Optimizing Pre-Trained Code Embeddings With Triplet Loss for Code Smell Detection by Ali Nizam, Ertugrul Islamoglu, Omer Kerem Adali, Musa Aydin

“…These results suggest that incorporating contrastive learning techniques into the generation process of code representation as a preprocessing step can enhance performance in code analysis.…”
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Boosting 3D Object Detection with Adversarial Adaptive Data Augmentation Strategy by Shihao Li, Jingsong Li, Jianghua Fu, Qiuyue Chen

“…In this context, the fusion of Lidar and cameras is vital for the accuracy of object detection. To this end, we propose an adversarial adaptive data augmentation strategy that introduces virtual adversarial perturbations during the image feature extraction process, effectively enhancing the robustness of 3D object detection methods and enabling them to maintain stable performance when facing environmental changes and data perturbations. …”
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Detection of Salmonella sp. on Bulk Meatballs and Packaged Meatballs at Sepanjang Market, Sidoarjo by Rahmania Zain, Sri Hidanah, Ratna Damayanti, Sunaryo Hadi Warsito

“…Meatballs are one of the foods that are in demand by the public and foods that can be contaminated with salmonella bacteria. Detection of Salmonella sp bacteria can determine the quality of bulk meatballs and packaged meatballs. …”
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Community detection algorithm of hybrid node analysis and edge analysis in complex networks by Kun DENG, Qingfeng JIANG, Xingyan LIU

“…The community detection of hybrid node analysis and edge analysis in complex networks (CDHNE), a novel community detection algorithm, was proposed aiming at the problem that both edge community detection and node-based community detection algorithms had corresponding shortcomings in the process of detecting communities, which affected the quality of complex network community detection.The relatively stable characteristics of the edge in the networks were firstly used by the algorithm to construct a more accurate community structure through edge community detection at the early stage of algorithm execution.Then, after the formation of the edge communities, the flexible characteristics of the node were used to accurately detect the boundary of edge communities, so as to more accurately detect the community structure in the complex networks.In the computer-generated network experiments, when the community structure of the network gradually became fuzzy, the number of overlapping nodes and the number of communities to which the overlapping nodes belonged kept increasing.Compared to traditional algorithms, the accuracy of community detection and overlapping nodes detection were improved by an average of 10% and 15%, respectively, by the CDHNE algorithm.In the real network experiments, the tightness of the community structure detected by the CDHNE algorithm was better.Especially when facing large-scale networks with more than 100 000 nodes, the detection task was completed by the CDHNE algorithm with high quality, and the EQ value reached 0.412 1.The experimental results show that the CDHNE algorithm has advantages in operational stability and handling large-scale networks.…”
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Application of CoLD-CoP to Detecting Competitively and Cooperatively Binding Ligands by Shiva V. Patnala, Roberto Robles, David A. Snyder

“…NMR utilization in fragment-based drug discovery requires techniques to detect weakly binding fragments and to subsequently identify cooperatively binding fragments. …”
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FB-YOLOv8s: A fire detection algorithm based on YOLOv8s by Yuhang Liu, Chunjuan Bo, Chong Feng

“…Finally, we adopt the WIoUv3 loss function to optimize the training process and improve detection accuracy. The experimental results demonstrate that compared to the original algorithm, the mAP0.5 of FB-YOLOv8s increases by 2.0 %, and the number of parameters decreases by 25.23 %. …”
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A Detection Method of Lentinus Edodes Based on Improved YOLOv4 Algorithm by HUANG Ying-lai, LI Da-ming, LU Xin, YANG Liu-song

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Ensemble learning for multi-class COVID-19 detection from big data. by Sarah Kaleem, Adnan Sohail, Muhammad Usman Tariq, Muhammad Babar, Basit Qureshi

“…This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. …”
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Enhanced APT detection with the improved KAN algorithm: capturing interdependencies for better accuracy by Weiwu Ren, Hewen Zhang, Yu Hong, Zhiwei Wang

“…Abstract In real-world network environments, advanced persistent threats (APTs) are characterized by their complexity and persistence. Existing APT detection methods often struggle to comprehensively capture the complex and dynamic network relationships and covert attack patterns involved in the attack process, and they also suffer from insufficient detection effectiveness. …”
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A novel pedestrian detection algorithm based on data fusion of face images by Jianhu Zheng, Jinshuan Peng

“…Face recognition is the most popular method to detect and track pedestrian movement. During the face recognition process, feature classification ability and reliability are determined by the feature extraction methods. …”
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Feature selection‐based android malware adversarial sample generation and detection method by Xiangjun Li, Ke Kong, Su Xu, Pengtao Qin, Daojing He

“…In addition, to enhance the robustness of adversarial sample classification detection, a multiple feature set detection algorithm is designed and implemented. …”
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A lightweight steel surface defect detection network based on YOLOv9 by Tianyi Zheng, Ling Yu, Yongbao Shi, Fanglin Niu

“…In the steel production process, surface defect detection is crucial for ensuring product quality. …”
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Detect Multi Spoken Languages Using Bidirectional Long Short-Term Memory by Fawziya Ramo, Mohammed Kannah

“…This detection process is based on audio files of (1 or 2) seconds whereas most of the previous languages Classification systems were based on much longer time frames (from 3 to 10 seconds). …”
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Uncertainty Reduced Novelty Detection Approach Applied to Rotating Machinery for Condition Monitoring by S. Ma, S. M. Li, Y. P. Xiong

“…It is found that the associated multifractal coefficient and Kullback-Leibler Divergence operate well in the uncertainty reduction process. As shown by in situ applications to abnormal rotor with pedestal looseness, it is demonstrated that the abnormal states are detected. …”
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Design of Network Security Anomaly Detection Model Based on SLNA Cell Structure by Guiling Fan, Shaojun Chen

“…However, the renewal of intelligent devices makes network traffic show blowout growth, which brings more difficulties to network security anomaly detection. As a result, the study suggests a model for detecting anomalies in network security that is based on the stabilized layer normalized attention unit structure. …”
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Radar Obstacle Detection Based on Pre-Built Maps in Rail Transit Scenarios by LI Yuanzhengyu, PAN Wenbo, HUANG Wenyu, CHEN Zhiwei, YUAN Chao, YANG Zhenyu

“…This paper proposes a radar obstacle detection system based on pre-built maps to overcome this issue. …”
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A Guidance Framework for Resolving Problems of Credit Card Churn Detection Systems by Fehim Altınışık, Ahmet Sayar

“…The study examines possible solutions to resolve problems that occur in the process of operationalizing systems that predict the financial services that customers will terminate using machine learning and business intelligence approaches. …”
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Decoupled Sematic Distance Based Multi-class Defect Scene Detecting for Substations by Xin ZHANG, Junjie YE, Yao CUI, Xin HUANG, Linlin ZHONG

“…Due to the complexity and differences of defect types in substations, traditional deep learning models for defects detection lack comprehensive response ability. It proposes a sematic distance based decoupling detection model. …”
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GCN-based weakly-supervised community detection with updated structure centres selection by Liping Deng, Bing Guo, Wen Zheng

“…Community detection is a classic problem in network learning. …”
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Battery System Fault Detection: A Data-Driven Aggregation and Augmentation Strategy by Zhiming Zhang, Dan Zhang, Dejun Li, Yi Liu, Jiong Yang

“…In applying machine learning to battery system fault detection, current methods encounter some challenges. …”
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