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internet of Things Enabled Smart Hand Gesture Virtual Mouse System 14

Internet of Things Enabled Smart Hand Gesture Virtual Mouse ...

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14th IEEE international conference on Communication systems and Network Technologies, CSNT 2025

作者： Chelekkodan, Danish Muneer Jayapandian, N. Abhinav, S. Shyjan, Aaron Department of Computer Science and Engineering Christ University Kengeri Campus Bangalore India

ISBN: (纸本)9798331531935

This research is aim to focus on IoT based hand gesture model. Mouse is one of the most important input devices of a computer. It works as a pointing device and allows the user to move the pointer as needed by the user. In the early days, a wired mechanical mouse was used for this purpose. In mechanical mouse a ball is fixed underneath the mouse, which rotates as the user moves the mouse. This movement of the ball is used to move the mouse pointer on the screen. Now mostly we use optical mouse which can be wired or wireless. An optical mouse has a high-power laser below it, which takes more than thousand pictures of the surface below the mouse. An image comparator compares the images and sends the signal to move the mouse pointer as the texture of the image changes. Both the types of mouse works based on old technology. As technology leaps to greater heights, the need for simplicity also increases. With the invention of different kind of sensors, microcontrollers and other electronics, we can eliminate the mouse as an input device and instead use our hands to do the work of a mouse. This prototype is an embedded system which runs with the help of an arduino microcontroller. Flex sensors are used to capture the hand gestures. The proposed IoT based hand gesture model is providing high accuracy rate compare to the regular model. The proposed model is analyzed with accuracy level, the average accuracy level of proposed model is more than 90%. © 2025 IEEE.

关键词： Comparators (optical)

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Research on defect recognition method of substation inspection images based on faster R-CNN

Research on defect recognition method of substation inspecti...

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2021 international conference on internet of Things and Machine Learning, IoTML 2021

作者： Yu, Xinxi Meng, Xian Jiang, Xiping Zhu, Zhu Li, Xiaoping State Grid Chongqing Electric Power Company\ Chongqing Electric Power Research Institute Chongqing401123 China

ISBN: (数字)9781510653269

ISBN: (纸本)9781510653252

Aiming at the problems of low efficiency and poor accuracy in manual identification of tens of thousands of substation inspection images in the power grid, one method and system for identifying defects in substation inspection image data based on Faster R-CNN is proposed. The inspection images are preprocessed through data analysis module, image annotation module, image cleaning module, firstly. A convolutional neural network (CNN) is used to perform feature extraction on the processed images to obtain the target features, secondly. A Region Proposal Network (RPN) is introduced, which generates multiple candidate anchor frames on the target feature images, thirdly. Region of Interest (ROI) pooling is performed on the multiple candidate anchor frames to obtain a feature matrix of a fixed size, fourthly. Then, the frame regression and classification recognition are applied to obtain the defect recognition result and target anchor frame position. Finally, high-precision identification of 25 types of defects of substation inspection images such as oil leakage, insulator damage, mark damage, metal corrosion 1/4 is realized. The average accuracy of the method is as high as 93.44%, which is about 27% and 23% higher than the traditional systems with R-CNN and Fast R-CNN algorithms. The time of single image recognition is shortened to (150ms) millisecond level. This method greatly saves the detection time, which can effectively reduce manpower input, improve work efficiency more effectively, and provide a foundation for the digital transformation of the power grid. © SPIE.

