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

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An Experimental Analysis Into Blockchain Cyber Security Attacks 3

An Experimental Analysis Into Blockchain Cyber Security Atta...

引用

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

作者： Gupta, Sumeet Bhowmick, Apoorva Umamageswari, K. K., Joshi H., Bhalla D., Kaushal Upes Core Cluster School of Business India University Department of Computer Science Rajasthan Ajmer India Department of Computer Science with Artificial Intelligence Chennai India Uttaranchal University Uttaranchal Institute of Technology India Galgotia College of Engineering Computer Science India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

WSNs are utilized in various fields, including smart urban communities, medical services, smart structures, and transportation. These networks distribute private information among numerous transceivers, smart devices, and sensor nodes. The environment of WSNs contains sensitive data that is vulnerable to numerous cyber threats and attacks. To deal with threats, attacks, and security hardships in WSNs, a viable security system is required. To decide the best countermeasures to distinguish the pernicious and security gambles using Blockchain innovation, this article fostered another methodology utilizing heuristic, signature, and voting detection techniques. In our system, the three detection methods together with Blockchain are used by the cluster head node (CN) to identify rogue sensor nodes. Also, CN utilizes vital factors like voting degree for malicious nodes, sensor node hash value, and node signature to identify malicious nodes in WSNs. The general discoveries measurement uncovered that during the reenactment of our technique, 94.9% of malicious messages were really perceived and distinguished. © 2023 IEEE.

关键词： Network security

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A Review of Breast Cancer detection Using the Internet of Things and Machine Learning 3

A Review of Breast Cancer Detection Using the Internet of Th...

引用

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

作者： Sandhu, Jasjeet Kaur Kaur, Amandeep Kaushal, Chetna Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350338263

Using the Internet of Things (IoT) and Machine Learning (ML), it is possible to find breast cancer quickly and accurately, which is important for getting better and getting treatment right away. In recent years, the IoT has changed in a way that makes it possible to use artificial intelligence (AI) and ML to look at both real-Time and historical data. Many imaging methods, such as mammography, thermography, CT, ultrasonography, histology, etc., could find breast cancers in their earliest stages. Medical imaging studies are now heavily reliant on machine learning (ML). With the advancement of machine learning, computer-Aided diagnostic (CAD) systems are becoming increasingly smarter and more capable of operating independently. The Internet of Medical Things (IoMT) is a novel method of connecting systems and apps to healthcare services and data sources for monitoring and tracking patients. It can completely change how hospitals work to find and predict cancers with higher care and customer satisfaction. The analysis found that the SVM method had the highest accuracy (98.1%) compared to the other methods. The purpose of this study is to explore the application of imaging and machine learning in conjunction with internet-of-Things systems in the detection of breast cancer at an early stage. © 2023 IEEE.

关键词： Internet of things

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A Novel Transfer Learning Approach for Eye Tumour detection using Pre-Trained CNN Models 3

A Novel Transfer Learning Approach for Eye Tumour Detection ...

引用

3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Prabaharan, G. Rajasekar, Vani Sarveshwaran, Velliangiri B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narasapur India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Erode India Srm Institute of Science and Technology Department of Computational Intelligence TamilNadu Kattankulathur India

ISBN: (纸本)9798350370867

According to a recent study, using retinal imaging to identify the tumour in the eye is difficult. When an image is analyzed, the affected region of the lesion impacted retinal picture is not discovered in the conventional system due to uneven illumination, low contrast, and blurring, which are symptoms of an eye disease. The proposed methodology was created to identify the eye tumour in the earlier stage with enhanced image quality and increased accuracy. The conventional system has a few drawbacks such as lower accuracy and more computational time to analyze the image. Convolutional neural networks (CNN) have recently demonstrated its result of their capacity to learn various category image denotions that aid in image classification in various sectors. These networks have a tremendous ability to extract the proper features from input images with training on millions of images, which produces accurate categorization. The deep learning-based transfer learning approach is suggested for earlier detection of eye tumours. The five pre-trained models such as AlexNet, ResNet, DensNet, VGG 19 and Inception model were used for efficient eye tumour classification. The performance analysis of the proposed approach has shown that higher accuracy of 98.63% and this model shows enhanced performance in better classification. © 2023 IEEE.

关键词： Tumors

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Fault Diagnosis Method for Distribution Network Transformer Based on Artificial intelligence 3

Fault Diagnosis Method for Distribution Network Transformer ...

