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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LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning by Xu Liu, Yuewen Liu, Miaomiao Wei, Peng Xu

“…First, we propose a time-aware LSTM prediction neural network to improve the accuracy of service dependency mining. Secondly, based on the service dependency graph, we propose an anomaly detection method based on log-metric fusion, which can more accurately describe the running status of microservices, thereby improving the accuracy of anomaly detection. …”
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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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Dual Branch Encoding Feature Aggregation for Cloud and Cloud Shadow Detection of Remote Sensing Image by Naikang Shi, Haifeng Lin, Huiwen Ji, Min Xia

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Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets. by Der-Chiang Li, Susan C Hu, Liang-Sian Lin, Chun-Wu Yeh

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Multi-label classification for image tamper detection based on Swin-T segmentation network in the spatial domain by Li Li, Kejia Zhang, Jianfeng Lu, Shanqing Zhang

“…Furthermore, they only support a single image tampering type. Our method introduces three key innovations: (1) A spatial perception module that combines the spatial rich model (SRM) with constrained convolution, enabling focused detection of tampering traces while suppressing interference from image content; (2) A hierarchical feature learning architecture that integrates Swin Transformer with UperNet for effective multi-scale tampering pattern recognition; and (3) A comprehensive optimization strategy including auxiliary supervision, self-supervised learning, and hard example mining, which significantly improves model convergence and detection accuracy. …”
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Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review by Boris I, Kseniia Barashok, Yongjoon Choi, Yeongil Choi, Mohammed Aslam, Jaesun Lee

“…Among the various NDE techniques, ultrasonic methods are widely regarded as the most effective for damage detection and material characterization due to their high sensitivity and versatility. …”
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MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured Images by Shengbang Zhou, Song Zhang, Chuanqi Li, Shutian Liu, Dong Chen

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Comparison of Manhattan and Chebyshev Distance Metrics in Quantum-Based K-Medoids Clustering by Solikhun Solikhun, Muhammad Rahmansyah Siregar, Lise Pujiastuti, Mochamad Wahyudi, Deny Kurniawan

“…Clustering is a technique in data mining used to identify patterns that can support decision-making processes. …”
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A study on the technology of enhancing gas permeability and gas drainage by sectional hydraulic fracturing in directional long boreholes: a case study of the no. 8 coal seam in bao... by Zenghui Zhang, Jinlin Qiao, Sen Yang, Kaige Zheng, Di Zhang, Jian Zhang

“…By adopting the drag-type staged fracturing process with double-packers and a single-slip, and combining it with methods such as the analysis of pump injection stress curve stages, gas extraction concentration analysis, and in-hole transient electromagnetic detection, the laws of fracture propagation and the mechanism of permeability modification are revealed. …”
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Enhanced Cloud Detection Using a Unified Multimodal Data Fusion Approach in Remote Images by Yan Mo, Puhui Chen, Wanting Zhou, Wei Chen

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Improved Denclue Outlier Detection Algorithm With Differential Privacy and Attribute Fuzzy Priority Relation Ordering by Huangzhi Xia, Limin Chen, Dongyan Wang, Xiaotong Lu

“…Outlier detection is an important method in data mining. Although Denclue algorithm is particularly good at finding clusters of arbitrary shape and detecting outliers, it does not protect the user’s privacy well in the operation process. …”
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Reliable identification method of intrusion personnel in front of underground moving equipment by Bin LIANG, Guang LI, Guangpeng SHAN

“…A reliable registration method of infrared and visible dual-modal images is designed and proposed to solve the problem of reliable identification of intruders in front of the moving equipment in underground coal mines. …”
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Human Action Recognition Method Based on Multi-channel Fusion by Zhiyong TAO, Xijun GUO, Xiaokui REN, Ying LIU, Zemin WANG

“…In healthcare, real-time action recognition enhances patient care, detects potential falls, and assists elderly individuals. …”
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Research on the problem of landmines and explosive remnants of war worldwide and methods of demining by Dzenan Cosic, Alan Catovic

“…Additionally, it explores the most effective method for mine removal, and mechanical demining, providing a detailed overview of the operation and characteristics associated with this approach. …”
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Hyperspectral Detection of Metal Element Concentration in Vegetation Canopies: A Case Study of Fe and Mo by Daming Wang, Veronika Kopačková-Strnadová, Bo Zhang, Jing Zhang, Feicui Wang, Junquan Yang

“…Remote and proximal sensing has proven to be highly effective in pinpointing surface-exposed alteration minerals and detecting potential mining sites in previously unproductive areas. …”
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Enhancing Malware Analysis Using Data Visualization Through Shared Code and Attribute Analysis by Narayandas Sai Ramana Vashista, K. Abhimanyu Kumar Patro

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Joint Tomek Links (JTL): An Innovative Approach to Noise Reduction for Enhanced Classification Performance by Goksu Tuysuzoglu, Yunus Dogan, Elife Ozturk Kiyak, Mustafa Ersahin, Bita Ghasemkhani, Kokten Ulas Birant, Derya Birant

“…Mathematical methods are crucial in tackling this obstacle, particularly in optimizing noise detection and data preprocessing. …”
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A real-world pharmacovigilance study of efgartigimod alfa in the FDA adverse event reporting system database by Yunlin Yang, Jinfeng Liu, Wei Wei, Wei Wei, Wei Wei

“…Disproportionality analysis was used in data mining to quantify efgartigimod alfa-related AE signals.ResultsA total of 3,040 reports with efgartigimod alfa as the primary suspect and 12,487 AEs were retrieved from FAERS. …”
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Prospects of Improving the Vibroacoustic Method for Locating Buried Non-Metallic Pipelines by Vladimir Pshenin, Alexander Sleptsov, Leonid Dukhnevich

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