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Revolutionizing Road Surface Classification: Integrating Vibration data and MCNN for Enhanced Pothole Detection 2

Revolutionizing Road Surface Classification: Integrating Vib...

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2nd International conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Imran Hussain, S. Athunan, M. Maadesh Sivam, S. Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9798331541217

The existing techniques for categorizing road surfaces mainly rely on visual inspection or image analysis whose reliability and performance are limited in ugly climate ones. The unique contribution of the study is a proposed learning based novel solution that combines accelerometer-based vibration data fed into multiple channels of features to accurately identify potholes and rigid surface irregularities via multi-channel neural network (MCNN). Using motion responses detected by the accelerometer in conjunction with live imagery taken from a Raspberry Pi camera, the system provides accommodating detection of variations under different scenarios. Testing reveals the proposed configuration attains a precision of 94.5% and an exceptionally low false positive rate of only 5.4%, handily outperforming past solutions. The creative integration of diverse inputs not only strengthens detection capabilities but also enables quick processing, permitting immediate feedback to optimize upkeep, fundamentally changing infrastructure management and ensuring enhanced safety for all road users. © 2024 IEEE.

关键词： Vibration analysis

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Wheat Leaf Disease Detection Using CapsNet in Agri-Tech 2

Wheat Leaf Disease Detection Using CapsNet in Agri-Tech

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2nd International conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Subash, Nemani Sushanth, Samala Shivani, Tumu Sai Sooraj, Puppala Department of ECE VNR VJIET Hyderabad India

ISBN: (纸本)9798350388664

Various diseases that affect the growth of wheat are important threats to food security in the world. This project aims at developing a system of wheat diseases diagnosis based on the integration of CapsNet into IoT. This dataset includes the images of different types of wheat diseases to include STB, stem rust, Septoria, tannin spot and powdery mildew, that affects Tunisia. Pre-processing and data augmentation bring in conformity to methods of training and improving the non uniformity and a diversified type of the dataset. CapsNet also has the feature called affiliative invariance which helps to solve the problem of varying camera angles and increases the model's accuracy. Even deeper capabilities can be achieved in transfer learning within hyperparameter tuning of the pre-trained CapsNets models. Applications of IoT empowers a continuous monitoring of the disease that affects crops, extensive and efficient spraying of biopesticides in agriculture. Wheat grains that are transformed into sensors are able to track changes in their environment;on the other hand, cameras of specific functions capture image data. Prompts and advisories from time to time assist farmers in preventing such challenges;hence, enhancing the productions and qualities. This work is helpful to agricultural in general and more specifically it is a supplement to the process of crop health monitoring and early signs of diseases detection, thus being a boost to global food security. © 2024 IEEE.

关键词： image enhancement

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Multi-Scale Object Detection and Classification using Machine Learning and image Processing 2

Multi-Scale Object Detection and Classification using Machin...

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2nd International conference on data Science and Information System, ICDSIS 2024

作者： Yuvaraj, N. Rajput, Kapil Suganyadevi, K. Aeri, Manisha Shukla, Rishi Prakash Gurjar, Hariom Computer Science And Engineering Sri Shakthi Institute Of Engineering And Technology Tamil Nadu Coimbatore India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Sri Eshwar College Of Engineering Department Of Ece Tamil Nadu Coimbatore India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Chandigarh University Ait Management Mohali India Manipal University Jaipur India

ISBN: (纸本)9798350381665

Multi-scale object detection and category has grown to be important duties in numerous domain names, including laptop imaginative and prescient, autonomous driving, and surveillance. The task aims to develop a complete and green approach for multi-scale item detection and classification with the usage of machine cutting-edge and image processing strategies. The proposed framework contains three principal components - object detection, function extraction, and type. First of all, the item detection module modern-day object detection algorithms, consisting of faster R-CNN or YOLO, discover and localize items within the input picture. Then, the function extraction module extracts capabilities from those detected gadgets through the use of strategies like deep convolutional neural networks (CNNs). Those functions are then fed into a category version, which predicts the elegance label ultra-modern, the detected item. © 2024 IEEE.

关键词： Object detection

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A Comprehensive Analysis of Anti-image Manipulation Techniques: Advancements and Challenges 2

A Comprehensive Analysis of Anti-Image Manipulation Techniqu...

