A Two-Step Approach for Airborne Delay Minimization Using Pretactical Conflict Resolution in Free-Route Airspace by Ramazan Kursat Cecen, Cem Cetek

“…According to this comparison, both metaheuristics algorithms produce near optimal solutions in a reasonably short time. …”
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In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes. by Alireza Naghizadeh, Wei-Chung Tsao, Jong Hyun Cho, Hongye Xu, Mohab Mohamed, Dali Li, Wei Xiong, Dimitri Metaxas, Carlos A Ramos, Dongfang Liu

“…Specifically, we used artificial neural networks (ANN) to incorporate object detection into segmentation. The proposed ANN model extracts the most useful information to differentiate different IS datasets. …”
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Machine learning discovery of the dielectric properties of strontium-containing condensed matter by Dongyang Huang, Jiaxing Fu, Chenghao Yu

“…In this work, machine learning models were successfully developed to capture the relationship between composition and dielectric properties of strontium-containing dielectrics using different algorithms, with hyperparameter optimization performed via grid search. …”
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Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments by Amin Taheri-Garavand, Mojgan Beiranvandi, Abdolreza Ahmadi, Nikolaos Nikoloudakis

“…The current research was carried out to develop a genetic algorithm-based artificial neural network (ΑΝΝ) model able of simulating the levels of antioxidants in savory when using soil amendments [biochar (BC) and superabsorbent (SA)] under drought. …”
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Enhanced Occupational Safety in Agricultural Machinery Factories: Artificial Intelligence-Driven Helmet Detection Using Transfer Learning and Majority Voting by Simge Özüağ, Ömer Ertuğrul

“…The proposed model achieved an accuracy of 90.39%, with DenseNet201 producing the most accurate results. …”
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A 3D acid fracturing design calibrated to describe the productivity index in several southwestern Iranian oil fields by Mohammad Mirhashemi, Kaveh Ahangari, Ali Naghi Dehghan, Kamran Goshtasbi

“…By assessing these input parameters, the study aims to optimize the conditions under which acid fracturing can be most effective. …”
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Distinguishing novel coronavirus influenza A virus pneumonia with CT radiomics and clinical features by Lianyu Sui, Huan Meng, Jianing Wang, Wei Yang, Lulu Yang, Xudan Chen, Liyong Zhuo, Lihong Xing, Yu Zhang, Jingjing Cui, Xiaoping Yin

“…After incorporating clinical features, the clinical model’s discriminatory and predictive efficacy further improved in testing sets (AUC, 0.669 vs. 0.820, P = 0.002). …”
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A Hierarchical Control Framework for Coordinating CAV-Dedicated Lane Allocation and Signal Timing at Isolated Intersections in Mixed Traffic Environments by Feng Chen, Cunbao Zhang, Yu Cao

“…With the rapid development of connected and automated vehicles (CAVs), numerous studies have demonstrated that CAV-dedicated lanes (CAV-DLs) can significantly enhance traffic efficiency. However, most existing studies primarily focus on optimizing either CAV trajectory planning or traffic signal control, and the integration of CAV-DLs and signal control for improved spatiotemporal resource utilization remains underexplored. …”
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Efficient Material Flow and Storage Space Determination in Automated Distribution Centers by Mohammed Alnahhal, Nikola Gjeldum, Mohammed Ruzayqat

“…Items with relatively large demand levels have scenario 3 as the optimal one. Results also showed that the model reduces both total costs and stacker crane utilization while improving system flexibility.…”
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Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector by Ruixing Ming, Osama Mohamad, Nisreen Innab, Mohamed Hanafy

“…Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. …”
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Problems and perspectives of family doctors training on the undergraduate stage by Yu. M. Kolesnik, V. D. Syvolap, N. S. Mikhaylovskaya, T.O. Kulinich

“…Computer presentations, videos, case-technology and other innovative methods are widely used for training optimization. For working on practical part of family doctors basic skills it is planned to organize educational and training center at the family ambulatory, and its equipment with the necessary visual means, phantoms, models, simulators, diagnostic, medical apparatus and instruments. …”
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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…The results show that the proposed model achieves more accuracy (in average % 10 and at most % 71 improvements) compared to the baseline machine learning models in the literature.…”
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Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study by Rishishankar E. Suresh, M S Zobaer, Matthew J. Triano, Brian F. Saway, Parneet Grewal, Nathan C. Rowland

“…Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). …”
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Data-driven approach to mid-latitude coherent scatter radar data classification by Berngardt Oleg

“…Based on 2021 data, a solution of the problem of automatic data classification is presented without their labeling by an expert and without postulating the number of classes. The algorithm automatically labels the data, determines the optimal number of signal classes observed by the radars, and trains a two-layer classifying neural network of an extremely simple structure. …”
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Comparison of artificial intelligence approaches for estimating wind energy production: A real-world case study by Mohamed Bousla, Mohamed Belfkir, Ali Haddi, Youness El Mourabit, Badre Bossoufi

“…The precise prediction of wind power is essential not only for the smooth integration into the power grid but also for the optimization of unit commitment, maintenance scheduling, and the improvement of power traders' profitability. …”
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Selective Cleaning Enhances Machine Learning Accuracy for Drug Repurposing: Multiscale Discovery of MDM2 Inhibitors by Mohammad Firdaus Akmal, Ming Wah Wong

“…The optimized model was integrated with structure-based virtual screening via molecular docking to prioritize repurposing candidate compounds. …”
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Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping by Shangpeng Wang, Chenyuan Zhang, Zihan Su, Limin Liu, Jun Long

“…First, we introduce a graph theoretic model to represent the task dependency and priority relationships explicitly, combined with a novel algorithm for task decomposition. …”
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Underwater Target 3D Reconstruction via Integrated Laser Triangulation and Multispectral Photometric Stereo by Yang Yang, Yimei Liu, Eric Rigall, Yifan Yin, Shu Zhang, Junyu Dong

“…At the same time, we propose to optimize the laser place calibration and laser line separation processes, further improving the reconstruction performance of underwater laser triangulation and multispectral photometric stereo. …”
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Beyond Linearity: Uncovering the Complex Spatiotemporal Drivers of New-Type Urbanization and Eco-Environmental Resilience Coupling in China’s Chengdu–Chongqing Economic Circle with... by Caoxin Chen, Shiyi Wang, Meixi Liu, Ke Huang, Qiuyi Guo, Wei Xie, Jiangjun Wan

“…The results reveal the following: (1) NTU and EER levels steadily improved from 2004 to 2022, although coordination between cities still requires enhancement; (2) CCD exhibited a temporal pattern of “progressive escalation and continuous optimization,” and a spatial pattern of “dual-core leadership and regional diffusion,” with most cities shifting from NTU-lagged to synchronized development; (3) environmental regulations (MAR) and fixed asset investment (FIX) emerged as the most influential CCD drivers, and significant nonlinear interactions were observed, particularly those involving population size (HUM); (4) CCD drivers exhibited complex spatiotemporal heterogeneity, characterized by “stage dominance—marginal variation—spatial mismatch.” …”
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Predicting hospital outpatient volume using XGBoost: a machine learning approach by Lingling Zhou, Qin Zhu, Qian Chen, Ping Wang, Hao Huang

“…Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allocation of medical resources. This study aims to develop a predictive model for daily hospital outpatient volume using the XGBoost algorithm. …”
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