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Real-Time Facial Recognition in computer Vision for Industrial Security 3

Real-Time Facial Recognition in Computer Vision for Industri...

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

作者： Kaliappan, S. Maranan, Ramya Ali, H. Mohammed Sathyavani, B. Lovely Professional University Division of Research and Development Punjab Phagwara India Srm Institute of Science and Technology Dept. Mechanical Engineering Ramapuram TN Chennai India Saveetha School of Engineering Simats Dept. of Research and Innovation TN Chennai India Sr University Dept. of Eee Telangana Warangal India

ISBN: (纸本)9798350343274

In industrial security, facial recognition is crucial because it allows for precise and timely identification of people, which is necessary to protect assets and guarantee worker safety. This research presents a unique solution to the problems of real-time face recognition in industrial settings by combining state-of-the-art technology. 'Deep Face Recognition with FaceNet,' 'Real-Time Face Detection with Faster R-CNN,' and 'Privacy-Preserving Techniques with Homomorphic Encryption' are all integrated into the suggested way to produce a secure and private system. Through a series of tests, we show in our studies how successful this strategy is. Firstly, Faster R-CNN achieves high accuracy face detection with great efficiency in real-time video streams. FaceNet then does a great job of identifying these faces and associating them with recognized people, providing accurate identification. Our method relies heavily on privacy preservation, which is accomplished by using homomorphic encryption. This guarantees the confidentiality of face data even as it is being recognized. It uses deep learning to adjust to changing situations and provides real-time accuracy for quick recognition. Moreover, it is unique due to its use of homomorphic encryption, which protects users' privacy while identifying them. To sum up, our method is a major step forward in real-time face recognition for industrial security. © 2023 IEEE.

关键词： Face recognition

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Sewage Treatment Process Performance Evaluation Index Weight Determination Model Design and Research 3

Sewage Treatment Process Performance Evaluation Index Weight...

引用

3rd international conference on Machine Learning and computer Application, ICMLCA 2022

作者： Guo, Yi Xu, Yanhui Di, Zhuobing Logistics Engineering College Shanghai Maritime Univ Shanghai China

ISBN: (纸本)9781510664814

Sewage treatment technology is developing day by day, a systematic, objective, complete evaluation method to evaluate the sewage treatment process is imminent. Sewage treatment AAO process as a domestic sewage treatment plant land is relatively more process, we in comprehensive evaluation, the AAO process evaluation index system in the role in the whole sewage treatment process is different, to the final treatment of water quality weight is different, which requires us to design a scientific and reasonable sewage treatment AAO process performance evaluation index weight determination model. This paper combines AAO process performance evaluation index system with BP neural network model, design and study it, and the evaluation index weight is determined through BP neural network model, which improves the effectiveness and objectivity of process performance index weight. © 2023 SPIE.

关键词： Water quality

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Hate Speech Detection on Twitter: A Comparative Evaluation of Different Machine Learning Techniques 1

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3rd international conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Rastogi, Aryan Kumar, Arjit Dwivedi, Daarshik Singh, Abhishek Pratap Saberwal, Suruchi Alam, Mehboob Department of Computer Science JSS Academy of Technical Education Uttar Pradesh Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

The necessity for robust and fast detection techniques has become critical as social media hate speech has grown. This study investigates ways to identify hate speech comments present on Twitter using language processing methods. In this work, we suggest a cutting-edge method for effectively identify hate speech in tweets that combines linguistic elements and machine learning techniques. Using a sizable dataset of annotated tweets, we test our model, and we get good F1-score and accuracy. The findings of this study present the possibilities of using techniques for processing natural language to identify hateful speech on Twitter and can assist in direct the creation of efficient regulations and interventions to lessen the negative consequences of hate speech on social media sites. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Analysis For Stock Market Investment Decision Using ML Models 3

Analysis For Stock Market Investment Decision Using ML Model...

引用

3rd international conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Singh, Akshat Devaser, Virrat School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350336238

For the purpose of increasing their profits, investors are taking advantage of the buy and sell advice offered by brokers and experts. When brokers or software try to forecast the share price, mistakes are typically made in the projection. The worth of stocks on the market may be determined by a variety of approaches, which are together referred to as valuation techniques. Therefore, if you want to make an investment, it is quite important to locate the valuation methods that provide the most accurate predictions of share prices. Although there are a lot of difficult financial indications and the fluctuations of the stock market are quite violent, the chance to generate a consistent fortune from the stock market is increasing as technology advances, and it also helps financial professionals make more accurate predictions. The stock market is notorious for its volatility, dynamism, and non-linearity. Accurate stock price forecasting is extremely difficult due to a variety of factors including politics, the current state of the global economy, unexpected events, and the financial performance of a company, amongst others. However, this does imply that there is a significant quantity of data to sift through in order to uncover patterns. The newest, most cutting-edge, most technologically advanced exchange in the world is the National Stock Exchange. This paper provides a concise summary of several recent studies carried out in the subject of stock market prediction. It will assist us in determining how to go about developing a model that would enable investors to make greater returns on their investments. This article will also play an essential part in determining the optimal model to adopt in future research, which will play a role that will have a significant impact. © 2023 IEEE.

