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检索条件"主题词=Detection Algorithms"

共 7403 条记录，以下是611-620 订阅

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Identifying Social Connections: An Empirically-Driven Evaluation of Community detection Methods on Twitter

Identifying Social Connections: An Empirically-Driven Evalua...

引用

International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Ceren Bahar Yilmaz Zuleyha Akusta Dagdeviren Murat Osman Unalir Department of Computer Engineering Ege University Izmir Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

The rise of social networking platforms has increased the virtual communities that reflect users’ interests and connections. Detecting social network communities is becoming increasingly important to decipher these structures and facilitate the understanding of their various applications. This paper comprehensively evaluates community detection algorithms applied in Twitter networks. Our aim is to provide an assessment shedding light on the performance of community detection algorithms, software libraries, and visualization methods on various network sizes within the Python ecosystem. From our extensive findings, the Louvain demonstrates high performance in runtime and modularity measurements of Greedy algorithm. On the other side, Girvan-Newman faced difficutlies in completion. Adaptations of NetworkX and matplotlib libraries have proven valuable for visualizing smaller communities, while Gephi has been identified as a commendable tool for large-scale network visualization.

关键词： Visualization Accuracy Software libraries Runtime Social networking (online) Blogs Data visualization Directed graphs detection algorithms Python

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Exploring Network Dynamics: Community detection and Influencer Analysis in Multidimensional Social Networks

Exploring Network Dynamics: Community Detection and Influenc...

引用

IEEE International Conference on Big Data

作者： Andreas Kanavos Gerasimos Vonitsanos Ioannis Karamitsos Khalil Al-Hussaeni Department of Informatics Ionian University Corfu Greece Computer Engineering and Informatics Department University of Patras Patras Greece Rochester Institution of Technology Dubai UAE Computing Sciences Department Rochester Institute of Technology Dubai UAE

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In the digital era, multidimensional social networks have become integral to daily communication, catering to diverse relational needs, from interpersonal to professional and commercial. This study utilizes two comprehensive datasets from Twitter to explore and visualize user interactions within these networks. Focusing on advanced community detection algorithms, we apply the Louvain and Label Propagation methods to delineate the structure of these communities and identify influential users effectively. Through systematic analysis, our research reveals significant insights into the dynamics of network clusters and the pivotal role of influencers. We demonstrate that community structures significantly influence in formation dissemination and user engagement, providing key data to optimize digital communication strategies in complex environments. The findings underscore the importance of strategic influencer engagement and tailored community management in enhancing interaction within multidimensional social networks. Additionally, our results suggest that understanding the network’s structural nuances can aid in developing targeted interventions that leverage influencer capabilities to maximize communication impact, illustrating potential applications across various sectors, including marketing, politics, and public health.

关键词： Systematics Social networking (online) Heuristic algorithms Blogs Focusing Media Big Data Digital communication Public healthcare detection algorithms

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Accelerometer-Based Person-in-Bed detection Challenge

Accelerometer-Based Person-in-Bed Detection Challenge

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lauren Mentzer Ravi Kiran Raman Atulya Yellepeddi Analog Devices Inc.

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In-bed, non-contact vital signs monitoring enables patient monitoring without the use of intrusive sensors that interfere with patients’ sleep. Accelerometers incorporated into mattresses can be used to estimate insights related to vitals and sleep quality when someone lays on the mattress. A necessary first step to this is detecting whether someone is lying on the bed. In this signal processing grand challenge, we consider the problem of person-in-bed detection using only 3-axis accelerometer data obtained using the ADXL355 sensor. In particular, we focus on low-cost, low-latency detection algorithms through two challenge tracks, the first focused on detection over limited windows and the second on state tracking aimed to detect changes quickly.

关键词： Accelerometers Patient monitoring Signal processing Acoustics Sensors Speech processing Low latency communication detection algorithms

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A Novel Roof detection Algorithm Based SLSC Supervoxel for Airborne LiDAR Point Cloud Data

A Novel Roof Detection Algorithm Based SLSC Supervoxel for A...

引用

Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Lili Yue Mengqing Yun Di Han School of Marxism Jilin Communications Polytechnic Changchun China Cloud Computing Center China Telecom Chengdu China International Education Jilin Communications Polytechnic Changchun China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

A new roof detection framework is proposed in this article for detecting roofs from raw point cloud obtained from ALS (Airborne Laser Scanner). There are two main problems solved by the framework, the first problem is that the traditional supervoxel-based algorithm cannot provide accurate feature extraction regions, leading to degradation of classification performance. The second problem is that traditional individual roof segmentation algorithms suffer from the difficulty of ensuring a balance between efficiency and accuracy. To address the first problem, a SLSC (Supervoxel-based Linear Spectral Clustering Segmentation) algorithm is proposed for the feature extraction, which takes into account the correlation of local neighbor changes in terms of supervoxels and provides suitable neighbors for feature computation. Experiments are conducted on two ALS point cloud datasets to evaluate the performance of the algorithms in this article. This algorithm achieved advanced results on two datasets, with overall accuracies of 73.29% and 81.21%, respectively.

