CUSE-TD Algorithm for Precision Detection of Coherent and Uncorrelated Signals by Rohini Dakulagi, Ravi Raushan, Soham Dutta, Mohammed Theeb Alosaimi, Miguel Villagomez-Galindo

“…The usefulness of the CUSE-TD algorithm lies in its applicability CUSE -TD to real-world MIMO systems, particularly in complex environments such as radar, wireless communications, and surveillance systems, where accurate source detection is critical for operational effectiveness. …”
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Robust UAV Target Tracking Algorithm Based on Saliency Detection by Hanqing Wu, Weihua Wang, Gao Chen, Xin Li

“…In response to this problem, this paper proposes a robust UAV target tracking algorithm based on saliency detection (SDBCF). Using saliency detection methods, the DCF tracker is optimized in three aspects to enhance the robustness of the tracker in complex scenes: feature fusion, filter-model construct, and scale-estimation methods improve. …”
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Multi-granularity Android malware fast detection based on opcode by Xuetao ZHANG, Meng SUN, Jinshuang WANG

“…The detection method based on opcode is widely used in Android malware detection,but it still contains some problems such as complex feature extraction method and low efficiency.In order to solve these problems,a multi-granularity fast detection method based on opcode for Android malware was proposed.Multi-granularity refers to the feature based on the bag of words model,and with the function as basic unit to extract features.By step-by-level aggregation feature,the APK multi-level information is obtained.The log length characterizes the scale of the function.And feature can be compressed and mapped to improve the efficiency and construct the corresponding classification model based on the semantic similarity of the Dalvik instruction set.Tests show that the proposed method has obvious advantages in performance and efficiency.…”
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Method for Detecting Tiny Defects on Machined Surfaces of Mechanical Parts Based on Object Recognition by Haotian Li, Zhen Wang, Lipeng Qiu, Xichu Wei

“…Practical results demonstrate that this method outperforms traditional approaches in terms of missed detection rates and detection efficiency, effectively addressing the challenge of detecting complex machining surface defects, and providing a high-precision, high-efficiency defect detection solution for the mechanical part machining field.…”
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Improving Threat Detection in Wazuh Using Machine Learning Techniques by Samir Achraf Chamkar, Mounia Zaydi, Yassine Maleh, Noreddine Gherabi

“…The increasing complexity and sophistication of cyber threats underscore the critical need for advanced threat detection mechanisms within Security Operations Centers (SOCs) to effectively mitigate risks and enhance cybersecurity resilience. …”
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Detection of Maize Pathogenic Fungal Spores Based on Deep Learning by Yijie Ren, Ying Xu, Huilin Tian, Qian Zhang, Mingxiu Yang, Rongsheng Zhu, Dawei Xin, Qingshan Chen, Qiaorong Wei, Shuang Song

“…Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. …”
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Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots by Xiaojie Shi, Shaowei Wang, Bo Zhang, Xinbing Ding, Peng Qi, Huixing Qu, Ning Li, Jie Wu, Huawei Yang

“…The accuracy of object detection and location is directly related to the picking efficiency, quality and speed of fruit-harvesting robots. …”
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A Comparative Study on Detection and Recognition of Nonuniform License Plates by Mehak Arshid, Muhammad Raees Azam, Zahid Mahmood

“…In addition, a detailed computational complexity comparison on various image resolutions revealed that E2E and the CA-CenterNet are more efficient than their counterparts during detection and recognition tasks, respectively.…”
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Attribute grouping-based categorical outlier detection using causal coupling weight by Yijing Song, Jianying Liu, Jifu Zhang

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CNN and Transfer Learning methods for enhanced dermatological disease detection by Habibulla C. Venkataiah, T. Jayachandra Prasad, G. Gopinath, B. Charitha, G. Dharma Teja, B. Lomith Reddy

“…This final model has been a pragmatic and accessible tool for early detection and diagnosis of skin disease. The feature here is an attempt to provide a more accurate, efficient, and user-friendly diagnostic solution through the incorporation of advanced methods of Transfer Learnin3g and visualization.…”
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A Comprehensive Investigation of Fraud Detection Behavior in Federated Learning by Sun Rui

“…While ANN and CNN demonstrate strong capacity in identifying complex fraud patterns, their communication efficiency and overfitting challenges are significant. …”
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An annotated Dataset and Benchmark for Detecting Floating Debris in Inland Waters by Guangchao Qiao, Mingxiang Yang, Hao Wang

“…However, complex light conditions in the water, small size objects and other factors pose a huge challenge for floating object detection. …”
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Coral reef detection using ICESat-2 and machine learning by Gabrielle A. Trudeau, Kim Lowell, Jennifer A. Dijkstra

“…These obstacles were mitigated through the integration of algorithmically derived pseudo-rugosity and slope metrics as innovative proxies for seafloor complexity, significantly improving predictive performance. …”
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Image Target Detection and Recognition Method Using Deep Learning by Hongyan Sun

“…Finally, the experimental results showed that the image target detection and recognition algorithm proposed in this paper can be better applied to image target detection and classification in complex environment and had higher detection efficiency and recognition accuracy than the existing models. …”
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Improved RRT-Based Obstacle-Avoidance Path Planning for Dual-Arm Robots in Complex Environments by Jing Wang, Genliang Xiong, Bowen Dang, Jianli Chen, Jixian Zhang, Hui Xie

“…To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). …”
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Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots by Li Wan, Zhenjiang Li, Changan Zhang, Guangyong Chen, Panming Zhao, Kewei Wu

“…Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. …”
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HCRP-YOLO: A lightweight algorithm for potato defect detection by Haojie Liao, Guanping Wang, Siyu Jin, Yan Liu, Wei Sun, Sen Yang, Lu Wang

“…In response, this study proposes HCRP-YOLO system, which is based on the YOLOv8n architecture and aims to further enhance lightweight, precision, and efficiency. The method first introduces the lightweight HGNetv2 backbone network and incorporates a channel scaling feature to reduce model complexity, while dynamically adjusting the number of feature channels, thereby improving detection performance. …”
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Inner Ear Multiple Primary Cell Type Detection System by Yu-Ting Li, Ching-Yun Chen, Bing-Siang Wang, Po-Hsuan Hung, Chia-Yu Lin

“…Overall, IEP-CDS effectively improves the efficiency of IEP detection in developing cell therapy for inner ear organoids.…”
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Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…It enhances multi-scale feature fusion capability via weighted feature fusion, significantly reducing parameter count while improving small target detection capability. Additionally, it designs a detection decoupling head with a multi-head attention mechanism to improve the issue of network complexity caused by convolutional layer redundancy, processes spatial dimensions to focus on capturing target features, reduces interference from irrelevant backgrounds, and enhances the accuracy of occluded target recognition.urthermore, it constructs a lightweight neural network with dual convolution (CDC), enhancing inter-channel information flow, improving model feature expression capability, and reducing model complexity. …”
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Evaluating Lightweight Transformers With Local Explainability for Android Malware Detection by Fatima Bourebaa, Mohamed Benmohammed

“…Traditional deep learning-based Android malware detection, reliant on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), struggles to capture long-range dependencies, which are critical for identifying complex malware patterns. …”
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