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A Framework of internet of Things (IOT) for the Manufacturing and image Classifaication System 2

A Framework of Internet of Things (IOT) for the Manufacturin...

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2nd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Gupta, Ayan Das Rafi, Shaik Mohammad Singh, Nagendra Gupta, Vikram Kumar Jaiswal, Sushma Gangodkar, Durgaprasad Chandernagore Government College Department of Geography West Bengal Hooghly India Chandernagore Government College Affiliated to the University of Burdwan India Artificial Intelligence and Information Technology Sri Mittapalli College of Engineering Andhrapradesh Guntur India Embedded Systems Centre for Development of Advanced Computing Noida India Quality and Reliability IIT Kharagpur India CG Bilaspur India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

Real-time identification of industrial processes and real-time image classification are necessary for recent days to reduce the excess power consumption and detect water pollution respectively. Usually, robotic technology in manufacturing industries is a costly initiative;thus, researchers are searching for a low-cost and effective loT (internet of Things) technology that will detect and measure real time-condition of industrial machines. Moreover, the loT technology is useful in image classification as well for detecting water pollution. This research has explained the benefits and cost-effectiveness of available loT along with a comparison between image classification techniques. A primary quantitative survey method has been carried out to collect practical information based on the judgement of 60 respondents. Thereafter, the 'Purposive' sampling technique has been applied to discuss the primary data and uphold a significant judgement. Findings and analysis suggested that WSN (Wireless Sensor Network) is a cheaper technology that can be integrated into small- and large-scale manufacturing industries. image classification loT has been identified for detecting water pollution where texture analysis is cheaper than spatiotemporal analysis. © 2022 IEEE.

关键词： internet of things

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Research on Semantic Object Measurement Algorithm based on Object Detection 10th

Research on Semantic Object Measurement Algorithm Based on O...

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10th international conference on Communications, signal Processing, and systems

作者： Wu, Wanqing Ma, Lin Wang, Bin Zhang, Zhongwang Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China China Natl Aeronaut Radio Elect Res Inst Shanghai 200233 Peoples R China

ISBN: (纸本)9789811903908;9789811903892

As a branch of machine vision, stereo vision has become a research hotspot in recent years. With the development of Augmented Reality (AR), image measurement technology has been proposed. We mainly study the algorithm of using RGB-D image to measure the size of objects in indoor environment, and comprehensively use computer vision technology and image target detection technology to realize the image measurement of objects. Aiming at the problem that there are many kinds of objects in the complex indoor environment and the measurement time in the actual environment, we propose a semantic target measurement algorithm based on target detection. Experimental results show that the target detection network we proposed can effectively identify the object category in indoor environment and measure the size of the recognized semantic target. At the same time. At the same time, the measurement time of the algorithm we proposed is very small, so the measurement efficiency can be improved.

关键词： Indoor Target detection 3D image image measurement

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Design of Cloud-Edge-End Collaborative Framework for Power Supply Intelligent Inspection Operation Scenario based on Unmanned Aerial Vehicle 8

Design of Cloud-Edge-End Collaborative Framework for Power S...

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8th IEEE Information technology and Mechatronics Engineering conference, ITOEC 2025

作者： Wang, Wei Qiu, Ping State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Zhejiang International Studies University Hangzhou China

ISBN: (纸本)9798331517915

As a new type of tool with powerful perception, monitoring, and emergency response capabilities, unmanned aerial vehicles (UAVs) have the characteristics of flexibility, maneuverability, and high-altitude inspection. UAVs can greatly improve the efficiency of power facility maintenance and repairing, reduce personnel risks, and have gradually become an important auxiliary means for power supply operation and management. Currently, research on cloud-edge collaborative computing using UAVs has been applied to power systems, especially in their daily maintenance. However, there is not much discussion on building a systematic UAV-based cloud-edge collaboration system, especially to meet the requirements of power supply companies. Aiming at the problems such as the limited distance of image transmission, the lack of timeliness and visualization of image data processing in the application process of UAVs, in this paper, a "cloud-edge-end"collaborative framework has been proposed to achieve fast data return over the distance under signal coverage, and use the characteristics of local edge computing to quickly solve image preprocessing. © 2025 IEEE.

