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Blind Navigation system Based on NS-YOLOV5

Blind Navigation System Based on NS-YOLOV5

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Intelligent Communications and Computing (ICC), international conference on

作者： Geng Cong Liu Yang Hongyan Liu Han Chao Chenzhu Niu Zaozhuang University Artificial Intelligence Institute ZaoZhuang China

In response to the challenges faced by visually impai red individuals when navigating their surroundings, this paper pr esents an enhanced machine vision object detection algorithm kno wn as NS-YOLO, which builds upon YOLOV5s. NS-YOLO aims to achieve efficient and precise detection of complex scenarios in p edestrian walkways. Given the real-time demands of this applicati on, several enhancements have been made to YOLOV5s. Firstly, t he backbone feature extraction network of YOLOV5s has been re placed with MobileNetV3, leading to a significant reduction in the number of parameters and computational load through the use o f depthwise separable convolutions. Secondly, the NAM attention mechanism has been introduced to improve the model's computat ional efficiency. Lastly, a weighted attention mechanism (SimAM) has been incorporated before the prediction stage to dynamically adjust the model's focus, further improving feature representatio n capabilities in complex scenarios. Experimental results demonst rate that the NS-YOLO model enhances mAP50 by 4.5 compared to the original YOLOV5s model, all while achieving a detection sp eed of 90.88 FPS. NS-YOLO elevates detection accuracy while ma intaining detection speed, making it significantly superior to com parative algorithm models and meeting practical application requ irements, thus possessing substantial practical value.

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Automated Motorcycle Helmet detection Using The Combination of YOLO AND CNN

Automated Motorcycle Helmet Detection Using The Combination ...

引用

Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Mary Anne Sanchana Sherin Eliyas Department of Computer Applications Hindustan Institute of Technology and Science Padur

Motorcycle accidents are a significant public concern worldwide. Accidents have been increasing rapidly which leads to the loss of several lives. One of the main contributing factors to the severity of injuries in these accidents is the lack of helmet use. Though the usage of helmets is necessary, it is also considered a piece of mandatory safety equipment among drivers, but they do not make use of it which causes unfortunate incidents to take place. Traffic police do try to bring awareness among the people but most of the population refuses to follow them. To avoid such incidents and to detect people from breaking the rules, this research was introduced. Here, we present a study based on an automated helmet detection system for motorcyclists using the combined techniques of YOLO and CNN. The combination of these image processing techniques such as You Only Look Once (YOLO) and Convolutional Neural Network (CNN) helps in the identification of helmets from image datasets. Because of this combination, the model can accurately identify helmets with a high degree of reliability due to the use of image datasets. And due to this combination, our system can accurately detect helmets with an accuracy of 94.29%.

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Machine Learning Based Malware detection system

Machine Learning Based Malware Detection System

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Gunjan Varshney Sudeep Varshney Amrit Suman Kuldeep Chouhan Preetam Suman Department of Electrical Engineering JSS Academy of Technical Education Noida Uttar Pradesh India Computer Science and Engineering Sharda School of Engineering & Technology Greater Noida Uttar Pradesh India Computer Science and Engineering VIT University Bhopal Madhya Pradesh India

As today's time is more dependent towards the internet, there are various types of malwares are being developed on daily basis, as per report presented by Kaspersky around 560,000 instances of malware created daily. Malware are being developed in such a way that they can replicate and change their signature based on the types of detection system present in organization. Due to urge of variety of malwares it is enables security researchers to shift towards use of Machine Learning for better protection towards variety of security systems. Use of machine learning makes the whole thing more productive and responsive. In this research we have suggested a system that detects diversity of viruses depending on their types response to the system, for the initial phase we have taken a Dataset of Malwares which contains 96,724 malware samples and 41,323 binaries, executable and dynamic link libraries which are legit files. Also we have contrasted the different types of machine learning Techniques which can be used for creating these types of system.

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Phishing URL detection and Reporting system Using Machine Learning Approach

Phishing URL Detection and Reporting System Using Machine Le...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： D. Aman J. Kartheek Reddy B. Manoj Sai D. Dhanalakshmi Shanmugasundaram Hariharan Vinay Kukreja Department of CSE Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

Phishing attacks continue to pose a threat to the internet users due to the consequences they bring, including the compromise of sensitive information, financial harm and reputational damage. In this research paper, we propose to create a free access, ad-free and non-profit website that uses machine learning algorithms to identify phishing URLs. This website shall be designed to provide users with a reliable and user-friendly tool to evaluate the legitimacy of the URLs they come from. Using different data-sets containing phishing and legitimate URLs, extract important features to train the classification model. The model will be embedded in website, which will evaluate user-submitted URLs and provides true phishing detection. There exists report, which claims on the website where users can contribute to the collective fight against phishing attacks by reporting phishing URLs they encountered. In this way users actively contribute to the development of the and improved efficiency of the system.

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Faster Region-Convolutional Neural Network with ResNet50 for Video Stream Object detection

Faster Region-Convolutional Neural Network with ResNet50 for...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Hassan M. Al-Jawahry A. H. A. Hussein G Sunil Mohammed Ayad Alkhafaji Piyush Kumar Pareek The Islamic university Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq School of Computer Science & Artificial Intelligence SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

Detecting objects through video frames is a difficult process due to several issues, such as poor quality of frames, pose changes, occlusions, video defocusing, and motion blur. The closely arranged frames make it more complicated to detect the object in video streams. A common strategy employed to identify the problem is to correctly detect objects through video sequences. Considering these problems, the Faster Region-Convolutional Neural Network (FRCNN) is implemented to detect objects in video streams. At first, the Region proposal network (RPN) is used to generate the objects feature, and then the Convolutional Neural Network (CNN) classifies the RPN objects' features. Then the proposed architecture is combined with the ResNet50, which is the backbone of the Faster R-CNN pipeline, defining the FRCNN-ResNet50 object detector that constant detects objects in video streams. Then the model is evaluated in the DAVIS dataset and gets the F-measure 0.893, S-Measure 0.859, and Mean Absolute Error (MAE) 0.016 respectively. Compared to recent works like CSAttention-ConvLSTM, Deep-GCN, CFCN-MA, the proposed model achieves a higher detection rate and lower false detection.

