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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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Object detection Algorithm based on Improved Yolov5 3

Object Detection Algorithm based on Improved Yolov5

引用

3rd international conference on Artificial intelligence and Computer Engineering, ICAICE 2022

作者： Wang, Hua Yin, Jiang Zhang, Shuang Hou, Daishuang School of Mechatronic Engineering Changchun Institute of Technology Changchun130012 China School of Mechatronic Engineering Changchun University of Science and Technology Changchun130012 China

ISBN: (纸本)9781510663473

A more accurate target detection model is proposed in this research based on Yolov5 target detection algorithm, aiming at its low regression accuracy to the target boundary box. Firstly, coordinate attention mechanism is added to the backbone network to improve the position information of the perceived target in the underlying feature information;Secondly, GIOU is replaced with EIOU to improve the convergence speed. Finally, the feature extraction network is replaced with BiFPN to more efficiently fuse different feature information. Using PASCAL VOC 2007 and 2012 datasets and redividing the training set and verification set, this algorithm is better than the original algorithm mAP@0.5 increased by 2.9%, mAP@0.5:0.95 increased by 1.4%. © 2023 SPIE.

关键词： Object detection

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Ransomware Threat detection and Mitigation using Machine Learning Models 3

Ransomware Threat Detection and Mitigation using Machine Lea...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

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

ISBN: (纸本)9798350343274

The relentless evolution of ransomware threats poses an increasingly severe challenge to cybersecurity. In response to this growing menace, this study presents a comprehensive approach for Ransomware Threat detection and Mitigation using Machine Learning Models. Leveraging the power of advanced machine learning techniques, the proposed method seeks to revolutionize the landscape of ransomware defense. Our methodology commences with the collection and preprocessing of diverse datasets encompassing system and network activities. Feature engineering techniques extract relevant behavioral attributes. A trio of machine learning algorithms, including Self-Organizing Maps (SOM), Random Forest Classifier, and Long Short-Term Memory (LSTM) networks, is deployed for behavioral analysis. These algorithms excel in identifying intricate patterns and temporal dependencies within the data. Threshold-based classification ensures efficient identification of potential threats. Automated response mechanisms further fortify the system, enabling rapid isolation of affected systems and termination of malicious processes. Performance evaluation reveals the superiority of the proposed method over traditional approaches. High detection Accuracy (93.0%) and Precision (97.0%) attest to its efficacy in accurate ransomware threat identification while minimizing false alarms. The use of LSTM networks bolsters sequential behavior analysis, a crucial factor in combating evolving ransomware tactics. The Proposed methodology's adaptability shines through its capacity to continuously learn and evolve with new data, thereby staying ahead of emerging threats. The inclusion of automated responses adds a layer of proactive defense, crucial in today's fast-paced threat landscape. © 2023 IEEE.

关键词： Malware

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Deep Learning Based Smoking detection system For Public Areas 3

Deep Learning Based Smoking Detection System For Public Area...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Retinderdeep Sharma, Neha Chauhan, Rahul Choudhary, Ankur Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350319125

Cigarette smoking continues to be a major worldwide health concern, with multiple negative implications for both people and society. Effective surveillance and early identification of smokers are critical steps in tackling this public health issue. This study employs deep learning CNN architecture to create a robust and accurate system for recognising cigarette smokers in public areas. In the research, a large collection of face photos was obtained from both smokers and nonsmokers, assuring variety in age, gender, and ethnicity. For feature extraction and classification, we used the convolutional neural network (CNN) architecture, a well-established deep learning model. The experimental findings illustrate the efficiency of the suggested technique, with a remarkable accuracy rate of 90% in discriminating cigarette smokers from non-smokers. Furthermore, the model's performance was assessed using important measures such as accuracy, recall, and F1-score, demonstrating its ability to balance false positives and false negatives. © 2023 IEEE.