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

作者： Zhang, Qiuyu Jiang, Yunfeng Wang, Kuitao State Grid Xingtai Power Supply Company China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Distribution network transformers have the function of controlling power equipment, but faults and anomalies often occur during practical applications. Therefore, a fault diagnosis method for distribution network transformers based on artificial intelligence is designed. Select characteristic fault diagnosis parameters, design an intelligent closed-loop transfer fault diagnosis model, and use cost matrix to complete the fault diagnosis of intelligent transformers. The final test results show that compared to the traditional AdaBoost sensitive fault diagnosis test group and the traditional parallel fault diagnosis test group, the maximum absolute difference obtained by the artificial intelligence fault diagnosis test group designed in this article is relatively small, indicating that the application fault diagnosis error of this method has been greatly controlled, with better results, and has practical application value. © 2023 SPIE.

关键词： Artificial intelligence

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Learning Method to Detect Anomalies in Sewer Pipe Images Using Object detection 3

Learning Method to Detect Anomalies in Sewer Pipe Images Usi...

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3rd IEEE international conference on Software Engineering and Artificial intelligence, SEAI 2023

作者： Igarashi, Ryutaro Ogawa, Tomomi Tokyo Denki University Department of Science and Engineering Tokyo Japan

ISBN: (纸本)9798350337952

Object detection has have been used in a wide range of fields, e.g., manufacturing, construction, and medicine. In this study, we investigated the possibility of applying an object detection method to the sewer pipe inspection task. Currently, many sewer pipes are aging, and there is a shortage of personnel and funding for maintenance work. To address this problem, a system is required to automatically detect sewer pipe anomalies. In a previous study, we classified 15 types of sewer pipe anomalies from a total of 58 types, and we trained using the YOLOv5 object detection method, resulting in an overall accuracy rate of 60%. However, higher accuracy is required for practical application. Thus, in this study, we optimized the YOLOv5 learning process by dividing the sewer pipe images into direct-view and side-view images and by the types of pipes, and we achieved an overall accuracy rate of 70.2% on images categorized according to the types of pipes. © 2023 IEEE.

关键词： Object detection

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Vehicle detection and Classification based on C-DSO Dataset using YOLO v3 with SRBD Method for Intelligent Transportation Applications 3

Vehicle Detection and Classification based on C-DSO Dataset ...

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Anitha, R. Prabakaran, P. Vels University v i S T A S Department of Computer Science Chennai India

ISBN: (纸本)9781665494007

The tremendous evolution of Computer vision and technology, the most challenging application of vehicle category detection is an important role in our Indian Traffic system. The most of the vehicle detection algorithms facing some struggles with vehicle object detection and recognition rate. Using Deep Learning with Conventional Neural networks to undercover the problem of vehicle detection and to understand the right way of vehicle classification, detection, and vehicle recognition. This paper proposes our model of designing a dataset (C-DSO) and combines the method YOLO(You Only Look Once) V3 algorithm with SRBD to introduce the SRBD-C-DSO algorithm, it detects the smart way of integrating the pre-training of the image, labeling of vehicle image, image smoothing, and Annotation. Furthermore, renovating the dataset, adding the various categories of vehicle images, and the experimental results show a better detection rate and reduction of loss of validation in the dataset. © 2023 IEEE.

关键词： Vehicles

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Research on Algorithm for License Plate detection in Complex Scenarios Based on Artificial intelligence 3

Research on Algorithm for License Plate Detection in Complex...

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

作者： Ren, Fei Zhang, Tao Zhang, Chunmei Wang, Jian Yao, Menghan Liu, Sheng Doma, Bonifacio T. Intelligent Networking Engineering Technology Research Center Nanjing Intelligent Transportation Information Co. Ltd. Nanjing210000 China School of Information technology Mapua University Manila999005 Philippines Nanjing Intelligent Transportation Information Co. Ltd. Nanjing210000 China Technology Engineering Department Nanjing Intelligent Transportation Information Co. Ltd. Nanjing210000 China

ISBN: (纸本)9798350359664

With the advancement of transportation technology, the management of vehicles on the roadside has become a pressing issue for traffic management and parking authorities. Consequently, non-fixed-scene license plate detection has become a focal point for academic and industrial research and solutions. In response to the inadequacies of existing license plate detection algorithms in complex environments, such as different locations and angles, the papper employed the PaddleOcrV4 as license plate detection model based on deep learning. This model utilized PaddlePaddle as its foundational framework and replaces MobileNetv3 with PP-LCNetV3 in the license plate detection network to reduce interference from complex environmental backgrounds. In the license plate recognition network, PP-HGNet served as the backbone network, effectively improving detection accuracy. This paper primarily designed a solution for license plate detection and text recognition based on OCR technology. Experimental results indicated that the algorithm presented in this paper outperforms PaddleOcrV3 and PaddleOcrV3 in terms of detection accuracy, achieving a precision rate of 96% on the test dataset. © 2023 IEEE.