关键词： Point cloud compression Accuracy Buildings Lasers Clustering algorithms Feature extraction Power electronics Classification algorithms detection algorithms Random forests

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P-ODA: A Fast and Robust Parallel Ommatidia detection Algorithm for High-Throughput Insect Ommatidia detection

P-ODA: A Fast and Robust Parallel Ommatidia Detection Algori...

引用

Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Wentao Zhu Chaofan Zhang Tianhao Zhang Zhenyu Gao Siqin Ge Yong Liu Anhui Institute of Optics and Fine Mechanics Hefei Institutes of Physical Science Chinese Academy of Sciences University of Science and Technology of China Anhui China Anhui Institute of Optics and Fine Mechanics Hefei Institutes of Physical Science Chinese Academy of Sciences Anhui China Key Laboratory of Animal Biodiversity Conservation and Integrated Pest Management Institute of Zoology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of Animal Biodiversity Conservation and Integrated Pest Management Institute of Zoology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

The automatic detection and analysis of ommatidia, the fundamental visual units of insect compound eyes, is vital for advancing biological research and understanding insect vision. Compound eyes, with their multi-lens structure, provide a broad field of view and high visual resolution. Researchers typically obtain ommatidia structures through CT imaging of insect compound eyes. However, processing these high-resolution 3D images requires significant computational resources due to the complexity and scale of the data. Existing methods struggle with inefficiency and poor robustness, particularly when dealing with large and complex datasets. To overcome these challenges, we introduce a groundbreaking framework, P-ODA, for fast and robust ommatidia detection by integrating parallel computing into the CT image analysis insect compound eyes. By harnessing the power of both Graphics Processing Units (GPUs) with Compute Unified Device Architecture (CUDA) and multicore Central Processing Units (CPUs), we present a novel approach to enhance the Ommatidia detection Algorithm (ODA). Our method efficiently processes large-scale datasets and demonstrates unprecedented scalability. We offer two independent implementations: one utilizing CUDA for GPU acceleration, and another leveraging Python's multicore CPU capabilities. Experimental results show that P-ODA dramatically improves execution time and scalability over traditional ODA methods. Moreover, the system is designed for ease of use with a Linux graphical interface. This innovative framework sets the stage for the next generation of biological dataset processing and hybrid parallel computing systems.

关键词： Visualization Insects Computed tomography Scalability Graphics processing units Parallel processing Biology Central Processing Unit Compounds detection algorithms

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Research on X-Ray Image detection Algorithm Based on YOLO-MCA

Research on X-Ray Image Detection Algorithm Based on YOLO-MC...

引用

Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Zhang Yu Chen Yanzhou Pan Keda Deng Baichuan Wen Ge Wu Huan Cheng Tianyu Zhang Hanfei Wu Junchen Southern Power Grid General Aviation Service Co. Ltd. Guangzhou Guangdong China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

A novel object detection method for X-ray image detection is realized by using the YOLO-MCA algorithm. This algorithm utilizes a convolutional neural network (CNN) to perform powerful feature extraction, and the combined nature of two-stage and single-stage object detection algorithms is aimed toward increasing mostly the efficiency and accuracy. Progressive development in the YOLO family with added optimization in the structure of the network builds a good base by intelligent design of anchors and precise border regression for object detection. In this study, a YOLO-MCA architecture has been constructed, putting emphasis on increasing the sensitivity and adaptability of the network toward various targets through the use of a multi-channel attention (MCA) mechanism. The precise design of the training module and the appropriately coordinated detection module make it very effective, and experimental data shows that it is very helpful in improving the recognition of complex shapes in X-ray images. The experimental results show that the YOLO-MCA algorithm has high performance in terms of accuracy and speed, which provides new ideas and potential application value for future medical image analysis.

关键词： YOLO Training Accuracy Sensitivity Shape Convolutional neural networks detection algorithms X-ray imaging Optimization Medical diagnostic imaging

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Out-of-Distribution detection for Contrastive Models Using Angular Distance Measures

Out-of-Distribution Detection for Contrastive Models Using A...

