Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer by Junyi Li, Shixin Li, Dongpo Zhang, Yibing Zhu, Yue Wang, Xiaoxiao Xing, Juefei Mo, Yong Zhang, Daixiang Liao, Jun Li

“…To address these limitations, this study systematically analyzed RNA-seq high-throughput data and combined 10 machine learning algorithms to construct 117 models. The optimal algorithm combination, StepCox[both] and ridge regression, was identified, and an immune-related gene signature (IRGS) composed of 12 genes was developed. …”
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Research on Status Assessment and Operation and Maintenance of Electric Vehicle DC Charging Stations Based on XGboost by Hualiang Fang, Jiaqi Liao, Shuo Huang, Maojie Zhang

“…The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. …”
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Overhead line path planning based on deep reinforcement learning and geographical information system by Jiahui Chen, Yi Yang, Ling Peng, Lina Yang, Yinhui Han, Xingtong Ge

“…Experimental verification of real data shows that compared with existing algorithms, the DSOP method is not only more consistent with the manual line selection effect (improved by more than 3%), but also has a high success rate. …”
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Post-Quantum Cryptography Resilience in Telehealth Using Quantum Key Distribution by Don Roosan, Rubayat Khan, Saif Nirzhor, Fahmida Hai

“…Methods A multi-layered design approach was adopted. PQC algorithms (e.g., CRYSTALS-Dilithium) were integrated at the blockchain consensus layer to resist quantum attacks. …”
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Intelligent Data Reduction for IoT: A Context-Driven Framework by Laercio Pioli, Douglas D. J. De Macedo, Daniel G. Costa, Mario A. R. Dantas

“…With these predictions based on existing datasets, a selector algorithm module is adopted to identify the most suitable data reduction approach for specific IoT applications. …”
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Research on Urban Traffic Signal Control Systems Based on Cyber Physical Systems by Li-li Zhang, Qi Zhao, Li Wang, Ling-yu Zhang

“…Finally, considering China, the system designs a general control strategy API to separate data from control strategy. Most of the popular communication protocols between signal controllers and detectors are private protocols. …”
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DS-AdaptNet: An Efficient Retinal Vessel Segmentation Framework With Adaptive Enhancement and Depthwise Separable Convolutions by Shuting Chen, Chengxi Hong, Hong Jia

“…Second, we develop a Context-Aware Adaptive Threshold Optimization (CA-ATO) algorithm that dynamically determines optimal thresholds by integrating multi-scale contextual information and uncertainty estimates, substantially improving boundary delineation accuracy and fine structure preservation. …”
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City-scale industrial tank detection using multi-source spatial data fusion by Zhibao Wang, Mingyuan Zhu, Lu Bai, Jinhua Tao, Mei Wang, Xiaoqing He, Anna Jurek-Loughrey, Liangfu Chen

“…To address this, high-resolution remote sensing images and deep learning algorithms are used to improve the accuracy of industrial storage tank detection at the city scale. …”
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Lightweight security architecture design for wireless sensor network by Chao WANG, Guang-yue HU, Huan-guo ZHANG

“…Most previous security proposal did not consider key management or their authentication efficiency was very low.Lightweight security architecture and lightweight security algorithm were proposed for wireless sensor network,The problem of network encounters malicious nodes maybe occur in the procedure of backbone networks networking could be solved by threshold secret sharing mechanism.The lightweight ECC was proposed to optimize the CPK architecture based on normal ECC,authentication was efficient without the third-party CA,and could reduce the computational complexity,the key management could meet the resource limit in wireless sensor network,and the key security depended on the exponential computation complexity of the elliptic discrete logarithm decomposition.The scheme used the improved two-way authentication to ensure the communication security between common node and sink node,which could prevent man-in-the-middle attack.…”
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Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications by Dasheng Wu, Na Liu, Rui Ma, Peilong Wu

“…Medical images (9/26, 34.6%) and electronic medical records (7/26, 26.9%) were the most commonly used data types. Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. …”
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Recent technical advancements and clinical applications of MR-guided radiotherapy in lung cancer treatment by Chi Ma, Xiao Wang, Ke Nie, Zhenyu Xiong, Keying Xu, Ning Yue, Yin Zhang

“…Additionally, MRgRT could potentially allow multileaf collimator (MLC) tracking to further improve the treatment efficiency. Recent technological innovations, including AI-powered auto-contouring algorithms, deep-learning (DL) based prediction models, and adaptive treatment strategies, further optimize MRgRT by improving workflow efficiency and reducing treatment time. …”
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Capacity-Constrained Contraflow Adaption for Lane Reconfiguration in Evacuation Planning by Wu Ni, Wenbo Li, Hailei Wang, Chengxin Xiong, Dan Guo

“…This paper presents a heuristic contraflow-based reconfiguration evacuation algorithm, which is named Capacity-Constrained Contraflow Adaption (CC-Adap). …”
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Space-time coding scheme for the paired weak user in MIMO-NOMA systems by Mingyan GONG, Zhen YANG

“…In view of the paired weak user’s poor outage performance in multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems,Alamouti code was adopted to encode for the weak user in order to improve its outage performance by means of diversity,and the closed-form expression of the strong user’s ergodic capacity as well as the boundary-form expressions of the weak user’s ergodic capacity and outage probability was derived in the proposed model.Moreover,a power allocation algorithm for optimizing the system’s throughput was proposed.Finally,the numerical results show the accuracy of the derived expressions,the efficacy of the proposed algorithm,and that the weak user’s outage performance in the proposed coding scheme is far superior to that in the current coding scheme only adopting vertical Bell lab layered space-time (V-BLAST) code.…”
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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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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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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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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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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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