关键词： Machine learning

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Visual Question Answering Model Based on Fusing Global-Local Feature 3

Visual Question Answering Model Based on Fusing Global-Local...

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3rd international conference on computer Vision and Pattern Analysis, ICCPA 2023

作者： Yang, Guan Zhang, Ziming Liu, Xiaoming School of Computer Science Zhongyuan University of Technology Henan Zhengzhou China China Internet Public Opinion Research Center Henan Zhengzhou China

ISBN: (纸本)9781510667563

In order to solve the problem of insufficient learning ability of mutual representation of two modal information in existing visual question answer models, the Global-Local Attention Network (GLAN) approach to visual question answer model with global-local features is proposed by analyzing the reasons why the model cannot distinguish the relationship between similar targets in images and the reasons for the model's reliance on text information. To supplement the missing local information in the global features, the model is designed with a convolution guided attention module to enhance the image representation capability and increase the bias of image information in the model. Experiments are conducted on VQAv2.0 and GQA datasets, and the accuracy of the proposed model is improved by 0.60% and 0.38%, respectively, compared with Baseline, which can effectively improve the inference ability of the model. © 2023 SPIE.

关键词： Convolutional neural networks

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Design of a guiding cane for human-computer interaction using sensors 3

Design of a guiding cane for human-computer interaction usin...

引用

3rd international conference on Control and Intelligent Robotics, ICCIR 2023

作者： Zhu, Huijuan East China University of Science and Technology Shanghai China

ISBN: (纸本)9781510671867

Human society is a comprehensive group structure. As a special group, blind people occupy a large proportion in society. It is inevitable and very difficult for blind people to walk alone in their life. First, they are easy to lose their way at intersections. Second, some imperceptible obstacles will hurt them. Finally, the Blind Way occupied will make their walk more difficult. However, with the progress of computer technology, there are more and more products that can help the blind to travel, such as wearing the obstacle warning clothing on the body of the blind, or the obstacle recognition device in the form of glasses. However, most of the products are not only very complex in use, but also change the original travel habits of the blind, making them have a kind of resistance. This research is based on the concept of human-centred design from the perspective of helping the blind, with the focus on not changing the original habits of the blind, using the concept of barrier-free design as a guideline to discover the travel needs of the blind in their daily lives, and using the sensors as the collection end, and using the computer and the device for interaction, in order to complete the design of mobility products for the blind. It is hoped that the design and research in this paper will help the development of guide products and serve as a reference for future research on such products, and that more designers will pay attention to these visually impaired people and give them more and better designs in the future. © 2023 SPIE.

关键词： Human computer interaction

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BIRM: Blockchain Based Integrity References Management 3

BIRM: Blockchain Based Integrity References Management

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3rd international conference on computer Science and Blockchain, CCSB 2023

作者： Zhang, Xiao Zhao, Baohua Wang, Zhihao Wang, Ning Qi, Longyun Liu, Ran Yu, Fajiang Yu, Xiangtao State Grid Corporation of China Beijing China School of Computer Science Beijing University of Technology Beijing China Research Institute Co. Ltd. State Grid Smart Grid Beijing China Beijing Electric Power Company State Grid Beijing China Jiangsu Nanjing China Xinjiang Electric Power Co. Ltd. State Grid Xinjiang Urumqi China School of Cyber Science and Engineering Wuhan University Hubei Wuhan China

ISBN: (纸本)9798350359077

Maintaining the integrity of programs is crucial for ensuring the security of the platform they operate on. The integrity of the programs on a trusted computing platform is measured and reported to a challenger by utilizing a Trusted Platform Module integrated on the platform. The challenger then judges whether the programs maintain their integrity by comparing the measurements with the integrity references. Therefore, managing the integrity references and ensuring their correctness is of utmost importance. There is currently no system where a challenger can obtain all integrity references directly. A challenger typically has to obtain the integrity references from each platform with 'good' measurements. This paper presents BIRM, a consortium Blockchain-based Integrity References Management system. BIRM enforces access controls for publishing integrity references. The integrity reference files in BIRM use a unified syntax based on the JSON Web Signature specification and are stored separately in a distributed storage network. Only identity and storage index tuples are recorded in the blockchain to reduce its load. The blocks in BIRM are organized as a Merkle Mountain Range. When an inquiry is made, BIRM not only returns the requested identity and storage index tuple directly from the key-value database but also generates proof data by parsing block data to ensure the tuple's correctness. BIRM supports multiple tuple inquiries in a single request, using multiple paths in the Merkle Mountain Range tree. The amount of proof data and proof generation and checks are logarithmic in complexity relative to the total number of blocks. © 2023 IEEE.

关键词： Blockchain

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Spearman-Coefficient-Based Improved D-S Evidence Theory Algorithm Considering the Initial Importance of Evidence Body and Its Application on Multi-Source Information Fusion 3

Spearman-Coefficient-Based Improved D-S Evidence Theory Algo...

