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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

共 2097 条记录，以下是251-260 订阅

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Creating Reporting Guidelines for Medical Artificial intelligence (Minimum Information for Medical AI Reporting, or MINIMAR) 3

Creating Reporting Guidelines for Medical Artificial Intelli...

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Mittal, Aman Chitkara University Chitkara University Institute of Engineering and Technology Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350399264

Significant advances in artificial intelligence (AI) have been made possible by the growth of digital data and computer power. As an outcome, medical judgement for diagnostics, treatments, and prognostic is improved through the application of categorization & forecasting algorithms in health. Nevertheless, these developments are constrained by the absence of reported standard for the information utilized to build these systems, the system design, including the modeling reviewing & verification procedures. The MINIMAR (Min Information for Medicinal AI Reports) concept, which defines the basic information needed to understand intentional forecasts, targeted population, latent biases, & the generalisation of such cutting-edge technologies, is presented in this article. We demand the creation of a standard for the ethical and truthful usage of AI in the medical industry. This will address difficulties with accuracy and bias while also simplifying the development and usage of connected medical decision assistance technologies. while encouraging the creation and use of such models. © 2023 IEEE.

关键词： Artificial intelligence

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Anomaly detection based on traffic packets in cloud environment 3

Anomaly detection based on traffic packets in cloud environm...

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3rd IEEE international conference on Information technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Yin, Xiaoyu Wu, Lisha Zhang, Yicao Zhang, Min Meng, Yibo State Grid Anhui Electric Power Co.LTD. Information & Telecommunication Branch Hefei China State Grid Anhui Electric Power Co. LTD. Anqing Power Supply Company Anqing China

ISBN: (纸本)9781665490788

Because the network traffic data is large and the flow rate is high, the abnormal traffic can provide support for the host vulnerability warning, which makes the real-time detection of abnormal network traffic become a key problem. This paper proposes an online anomaly detection model. Firstly, based on the encrypted traffic features of network traffic header, a threshold is set for the feature quantity and the extraction time of each stream to ensure the timeliness of feature extraction. On this basis, an anomaly classification model is constructed based on the classical LeNet-5. Finally, a simulation experiment was carried out to evaluate the anomaly detection accuracy of the above anomaly detection model. The experimental results showed that a small number of header messages could achieve a high anomaly detection accuracy, which provided certain proof for the feasibility of online anomaly detection, also provided support for the subsequent host vulnerability warning, and protected the security of cloud platform. Reduce the impact of attack traffic on the server. © 2023 IEEE.

关键词： Anomaly detection

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Fake Review detection and Classification Using Improved Convolutional Neural Network on Amazon Dataset 3

Fake Review Detection and Classification Using Improved Conv...

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Sasikala, C. Ramya, S. RajeshKumar, S. Ramachandramoorthy, K.B. VijayaKumar, S. Umapathi, K. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350322842

Fake reviews are a growing concern for e-commerce websites and other online platforms. To tackle this issue, researchers have developed an advanced convolutional neural network system that can detect and classify fake reviews. This system has been specifically designed to analyze the Amazon review dataset, and by integrating various text pre-processing methods and an innovative attention mechanism, it has significantly improved its classification accuracy. In fact, the proposed model outperforms existing state-of-the-art techniques in terms of accuracy and F1-score, with an impressive accuracy of 0.97. These results demonstrate the effectiveness of the improved CNN model in accurately detecting and classifying fake reviews, and provide a valuable tool for maintaining the authenticity and trustworthiness of online reviews. © 2023 IEEE.

关键词： Convolutional neural networks

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Deep Learning for Enhanced Security in the Internet of Nano Things: A Study on Data Classification for Normal and Abnormal Behavior 3

Deep Learning for Enhanced Security in the Internet of Nano ...

