Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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Identification of Low‐Value Defects in Infrared Images of Porcelain Insulators Based on STCE‐YOLO Algorithm by Shaotong Pei, Weiqi Wang, Chenlong Hu, Keyu Li, Haichao Sun, Mianxiao Wu, Bo Lan

“…To solve the above problems, this paper optimizes the small target and complex environment problems in the low‐value defect recognition of insulator infrared images, and proposes the STCE‐YOLO algorithm: based on YOLOv8, the deformable large kernel attention is used to improve the detection ability of small targets; then the cross‐modal contextual feature module is applied to Integrate the features of different scales to reduce the computation of the model. …”
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Performance Evaluation of Hybrid Bio-Inspired and Deep Learning Algorithms in Gene Selection and Cancer Classification by Shahad S. Alkamli, Hala M. Alshamlan

“…This study explores the performance of hybrid bio-inspired algorithms and deep learning techniques for gene selection and cancer classification. …”
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LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement by Zhipeng Fan, Yayun Wu, Wei Liu, Ming Chen, Zeguo Qiu

“…Evaluations on the SWHD dataset confirm the effectiveness of the LG-YOLOv8 algorithm. Compared to the original YOLOv8-n algorithm, our approach achieves a mean Average Precision (mAP) of 94.1%, a 59.8% reduction in parameters, a 54.3% decrease in FLOPs, a 44.2% increase in FPS, and a 2.7 MB compression of the model size. …”
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Mechanism of Immune System Based Multipath Fault Tolerant Routing Algorithm for Wireless Sensor Networks by Hongbing Li, Qingyu Xiong, Weiren Shi, Liwan Chen, Xin Shi, Qiang Chen

“…Mechanism of immune system is applied to do the variation on the initial antibody population, namely, the multiple disjoint paths, to establish the final optimal transmission paths. Mathematical model is established to do the theoretical analysis on the performance of the algorithm. …”
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Comprehensive Comparison and Validation of Forest Disturbance Monitoring Algorithms Based on Landsat Time Series in China by Yunjian Liang, Rong Shang, Jing M. Chen, Xudong Lin, Peng Li, Ziyi Yang, Lingyun Fan, Shengwei Xu, Yingzheng Lin, Yao Chen

“…These findings highlight the necessity of region-specific calibration and parameter optimization tailored to specific disturbance types to improve forest disturbance monitoring accuracy, and also provide a solid foundation for future studies on algorithm modifications and ensembles.…”
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Investigating the performance of random oversampling and genetic algorithm integration in meteorological drought forecasting with machine learning by Tahsin Baykal, Özlem Terzi, Gülsün Yıldırım, Emine Dilek Taylan

“…The study found that the integration of ROS significantly enhanced data balance, leading to more robust model training, while the use of GA for hyperparameter tuning consistently improved model accuracy. …”
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Mobile Robot Path Planning Based on Fused Multi-Strategy White Shark Optimisation Algorithm by Dazhang You, Junjie Yu, Zhiyuan Jia, Yepeng Zhang, Zhiyuan Yang

“…A novel multi-strategy fusion white shark optimization algorithm is proposed, focusing on actual scenario requirements, to provide optimal solutions for mobile robot path planning. …”
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A review of state-of-the-art resolution improvement techniques in SPECT imaging by Zhibiao Cheng, Ping Chen, Jianhua Yan

“…It delves into advancements in detector design and modifications, projection sampling techniques, traditional reconstruction algorithm development and optimization, and the emerging role of deep learning. …”
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Set-Based Differential Evolution Algorithm Based on Guided Local Exploration for Automated Process Discovery by Si-Yuan Jing

“…Thirdly, a fine-grained evaluation technique is designed to assign score to each node in a process model, which is employed to guide the local exploration and improve the efficiency of the algorithm. …”
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A cooperative dynamic target search approach for multi-UAV systems utilizing the MAPPO algorithm by Peiyan Zhang, Guodong Li

“…In response to these issues, this study proposes an improved multi-agent proximal policy optimization algorithm (AS-MAPPO). …”
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A self-learning method with domain knowledge integration for intelligent welding sequence planning by Weidong Shen, Xuewen Wang, Juanli Li, Yong Wang, Xiaojun Qiao

“…To improve the interpretability of the results, domain knowledge was integrated into the construction and training processes of a self-learning model. …”
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Wood Panel Defect Detection Based on Improved YOLOv8n by Rui Li, Shilu Zhong, Xuemei Yang

“…However, the accuracy and convergence speed of existing defect detection techniques still require improvement. In this paper, an improved algorithm based on YOLOv8n was designed for accurate detection of wood panel defects. …”
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The Application of the SubChain Salp Swarm Algorithm in the Less-Than-Truckload Freight Matching Problem by Yibo Sun, Lei Yue, Yi Liu, Weitong Chen, Zhe Sun

“…Traditional LTL matching methods are challenged by delays in updating logistic information and higher distribution costs. In order to solve LTL challenges, we developed a novel SubChain Salp Swarm Algorithm (SSSA) by improving the traditional Salp Swarm Algorithm with the utilization of a SubChain operation. …”
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Evaluating the generalisability of region-naïve machine learning algorithms for the identification of epilepsy in low-resource settings. by Ioana Duta, Symon M Kariuki, Anthony K Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W Sander, Charles R Newton, Arjune Sen, Gabriel Davis Jones

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VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes by Yunxiang Liu, Yuqing Shi

“…To overcome these challenges, this paper proposes an improved VRU detection algorithm based on YOLOv8, named VRU-YOLO. …”
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Critical Component Overheating Monitoring Algorithm Integrating Temperature-sensing Patches and Computer Vision Trains by SHU Dong, ZHANG Beijia, YANG Hongtai

“…In the test on a real dataset, the improved algorithm achieves an accuracy rate of 99.30%.…”
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Development and Comparison of Interrupt-Based and Analog-to-Digital Converter Algorithms for Seed Counting in Precision Planters by A. Ghaffarnezhad, H. Navid, H. Karimi

“…Both developed circuits featured the deployment of the STM32F103C8T6 microcontroller, renowned for its robust capabilities and cost efficiency.In the interrupt-based algorithm's development, the microcontroller's external interrupt was used, selecting its sensitivity to detect both rising and falling edges. …”
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Machine learning algorithms to predict stroke in China based on causal inference of time series analysis by Qizhi Zheng, Ayang Zhao, Xinzhu Wang, Yanhong Bai, Zikun Wang, Xiuying Wang, Xianzhang Zeng, Guanghui Dong

“…Introduction of dynamic causal inference features has significantly improved the performance of almost all models. The area under the ROC curve of each model ranged from 0.78 to 0.83, indicating significant difference (P < 0.01). …”
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Integrating solar and wind energy into the electricity grid for improved power accessibility by Collins N. Nwagu, Chika Oliver Ujah, Daramy V.V. Kallon, Victor S. Aigbodion

“…It was observed that developments of demand-side management, energy storage technology, and hybrid system optimization algorithms would improve the dependability, effectiveness, and cost of the hybrid system. …”
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