of Condenser Spatial State Model Based on Dynamic Thresholding of Environmentally Adaptive Multi-Dimension Eigenvalues by Qian Hong, Sun Shuyin, Wang Xuehua, Li Zhenpeng

“…Then, utilizing seawater-temperature and seawater-level as environmental parameters, the partitioning of the condenser marine environmental region is optimized based on the CalinskiHarabasz index. Subsequently, the multi-dimension eigenvalues are used to calculate to get the multidimension eigenvalues dynamic thresholds and mean corresponding to the marine environmental regions by improved Gaussian mixture algorithm (GMM). …”
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Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model by Gang Li, Ruijiang Ran, Jun Fang, Hao Peng, Shengmin Wang

“…Then, the LSSVM with the strongest nonlinear modelling ability was selected to build the bridge construction early-warning model and adopted the SSA to optimize the LSSVM parameter combination, improving the early warning accuracy. …”
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Fusion of multi-scale attention for aerial images small-target detection model based on PARE-YOLO by Huiying Zhang, Pan Xiao, Feifan Yao, Qinghua Zhang, Yifei Gong

“…Evaluation on the VisDrone2019 dataset indicates that PARE-YOLO achieves a 5.9% improvement in mean Average Precision (mAP) at a threshold of 0.5, compared to the original YOLOv8 model. …”
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A Recognition Method for Adzuki Bean Rust Disease Based on Spectral Processing and Deep Learning Model by Longwei Li, Jiao Yang, Haiou Guan

“…Second, the competitive adaptive reweighted sampling (CARS) algorithm was implemented in the range of 425–825 nm to determine the optimal characteristic wavenumbers, thereby reducing data redundancy. …”
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AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes by Ang Li, Yunxin Wang, Hongxu Chen

“…This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in biomedical diagnostics, particularly for AI-driven arrhythmia detection, hypertrophic cardiomyopathy (HCM) in athletes, and personalized medicine. …”
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Interpretability Study on the Fault Diagnosis Model of the Heat pipe/ Vapor Compression Composite Air Conditioning System by ZHANG Yiqi, HUANG Shuoquan, LI Xiuming, DI Yanqiang, SONG Mengjie, HAN Zongwei

“…Applying data-driven fault diagnosis models to data center air conditioning systems can significantly improve operational reliability. …”
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Robust Multi-Stage Planning of Park-Level Integrated Energy System Considering Source-Load Uncertainties by JIANG Xunpu, BAO Zhejing, YU Miao, GUO Chuangxin, GUO Yuanyue, WANG Jian

“…Instead， an additional nested layer is required to solve the model. Therefore， this paper employs the nested column-and-constraint generation （NC&CG） algorithm to solve the model. …”
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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…To simplify the analysis and reduce computational complexity, a subset of key spectral wavelengths was selected using a successive projection algorithm. Deep learning models were trained on these selected wavelengths to directly learn patterns from the raw spectral data. …”
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XGBoost based enhanced predictive model for handling missing input parameters: A case study on gas turbine by Nagoor Basha Shaik, Kittiphong Jongkittinarukorn, Kishore Bingi

“…The model is built to anticipate the gas turbine's Energy Yield (EY) output, optimize energy production efficiency, improve maintenance schedules, and enable operational decision-making within the power plant. …”
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Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC–DC Side by Abdullah Berkay Bayindir, Ali Sharida, Sertac Bayhan, Haitham Abu-Rub

“…To address this challenge, the proposed method employs an adaptive estimation strategy utilizing a recursive least square algorithm for online dynamic model estimation. This real-time estimated model enables IMPC to predict optimal switching states with improved reliability. …”
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Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems by Fukui Wu, Hanzhong Tan, Linfeng Zhang, Shuangbing Wen, Tao Hu

“…Real-time vehicle data are collected using cameras deployed along highways, and key traffic parameters such as flow, density, and speed are precisely extracted using the YOLOv8 object detection model. Subsequently, five machine learning algorithms and three deep learning algorithms are employed to predict traffic flow. …”
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