引用

3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： El-Gendy, Sherif Abdelbaki, Nashwa Azer, Marianne A. School of Information Technology and Computer Science Nile University Cairo Egypt National Telecommunication Institute Nile University Cairo Egypt

ISBN: (纸本)9798350306231

The Internet of Nano Things (IoNT) is a new frontier in technology that has opened up vast possibilities in the field of internet connectivity. With the proliferation of nanoscale devices and the ability to network them, the potential for a more efficient and effective Internet of Things (IoT) has become a reality. However, increased connectivity has also raised security issues that need to be addressed. This research paper aims to provide a comprehensive review of the security challenges posed by the IoNT and proposes the use of Artificial intelligence (AI) and Machine Learning (ML) techniques to detect and prevent cyber-attacks. The paper presents experimental and evaluative work on selected ML algorithms using a real-world dataset to demonstrate and analyze the effectiveness of ML in securing IoNT. © 2023 IEEE.

关键词： Intrusion detection

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Algorithm for minimum bounding rectangle based on key point set 3

Algorithm for minimum bounding rectangle based on key point ...

引用

3rd international conference on Artificial intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Liang, Fengqi Wang, Licai Yu, Jintao North China Institute of Computing Technology Big Data R&D Center China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

This paper proposes a ship detection method based on contour extraction networks and minimum bounding box generation algorithms. The proposed method utilizes contour extraction networks to extract the contour of the ship target and generate a set of key points. Then, the minimum bounding box generation algorithm is used to transform these key points into the rotational detection box of the target, which can more accurately describe the shape and orientation of ships, thus improving the accuracy and reliability of detection. The experimental results show that the proposed method can effectively detect ships of various orientations and postures, and has better performance than traditional rectangular box detection methods. The paper compares rotated bounding box detection methods based on traditional machine learning and deep learning, and concludes that rotated bounding box detection methods based on deep learning can better solve the problem of ship detection in complex situations, but require a large amount of data for training and may require a higher computational cost. Finally, the paper introduces the Minimum Bounding Rectangle Algorithm used in the instance segmentation process. © 2023 SPIE.

关键词： Object detection

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

3rd IEEE International Conference on Mobile Networks and Wir...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

ISBN: (纸本)9798350317022

The proceedings contain 282 papers. The topics discussed include: design of automatic correction system for English pronunciation errors based on HMM algorithm;detection of unwanted information on Quora using support vector machine and AlexNet;a real-time spam identification scheme over social networking environment using deep learning principles;hybrid feature extraction improves image retrieval by fusing diverse methods for enhanced content-based search;tennis stroke tracking and recognition system (TARS) based on D-P algorithm;identification of fingerprint orientation using improved generative adversarial network with support vector machine;application research of deep learning in clothing style design;and implementation of artificial intelligence in engineering to achieve sustainable development goals (SDGs).

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Action Recognition system Based 5G Communication and Edge Computing for Service Robot 3

Action Recognition System Based 5G Communication and Edge Co...

引用

3rd IEEE international conference on Computer Communication and Artificial intelligence, CCAI 2023

作者： Zhang, Yanming Wei, Cailun Sun, Gangcan Zhang, Dawei Zhengzhou University School of Electrical and Information Engineering Zhengzhou China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China

ISBN: (纸本)9798350335262

Action recognition is one of the most critical technologies for the robot to provide service excellently. In order to solve the problems of low accuracy, limited arithmetic power, and low intelligence of service robot's action recognition, an action recognition system based on 5G communication and edge computing is proposed. Communication and data interaction between robot and edge computing device is realized by using 5G routers and technologies such as NAT and Socket. A LightSlowfast network is designed for the service robot, which improves action recognition accuracy while reducing the computational effort. The experimental results show that the Light-Slowfast network achieves 72.9% accuracy on the HMDB51 dataset. The system performs well in practical tests, and the low latency of data transmission can satisfy the robot's demand for real-time action recognition. © 2023 IEEE.

关键词： Edge computing

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Small Target detection Algorithm Based On Multi-target detection Head And Attention Mechanism 3

Small Target Detection Algorithm Based On Multi-target Detec...