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2nd International conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Ilavendhan, A. Vignesh, N. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350340235

Every individual relies on technology either directly or indirectly. Living in an Information Era means that even one's birthday is known to others who haven’t been se en for years, via social media. This is possible because of the data that the social media extracts from the user and keeping it securely in their database as information. Even with the assurance given by the media companies about the information they store, many of us still refuse to share the personal data online because of the factors like data Breaches, data phishing, etc. Even if personal data access is granted, the images posted on social media undergo unsightly alterations through various AI modification techniques employed by certain individuals. With these image alterations ranging from subtle enhancements to sophisticated forgeries, the need for robust solutions to detect and mitigate these manipulations has become very crucial. This research study is based on a novel approach to traverse through various approaches in preventing images getting altered through AI. © 2023 IEEE.

关键词： data Breaches data phishing database Robust Sophisticated

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Insulator image data enhancement based on BIG-GAN 2

Insulator image data enhancement based on BIG-GAN

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2nd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Liang, Chen Yaohong, Liu Gang, Cao Tong, Xu Wei, Yi Meishan Power Supply Company of State Grid Sichuan Electric Power Company Meishan China

ISBN: (纸本)9781665442763

The data set of power grid equipment is difficult to obtain on a large scale due to the complex working environment of the power grid and many high-altitude operation scenarios, resulting in scarce data in many scenarios. Aiming at the problem of lack of image data of insulators on transmission lines, a data augmentation network based on BIG-GAN network is proposed for the first time. This network combines convolutional neural networks with batch standardization and sampling truncation techniques added to the convolutional neural network, which effectively improve the stability of the GAN network training process and the reconstruction effect of the generative model. © 2022 IEEE.

关键词： Convolutional neural networks

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image Style Transfer Based on Deep Feature Rotation and Adaptive Instance Normalization 2

Image Style Transfer Based on Deep Feature Rotation and Adap...

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2nd International conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2023

作者： Yang, Zichuan Wu, Yan Cao, Xuan He, Changyi Xjtlu School of Intelligent Engineering Su Zhou215123 China Nuaa Nan Jing211100 China Hong Kong Baptist University Department of Computer Science 000000 Hong Kong International Department of Chongqing Bachuan Quantum School Chongqing400000 China

ISBN: (纸本)9798350339765

Style transfer is an attractive point in recent years. This tech needs an image to provide its style, and a neural network is applied to transfer the style onto a content target. Most of the existing methods aim to obtain the weight parameters by training on a single image feature and continuously optimizing the network structure to improve the computational efficiency and image quality, but these methods only consider the image features from a certain perspective, which may lead to some information loss. In this paper, we add Deep Feature Rotation (DFR) to the AdaIN network, which enables us to generate multiple features from one image feature by rotation and train these features synthetically. By this method, we can perform comprehensive feature extraction on a stylized image to preserve more complete feature information. We have tried different combinations of angles and also compared them with other methods. The code is available at https://***/SP-FA/Style-Transfer-Based-on-DFR-and-AdaIN © 2023 IEEE.

关键词： Rotation

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Design of a simulation system for authenticity reproduction of cultural relics images in digital museums based on data mining 2

Design of a simulation system for authenticity reproduction ...

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2nd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Fu, Yi Fu, Tianen Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9781665442763

The traditional method first converts images of different dimensions, and then introduces the characteristics of the human visual system, but ignores the smoothing of the image, which leads to the unsatisfactory effect of image reproduction. Based on the data mining algorithm, this paper has done a research on the authenticity of the digital museum's cultural relic image. A method of authenticity reproduction of cultural relics images based on data mining is proposed. In order to realize the authentic reproduction of cultural relics images, this paper designs a digital museum cultural relics image authenticity reproduction simulation system to solve the problems of poor noise suppression, missing holes, poor reproduction quality of cultural relics, and insufficient details in traditional systems. It is necessary to first calculate the optimal neighborhood range of the cultural relic image pixel, and then perform the restrictive smoothing of the image edge to complete the authenticity restoration of the cultural relic image. The hole phenomenon in the process of data mining triangulation is filled in. The spline function least squares method is selected for surface fitting, two-dimensional texture coordinates are established through data mining algorithm mapping, and the image texture is mapped to the surface grid to realize the three-dimensional reconstruction of the cultural relic image. © 2022 IEEE.

