RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 by Wenjie Tang, Yangjun Deng, Xu Luo

“…This integration effectively optimizes feature representation capabilities while significantly reducing the model’s parameter count. …”
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Line-Structured Light-Based Three-Dimensional Reconstruction Measurement System with an Improved Scanning-Direction Calibration Method by Jia Chen, Shantao Ping, Xiaowei Liang, Xulong Ma, Shiyan Pang, Ying He

“…In this study, we propose an improved method to calibrate the sensor’s scanning direction that iteratively optimizes control points via plane transformation while leveraging the rotational invariance of the rotation matrix during translation. …”
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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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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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A seismic random noise suppression method based on CNN-Mamba by Xiujuan WEI, Xingye LIU, Huailai ZHOU

“…The hardware-aware parallel algorithm of Mamba was employed to reduce the computational resource consumption, thus ensuring the denoising performance while enhancing computational efficiency. …”
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Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients by Zhenmeng Lin, Zhenmeng Lin, Hao He, Mingfang Yan, Xiamei Chen, Hanshen Chen, Jianfang Ke

“…While RF showed marginally higher predictive performance, the nomogram offers superior clinical interpretability, making it a practical option for individualized risk stratification.…”
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Aryana-bs: context-aware alignment of bisulfite-sequencing reads by Hassan Nikaein, Ali Sharifi-Zarchi, Afsoon Afzal, Saeedeh Ezzati, Farzane Rasti, Hamidreza Chitsaz, Govindarajan Kunde-Ramamoorthy

“…It outperforms existing methods, such as BSMAP, bwa-meth, Bismark, BSBolt, and abismal, particularly in robustness against genomic biases and alignment of longer, higher-error reads, demonstrating suitability for cancer research and cell-free DNA studies. While the Expectation-Maximization (EM) algorithm provides only modest initial improvements, it establishes a valuable framework for future refinement and potential enhancements in sensitive applications.…”
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A Framework for Autonomous UAV Navigation Based on Monocular Depth Estimation by Jonas Gaigalas, Linas Perkauskas, Henrikas Gricius, Tomas Kanapickas, Andrius Kriščiūnas

“…The solution utilizes a depth image estimation model to create an occupancy grid map of the surrounding area and uses an A* path planning algorithm to find optimal paths to end goals while navigating around the obstacles. …”
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Toward Digital Twin of Off-Road Vehicles Using Robot Simulation Frameworks by Arianna Rana, Antonio Petitti, Angelo Ugenti, Rocco Galati, Giulio Reina, Annalisa Milella

“…The paper presents the details of model design and implementation while investigating the best choice in developing the digital twin of off-road vehicles operating in the field. …”
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Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database by De Su, Jie Zheng, Yue-kai Shao, Jun-ya Liu, Xin-xin Liu, Kun Yu, Bang-hai Feng, Hong Mei, Song Qin

“…LASSO regression was employed for feature selection, and various machine learning algorithms were utilized to train models, including logistic regression (LR), random forest (RF), and gradient boosting (GB), among others. …”
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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway by Jun-luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-shun Liang, Hong-hao Zhou, Zhao-qian Liu, Wen-xu Hong, Ji-ye Yin

“…We used machine learning algorithms to analyze small RNAs with significant differences between the two groups, fit the model through training sets, and optimize the model through testing sets. …”
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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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Monitoring and Comparative Analysis of NO₂ and HCHO in Shanghai Using Dual-Azimuth Scanning MAX-DOAS and TROPOMI by Hongmei Ren, Ang Li, Zhaokun Hu, Nannan Shao, Xinyan Yang, Hairong Zhang, Jiangman Xu, Jinji Ma

“…These findings not only reveal the spatiotemporal distribution characteristics of regional pollutants but optimize the sampling time and distance parameters for satellite–ground observation validation, providing data support for improving and enhancing the accuracy of satellite retrieval algorithms.…”
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