Outlier detection algorithm based on fast density peak clustering outlier factor by Zhongping ZHANG, Sen LI, Weixiong LIU, Shuxia LIU

“…For the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estimate, which adopted the KD-Tree index data structure calculation of k close neighbors of data objects, and then the way of the product of density and distance was adopted to automatic selection of clustering centers.In addition, the centripetal relative distance and fast density peak clustering outliers were defined to describe the degree of outliers of data objects.Experiments on artificial data sets and real data sets were carried out to verify the algorithm, and compared with some classical and novel algorithms.The validity and time efficiency of the proposed algorithm are verified.…”
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Land Target Detection Algorithm in Remote Sensing Images Based on Deep Learning by Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

“…Additionally, it reduced model parameters and computational complexity by 0.9 M and 2.9 GFLOPs, respectively. These results demonstrate the enhanced detection performance and efficiency of the YOLOv5s-CACSD model, making it suitable for practical applications in land target detection for remote sensing imagery.…”
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Automatic detection of foreign object intrusion along railway tracks based on MACENet. by Xichun Chen, Yu Tian, Ming Li, Bin Lv, Shuo Zhang, Zixian Qu, Jianqing Wu, Shiya Cheng

“…Ensuring high accuracy and efficiency in foreign object intrusion detection along railway lines is critical for guaranteeing railway operational safety under limited resource conditions. …”
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Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach by John Blake, Abu Saleh Musa Miah, Krzysztof Kredens, Jungpil Shin

“…This paper presents a novel algorithm that leverages cutting-edge machine-learning techniques to accurately and efficiently detect AI-generated texts. Rapid advancements in natural language processing models have led to the generation of text closely resembling human language, making it increasingly difficult to differentiate between human and AI-generated content. …”
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Redundancy and conflict detection method for label-based data flow control policy by Rongna XIE, Xiaonan FAN, Suzhe LI, Yuxin HUANG, Guozhen SHI

“…To address the challenge of redundancy and conflict detection in the label-based data flow control mechanism, a label description method based on atomic operations has been proposed.When the label is changed, there is unavoidable redundancy or conflict between the new label and the existing label.How to carry out redundancy and conflict detection is an urgent problem in the label-based data flow control mechanism.To address the above problem, a label description method was proposed based on atomic operation.The object label was generated by the logical combination of multiple atomic tags, and the atomic tag was used to describe the minimum security requirement.The above label description method realized the simplicity and richness of label description.To enhance the detection efficiency and reduce the difficulty of redundancy and conflict detection, a method based on the correlation of sets in labels was introduced.Moreover, based on the detection results of atomic tags and their logical relationships, redundancy and conflict detection of object labels was carried out, further improving the overall detection efficiency.Redundancy and conflict detection of atomic tags was based on the relationships between the operations contained in different atomic tags.If different atomic tags contained the same operation, the detection was performed by analyzing the relationship between subject attributes, environmental attributes, and rule types in the atomic tags.On the other hand, if different atomic tags contained different operations without any relationship between them, there was no redundancy or conflict.If there was a partial order relationship between the operations in the atomic tags, the detection was performed by analyzing the partial order relationship of different operations, and the relationship between subject attribute, environment attribute, and rule types in different atomic tags.The performance of the redundancy and conflict detection algorithm proposed is analyzed theoretically and experimentally, and the influence of the number and complexity of atomic tags on the detection performance is verified through experiments.…”
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Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities by Denis Leite, Emmanuel Andrade, Diego Rativa, Alexandre M. A. Maciel

“…Integrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). …”
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Integrating ANN and ANFIS for effective fault detection and location in modern power grid by Yadav Goutam Kumar, Kirar Mukesh Kumar, Gupta S.C., Rajender Jatoth

“…The increasing complexity and demand for reliability in modern power systems necessitate advanced techniques for fault detection, classification, and location. …”
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Intelligent Casting Quality Inspection Method Integrating Anomaly Detection and Semantic Segmentation by Min-Chieh Chen, Shih-Yu Yen, Yue-Feng Lin, Ming-Yi Tsai, Ting-Hsueh Chuang

“…Customized optical path design is often required, especially when conducting internal and external defect inspections, which increases overall operational complexity and reduces inspection efficiency. We developed an automated optical inspection (AOI) system to address these challenges. …”
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ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection by Zezhong Ding, Yanfang Li, Bin Hu, Zhiwei Chen, Houzhen Jia, Yali Shi, Xingmin Zhang, Xuesong Zhu, Wenjie Feng, Chunwang Dong

