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Electronic Transaction system Construction Utilizing the Internet of Things 3

Electronic Transaction System Construction Utilizing the Int...

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

作者： Sharma, Varsha Sethi, Gaurav Ray, Jageshwar Deepan, S. Al-Taee, Mustafa Alazzam, Malik Bader Bhagwan Parshuram Institute of Technology India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Al-Farahidi University Computer Science and IT Baghdad10021 Iraq Ajloun National University Information Technology College Jordan

ISBN: (纸本)9798350399264

Internet - of - things (IoT) technologies must be developed for e-payments due to the surge in cell use. The payment network is yet another significant IoT challenge. The links will be successfully used in the future to improve and streamline life by taking funds in exchange for the goods and services. In this inquiry, money is transferred. It consists of a number of interconnected components, including a transmitter, a Rfid tag, a NodeMCU microcontroller, and compatible programming. A tab serves as a representation of the shopping excursion, or user data is stored on the data tier. In order to maintain the i.d. of sectors, including payment commerce, where one allows clients to complete all having to buy debt securities tech, a security mechanism for the a basic Smart things price strategy is articulated to use user proof in the sensor of cellular telephones, such as identity verification and designation. Management efficiency and conventional approaches are still used by several institutions and bank transactions. Multiple documentation must be filed for each trade, which might take time. As a result, the mechanism for sending money is a crucial component of online shopping. It is a financial service that makes use of lan, PCB pc (servo controller) cards, anonymity, and other communication and information technologies. © 2023 IEEE.

关键词： Polychlorinated biphenyls

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A Comparative Study of Prediction of Pneumonia and COVID-19 Using Deep Neural Networks 3

A Comparative Study of Prediction of Pneumonia and COVID-19 ...

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3rd international conference on Electronic and Electrical Engineering and Intelligent system, ICE3IS 2023

作者： Haque, Md Sabbirul Taluckder, Md Siam Bin Shawkat, Siam Shahriyar, Md Ahnaf Sayed, Md Abu Modak, Chinmoy Ieee Professional Community Ieee Lamar University Phillip M. Drayer Department of Electrical Engineering United States American International University Computer Science Department Bangladesh College of Technology and Engineering Westcliff University IrvineCA United States New Jersey City University Jersey City Department of Professional Security Studies NJ United States Florida Polytechnic University Department of Computer Science LakelandFL United States

ISBN: (纸本)9798350327762

Pneumonia, a contagious respiratory infection caused by both bacteria and viruses, carries significant global implications. Rapid identification of infected individuals is paramount to effectively curb its transmission. This study explores the application of medical image analysis to address this challenge by proposing machine learning techniques for pneumonia prediction using chest X-ray images. Chest X-ray imaging, known for its accessibility and cost-effectiveness, plays a pivotal role in pneumonia diagnosis. However, the interpretation of X-rays for pneumonia detection can be intricate due to potential overlaps with radiographic features of other respiratory conditions. In this research, various machine learning models, including DenseNet121, Inception Resnet-v2, Inception Resnet-v3, Resnet50, and Xception, are evaluated and compared using chest X-ray images from pneumonia patients. Performance metrics and confusion matrices are employed to assess and differentiate these models. The findings highlight DenseNet121 as the top-performing model, achieving an impressive accuracy rate of 99.58%. This study underscores the significance of machine learning in accurately detecting pneumonia through the analysis of chest X-ray images. It offers valuable insights into the potential of technology to mitigate pneumonia's spread by facilitating precise diagnostic tools. © 2023 IEEE.

关键词： Cost effectiveness

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A Study of Data Augmentation for Imbalance Problem in Network Traffic 3

A Study of Data Augmentation for Imbalance Problem in Networ...

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3rd international conference on Blockchain technology and Information Security, ICBCTIS 2023

作者： Wang, Xu Sun, Shimin Han, Li School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350340402

The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for Previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems. © 2023 IEEE.

关键词： Anomaly detection

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Prediction of Auto Insurance Claims as an Imbalance Data Distribution Problem 3rd

Prediction of Auto Insurance Claims as an Imbalance Data ...