关键词： Drones

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Performance Analysis of image Inpainting using K-Nearest Neighbor 4

Performance Analysis of Image Inpainting using K-Nearest Nei...

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4th international conference on Smart systems and Inventive technology, ICSSIT 2022

作者： Hudagi, Manjunath R. Soma, Shridevi Biradar, Rajkumar L. Tatyasaheb Kore Institute of Engineering and Technology India Poojya Doddappa Appa College of Engineering India India

ISBN: (纸本)9781665401180

image inpainting is a method that focuses to fill the disappeared regions to restore the images back to provide a complete meaningful image. However, the classical methods still struggle with solving the data loss problem because of object and its background. This research analyzes the performance of inpainting model using K-Nearest Neighbor (KNN)-based search method with Bhattacharya distance and the patch extraction is performed using the concept of Bhattacharya distance. Here, the analysis is done by varying the patch size in KNN-based search model for the considered image depending on the percentage of scratch. Moreover, the performance is estimated by considering metrics like Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Second-Derivative-like Measure of Enhancement (SDME), Universal image Quality Index (UQI), Multi-Scale Structural Similarity (MS -SSIM) and Mean Square Error (MSE). From the analysis, it is clear that the KNN + Bhattacharya distance provides effective performance with respect to evaluation metrics and also shows higher efficiency when compared with any other traditional approaches. When the percentage of scratch is 100%, the MSE attained by KNN + Bhattacharya distance with patch size 20x20 is 0.190, MS - SSIM is 0.925, PSNR is 29.01, SDME is 69.02, SSIM is 0.914, UQI is 0.190 for dataset 3. © 2022 IEEE

关键词： Mean square error

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Analysis of Deep Learning based Secure and Robust System for the Renovation of Medical images on IoMT

Analysis of Deep Learning Based Secure and Robust System for...

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international conference on Computer and Communications Technologies (ICCCT)

作者： Preethi S K. Manoharan Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Electronics and Communication Engineering SNS College of Technology Coimbatore India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Medical healthcare transformations resulted from internet of Medical Things (IoMT) integration because this technology allows physicians to carry out medical imaging examinations promptly and diagnose patient conditions in real time. Medical imaging systems built on the internet of Medical Things framework experience crucial issues that involve weakening of images and privacy risks as well as cyber security threats. This research delivers an extensive investigation about an advanced DL framework which ensures secure medical image restoration inside IoMT platforms. GANs coupled with a Residual Attention Mechanism (RAM) allow the proposed framework to recover medical images that become degraded while retaining essential anatomical elements. A Hybrid Homomorphic Encryption (HHE) scheme brings heightened security through complete confidentiality of data whether it is transmitted or stored. The Adaptive Noise Reduction (ANR) model serves as an integral part to reduce transmission errors and their resulting artifacts and distortions. The model proves its superiority through evaluations on medical imaging benchmarks which yield better Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values compared to contemporary state-of-the-art approaches while users report improved subjective judicial scores (MOS) at the same time. The framework has established its capability for improving image clarity and data security while maintaining patient privacy roles as a promising solution for secure medical image restoration in IoMT platforms.

关键词： Deep learning Surveys Adaptation models PSNR Attention mechanisms internet of Medical Things Real-time systems image restoration Homomorphic encryption Medical diagnostic imaging

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2nd international conference on Mechanical and Energy Technologies , ICMET 2021

2nd International Conference on Mechanical and Energy Techno...

