IoT-driven smart assistive communication system for the hearing impaired with hybrid deep learning models for sign language recognition by Mashael Maashi, Huda G. Iskandar, Mohammed Rizwanullah

“…Finally, the attraction-repulsion optimization algorithm (AROA) adjusts the hyperparameter values of the CNN-BiGRU-A method optimally, resulting in more excellent classification performance. …”
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Integrated Bidding and Battery Scheduling in a Microgrid for Sealed-Bid Double Auction Power Trading With Peer Microgrids Under Uncertainty and Its Blockchain-Based Implementation by Zubin J. B., Sunitha R., Gopakumar Pathirikkat

“…In parallel, battery operations are optimized using a hybrid method that combines Genetic Algorithm (GA) and Simulated Annealing (SA), explicitly incorporating the bid buffer capacity to align scheduling with market commitments. …”
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A Three-Dimensional Feature Space Model for Soil Salinity Inversion in Arid Oases: Polarimetric SAR and Multispectral Data Synergy by Ilyas Nurmemet, Yilizhati Aili, Yang Xiang, Aihepa Aihaiti, Yu Qin, Bilali Aizezi

“…The random forest (RF) feature selection algorithm identified three optimal parameters: Huynen_vol (volume scattering component), RVI_Freeman (radar vegetation index), and NDSI (normalized difference salinity index). …”
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A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks by Qing An, Santiago Segarra, Chris Dick, Ashutosh Sabharwal, Rahman Doost-Mohammady

“…In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose SMART, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. …”
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Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures by Tingting Liu, Lifan Zhong, Xizhe Sun, Zhijiang He, Witiao Lv, Liyun Deng, Yanfei Chen

“…The artificial intelligence model personalizes therapy by leveraging patient-specific molecular profiles, optimizing drug selection and dosage while minimizing side effects. …”
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A Composite Network for CS ISAR Integrating Deep Adaptive Sampling and Imaging by Lianzi Wang, Ling Wang, Miguel Heredia Conde, DaiYin Zhu

“…However, the existing CS ISAR imaging methods based on deep learning (DL) mainly focus on improving the performance of the reconstruction algorithm while ignoring the potential room for improvement given by the design of the measurement matrix. …”
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Energetically self-sufficient robot group study kit by M. A. Rovbo, A. A. Malyshev

“…In addition to performing the target task, ancillary tasks such as maintaining the battery level, communication with other team members in a multi-agent system, should be considered in the control algorithm of the robot, which allows to study the distribution of priorities between these objectives, as shown in the example of multicriteria optimization. …”
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Study on the calculation of surface residual deformation in goaf based on the Gudermann time function by Haiguang Du, Wenchang Wang, Zhilin Dun, Lianwei Ren, Yakun Nan

“…In order to comprehensively and effectively grasp the dynamic evolution process of surface movement and deformation, and realize the scientific and accurate residual deformation calculation of each key point of the surface in goaf, the Gudermann time function for dynamic anticipation is established by introducing the Gudermann function and optimizing it, the spatial and temporal characteristics of this function in the subsidence prediction are analyzed, and the influence law of parameter changes on the surface subsidence, subsidence velocity and subsidence acceleration curve patterns of the Gudermann time function is discussed, and the method of extracting the optimal values of the function parameters by Simulated Annealing Algorithm (SAA) is presented. …”
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Machine Learning to Select Experiments Driven by Fundamental Science and Applications for Targeted Nuclear Data Improvement by D. Neudecker, T. E. Cutler, M. Devlin, P. Brain, N. Gibson, M. J. Grosskopf, M. W. Herman, J. Hutchinson, T. Kawano, A. Khatiwada, N. Kleedtke, E. Leal-Cidoncha, R. C. Little, A. E. Lovell, A. Stamatopoulos, E. C. Thompson, S. A. Vander Wiel, E. Williamson

“…We chose as differential measurements those that investigate ^{63}Cu and ^{239}Pu total cross sections, based on D-optimality rank and feasibility constraints. Two integral (criticality) experiments were selected: An experiment with Al_{2}O_{3} and graphite interleaved with Pu and a thick Cu reflector explores 1–30 keV, while we target the 30–600 keV range with an experiment that swaps boron in place of graphite with a different geometry.…”
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The Forecasting Yield of Highland Barley and Wheat by Combining a Crop Model with Different Weather Fusion Methods in the Study of the Northeastern Tibetan Plateau by Peng Li, Liang He, Xuetong Wang, Mengfan Zhao, Fan Li, Ning Jin, Ning Yao, Chao Chen, Qi Tian, Bin Chen, Gang Zhao, Qiang Yu

“…For HB, sequential selection and an improved KNN algorithm were optimal, while for wheat, sequential selection performed best. …”
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Designing of Loss Optimum Regulator for Control of D.C. Electric Drive with Varying Inertia Moment in CoDeSys Programming System by S. O. Novikov, A. V. Paschenko