关键词： data mining

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A seven layer DNN approach for social-media multilevel image-based text classification 2nd

A seven layer DNN approach for social-media multilevel image...

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Tripathi, Veena Upadhyay, Navin Mani Sharma, Saurabh Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9781032738659

In recent years, social media platforms have played a significant role in disaster management. To extract valuable insights and understand the meaning behind social media text content, using conventional machine learning methods, text mining solutions have been created. Through these techniques, messages are intended to be categorized into several themes, such counsel and caution. Nevertheless, these methods frequently focus on a single event and have trouble generalizing to cross events classifications. The effectiveness of traditional models trained on historical data for classifying social media messages from future events remains unclear. Our research focuses on the efficacy of a CNN model in classifying Twitter topics across diverse events. Three geotagged datasets from twitter gathered during the COVID-19 pandemic’s management are the subject of this study. Two conventional machine learning techniques (LR and SVM) are contrasted with the CNN model’s performance. Our experiments conclusively show that the CNN model significantly surpasses the performance of both SVM and LR models in terms of accuracy, regardless of whether the evaluation is based on individual events or across different events. This implies that the CNN model may efficiently categorize messages for impending events and pre-train on Twitter data from past occurrences, improving situational awareness. In conclusion, this study demonstrates the CNN model’s capability compared to traditional machine learning techniques for disaster management. © 2025 The Author(s).

关键词： Disasters

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A Comprehensive Study on Various Encryption Techniques for Digital images in Multimedia Networks 2

A Comprehensive Study on Various Encryption Techniques for D...

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2nd IEEE International conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Santhosha, Benaka Sudheesh, K.V. Kiran, K. Parameshachari, B.D. Dayanand Sagar University Harohalli Bengaluru Visvesveraya Technological University Department of CSE Belagavi India Vidyavardhaka College of Enginerring Mysuru Visvesveraya Technological University Department of ECE Belagavi India Nitte Meenakshi Institute of Technology Department of ECE Bengaluru India

ISBN: (纸本)9798350317558

image encryption is a fundamental component of modern data security that guarantees the integrity, privacy, and confidentiality of sensitive visual content. This paper provides a thorough examination of image encryption, comparing and contrasting different encryption techniques and their benefits and limitations as well as real-world uses. In order to strengthen image data security against unwanted access and manipulation, we first offer some basic understanding of image encryption. The suitability of symmetric, asymmetric, and hybrid encryption techniques in various contexts is examined. We also explore the assessment criteria used to evaluate encryption algorithms, highlighting the significance of using suitable measures to precisely gauge security and effectiveness. We also discuss common issues with image encryption, like complicated key management and computational complexity. The review also explores future directions that picture encryption might take, including multi-media encryption techniques, resistance to cryptanalysis, and quantum image encryption. We also highlight how important image encryption is in a variety of fields, such as finance, healthcare, journalism, intellectual property protection, and military operations. We will conduct a thorough analysis of current cryptography schemes and multimedia encryption algorithms to provide a comprehensive overview of the current security landscape tailored to digital multimedia technology. The findings from this survey will enhance our understanding of the efficacy and dependability of secure multimedia encryption schemes, ultimately aiding in the development of more efficient and robust encryption methods for the future. This article aims to summarize and assess numerous algorithms using different methodologies based on multiple characteristics such as MSE, PSNR, NC, BER, and so on. © 2024 IEEE.

关键词： Cryptography image Multimedia Networks Security Attacks Steganography Watermarking

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2.5D Deep Learning Approach for Precise image Segmentation of Gastro-Intestinal Tract 2

2.5D Deep Learning Approach for Precise Image Segmentation o...

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2nd IEEE International conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Marreddi, Jayanth Sai Punn, Narinder Singh ABV-IIITM Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350360523

This paper explores a novel 2.5D training approach for Gastro-Intestinal(GI) image Segmentation where 3D data is trained as 2D images effectively by stacking consecutive magnetic resonance imaging (MRI) scans as channels. This approach minimizes computational and memory demands, simplifies pipelines, and improves inference efficiency over traditional 3D training methodologies. Empirical results on the dataset from the Kaggle competition - University of Wisconsin Madison gastrointestinal tract image segmentation demonstrate improved scores, attributed to the enriched depth information. This innovative method offers promising opportunities to advance medical image analysis while optimizing resource utilization. © 2024 IEEE.

关键词： image segmentation

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