关键词： CNN Deep Learning EfficientNet Passive Smoking Smoking

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Monocular 3D Object detection Using Depth Fusion 3

Monocular 3D Object Detection Using Depth Fusion

引用

2023 3rd international conference on Artificial intelligence and Industrial technology Applications, AIITA 2023

作者： Qiao, Dewen Niu, Hongxia Key Laboratory of Optoelectronic Technology and Intelligent Control of Ministry of Education Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Plateau Traffic Information Engineering and Control of Gansu Province Lanzhou Jiaotong University Lanzhou730070 China School of Automation and Electrical Engineering Lanzhou Jiaotong University China

It is an important task to estimate a 3D bounding box from monocular images for autonomous driving. However, the monocular pictures do not have distance information, so it is difficult to acquire accurate results. For the sake of solving the trouble of low accuracy of the monocular image in 3D target detection because of lacking distance information, an improved monocular three-dimensional target detection algorithm based on GUPNet and neural network was proposed to promote the precision of target detection. First, based on the geometric method proposed by GUPNet, the depth, and uncertainty are obtained by direct regression using a neural network. According to the difference in the accuracy of the two methods, a parameter α was introduced, and their depth scores are obtained from the uncertainty. According to the depth score and parameter α, the depth obtained by the two methods is fused to get the final depth. Test results prove that the proposed algorithm promotes average detection precision of KITTI data set in simple, medium, and difficult cases. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Network Intrusion detection: A Study on Various Learning Approaches 3

Network Intrusion Detection: A Study on Various Learning App...

引用

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

作者： Sadhwani, Sapna Verma, Avani Muthalagu, Raja Pawar, Pranav Mothabhau BITS Pilani Dubai Campus Dept. of Computer Science Dubai United Arab Emirates

ISBN: (纸本)9798350338263

Internet of Things (IoT) is a comprehensive paradigm where millions of devices are connected to a network. These interconnected devices create a network of intelligent systems that exchange data without the need for any computer or human communication. The devices gather data that is important to humans and businesses. Standard high-end security solutions are ineffective for safeguarding an IoT system because IoT devices have limited storage and processing capability. Due to the proliferation of innovative attacks, network security is finding it difficult to identify breaches with good accuracy. As a result, it becomes necessary to provide smart security solutions that are portable, widely dispersed, and provide long term services. The monitoring of network traffic by an intrusion detection system (IDS), which protects against prospective intrusions and preserves the network's confidentiality, integrity, and availability, is one solution. However, IDS still has difficulties detecting intrusions and improving detection accuracy while lowering false alarm rates. When dealing with heterogeneous data of varied sizes, Machine Learning (ML) and Deep Learning (DL) have already demonstrated their importance. Many modern IDS are ML based models. In this paper, ML and DL learning models like Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XG Boost (XGB), Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) are used and compared. The best algorithm amongst these is compared with the existing state-of-Art models. The dataset used is UNSW-NB 15 Train set and Test set. The metrics used for comparison are Accuracy (Ac), Recall (Rc), Precision (Pr), F1 score, Mean Squared Error (MSE), training time, prediction time and total time. RF performs better than all other algorithms with Train Ac of 95.98% and Test Ac of 97.69%. It also outperforms the existing state-of-Art models achieving the highest a

关键词： Internet of things

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Research in Variable Period Flexible detection Scheduling Problem Based on Risk Assessment 3

Research in Variable Period Flexible Detection Scheduling Pr...

引用

2023 3rd international conference on Big Data, Artificial intelligence and Risk Management, ICBAR 2023

作者： Feng, Yewei Ao, Liang Li, Xinxin Deng, Xueli Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Reliability Assurance Center of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400716478

This article takes the component testing laboratory as the research object, and focuses on the problem of plan invalidation caused by order returns, delays, rework, and other situations during the component testing process. This article proposes a scheduling model and algorithm based on the scheduling problem of component inspection orders with changes in time, taking into account the factors of changes in inspection test time. The optimization objectives are the average inspection cycle and the risk of deviation from the scheduling plan. According to the verification of numerical examples, the improved algorithm proposed in this article can obtain a scheduling scheme that is more suitable for practical situations, with better computational speed, to support efficient order processing and resource allocation work under complex tasks © 2023 ACM.

关键词： Risk assessment

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Research on the improved PBFT consensus algorithm based on credit mechanism 3

Research on the improved PBFT consensus algorithm based on c...

引用

3rd international conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Wang, Weixing Xu, Guangxia Ye, Liuqin Ma, Chuang School of Artificial Intelligence The Open University of Guangdong China The Cyberspace Institute of Advanced Technology Guangzhou University China Department Economics & Management Beihai Vocational College China School of Software Engineering Chongqing University of Posts and Telecommunications China

ISBN: (纸本)9781510671485

The PBFT consensus algorithm commonly used in the consortium chain has the disadvantages of three-stage consensus process, resulting in high communication costs. This paper proposes an enhanced PBFT algorithm that utilizes a credit mechanism to select master nodes that can improve system availability. The three-stage consensus process of PBFT is optimized, and the master node collects the confirmation messages broadcast by the whole network during the confirmation stage, effectively reducing communication overhead. At the same time, the block height synchronization mechanism is used to ensure the high consistency of consensus node blocks, restore the block height of failed nodes, and achieve fast consensus. Empirical results show that the improved P-PBFT consensus algorithm effectively reduces resource consumption and latency. © 2023 SPIE.