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Research on the Application of Relationship Graphs in Data Analysis Algorithm Design

Research on the Application of Relationship Graphs in Data A...

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Electronic technology, Communication and Information (ICETCI), IEEE international conference on

作者： Zhu Yancheng College of Artificial Intelligence Shenyang Aerospace University Shenyang China

With the continuous development of statistics and computer science, people’s demand for data mining and potential information analysis is increasing. There are a lot of relational data in the world. As the core of data analysis and mining, data analysis algorithms significantly impact the effectiveness and results of data analysis. A relationship graph is an essential tool for in-depth association analysis. This paper proposes a graph-based data screening and analysis evaluation system using the relationship graph. First, this paper proposes an entity retrieval and location algorithm to establish the entity and relationship network for the research. Then, a relationship graph is established to extract frequent data items for the relationship network. In addition, to facilitate the subsequent data scoring and analysis, we have established a graph-based data screening process and completed the above tasks by defining an evaluation matrix. Finally, we practice the constructed relationship graph in the application scenario of scientific and technological data analysis. The research is based on statistics and computer science, with in-depth research and applications on relationship graphs. It is expected to provide some references for designing and applying data analysis algorithms.

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Development of non-contact detection device for coal moisture based on microwave transmission method

Development of non-contact detection device for coal moistur...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Ruihao Chai Xu Kong Xiaomin Wang College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

In order to quickly and accurately detect the moisture content of coal and realize online non-destructive testing. An on-line measurement system of coal moisture content based on the principle of microwave transmission is designed. The measurement principle is introduced emphatically, and a method for quickly detecting the moisture content of coal by detecting the voltage signal is proposed. Different fitting models between decay voltage and water content were established, and the best calibration equation was determined by comparing different fitting models. On this basis, a coal moisture detection device applied to the conveyor belt was developed. The device consists of a 10GHz microwave signal source, a horn antenna, a detector, a single-chip microcomputer, an electric conveyor belt machine and a sample holder. Finally, coals with different moisture contents were evaluated experimentally. The results show that the measurement accuracy and deviation of the system are within the acceptable range.

关键词： Microwave measurement Temperature measurement Microwave integrated circuits Voltage measurement Moisture measurement Fitting Moisture

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Analysis of Cyber Security Threats in Payment Gateway technology

Analysis of Cyber Security Threats in Payment Gateway Techno...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Hari Prapan Sharma Somanchi Hari Krishna Vijay Prakash R Mohit Tiwari Tripti Tiwari Madarapu Naresh Kumar Institute of Business Management GLA University Mathura Uttar Pradesh Department of Business Management Vignana Bharathi Institute of Technology School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi Department of Management Studies BVIMR Delhi Ministry of Electronics and Information Technology National Informatics Centre Delhi

There has been a meteoric rise in the use of online payment systems recently. Multiple e-payment systems exist, each designed to provide the highest degree of security while maintaining the greatest possible convenience for online shoppers. Cyber-attack techniques, on the other hand, are developing at the same rapid pace as security mechanisms. The authors of this paper examine the history of electronic payment systems, from the development of the corresponding language through the emergence of today's standard electronic payment systems. It also reveals a lack of security measures and approaches to fixing the problem. The current survey research makes a significant contribution by outlining the state of the electronic payment system framework and the possibilities it presents for the development of e-commerce in the future. The rates of suspicious purchases, which will serve as a yardstick in the creation of a trustworthy e-payment system, have been briefly discussed and analyzed.

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Covert Channel detection and Generation Techniques: A Survey

Covert Channel Detection and Generation Techniques: A Survey

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Emerging Smart Technologies and Applications (eSmarTA), international conference on

作者： Nesmah A. AL-Khulaidi Ammar T. Zahary Muneer A.S Hazaa Adel A. Nasser Department of Computer Networks Yemen Academy for Graduate Studies Sana'a Yemen Department of Computer Science Faculty of Computing and IT University of Science and Technology Sana'a Yemen Department of Computer Science Faculty of Computer and Information System Thamar University Thamar Yemen Department of Information Systems and Computer Science Faculty of Sciences Sa'adah University Sa'adah Yemen

The use of covert communications has become more widespread recently as a solution to the information security issue. Information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. system security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment.

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A Lightweight Improved Yolov5 for Insulator Defect detection

A Lightweight Improved Yolov5 for Insulator Defect Detection

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Computer Science, Electronic Information Engineering and Intelligent Control technology (CEI), IEEE international conference on

作者： Xinping Zhu South China University of Technology Guangzhou China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

The safety of insulators is related to the stability of power transmission and economic livelihood. UAV inspection has the importance of improving detection efficiency and adapting to complex terrain. In this paper, based on MobileNetV3 and coordinate attention, the YOLOv5 detection algorithm is improved. The number of parameters is reduced by 2.1 GFLOPs compared with the YOLOv5n model, and the ability of the network to learn the relative positions of defects and insulators is strengthened, so that the detection accuracy increases. It has the potential to be applied in UAV transmission line inspection.

关键词： YOLO Power transmission lines Power transmission Inspection Power system stability Insulators Autonomous aerial vehicles

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