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2nd international conference on Mechanical and Energy Technologies , ICMET 2021

ISBN: (纸本)9789811901072

The proceedings contain 59 papers. The special focus in this conference is on Mechanical and Energy Technologies. The topics include: Role of Artificial Intelligence in Energy and Power Engineering;the Opportunities and Challenges of Implementing Green internet of Things (IoT) Towards Energy Saving Practices in the Current Environment;Influential Effects of Process Parameters of Fused Deposition Modelling on Wear of a PLA Specimen: A Comprehensive Review;review based on Named Entity Recognition for Hindi Language Using Machine Learning Approach;effects of Covid-19 Lockdown: A Perception Survey;Improving Energy Efficiency in Piezoelectric Effect based Synchronous Multicast Protocol (PESM) in Wireless Sensor Networks;a Critical Analysis on the Application of Next-Generation internet of Things (Iot) for Building Efficient Energy systems for Improved Temperature Control and Better Energy Consumption;exploring the Essentials Elements of Data Analysis and Its Impact on Performance Management Framework;role of Data Mining in Detecting Theft and Making Effective Impact on Performance Management;railway Track Monitoring System;an Analytical Approach of Crime Prediction Using Machine Learning;consumer Behaviour on Digital Marketing Platforms—Specifically in Terms of Consumer Loyalty Using Machine Learning;a Review on Various Aerodynamics Control Techniques;the Role of Blockchain technology on Human Rights Management and Business Ethics—Utopia or Dystopia;machine Learning for the Manufacturing of Digital Marketing Techniques and Its Impact on Health Care System;benefits of Digital Education Over the Impact of Pandemic on Indian Higher Education System;decomposition Makes Better Rain Removal: An Enhanced Attention-Guided image De-raining Using Deconvolutions Network;preface.

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Baby Monitoring Using IOT and Deep Learning 15

Baby Monitoring Using IOT and Deep Learning

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Parvathavarthini, S. Bhuvaneswaran, U. Arun, U.M. Sengottayan, S. Kongu Engineering College Erode Tamil Nadu Perundurai638052 India Kongu Engineering College Department of Computer Technology Erode Tamil Nadu Perundurai638052 India

ISBN: (纸本)9798350370249

The way parents watch over and take care of their infants has changed dramatically as a result of the internet of Things (IoT) integration with baby monitoring devices. This technical innovation provides real-time information into the baby's surroundings and behaviors by utilizing a network of interconnected sensors, devices, and cloud-based platforms. The core functionality of IoT baby monitoring systems includes real-time environmental monitoring, sleep pattern analysis, audio and xv surveillance. Customizable alerts and notifications empower parents to set personalized thresholds, receiving timely updates on changes in temperature, unusual movements, or the baby waking *** research focuses on baby monitoring using three methods: Whether the child is weeping or not, urine should be monitored, and the child's emotions should be identified. When a baby cries, the cradle automatically moves to stop the baby's cry;if the infant stops crying, the movement will cease for the cry. This allows caregivers to keep an eye on the child regardless of whether they are crying or *** child's voice will be recorded using a monitoring microphone, and its output will be compared to the weeping sound that the Arduino code will already provide. In order to monitor urine, a soil moisture measuring sensor is placed under the cloth used in the cradle. If the cradle is wet, the soil moisture sensor detects the cloth is wet, and an alert is sent. The deep learning (DL) algorithm is used to detect the child's emotions. The DL algorithm receives an image as input and matches the child's face emotions with the ESP 32 camera module. The DL algorithm then predicts the child's emotion and sends the emotion to the parent mobile. © 2024 IEEE.