“…The given software is the most suitable for simulation and development of control system algorithms and execution of semi-full-scale tests without involvement of an actual object. …”
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Energy Demand Response in a Food-Processing Plant: A Deep Reinforcement Learning Approach by Philipp Wohlgenannt, Sebastian Hegenbart, Elias Eder, Mohan Kolhe, Peter Kepplinger

“…By leveraging the adaptive, self-learning capabilities of RL, energy costs in the investigated plant are effectively decreased. The RL algorithm was compared with the well-established optimization method Mixed Integer Linear Programming (MILP), and both were benchmarked against a reference scenario without DR. …”
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Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities by Weronika Jarych, Elżbieta Tokarczyk, Patryk Iglewski, Daria Ziemińska, Karina Motolko, Rafał Burczyk, Konrad Duszyński, Michał Kociński, Jan Reinald Wendt

“…In conclusion, the study highlights the promising role of AI in gastrointestinal endoscopy while underscoring the importance of continued research, algorithmic refinement, and the establishment of regulatory frameworks to fully harness its clinical potential. …”
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Retrieval of water quality parameters based on IOA-ML models and their response to short-term hydrometeorological factors by Wentong Hu, Donghao Miao, Chi Zhang, Zixian He, Wenquan Gu, Dongguo Shao

“…The best IOA-ML model for total phosphorus (TP), total nitrogen (TN), and permanganate index (CODMn) was extreme gradient boosting optimized by genetic algorithm (GA-XGB), while that for dissolved oxygen (DO) and turbidity was categorical boosting regression optimized by GA (GA-CBR). …”
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Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype by Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang, Hongjian Zhang

“…Compared with the traditional method, the innovation of this paper is that a non-destructive prediction method is proposed, which enables high-precision predictions of the crushing force by integrating multi-dimensional phenotypic features and an intelligent optimization algorithm. Comparative tests with a random forest regression, the K-nearest neighbor, a back propagation (BP) neural network, and a long short-term memory (LSTM) neural network have demonstrated that the PSO-SVM model outperforms these methods in terms of mean absolute error, root mean square error, and correlation coefficient, underscoring its effectiveness. …”
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TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning by Yanping Wang, Longcheng Zhang, Zhenguo Yan, Jun Deng, Yuxin Huang, Zhixin Qin, Yuqi Cao, Yiyang Wang

“…A flood optimization algorithm (FLA) is used to establish a hyperparameter collaborative optimization framework. …”
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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

“…Background: The advancement of big data analytics calls for careful selection of processing frameworks to optimize machine learning effectiveness. Choosing the appropriate framework can significantly influence the speed and accuracy of data analysis, ultimately leading to more informed decision making. …”
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Research on soybean leaf disease recognition in natural environment based on improved Yolov8 by Chen Chen, Chen Chen, Xiaolei Lu, Lei He, Lei He, Ruoxue Xu, Ruoxue Xu, Yi Yang, Yi Yang, Jing Qiu, Jing Qiu

“…Experimental results demonstrate that YOLOv8-DML achieves a mAP50 of 96.9%, marking a 1.8% improvement over the original YOLOv8 algorithm, while also achieving an 18.6% reduction in parameters. …”
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GNSS Precipitable Water Vapor Prediction for Hong Kong Based on ICEEMDAN-SE-LSTM-ARIMA Hybrid Model by Jie Zhao, Xu Lin, Zhengdao Yuan, Nage Du, Xiaolong Cai, Cong Yang, Jun Zhao, Yashi Xu, Lunwei Zhao

“…Enhanced by local mean optimization and adaptive noise regulation, the ICEEMDAN algorithm effectively suppresses pseudo-modes and minimizes residual noise, enabling its decomposed intrinsic mode functions (IMFs) to more accurately capture the multi-scale features of GNSS-PWV. …”
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Immunogenic cell death-related genes as prognostic biomarkers and therapeutic insights in uterine corpus endometrial carcinoma: an integrative bioinformatics analysis by Tianfei Yi, Zhenglun Yang, Peng Shen, Yan Huang

“…The ICD score exhibited positive correlations with immune cell infiltration, as verified by ESTIMATE, xCell, TIMER, MCPcounter, EPIC, and IPS algorithms. Finally, we found that hyper-immunogenicity may be sensitive to immunotherapy and certain drugs (AZD5991, Ibrutinib, Osimertinib, AGI-5198, Savolitinib, Sapitinib, AZ960, AZD3759 and Ruxolitinib), while PCI-34051 and Vorinostat showed sensitivity in patients with hypo-immunogenicity.DiscussionOur results demonstrate that ICD plays an important role in UCEC progression, suggesting that ICD-related markers could serve as potential targets for prognosis and treatment.…”
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