A hybrid framework for heart disease prediction using classical and quantum-inspired machine learning techniques by Ankur Kumar, Sanjay Dhanka, Abhinav Sharma, Rohit Bansal, Mochammad Fahlevi, Fazla Rabby, Mohammed Aljuaid

“…Subsequently, both classical and quantum-inspired models are trained and optimized. The classical models utilized Genetic Algorithms (CGA) and Particle Swarm Optimization (CPSO) for hyperparameter tuning, while the quantum-inspired models employed Quantum Genetic Algorithms (QGAs) and Quantum Particle Swarm Optimization (QPSO). …”
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Research on Channel Modeling and Communication Coverage of Wireless Sensor Networks in Barrier Area of Nuclear Power Plants by Zhi-Guang Deng, Qian Wu, Xin Lv, Bi-Wei Zhu, Mei-Qiong Xiang, Xue-Mei Wang, Jia-Liang Zhu

“…Based on the channel modeling, this paper optimizes the coverage of the network in the obstacle area by using the improved teaching and learning group intelligent algorithm. …”
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The evaluation model of engineering practice teaching with complex network analytic hierarchy process based on deep learning by Xianlong Han, Xiaohui Chen

“…This study aims to help reveal the relationship between students’ performance and teaching evaluation factors, deepen the understanding of the evaluation model of engineering practice teaching in colleges and universities, and provide valuable guidance for optimizing teaching.…”
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Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention by Sijia Fu, Rui Zhu, Feiyang Yu

“…In order to tune the hyperparameters in the structure of this prediction model, such as the learning rate, the size of the convolutional kernel, and the number of recurrent units, this study chooses to use the Golden Jackal Optimization Algorithm for optimization. …”
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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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A Novel Metaheuristic-Based Methodology for Attack Detection in Wireless Communication Networks by Walaa N. Ismail

“…Additionally, an optimized attention-based XGBoost classifier is utilized to improve model performance by combining the benefits of parallel gradient boosting and attention mechanisms. …”
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Mortality prediction of heart transplantation using machine learning models: a systematic review and meta-analysis by Ida Mohammadi, Setayesh Farahani, Asal Karimi, Saina Jahanian, Shahryar Rajai Firouzabadi, Mohammadreza Alinejadfard, Alireza Fatemi, Bardia Hajikarimloo, Mohammadhosein Akhlaghpasand

“…IntroductionMachine learning (ML) models have been increasingly applied to predict post-heart transplantation (HT) mortality, aiming to improve decision-making and optimize 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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NLP for computational insights into nutritional impacts on colorectal cancer care by Shengnan Gong, Xiaohong Jin, Yujie Guo, Jie Yu

“…Colorectal cancer (CRC) is one of the most prominent cancers globally, with its incidence rising among younger adults due to improved screening practices. However, existing algorithms for CRC prediction are frequently trained on datasets that primarily reflect older persons, thus limiting their usefulness in more diverse populations. …”
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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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Implementation of XGBoost Models for Predicting CO₂ Emission and Specific Tractor Fuel Consumption by Nebojša Balać, Zoran Mileusnić, Aleksandra Dragičević, Mihailo Milanović, Andrija Rajković, Rajko Miodragović, Olivera Ećim-Đurić

“…Although not optimized for high precision, these models offer a valuable basis for preliminary assessments and highlight the potential of data-driven approaches for improving energy efficiency and environmental sustainability in agricultural mechanization.…”
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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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NeuroAdaptiveNet: A Reconfigurable FPGA-Based Neural Network System with Dynamic Model Selection by Achraf El Bouazzaoui, Omar Mouhib, Abdelkader Hadjoudja

“…By adaptively selecting the most suitable model configuration, NeuroAdaptiveNet achieves significantly improved classification accuracy and optimized resource usage compared to conventional, statically configured neural networks. …”
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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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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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