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3rd international conference on Machine Vision and Augmented intelligence, MAI 2023

作者： Singh, Akhilesh Kumar Singh, Sumit Kumar Singh, Koushlendra Kumar SCSE Department Galgotias University Yamuna Expy Opposite Buddha International Circuit Sector 17A Uttar Pradesh Greater Noida203201 India Machine Vision and Intelligence Lab Department of CSE National Institute of Technology Jamshedpur Adityapur Jamshedpur831014 India

ISBN: (纸本)9789819743582

Claim accuracy is one of the major concerns of both clients and insurance companies. Claim accuracy is directly associated with predictive modeling in the insurance business. Predictive modeling applications have played a vital role in the insurance business in areas such as fraud detection and claim prediction. Predictive modeling heavily depends on the data distribution. The present work addresses the challenges of an imbalanced dataset in the context of insurance claim prediction for autos. Prediction of the occurrence of claims in auto insurance has been employed in the present work. XGBoost, LR, SVM, RF, KNN, DT, and GNV have been used to calculate the claim prediction. The predictive model and random oversampling, random undersampling, and SMOTE have been applied, respectively. It has been observed that XGBoost is giving good results in all three cases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Classification Confusion matrix Imbalance data Predictive modeling

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Video based behavior detection algorithm 3

Video based behavior detection algorithm

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3rd international conference on Computer, Big Data and Artificial intelligence, ICCBDAI 2022

作者： Wang, Yonghui Wang, Mengjie Meng, Qingquan School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China

Action recognition based on video surveillance becomes possible because of the rapid development of action recognition, temporal action recognition and spatial-temporal action recognition technology. A video-based behavior detection algorithm designed to find information of interest from videos. In the process of video detection, feature extraction is often carried out from space and time dimensions. However, the calculation amount of videos sent into the deep convolutional network is much higher than that of pictures. Therefore, the design of lightweight convolutional network is conducive to reducing training costs, realizing efficient calculation, and enabling researchers to quickly apply it to practical scenarios. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Deep Learning based Real-time Stolen Vehicle detection Model with Improved Precision and Reduced Look Up Time 3

Deep Learning based Real-time Stolen Vehicle Detection Model...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Shafi, Shaik Pavan Sai Kumar Reddy, T. Silla, Rohan Yasmeen, Musrath B V Raju Institute of Technology Dept of ECE Medak India

ISBN: (纸本)9798350338607

In the recent past, the number of vehicles stolen in India has surged substantially. As per the Acko Vehicle Theft Report, in Delhi-National Capital Region (NCR), for every 12 minute a vehicle is stolen and overall Delhi-NCR accounts for more than 56% of all vehicle thefts in the country. Further, almost 3 lakh automobiles were stolen in the national capital during 2011 and 2020. While Bengaluru, India's second-highest car theft city, accounts for 9% of vehicle theft. There exists plethora of studies for stolen vehicle tracking or detection models. The prior methods employed were time-consuming and inefficient, requiring a lot of human work to go through CCTV video recordings and trace the stolen vehicle. Some models are 96% accurate but are incapable of being employed in real time. Some are inaccurate, yet they are utilized for real-time detection. To overcome this time consuming process, a Deep Learning Based Real Time Stolen Vehicle detection Model is proposed in this paper. Our work aims to reduce the lookup time of the vehicle in CCTV footage besides the several tasks to accomplish, like vehicle and license plate detection, optical character recognition. To achieve this, a deep learning tool known as You Only Look Once (YOLO) v8 algorithm has been used for real-time object recognition in the system. In addition, TensorFlow, an open-source machine learning platform is used for vehicle number plate recognition and for image processing. Using this strategy, we can dramatically shorten the time it takes to find the stolen vehicle. Upon rigorous experiments, results showed that the proposed deep learning based real time framework is viable and can achieve decent detection. © 2023 IEEE.

关键词： Deep learning

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Third international conference on Image Processing and Intelligent Control, IPIC 2023

Third International Conference on Image Processing and Intel...

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3rd international conference on Image Processing and Intelligent Control, IPIC 2023

ISBN: (纸本)9781510668140

The proceedings contain 54 papers. The topics discussed include: image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems;a method of extracting outer eye corners of terra cotta warriors based on point cloud data;research on UI design and optimization of digital media with artificial intelligence;video description method with fusion of instance-aware temporal features;a multi-modal information fusion-based method for repairing cracks in train hooks;research on optical detection technology for underwater archaeology;discharging state recognition method of intelligent ring network cabinet based on audio signal spectrum analysis;numerical analysis and calculation of urban landscape spatial pattern;and sparse signal recovery for multispectral demosaicking using pseudo-panchromatic image.

