Heuristic thermal sensor allocation methods for overheating detection of real microprocessors by Xin Li, Xueting Wei, Wei Zhou

“…On this basis, a heuristic method based on genetic algorithm is proposed to find a near‐optimal thermal sensor allocation solution, which can make overheating detection probability significantly improved with a greatly reduced execution time. …”
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Monitoring of Transformer Hotspot Temperature Using Support Vector Regression Combined with Wireless Mesh Networks by Naming Zhang, Guozhi Zhao, Liangshuai Zou, Shuhong Wang, Shuya Ning

“…Subsequently, this study employed a Support Vector Regression (SVR) algorithm to train the sample dataset, optimizing the SVR model using a grid search and cross-validation to enhance the predictive accuracy. …”
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Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks by Olivia Nakayima, Mostafa I. Soliman, Kazunori Ueda, Samir A. Elsagheer Mohamed

“…The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.…”
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RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

“…The lightweight module RepGhost, the repeated weighted bi-directional feature extraction module BiFPN, and the multi-dimensional attention mechanism MCA were integrated, and different datasets were replaced to enhance the adaptability of the model and improve its generalization ability. The findings from the experiment indicate that the precision of the proposed model is as high as 0.988, the mAP@0.5(%) value and mAP@0.5:0.95(%) values increased by 10.49% and 36.62% compared to the original YOLOv8 model, and the inference speed reached 8.1GFLOPS. …”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…This optimization of the signal enhancement methodology significantly improved the fault diagnosis accuracy. …”
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Predicting weather-related power outages in large scale distribution grids with deep learning ensembles by L. Prieto-Godino, C. Peláez-Rodríguez, J. Pérez-Aracil, J. Pastor-Soriano, S. Salcedo-Sanz

“…This approach not only enhances prediction accuracy compared to individual learners but also improves the generalization ability and robustness of standalone DL models. …”
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Predicting Diabetic Retinopathy and Nephropathy Complications Using Machine Learning Techniques by D. R. Manjunath, J. J. Lohith, S. Selva Kumar, Abhijit Das

“…This paper shows the possibility of machine learning based frameworks in diabetic complication management by predicting accurately and in time. These models can be integrated into clinical decision support systems (CDSS) to give insights to clinicians, improve patient outcomes through personalized interventions and optimize resource allocation. …”
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Using machine vision to determine the rotational forces of electric motors by O.O. Shelukha, K.V. Molchanova, I.H. Babichev

“…The system has a user interface and allows visualizing the received data. To improve the dynamic characteristics of the system, it is proposed to optimize image processing algorithms, use more powerful computing resources, implement hardware acceleration, and optimize the transmission of the video stream.…”
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Research into prediction and influential factors of circuit breaker closing time using BFGS-NN by Longcheng Dai, Jiaying Yu, Zhihui Huang, Hui Ni, Yifan Zhang, Junting Dou

“…On-site operational data were analyzed to build a circuit breaker action time database. The BFGS algorithm trained on these data generated a closing time prediction model, achieving rapid convergence and optimal fit during learning. …”
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The Role of Immunohistochemistry as a Surrogate Marker in Molecular Subtyping and Classification of Bladder Cancer by Tatiana Cano Barbadilla, Martina Álvarez Pérez, Juan Daniel Prieto Cuadra, Mª Teresa Dawid de Vera, Fernando Alberca-del Arco, Isabel García Muñoz, Rocío Santos-Pérez de la Blanca, Bernardo Herrera-Imbroda, Elisa Matas-Rico, Mª Isabel Hierro Martín

“…Further research is required to determine the optimal combination of markers, establish a consensus diagnostic algorithm, and validate IHC through large-scale trials. …”
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Prediction of Flexural Ultimate Capacity for Reinforced UHPC Beams Using Ensemble Learning and SHAP Method by Zhe Zhang, Xuemei Zhou, Ping Zhu, Zhaochao Li, Yichuan Wang

“…Furthermore, multiple machine learning (ML) algorithms, including both traditional and EL models, are employed to develop optimized predictive models for the flexural ultimate capacity of reinforced UHPC specimens derived from the established database. …”
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Understanding levels of best practice: An empirical validation. by Huy P Phan, Bing H Ngu, Hui-Wen Wang, Jen-Hwa Shih, Sheng-Ying Shi, Ruey-Yih Lin

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ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments by Fei Gao, Yang Tian, Yongliang Wu, Yunxia Zhang

“…In summary, the ST-YOLOv8 model, by integrating advanced neural network architectures and optimization techniques, significantly improves detection accuracy and reduces false detection rates. …”
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Torque capability enhancement of commercial interior permanent magnet motors using t-shaped notching and merged barrier rotor topology by H.N. Lin, P. Seangwong, N. Fernando, A. Siritaratiwat, P. Khunkitti

“…A multi-objective genetic algorithm with sensitivity-based constraints is employed to optimizes key design parameters, and the electromagnetic performance is evaluated using 2D finite element analysis under no-load and on-load conditions. …”
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Integrating Social Determinants of Health in Machine Learning–Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study by Seung-Yup Lee, Leslie W Hayes, Bunyamin Ozaydin, Steven Howard, Alison M Garretson, Heather M Bradley, Andrew M Land, Erin W DeLaney, Amy O Pritchett, Amanda L Furr, Ashleigh Allgood, Matthew C Wyatt, Allyson G Hall, Jane C Banaszak-Holl

“… BackgroundThe use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes. …”
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An Ante Hoc Enhancement Method for Image-Based Complex Financial Table Extraction by Weiyu Peng, Xuhui Li

“…The filter module is based on a text semantic matching model and another heuristic algorithm. The experimental results show that the use of the proposed method can significantly improve the performance of different table extraction methods, with increases in F1 scores of between 5.10 and 14.36 points being recorded.…”
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Advancing Agricultural Machinery Maintenance: Deep Learning-Enabled Motor Fault Diagnosis by Xusong Bai, Qian Chen, Xiangjin Song, Weihang Hong

“…This article further discusses future research directions, such as optimizing DL models for real-time processing, improving robustness under varying agricultural conditions, and developing user-friendly interfaces for farmers and technicians. …”
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Predicting the presence of adjacent septic arthritis in children with acute hematogenous osteomyelitis by Shuting Lin, Donghao Gu, Peng Ning, Jingyu Wu, Zhixin Yang, Tianjing Liu

“…Graphical and logistical regression analysis was used to determine variables independently predictive of adjacent infection. Optimal cutoff values were determined for each variable and a prediction model was created. …”
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Multi-Source Attention U-Net: A Novel Deep Learning Framework for the Land Use and Soil Salinization Classification of Keriya Oasis in China with RADARSAT-2 and Landsat-8 Data by Yang Xiang, Ilyas Nurmemet, Xiaobo Lv, Xinru Yu, Aoxiang Gu, Aihepa Aihaiti, Shiqin Li

“…Moreover, the MS + SAR MSA-U-Net, in comparison to traditional machine learning methods and the baseline model, improved the OA and Kappa coefficient by 8.24% to 12.55% and 0.08 to 0.15, respectively. …”
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Semi-supervised anchorless single-engine grip detection by Yun Shi, Gang Zhang, Min Kong, Jie Fang

“…The extensive application of artificial intelligence in wireless communication, 3D reconstruction, and target location has successfully solved the modeling problem. Grasping specific objects in a stacked scene is a difficult task to achieve robot grasping. …”
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