关键词： image segmentation

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Misbehavior Detection in the internet of Vehicles using Machine Learning based Situation Identification 8

Misbehavior Detection in the Internet of Vehicles using Mach...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Kelagadi, Hemantaraj M. Chandra Sekhar, Jampani Rawat, Ruchira Dhaliwal, Navdeep Singh, Swati Saravana, B. Shanthi Kle Technological University School of Electronics and Communication Engineering Karnataka Hubballi India Nri Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Guntur India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Division of Research and Development Punjab Phagwara India Uttaranchal University Division of Research and Innovation Uttarakhand Dehradun India Adhiparasakthi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Kalavai India

ISBN: (纸本)9798350396638

To improve communications network efficiency, situation identification is essential. The requirement for real-time scenario identification of vehicular communications is growing more pressing provided a certain vehicle movement makes vehicle interaction circumstances vibrant and that there are significant variations in signal propagation, vehicle speeds, and vehicular concentrations among possible situations. On the other hand, it has been demonstrated that cryptography methods are less effective at preventing insider attacks that originate inside the automotive communication network. A misbehaviour detection system has been found to be more effective in identifying and preventing insider attacks. This research study presents a misbehaviour detection system based on machine learning and is learned utilizing datasets produced by numerical simulations in a genuine vehicular distributed system. Vehicle communication systems may choose the proper spatial multiplexing and system integration by correctly detecting the current conditions in order to maintain an efficient and reliable operating condition. A back-propagation neural network (BPNN) is trained, and a scenario identification model is developed, using datasets that comprise channel properties acquired from channel measurements in various scenarios. Additionally, the proposed identification model's best performance-enabling model configuration technique is analysed and offered. The identification accuracy is then verified using validation information from the relevant circumstances. © 2023 IEEE.

关键词： Vehicles

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37th international conference on Advanced Information Networking and Applications, AINA 2023

37th International Conference on Advanced Information Networ...

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37th international conference on Advanced Information Networking and Applications, AINA 2023

ISBN: (纸本)9783031284502

The proceedings contain 160 papers. The special focus in this conference is on Advanced Information Networking and Applications. The topics include: D-insta: A Decentralized image Sharing Platform;simulation Modeling of Human Aortic Valve Blood Flow;ramification of Sentiments on Robot-based Smart Agriculture: An Analysis Using Real-Time Tweets;the Digital Humanities Trend in Chinese Film History: A Case Study of Filmmaker Lvban;A Tool for Creation of Virtual Exhibits Presented as IIIF Collections by Intelligent Agents;recommender systems in the Museum Sector: An Overview;Towards the Enrichment of IIIF Framework with Semantically Annotated and Geo-Located images;Comparison of ML Solutions for HRIR Individualization Design in Binaural Audio;Performance Analysis of a BESU Permissioned Blockchain;AI-Powered Drone to Address Smart City Security Issues;range Proofs with Constant Size and Trustless Setup;sensorless Predictive Maintenance: An Example on a ‘Not 4.0’ Coffee Machine Production Process;An Integrated System for Vibration Suppression Using Fuzzy Control and 2D-LiDAR;attendance System via internet of Things, Blockchain and Artificial Intelligence technology: Literature Review;an Overview and Current Status of Blockchains Performance;AgriBIoT: A Blockchain-based IoT Architecture for Crop Insurance;distribution of the Training Data Over the Shortest Path Between the Servers;a Decentralized Architecture for Electric Vehicle Charging Platform;services and Operations of Electric Vehicle System by Virtual Power Plant in Rural Area;a Triangulation based Water Level Measuring System for a Water Reservoir Tank;A System Architecture for Heterogeneous Time- Sensitive Networking based on SDN;a Parking System based on Priority Scheme;DTAG: A Dynamic Threshold-based Anti-packet Generation Method for Vehicular DTN;prediction in Smart Environments and Administration: Systematic Literature Review;Optimal and Suboptimal Routing Protocols for WSN;CL-DECCM-SA: A Cluster-based Delauna

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Over-the-Air Distributed Neural Network in internet of Things with Threat Modeling for Replay Attacks 1

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international conference on Communications, signal Processing, and systems, CSPS 2023

作者： Ren, Chao Zeng, Chuyue Li, Yingqi Zhang, Haijun University of Science and Technology Beijng China