引用

3rd international conference on computer Science, Electronic Information engineering and Intelligent Control technology, CEI 2023

作者： Liu, Lin Sun, Na Wang, Juncheng Co. Ltd. China Automobile Technology & Research Center Co. Ltd. Tianjin China Co. Ltd. China Automobile Technology & Research Center Co. Ltd. Tianjin China

ISBN: (纸本)9798350372052

Decision-making technology based on multi-source information fusion plays an increasingly important role in the field of R&D, design and manufacturing in digital-intelligence industry at the present time. Dempster-Shafer (D-S) evidence theory is a method that can effectively integrate multiple sources of information, conduct uncertainty reasoning and support decision making. How to carry out information fusion under the condition of evidence body conflict is the key to the effective application of D-S evidence theory. Moreover, due to the different sources of evidence, the initial importance of each evidence body is also a necessary concern prerequisite for the effective application of D-S evidence theory in real-world decision-making problems. In this paper, we propose an improved D-S evidence theory algorithm based on Spearman correlation analysis. According to the importance of each evidence body, the basic probability assignment (BPA) of D-S evidence theory is revised firstly. Then, based on the correlation of the evidence bodies in the same recognition framework, Spearman correlation coefficient is used to further modify the weight of evidence bodies. Finally, the modified evidence bodies are fused based on the improved evidence combination rule, thus the inference result is obtained. The method proposed in this paper is compared with several existing improved D-S evidence theory algorithms, and the numerical case results show that the proposed method is more robust in obtaining results aligning with common sense. The application results of a reliability prediction case for a new type of armored vehicle show that, for taking the initial importance of each evidence body into account, the proposed method can lead us to a decision that more in line with intuition and field test results compared with other existing improved methods. © 2023 IEEE.

关键词： Decision making

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Research on baseline load calculation method of distributed photovoltaic users 3

Research on baseline load calculation method of distributed ...

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3rd international conference on Mechanical, Electronics, and electrical and Automation Control, METMS 2023

作者： Liu, Ying Bao, Xingchuan Tao, Jing Hou, Zhansheng State Grid Laboratory of Electric Power Communication Network Technology State Grid Smart Grid Research Institute Co. Ltd. Nanjing China

ISBN: (纸本)9781510666689

The baseline load assessment of users is an important prerequisite for compensating users. With the rapid growth of distributed power sources represented by distributed photovoltaic, its fluctuating load and photovoltaic power bring new challenges to accurate baseline load calculation. The fluctuating load and photovoltaic power of distributed photovoltaic users bring new challenges to accurate baseline load calculation. Based on this, the traditional baseline load calculation method is introduced firstly. Secondly, the baseline load calculation method of distributed photovoltaic users is proposed, including actual load estimation based on clustering, distributed photovoltaic estimation based on segment estimation, and the final baseline load calculation and effect evaluation. Finally, an example analysis shows that the accuracy of the baseline load calculation method proposed for distributed photovoltaic users is improved compared with the traditional common methods. © 2023 SPIE. All rights reserved.

关键词： Photovoltaic effects

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Pioneering Healthcare Transformation: Deep Learning and Radiographic Imaging for Rapid COVID-19 Diagnosis 3

Pioneering Healthcare Transformation: Deep Learning and Radi...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Leo, L. Megaan Prabha, B. Gopinath, K.M. Tamilselvi, C. Mohanaprakash, T.A. Naveen, P. Sathyabama Institute of Science and Technology Department of ECE Tamilnadu Chennai India SRM Institute of Science and Technology Chennai Kattankulathur India Panimalar Engineering College Department of ECE Chennai India Dr. MGR Educational and Research Institute Department of Information Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Department of Artificial Intelligence and Data Science St.joseph's College of Engineering OMR Chennai India

ISBN: (纸本)9798350343632

The COVID-19 pandemic has underscored the critical need for innovative diagnostic tools, prompting an exploration of the transformative potential of combining radiographic imaging with deep learning. Traditional diagnostic methods face challenges in accessibility, time constraints, and symptom variability, necessitating advanced technologies. Deep learning, a subset of artificial intelligence, emerges as a solution by employing complex algorithms to analyze radiographic images, such as chest X-rays and CT scans. Recent advancements in deep learning and its successful application in medical imaging are examined, emphasizing its proficiency in recognizing unique patterns associated with COVID-19. The proposed model, based on convolutional neural networks (CNNs), facilitates swift and accurate identification of infected individuals, overcoming limitations of current diagnostic approaches. The study also delves into the integration of extensive datasets and collaborative efforts to train robust deep learning models capable of handling diverse patient populations and imaging conditions. Ethical considerations, including data privacy and bias in AI models, are addressed, stressing responsible deployment in healthcare settings. Comparative analyses showcase the superior performance and efficiency of the proposed approach compared to traditional methods. This study concludes by highlighting the potential impact on healthcare systems, underscoring the importance of early and accurate diagnosis in controlling virus spread. © 2023 IEEE.

关键词： COVID-19 Deep learning Healthcare Radiographic X-rays

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