关键词： Blockchain

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Multiscale Wavelet-Driven Transformer for Blade Icing detection MWT 3

Multiscale Wavelet-Driven Transformer for Blade Icing Detect...

引用

2024 3rd international conference on Frontiers of Artificial intelligence and Machine Learning, FAIML 2024

作者： Xiang, Dong Cheng, Xu Chen, Sizhuo Liu, Mengna Shi, Fan Liu, Xiufeng School of Computer Science and Engineering Tianjin University of Technology China Department of Technology Management and Economics Technical University of Denmark Denmark

ISBN: (纸本)9798400709777

Currently, the global demand for green wind power generation is increasing day by day. However, the icing of fan blades can significantly reduce the efficiency of fan power generation, highlighting the increasing importance of efficiently detecting whether the wind turbine is icing. In the traditional approach, the operation status of wind turbines is manually collected. However, with technological advancements, the SCADA system now automatically collects the continuous operational status of wind turbines. Nevertheless, However, the monitored operational data suffers from data imbalance. In this study, we introduce a multi-level neural network built upon the Trans- former architecture. Specifically, to extract multi-level features from the time and frequency domains, we utilize discrete wavelet decomposition. By leveraging the self-attention mechanism of the Transformer, we enhance the feature extraction capabilities for predictive tasks. We investigate data resampling methods to address the data imbalance problem. Comprehensive studies demonstrate that our proposed algorithm improves F1 scores to 90.08%, 81.70%, and 76.21% on datasets processed using data resampling algorithms. The obtained icing detection results validate the applicability of our approach and demonstrate that adopting this algorithm can enhance the accuracy. © 2024 Copyright held by the owner/author(s).

关键词： Wavelet decomposition

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Passive FSR Development for Cars detection Crossing a Railroad 3

Passive FSR Development for Cars Detection Crossing a Railro...

引用

3rd international conference on Green Communication, Network, and Internet of Things, CNIoT 2023

作者： Wang, Rutao Dept. Electronic Information Engineering Zhonghuan Information College Tianjin University of Technology Tianjin China

ISBN: (纸本)9781510668867

Forward Scatter Radar is a cutting-edge technical Radar to detect target across the baseline. In the first part, simply introduce some background of FSR application area. According to the calculation of the power budget, we can verify the data of the FSR system. Then it is the most crucial part, target signature, after simulation in Matlab we can get some figures regarding output, after low-pass filter and matched filter figures. © 2023 SPIE.

关键词： Matched filters

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Construction of smart management model for power grid user side based on artificial intelligence and power BD 3

Construction of smart management model for power grid user s...

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3rd international conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Du, Wang Yongqiang, Guo Jiacheng, Du Yimin, Wei Daoqiang, Han China Henan Jiuyu Tenglong Information Engineering Co. Ltd China

ISBN: (纸本)9781510671423

The changes in the pattern of electricity energy supply and demand, as well as the trend of energy development, have led to the reform and progress of the domestic and international electricity industry market, accelerating the construction of smart grids in various countries. In recent years, the rapid growth of China's economy has driven the continuous progress of science and technology. Information technology (IT), big data (BD) technology and artificial intelligence (AI) are widely used in all walks of life. The traditional power grid is gradually developing towards user demand side response, making system regulation more flexible and allowing users to fully participate in the operation of the power system and energy market. In order to build a power grid user side intelligent management system based on AI algorithm driven by power BD, this article studies a user demand side management (DSM) method based on load transfer technology, and uses a demand side user response potential assessment model based on fuzzy optimization set to evaluate its management effectiveness. Through numerical simulation, the task allocation process of load aggregators is simulated, and the rationality, effectiveness, and feasibility of this assessment method are demonstrated from aspects such as task completion and aggregator benefits. The results show that the system can effectively control and manage the operation of various devices, keep the total consumption below the threshold, and manage the load based on priority. © 2023 SPIE.

关键词： Artificial intelligence

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