ISBN: (数字)9789819975020

ISBN: (纸本)9789819975556

Large scale distributed neural networks have demonstrated promise for various inference tasks in internet of Things (IoT) devices, including intelligent security monitoring and defense against network threats. However, the massive amounts of data generated by IoT applications and limited computational capabilities present significant challenges in implementing typical applications, such as secure protocols for data confidentiality. Over-The-Air (OTA) computation, a recently proposed physical layer computing architecture, has great potential to address these issues. In this paper, we propose an OTA distributed neural network with the mutual benefit of joint computing and communication. However, the open channel environment in which the network’s forward computation is implemented renders OTA-based joint computing and communication methods vulnerable to replay attacks, thereby compromising the accuracy of the network performance and wasting valuable bandwidth resources due to backpropagation of contaminated information during OTA computing. A threat model of network is established to investigate the impact of replay attacks during the iterative process. Our analysis and numerical results demonstrate that the replay attacks have a significantly impact on the network. Specifically, the test accuracy rate decreases from 85 to 35%, and the convergence rate decreases by an average of 40%. When the number of iterations is set to 500, the success probability of replay attacks is 0.378. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of things

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Mushroom image Classification and Recognition based on Improved Swin Transformer 6

Mushroom Image Classification and Recognition Based on Impro...

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2023 IEEE 6th international conference on Information systems and Computer Aided Education, ICISCAE 2023

作者： Zhao, Kexin Huo, Yukang Xue, Lin Yao, Mingyuan Tian, Qingbin Wang, Haihua National Innovation Center for Digital Fishery Beijing100083 China Ministry of Agriculture and Rural Affairs Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Beijing100083 China China Agricultural University College of Information and Electrical Engineering Beijing100083 China Beijing Union University Smart City College Beijing100101 China Beijing Engineering and Technology Research Center for Internet of Things in Agriculture Beijing100083 China

ISBN: (纸本)9798350313444

Classification of mushrooms are essential for preventing even life-threatening consequences of accidentally eating wild mushrooms. In this study, a dataset including 114 varieties of mushrooms is collected and built. Next, the Swin Transformer model with robust classification characteristics is improved to classif mushroom images. Also, the classification accuracy of the improved Swin Transformer with different parameters was compared. In addition, ResNet50 is compared with the improved Swin Transformer under the optimal parameters. The model's training speed is further enhanced by integrating the improved Swin Transformer with ResNet50, thereby proposing the Swin_ResNet classification algorithm. Experimental results show that the classification accuracy of the optimal improved Swin Transformer algorithm is 87.66%, which is 14.86% and 7.64% higher than the ResNet50 and Swin Transformer models, respectively. In addition, the classification accuracy of Swin_ResNet is 85.31%, and the training time is 86.57% shorter than that of the improved Swin Transformer. This greatly improves the training efficiency. © 2023 IEEE.

关键词： Classification and Recognition Convolutional Neural Network Deep Learning Mushroom image Swin Transformer

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Intelligent ID System for the Industrial internet of Things Using Machine Learning Algorithms

Intelligent ID System for the Industrial Internet of Things ...

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international conference on Advanced systems and Electric Technologies (IC_ASET)

作者： BACHA Sawssen LIOUANE Noureeddine Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

The Industrial internet of Things offers modern industrial firms numerous opportunities for significant growth. The internet of Things technology utilized in the sector has developed into a large-scale network as a result of the increasing amount of data and devices. Industrial IoT networks are by nature susceptible to hacking and intrusions. Thus, in order to ensure the security of IIoT networks, intrusion detection system IDS development is essential. This study proposes an IDS to defeat a variety of cyberattacks in industrial internet of things environments. The suggested approach reduces the dimension of the data characteristics and enhances the performance of anomaly identification by using the kernel principal component analysis technique. For binary classification, we use the kernel extreme learning machine to identify if the traffic flow is benign or attack. We use it to multiclass classification in order to classify the set of attacks based on the specific type of attack. The performance results are assessed and analyzed using the X-IloTDS dataset in order to prove the effectiveness of the suggested anomaly detection approach. The evaluation's findings show that the suggested anomaly detection strategy may greatly raise detection performance outcomes in terms of specificity, accuracy, sensitivity, and F1-score while also successfully increasing detection efficiency.