关键词： IoT components, Emotion Detection, Deep Learning Models, image Classification

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ED-NET: Educational Teaching Video Classification Network 1

引用

international conference on Computer Vision and Machine Intelligence, CVMI 2022

作者： Gautam, Anmol Hazra, Sohini Verma, Rishabh Maji, Pallab Balabantaray, Bunil Kumar National Institute of Technology Meghalaya Shillong India GahanAI Bengaluru India

ISBN: (数字)9789811978678

ISBN: (纸本)9789811978661

Convolutional neural networks (CNNs) are widely used in computer vision-based problems. Video and image data are dominating the internet. This has led to extensive use of deep learning (DL)-based models in solving tasks like image recognition, image segmentation, video classification, etc. Encouraged by the enhanced performance of CNNs, we have developed ED-NET in order to classify videos as teaching videos or non-teaching videos. Along with the model, we have developed a novel dataset, Teach-VID, containing teaching videos. The data is collected through our e-learning platform Gyaan, an online end-to-end teaching platform developed by our organization, GahanAI. The purpose is to make sure we can restrict non-teaching videos from being played on our portal. The models proposed along with the dataset provide benchmarking results. There are two models presented one that makes use of 3D-CNN and the other uses 2D-CNN and LSTM. The results suggest that the models can be used in real-time settings. The model based on 3D-CNN has reached an accuracy of 98.87%, and the model based on 2D-CNN has reached an accuracy of 96.34%. The loss graph of both models suggests that there is no issue of overfitting and underfitting. The proposed model and dataset can provide useful results in the field of video classification regarding teaching versus non-teaching videos. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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To Detect Spammer from image-based Spam E-mail through Adjacent-Duplicate Technique

To Detect Spammer from Image-Based Spam E-mail through Adjac...

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2022 IEEE international conference on Knowledge Engineering and Communication systems, ICKES 2022

作者： Patel, Kavita Kumar, Sachin Singh, Vijendra Pratap Singh, Sandeep Kumar Saha, Tapash Kumar Jaiswal, Priya Ashoka Institute of Technology and Management Computer Science and Engineering Department UP Varanasi India Computer Science and Applications Veer Bahadur Singh Purvanchal University UP Jaunpur India Computer Science and Applications Mahatama Gandhi Kashi Vidyapeeth UP Varanasi India Computer Science & Engineering Kashi Institute of Technology UP Varanasi India Computer Science and Applications Kashi Institute of Technology UP Varanasi India

ISBN: (纸本)9781665456371

An Email facility proves to be efficient and more convenient communication way. Now a day, an internet service grows continuously, so information available to user is transformed from text to enriched multimedia. Where, spammer uses a new approach in which text is embedded in images. Widely known image detection algorithm such as optical character recognition (OCR) algorithm is defeated by spam image building techniques where randomization method is used to make sure the entire image is look different to each other. Spam image e-mails are mostly forwarded in a group that have visual similarity but dissimilar due to randomization technique used. The proposed system uses adjacent-duplicate detection technique to identify image-based spam. The system uses conventional spam detection technique to identify image-based spam and then further, use of numerous image-based spam filter to identify all the images which have similarity among them. The system is implemented to gain high identification rate where other side have false positive rate (FPR) is less than 0.001%. © 2022 IEEE.

关键词： Data mining

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Perceptual Hashing based on Salient Region and NMF 1

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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： Wu, Xujun Cui, Chen Wang, Shen Heilongjiang Univ Sch Informat Sci & Technol Harbin Heilongjiang Peoples R China Guangxi Key Lab Cryptog & Informat Secur Guilin Guangxi Peoples R China Jiaxing Univ Coll Math Phys & Informat Engn Jiaxing Zhejiang Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China

ISBN: (数字)9789811910579

ISBN: (纸本)9789811910579;9789811910562

We propose a novel perceptual hashing based on salient region and a nonnegative matrix factorization (NMF) in this paper. Firstly, the input image is standardized and processed with low-pass filtering. Secondly, the salient region is extracted from the obtained preprocessed image. The minimum bounding rectangle of each salient region is extracted, and the pixels in all rectangles are rearranged to form a secondary image, then the preprocessed image is decomposed by NMF to obtain the coefficient matrix as the final image hash. The algorithm is robust to general content-preserving manipulations through the experiment. The proposed algorithm outflanks some best in the performances of perceptual robustness and discrimination indicated by identification accuracy performances.

关键词： image hashing NMF Salient region

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