关键词： Industrial research

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Application of an Improved Convolutional Neural Network-Based Method in Network Intrusion detection 3

Application of an Improved Convolutional Neural Network-Base...

引用

3rd international conference on Intelligent Communications and Computing, ICC 2023

作者： Chen, Huan You, Guirong Shiue, Yeouren College of Information Engineering Fujian Business University Fuzhou China Fujian Prov. Universities Eng. Research Center of Big Data Analytics for Business Intelligence Fujian Business University Fuzhou China National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu Taiwan

ISBN: (纸本)9798350308327

This study investigates the use of convolutional neural network (CNN) technology to solve the problem of network intrusion detection, specifically studying the impact of convolutional layer count. Experiments revealed that two convolutional layers yield optimal results. An AdaBoost+CNN model was proposed using this CNN with optimization strategies such as ensemble learning. Eight classical learning algorithms were compared with the proposed methods, and the results were evaluated using parameters such as accuracy, precision, and F-measurement. Compared with traditional machine learning, deep learning, and other methods, the method proposed in this study achieved higher classification accuracy and successful results in detecting network attacks. © 2023 IEEE.

关键词： Adaptive boosting

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Three-Dimensional Trajectory Planning for Multi-robot Collaboration in Complex Components with Heterogeneous Materials 3

Three-Dimensional Trajectory Planning for Multi-robot Collab...

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3rd international conference on Robotics, Automation and Artificial intelligence (RAAI)

作者： Wang, Xinyu Wu, Yueyu Zhang, Gong Qi, Jianzheng Wang, Heyu Di, Si Lin, Qunxu Southern Univ Sci & Technol Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Guangzhou Inst Adv Technol Guangzhou Peoples R China Fujian Univ Technol Fuzhou Peoples R China Wuyi Univ Jiangmen Peoples R China

ISBN: (纸本)9798350307573;9798350307566

A multi-robot collaboration can reliably perform complex tasks that would be difficult for a single robot to accomplish, especially advanced welding operations in complex components with heterogeneous materials. We focused on a three-dimensional (3D) trajectory planning for complex components with heterogeneous materials, and carries out the research of multi-robot collaboration. By using the software of robot simulator CoppeliaSim, the comparative simulation analysis of multi-robot active-slave and distributed in 3D intersecting line was proposed. Through the establishment of multi-robot collaborative experiment system, the trajectory planning of multi-robot axial motion with semi-8 was put forward. Simulation and experimental results show that our proposed multi-robot system is feasible and is capable of realizing 3D trajectory planning for complex components with heterogeneous materials.

关键词： multi-robot 3D complex trajectory trajectory planning robotics simulator CoppeliaSim

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Audio-Visual Deepfake detection system Using Multimodal Deep Learning 3

Audio-Visual Deepfake Detection System Using Multimodal Deep...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Parikh, Aman Pereira, Kristen Kumar, Pranav Devadkar, Kailas Sardar Patel Institute of Technology Department of Information Technology Mumbai India

ISBN: (纸本)9798350338607

With the rise of deepfake videos in today's digital landscape, there is a pressing need to develop advanced detection and mitigation technologies. Deepfake videos, which use machine learning algorithms to manipulate and alter audiovisual content, can spread false information, create confusion, and even cause harm. To address this issue, our work leverages Deep Learning technology to process audio-visual data in real-time through a web interface, specifically a browser plugin. Our approach uses a multimodal neural network that is fed extracted audio and visual features from a video for deepfake prediction. The model achieves a maximum validation accuracy of 90 percent and is used to build an API that is responsive, and low-latency. Our end-to-end solution is implemented as a Chrome extension using JavaScript that communicates with the API. With this solution, we aim to contribute to the development of advanced deepfake detection and mitigation technologies that can help prevent the spread of false information and its potential consequences. © 2023 IEEE.

关键词： Computer vision

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