关键词： Training Machine learning algorithms Accuracy Sensitivity Security Computer crime Kernel

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A PROOF OF CONCEPT: CYBERSECURITY LEARNING CONTENT IN LTI based, EDUCATIONAL systems

A PROOF OF CONCEPT: CYBERSECURITY LEARNING CONTENT IN LTI BA...

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international conference on Applied Computing 2022 and WWW/internet 2022

作者： Kannegieser, Ehm Dyck, Sergius Atorf, Daniel Fraunhofer Institute of Optronics System Technologies and Image Interpretation IOSB Fraunhoferstr.1 Karlsruhe76131 Germany

ISBN: (纸本)9781713863793

Serious Games are an important part of technology aided learning. Since the learning outcome of intrinsically motivated learners is generally higher, a learning system should support the learner in achieving this state. Serious Games utilize the natural intrinsic motivation of playing a game by incorporating learning objectives. Concepts such as Digital Game based Learning (DGBL) and Immersive Didactics achieve this by creating a learning environment that feels like an entertainment game by aligning learning and gaming goals. This puts players in a state of peaked intrinsic motivation which allows them to achieve increased levels of learning success - even while enjoying the game itself. Learning content for Serious Games is usually taken from existing courses or other materials. This work in progress explores the implementation of the interoperability standard LTI for integration purposes of third-party learning content into a Serious Game context. Copyright © (2022) by international Association for Development of the Information Society (IADIS). All rights reserved.

关键词： Interoperability

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37th international conference on Advanced Information Networking and Applications, AINA 2023

37th International Conference on Advanced Information Networ...

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37th international conference on Advanced Information Networking and Applications, AINA 2023

ISBN: (纸本)9783031286933

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Early Detection of Melanoma Skin Cancer Using image Processing and Deep Learning 17th

Early Detection of Melanoma Skin Cancer Using Image Processi...

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17th international conference on Intelligent Information Hiding and Multimedia signal Processing (IIH-MSP) / 14th international conference on Frontiers of Information technology, Applications and Tools (FITAT)

作者： Shah, Syed Asif Raza Ahmed, Israr Mujtaba, Ghulam Kim, Moon-Hyun Kim, Cheonyong Noh, Seo-Young Sukkur IBA Univ Dept Comp Sci CRAIB Sindh Pakistan KISTI Adv KREONET Ctr Daejeon 34141 South Korea Chungbuk Natl Univ Dept Comp Sci Cheongju South Korea

ISBN: (纸本)9789811910531;9789811910524

The skin occupies the largest portion of the human body, and thus, the risk of exposure to UV rays is high, and DNA damage can easily occur. Because humans benefit from sunlight, it is impossible to avoid sun exposure, so it is impossible to completely avoid diseases such as melanoma and skin cancer. Melanoma skin cancer is one of the most common cancers worldwide, with approximately 300,000 new cases reported in year 2018. However, this is likely to be an underestimated because the number of people diagnosed per year with different type of skin cancer is projected to increase over the next 20 years. In this paper, we propose an early detection mechanism for melanoma skin cancer using image processing and deep learning techniques. The image processing is used for image segmentation such as threshold, edge detection, and geometry-based feature extraction for melanoma features asymmetry, border, color, diameter, and evolving (ABCDE). Whereas, the deep learning is used trained the deep learning model to predict low/high risk of melanoma skin cancer. The results of our proposed solution are clearly showing that it is highly accurate to detect the melanoma skin cancer using our e-health application.

关键词： Deep learning Healthcare image segmentation Melanoma Skin cancer

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