Secure healthcare data sharing and attack detection framework using radial basis neural network by Abhishek Kumar, Priya Batta, Pramod Singh Rathore, Sachin Ahuja

“…Specifically, the Intelligent Voyage Optimization algorithm effectively tunes the model hyperparameters and the deployment of hybrid features contributes to the proposed model to detect attack patterns effectively. …”
Get full text

A Distributed Resilience Enhancement Strategy for Multi-microgrids Based on System of Systems Architecture by Linxinyan LIN, Junpeng ZHU, Yue YUAN

“…Firstly, the energy exchange process of the multi-microgrid systems is modeled based on the SoS architecture, and the distributed optimization algorithm is used to solve the model, which ensures the privacy of user information. …”
Get full text

Rapid calculation scheme for electricity quantity and green certificate allocation considering demand response within the municipal scope by FAN Qiannan, WANG Zhongrong, YU Liya, LIANG Xiaohang, BAO Jie, LIU Shuyong

“…This paper focuses on the day-ahead allocation scenario of electricity quantity and certificates considering demand response within the municipal scope, constructs a leader-follower game model, and solves it through the Kriging model surrogate optimization method. …”
Get full text

An adaptive video stream transmission control method for wireless heterogeneous networks based on A3C by Zhiqiang LUO, Wei WANG, Xiaorong ZHU

“…The adaptive bit rate (ABR) algorithm has become the focus research in video transmission.However,due to the characteristics of 5G wireless heterogeneous networks,such as large fluctuation of channel bandwidth and obvious differences between different networks,the adaptive video stream transmission with multi-terminal cooperation was faced with great challenges.An adaptive video stream transmission control method based on deep reinforcement learning was proposed.First of all,a video stream dynamic programming model was established to jointly optimize the transmission rate and diversion strategy.Since the solution of this optimization problem depended on accurate channel estimation,dynamically changing channel state was difficult to achieve.Therefore,the dynamic programming problem was improved to reinforcement learning task,and the A3C algorithm was used to dynamically determine the video bit rate and diversion strategy.Finally,the simulation was carried out according to the measured network data,and compared with the traditional optimization method,the method proposed better improved the user QoE.…”
Get full text

Indoor Positioning System based on SSA-ELM Neural Network for Visible Light by JIA Kejun, NIU Zhen, YU Kai, ZHANG Zhicong, PENG Duo, CAO Minghua

“…【Objective】The Extreme Learning Machine (ELM) neural network algorithm in the traditional indoor Visible Light Positioning (VLP) system suffers from unstable convergence and a tendency to get stuck in local optimal states, resulting in decreased positioning accuracy. …”
Get full text

Support Vector Machine Berbasis Feature Selection Untuk Sentiment Analysis Kepuasan Pelanggan Terhadap Pelayanan Warung dan Restoran Kuliner Kota Tegal by Oman Somantri, Dyah Apriliani

“…Sentiment analysis is used to provide a solution related to this problem by applying the Support Vector Machine (SVM) algorithm model. The purpose of this research is to optimize the generated model by applying feature selection using Informatioan Gain (IG) and Chi Square algorithm on the best model produced by SVM on the classification of customer satisfaction level based on culinary restaurants at Tegal City so that there is an increasing accuracy from the model. …”
Get full text

InvMOE: MOEs Based Invariant Representation Learning for Fault Detection in Converter Stations by Hao Sun, Shaosen Li, Hao Li, Jianxiang Huang, Zhuqiao Qiao, Jialei Wang, Xincui Tian

“…To overcome these issues, we propose InvMOE, a novel fault detection algorithm with two core components: (1) invariant representation learning, which captures task-relevant features and mitigates background noise interference, and (2) multi-task training using a mixture of experts (MOE) framework to adaptively optimize feature learning across tasks and address label sparsity. …”
Get full text

Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning by Jian Ma, Hua Su, Wan-lin Zhao, Bin Liu

“…The grid search method is introduced in this paper to optimize the hyperparameters of the proposed aircraft engine RUL prediction model. …”
Get full text

Research on Dynamic Storage Location Assignment of Picker-to-Parts Picking Systems under Traversing Routing Method by Xiangbin Xu, Chenhao Ren

“…Then, the adjustment gain model of dynamic storage location assignment is built, and a genetic algorithm is designed to find the final adjustment solution. …”
Get full text

ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition. by David Koslicki, Saikat Chatterjee, Damon Shahrivar, Alan W Walker, Suzanna C Francis, Louise J Fraser, Mikko Vehkaperä, Yueheng Lan, Jukka Corander

“…The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. …”
Get full text

Adaptive robust position control scheme for an electromagnetic levitation system with experimental verification. by Ziwei Wu, Kuangang Fan, Ping Yi

“…Firstly, a nonlinear model of the electromagnetic levitation ball system was established; Secondly, robust sliding mode control is combined with linear active disturbance rejection control, and an adaptive parameter tuning strategy is introduced for the PD module in LADRC; Meanwhile, an improved whale optimization algorithm was proposed to address the issue of excessive adjustable parameters in the controller; In addition, the stability and convergence of the control algorithm were proven using the Lyapunov equation; Finally, in order to verify the effectiveness of the control method, PID, LADRC, CS-LADRC, and I-LADRC were introduced for simulation analysis and experimental verification. …”
Get full text

Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibri... by Peter Ruppersberg, Steven Castellano, Philip Haeusser, Kostiantyn Ahapov, Melissa H. Kong, Stefan G. Spitzer, Stefan G. Spitzer, Georg Nölker, Andreas Rillig, Tamas Szili-Torok

“…However, the underlying machine learning strategy used to develop and refine the EGF algorithm has not yet been detailed. Here, we present how our EGF Model—trained on procedural outcomes from 199 fully anonymized retrospective patient datasets—identifies clinically significant sources of AF and how this machine learning–driven hyperparameter optimization underlies its clinical effectiveness. …”
Get full text

YED-Net: Yoga Exercise Dynamics Monitoring with YOLOv11-ECA-Enhanced Detection and DeepSORT Tracking by Youyu Zhou, Shu Dong, Hao Sheng, Wei Ke

“…Evaluated on the SND Sun Salutation Yoga Dataset released in 2024, the improved model achieves a real-time processing speed of 85.79 frames per second (FPS) on an RTX 3060 platform, with an 18% reduction in computational cost compared to the baseline. …”
Get full text

Impact of occupancy behavior on building energy efficiency: What’s next in detection and monitoring technologies? by Wenjie Song, John Calautit

“…Particular attention is paid to data-driven methods, including probabilistic models such as Hidden Markov Models (HMMs), classical machine learning algorithms such as Support Vector Machines (SVMs) and K-Nearest Neighbors (KNN), and deep learning architectures such as Convolutional Neural Networks (CNNs), all of which have demonstrated high accuracy in both laboratory and real-world settings. …”
Get full text

Lithium-Ion Battery State of Health Estimation Based on Feature Reconstruction and Transformer-GRU Parallel Architecture by Bing Chen, Yongjun Zhang, Jinsong Wu, Hongyuan Yuan, Fang Guo

“…The results show that the RMSE of the state of health estimation by the proposed method is 0.0071, which is an improvement of 61.41% in the accuracy of its baseline model.…”
Get full text

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. …”
Get full text

AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…The advanced WIoUv3 loss function further boosted the model's performance, achieving a mAP@0.5 of 84.5% and an F1 score of 83%, marking an approximate 3.4% improvement over the baseline, and showcasing a favorable balance between detection accuracy and model efficiency. …”
Get full text

Combating electricity fraud: Employing hybrid learning and computer vision for sustainable energy management by Jui-Sheng Chou, Nader Anwar Charaf, Dani Nugraha Limantono, Hoang-Minh Nguyen

“…Additionally, the study explores hybrid and ensemble AI models that combine the best-performing algorithms to improve prediction accuracy. …”
Get full text

Securing federated learning: a defense strategy against targeted data poisoning attack by Ansam Khraisat, Ammar Alazab, Moutaz Alazab, Tony Jan, Sarabjot Singh, Md. Ashraf Uddin

Get full text

Application of 5G + edge computing technology in intelligent monitoring construction of electricity business office by Wei Cui, Junwei Li, Wei Ge, Bo Zhang, Tianwei Liu

“…This paper also proposes an edge scheduling algorithm based on game theory, which enables each edge device to choose the optimal data processing task according to its own utility function through a non-cooperative game model, realizing the optimization of the efficacy and accuracy of data processing. …”
Get full text