“…To solve this technical problem in the industry, this article proposes a lightweight algorithm for detecting and sorting impurities in premium green tea in order to improve sorting efficiency and reduce labor intensity. …”
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3D Object Detection Based on Graph Network Fusion Sampling Strategy by LI Wenju, CHEN Zhilin, QU Jiantao, CUI Liu, CHU Wanghui, GAO Hui

“…In the 3D target detection technology based on point cloud, there are problems like high cost of point cloud calculation and large gap between target scales, which lead to low target detection efficiency. …”
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Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels by Ruigang Han, Ning Jia, Yufei Liu, Jianchun Huang, Suna Qu, Shengming Guo

“…Although Viterbi detection provides high performance, its complexity makes it unsuitable for computationally constrained UWA systems. …”
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Comprehensive fragmentation of cell-free repetitive DNA for enhanced cancer detection in plasma by Mingguang Zhang, Shuohui Dong, Wei Rao, Shiwen Mei, Gang Hu, Ling Liu, Zhen Wang, Jianqiang Tang

“…Five innovative repetitive fragmentomic features were designed: fragment ratio, fragment length, fragment distribution, fragment complexity, and fragment expansion. A machine learning-based multimodal model was developed using these features.ResultsThe multimodal model achieved high prediction performance for early tumor detection, even at ultra-low sequencing depths (0.1×, AUC = 0.9824). …”
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Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment by Praveen Vijai, Bagavathi Sivakumar P

“…Anomaly detection is critical for enhancing operational efficiency, safety, and maintenance in industrial applications, particularly in the era of Industry 4.0 and IoT. …”
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Criminal emotion detection framework using convolutional neural network for public safety by Jay Raval, Nilesh Kumar Jadav, Sudeep Tanwar, Giovanni Pau, Fayez Alqahtani, Amr Tolba

“…The proposed framework is evaluated with different evaluation metrics, such as training accuracy, loss, optimizer performance, precision-recall curve, model complexity, training time, and inference time. In crime detection, the CNN model achieves a remarkable accuracy of 92.45% and in criminal emotion detection, LeNet-5 outperforms other CNN architectures by offering an accuracy of 98.6%.…”
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CASSAD: Chroma-Augmented Semi-Supervised Anomaly Detection for Conveyor Belt Idlers by Fahad Alharbi, Suhuai Luo, Abdullah Alsaedi, Sipei Zhao, Guang Yang

“…Most studies on idler fault detection rely on supervised methods, which depend on large labelled datasets for training. …”
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RLita: A Region-Level Image–Text Alignment Method for Remote Sensing Foundation Model by Qiang Zhang, Decheng Wang, Xiao Yu

“…Experiments on five remote sensing datasets including object detection, semantic segmentation, and change detection show the effectiveness of the RLita method.…”
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An In-Depth Comparative Study of Quantum-Classical Encoding Methods for Network Intrusion Detection by Adam Kadi, Aymene Selamnia, Zakaria Abou El Houda, Hajar Moudoud, Bouziane Brik, Lyes Khoukhi

“…In today’s rapidly evolving cyber landscape, the growing sophistication of attacks, including the rise of zero-day exploits, poses critical challenges for network intrusion detection. Traditional Intrusion Detection Systems (IDSs) often struggle with the complexity and high dimensionality of modern cyber threats. …”
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A multiobjective evolutionary algorithm incorporating neighborhood detection for the vehicle routing problem with soft time windows by Hao Jiang, Zhiwei Zhang, Chao Wang, Xiaoshu Xiang

“…Subsequently, we proposed a Multi-Objective Evolutionary Algorithm with Neighborhood Detection (MOEAND), which utilizes this customized neighborhood to efficiently solve VRPSTWs. …”
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Heavy Equipment Detection on Construction Sites Using You Only Look Once (YOLO-Version 10) with Transformer Architectures by Ikchul Eum, Jaejun Kim, Seunghyeon Wang, Juhyung Kim

“…These findings underscore the method’s potential to strengthen on-site safety by providing more reliable and efficient heavy equipment detection in complex work environments, while also acknowledging areas for further refinement.…”
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AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters by Oana-Maria Butnaru, Monica Tatarciuc, Ionut Luchian, Teona Tudorici, Carina Balcos, Dana Gabriela Budala, Ana Sirghe, Dragos Ioan Virvescu, Danisia Haba

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