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International Conference on Machine Learning and Applications (ICMLA)

作者： M Shifat Hossain Sumit Kumar Jha Chase Walker Rickard Ewetz Department of Electrical and Computer Engineering University of Central Florida Orlando Florida USA Computer Science Department Knights Foundation School Florida International University Miami Florida USA Department of Electrical and Computer Engineering University of Florida Gainsville Florida USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Vision-language models have demonstrated extraordinary zero-shot image classification capabilities. Out-of-distribution (OOD) detection is the problem of determining if a model is operating within its knowledge limits. While distance-based detection algorithms have emerged as a promising approach to OOD detection, we observe that there is a disparity between the distance measures used for OOD detection and model training. Recent studies have attempted to mitigate this shortcoming by modifying the contrastive learning process, which is highly undesirable for foundation models. In this paper, we propose an Angular distance-based out-of-distribution detection method for Contrastive models (AEC), an OOD detection framework for foundational contrastive models based on an angular distance measure. The angular distance-based score is compliant with the standard training process and circumvents the need to modify the training process of the model. We also formulate a distance transformation and solve an optimization problem to determine an OOD score threshold value for in-distribution and out-of-distribution data. The experimental evaluation demonstrates that AEC outperforms state-of-the-art OOD detection models in terms of AUROC, FPR@95TPR, accuracy, and correct ID metrics. We obtained an overall AUROC and FPR@95TPR of 70.42 and 83.98 from the proposed algorithm, which is significantly better compared to the SOTA OOD detection algorithms.

关键词： Training Visualization Computational modeling Memory management Contrastive learning Machine learning Data models detection algorithms Standards Optimization

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Road Surface Damage detection Based on Improved YOLOv5 Algorithm

Road Surface Damage Detection Based on Improved YOLOv5 Algor...

引用

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Shan Luo Junwei Wu Jihong Liu Weiming Zhao School of Electrical and Information Engineering Panzhihua University Panzhihua China School of Health and Wellness Panzhihua University Panzhihua China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In view of the shortcomings of YOLOv5 road surface damage detection algorithm in detection accuracy and practicability, and combined with the characteristics of road surface damage, YOLOv5 model is improved. Preprocess the collected road image to improve the image quality. Add a squeeze incentive attention mechanism module between the last C3 module and the SPPF module of the backbone network to build an improved YOLOv5 model. The improved model can focus on the channel features with the largest amount of information and suppress unnecessary channel features, thus greatly improving the accuracy of damaged target detection. The improved YOLOv5 model is used for detection experiments on open source datasets and actual scene damaged images. The experimental results show that the improved model shows good adaptability and robustness, and the detection accuracy is also higher, which can more effectively detect damaged targets on the road surface.

关键词： YOLO Analytical models Adaptation models Accuracy Attention mechanisms Roads Noise reduction Feature extraction Robustness detection algorithms

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An Algorithm for Bubble detection on Exposed Concrete Surface Based on Convolutional Neural Network

An Algorithm for Bubble Detection on Exposed Concrete Surfac...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Feng Qiu An Xu Xiaodong Xiang Dekun Ye Linshan Shi China Construction Seventh Engineering Bureau Co. Ltd Zhengzhou Henan China Kunming University of Science and Technology Kunming Yunnan China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

The purpose of this article is to study the key problems of quality control of exposed concrete under different temperature differences, especially for the detection of bubbles on the surface of exposed concrete. Therefore, an algorithm for bubble detection on exposed concrete surface based on convolutional neural network (CNN) is proposed. In the experimental stage, three different temperature difference environments, low temperature, normal temperature and high temperature, were simulated and tested with the same exposed concrete surface image data set. By comparison and analysis, it is found that the algorithm proposed in this study can identify and locate bubbles stably under different temperature differences, showing good adaptability and robustness. The algorithm has achieved high values in accuracy, precision, recall, F1 score and average intersection ratio. The surface bubble detection algorithm of exposed concrete based on CNN provides a new technical means for the quality control of exposed concrete. The algorithm can maintain stable performance under different temperature difference conditions, and has high detection accuracy and efficiency.

关键词： Temperature distribution Accuracy Quality control Stability analysis Robustness Temperature control Convolutional neural networks detection algorithms Springs Thermal stability

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Research on Lightweight detection of Pests and Diseases Based on Improved YOLOv11

Research on Lightweight Detection of Pests and Diseases Base...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Bingfeng Qian Yan Ma School of Mechanics Shanghai Dianji University Shanghai China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Accurate detection of pests and diseases is crucial for agricultural production, as it can effectively reduce the use of pesticides and increase crop yields. However, existing pest and disease detection algorithms still have shortcomings in terms of lightweight and detection accuracy. This article proposes a lightweight detection algorithm for pests and diseases based on improved YOLOv11. By introducing PP LCNet backbone network, MPDIoU loss function, multi-scale SimAM attention mechanism, and channel pruning technology, the detection accuracy and running efficiency of the model are significantly improved. The experimental results show that the improved algorithm performs well in pest and disease detection tasks, with high accuracy, recall, and model lightweighting, providing an effective solution for pest and disease detection in practical applications.

关键词： Adaptation models Accuracy Attention mechanisms Computational modeling Feature extraction Data models Robustness Real-time systems detection algorithms Diseases

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