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Research on using Kernel Dynamic Tracking to Locate system Bug 3

Research on using Kernel Dynamic Tracking to Locate System B...

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

作者： Fan, Zichen Computer Project and Scientific Institute Sanjiang University Jiangsu Nanjing210000 China

ISBN: (纸本)9781510664814

Nowadays, with the increasing diversity of Internet programs, more and more programs have launched more functions. However, many system vulnerabilities have gradually surfaced, and the process of finding and repairing vulnerabilities has become an imminent problem for developers. Code snippets related to dynamic memory allocation and resource preemption operations are a major source of operating system failures. In this paper, a new fault detection technique based on dynamic kernel tracing is proposed. The fault source and fault type are determined by collecting kernel call stack and global state transition information. The fault detection technology is implemented as a loadable kernel module in Linux system, which can effectively collect kernel information without adding hardware or modifying the original system code. The experimental results of fault injection show that the proposed method can detect faults effectively, and the detection delay is smaller than the method based on timeout time and performance index. How to deal with these vulnerabilities is the subject of this article. © 2023 SPIE.

关键词： Fault detection

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Ensuring Safe and Reliable Wireless Charging of Unmanned Aerial Vehicles: The Imperative for Foreign Object detection Methods 3

Ensuring Safe and Reliable Wireless Charging of Unmanned Aer...

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3rd IEEE international conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Pradhan, Sushan Munsi, Avishek Aditya, Kunwar Iit Jodhpur India

ISBN: (纸本)9798350319972

Wireless power transfer (WPT) technology has gained widespread attention in recent times due to its advantages over conventional plug-in charging methods. Among the different WPT technologies, inductive power transfer (IPT) is the most established and widely used. However, the presence of metal objects such as coins or keys in the charging area can negatively impact the charging process and even cause fires in worst-case scenarios. Strong magnetic fields can also cause medical conditions in living beings with medical implant, such as pacemaker, due to eddy currents. Therefore, it is crucial to address these issues in WPT systems. This article explores various methods to detect foreign metal objects in WPT systems. For this purpose, WPT coils were designed for a 105W system and the effect of foreign metal objects was analysed using Ansys Maxwell 3D software. A comparison of magnetic flux density distribution with and without the presence of a foreign metal object at a particular position on the transmitter coil was evaluated. Ohmic loss in the metal object was calculated using the field calculator and thermal analysis was performed using Ansys Icepack. Overall, mitigating the impact of foreign metal objects is essential for the safe and efficient operation of WPT systems. © 2023 IEEE.

关键词： Energy transfer

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A Review On: Autism Spectrum Disorder detection by Machine Learning Using Small Video 3

A Review On: Autism Spectrum Disorder Detection by Machine L...

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3rd international conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Garg, Khushbu Das, Nripendra Narayan Aggrawal, Gaurav Manipal University Jaipur Department of Information Technology India Amity University Department of Information Technology Tashkent Uzbekistan

ISBN: (纸本)9781665453578

Autism spectrum disorder (ASD) is a mental ailment that can be diagnosed by the study of social media data and biopsy. Those with autism spectrum disorder (ASD), a neurodevelopment condition, may experience permanent changes to their facial appearance over time. The faces of children with ASD are easily identifiable from those of normally developing (TD) children. After the toddler years, specialists will typically look at a child's behaviour patterns to make a diagnosis of autism spectrum disorders (ASD). Quicker intervention and better long-term outcomes are possible after an early diagnosis of autism spectrum disorder. Machine learning uses data science to facilitate early autism diagnoses. This literature review aims to bridge a gap in understanding by bringing together the results of recent studies and technologies that use machine learning based approaches for ASD screening in infants and children younger than 18 months. Individuals on the autism spectrum have restricted interests and behaviors and struggle to communicate and interact socially with others. The prevalence of ASD has increased in recent years. The potential for innovative methods like machine learning to be integrated into established therapeutic practices is quite encouraging. How computers can be taught to recognize patterns in data is the focus of machine learning research. Artificially intelligent technologies can identify signs and symptoms, organize data, make diagnoses, and forecast outcomes. Machine learning is an area of artificial intelligence that focuses on teaching computers new behaviors by observing how they interact with the world. These days, there are a wide variety of machine learning methods available. In this piece, we look at the existing literature on the frequency of ASD in the general community. The main interest of this paper is the academic literature were sought by searching numerous databases. © 2023 IEEE.

关键词： Diagnosis

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