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3rd IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2023

作者： Zhu, Gaofeng Zhu, Fenghua Wang, Zhixue Xiong, Gang Tian, Bin Shandong Jiaotong University College of Rail Transit Shangdong China Institute of Automatization Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350318470

To solve the problem of low detection precision and accuracy of small and medium-sized targets in aerial UAV images, an improved YOLOv5 algorithm is proposed. Fine-grained feature detection of small targets is realized by fusing Depthwise Separable Convolution multi-target detection heads in feature enhancement networks. The CBAM attention mechanism module is embedded in the backbone network to reorganize different feature channels, and assign large weights to key features to highlight the semantic information and detailed features of small targets, effectively weaken background interference, and improve the algorithm's overall perception ability and detection accuracy. Experiments were carried out on the UAV aerial photography data set VisDrone2019, and the results show that the average accuracy of the improved algorithm is increased by 5.5% and the accuracy is increased by 4.2%, which has a better detection effect. © 2023 IEEE.

关键词： Object detection

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Discussion on the Technical Issues Faced in the Calibration of Multi-camera Vision detection systems 3

Discussion on the Technical Issues Faced in the Calibration ...

引用

3rd international conference on Communication technology and Information technology, ICCTIT 2023

作者： Jiang, Zhihao Luo, Wenli Hu, Tao Zhang, Gengpei Dou, Xiaohan Mei, Guangwei Yangtze University Hubei Jingzhou434023 China Research Institue of Engineer Technology CNPC Bohai Drilling Engineering Company Limited Tianjin300000 China

ISBN: (纸本)9798350359664

Calibrating a multi-camera vision detection system is a critical step in ensuring system accuracy and stability. However, there are several technical challenges in the calibration process. This article discusses some of the technical issues faced in calibrating multi-camera vision detection systems. Firstly, the issue of camera synchronization can affect image consistency and temporal alignment, requiring the identification of appropriate synchronization schemes. Secondly, lens distortion in cameras can affect geometric accuracy, necessitating distortion correction. Field of view coverage issues can affect the system's ability to capture the entire target area, thereby requiring considerations of camera layout and field of view angles. Furthermore, the selection and layout of calibration targets are crucial. Calibration targets need to cover the camera's field of view and provide sufficient feature points for calibration. Therefore, proper hardware design and suitable calibration methods can effectively address the technical challenges in calibrating multi-camera vision detection systems, improving system performance and reliability. © 2023 IEEE.

关键词： Calibration

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Comparing YOLOV3, YOLOV5 & YOLOV7 Architectures for Underwater Marine Creatures detection 3

Comparing YOLOV3, YOLOV5 & YOLOV7 Architectures for Underwat...

引用

3rd IEEE international conference on Computational intelligence and Knowledge Economy, ICCIKE 2023

作者： Shankar, R. Muthulakshmi, M. Amrita Vishwa Vidyapeetham Amrita School of Engineering-Chennai Dept. of Electronics and Communication Engineering India

ISBN: (纸本)9798350338263

The improvement of the marine economy depends on the object recognition of marine species. It is crucial for the intelligence of marine equipment to swiftly and reliably identify marine organisms in a challenging maritime environment. This research report suggests an architecture for a YOLO convolutional neural network trained on aquatic marine life. The most crucial step in object recognition is image pre-processing, which comes before YOLO processing. The pictures produced by the underwater system exhibit symptoms of inadequate lighting, low brightness, and the production of sea fret due to light absorption and dispersion under the sea. The image is improved using the Weighted L1-norm-based Contextual Regularization dehazing technique. This study used a variety of YOLO series to identify seven distinct marine animals, including fish, jellyfish, penguins, puffins, sharks, starfish, and stingrays. When the Enhanced YOLO series is contrasted with the Regular YOLO series, it is found that the former outperforms the latter significantly. Our research also reveals that the YOLOv7 framework performs better in terms of detection accuracy than the YOLOv5 and YOLOv3 architecture while retaining a slower inference time. The enhanced YOLOv7 exceeds the other five frameworks when comparing the YOLO series with enhanced YOLO frameworks, with a mAP of 0.82. © 2023 IEEE.

关键词： Object recognition

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