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Utilizing Deep Neural Network for Upgrading the Virtual Try-On 14

Utilizing Deep Neural Network for Upgrading the Virtual Try-...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sushma, D.S. Shamanth, K. Raj, Sinchana B. Kiran, V. Subiya, Syeda Department of Computer Science and Engineering Rajeev Institute of Technology Karnataka Hassan India

The twenty-first century has seen the emergence of fashion as a way of life. The amount and type of clothing worn may vary depending on physical stature, gender, as well as social and geographic considerations. The majority of people still associate shopping with their in-store shopping experience when they think about shopping. Although customers can try on clothing in-store, there aren't enough trial rooms, so the process takes too long. Our objective is to develop a virtual system that is dynamic, engaging, and astonishingly realistic and that enables users or clients to choose from a wide range of clothing designs before putting those outfits on virtual humans. In this study, we proposed a system that facilitates everyday attire synchronisation. The initiative to create a virtual dressing room with a live camera stream may change how someone shopping for and dresses themselves. By utilising "Virtual Reality," customers may try on a variety of things without really wearing them. The advantage of doing things this way is that trying the clothes on physically would take less time and effort. This initiative helps with market management by lowering the demand for shoppers to try on every piece of apparel. By not keeping a huge inventory on hand, businesses can also save time and space. © Grenze Scientific Society, 2023.

关键词： Image processing

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A Hybrid and Multi-objective Approach for Data Leak and Tamper Detection in Healthcare Cloud Data

引用

International Conference on Machine Vision and Augmented Intelligence, MAI 2022

作者： Roychowdhury, Saptarshi Singh, Binod Kumar Department of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur India

ISBN: (纸本)9789819901883

The emerging technology cloud computing has become a strong pillar beneath the digital healthcare systems. Storing and maintaining the medical records in cloud has been proven beneficiary for smooth and efficient running of the digital healthcare systems. Almost every large private and public healthcare organizations have evolved their system into a cloud-based e-healthcare system. In this digital healthcare environment, the electronic health records are the most important instrument and commonly face security threats and attacks. The data leak due to a malicious insider inside a digital healthcare system pile even more weight on the security concerns. Encryption techniques and watermarking approaches separately have been used to enhance the security of cloud data, but alone they are not strong enough to detect the insider attack or to identify any deformation of medical reports. To find the solution of this we have proposed one hybrid approach by combining the encryption technique and watermarking approach while aiming toward multiple objectives of identifying the malicious insider and the tampering detection in the medical images stored in any healthcare cloud. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Watermarking

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Traffic Sign Detection and Recognition Using Dense Connections in YOLOv4 7th

Traffic Sign Detection and Recognition Using Dense Connectio...

引用

7th International Conference on computer Vision and Image Processing, CVIP 2022

作者： Saxena, Swastik Dey, Somnath Department of Computer Science and Engineering Indian Institute of Technology Indore Indore India

ISBN: (纸本)9783031314162

A self-driving car is a growing technology in India where detection of traffic sign in an unconstrained environment is a challenging task due to its small size. With the development of deep neural networks, many models for object detection have been developed. In this work, we have used a single-stage detection model YoloV4 with further improvements in detection neck for detection of traffic signs. We have used dense connections in place of normal connections of the model for better feature propagation. This improves the accuracy with less inference time. We have conducted our experiments on bench-marked Chinese traffic sign dataset, Tshigua-Tenscent 100K dataset (TT-100K). We have achieved accuracy of 94.30% with 32 FPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Traffic signs

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Makaton Sign Language Recognition

Makaton Sign Language Recognition

引用

2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Prince, Nikhil R Thayil, Rohan Ousephachen Anto Praveena, M.D. Prince Mary, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350333244

Makaton is a widely used sign language system that is primarily used by individuals with communication difficulties, such as those with Autism, Down syndrome, and other developmental disorders. However, despite its widespread usage, there is a lack of technology for accurately recognizing and understanding Makaton signs. This paper proposes a model that combines MediaPipe Holistic with a neural network, such as SimpleRNN, LSTM, or GRU, to recognize Makaton sign language (MSL). The model is trained on a custom dataset of Makaton sign language videos using backpropagation and the Adam optimizer. The proposed network achieved a high accuracy on the test set, demonstrating its effectiveness in recognizing Makaton sign language. This study introduces an innovative method for advancing the development of systems that recognize sign language by utilizing MediaPipe Holistic in conjunction with a neural network. This can improve the way technology interacts with people who have hearing impairments. © 2023 IEEE.

关键词： Long short-term memory

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Real - Time AI and Machine Learning Applications in Healthcare Management 1

Real - Time AI and Machine Learning Applications in Healthca...

引用

1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Poornima, D. Premalatha, J. Sarojini Abirami, G. Triveni, K. Bobby, M.P. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Real-time Artificial Intelligence (AI) and Machine Learning (ML) programs have emerged as a number one motive force for considerably altering patient care and management inside the healthcare control area. Staff shortages and affected person document management are among the fundamental problems dealing with the healthcare industry. In an effort to fight those issues, actual-time AI and ML technology offer a demonstration of optimism with the aid of facilitating quick decisions and higher affected person care. Having those technologies work together ensures that time-sensitive healthcare statistics is processed quick, which improves both useful resource allocation and service shipping. However, there are challenges related to the implementation of real-time AI and ML packages in healthcare management. Interoperability, the mixing of disparate records sources, and privateness and security concerns all pose great difficulties. In addition, an intensive and powerful approach is needed to assure the processing of healthcare facts in real-time. To better manipulate patient facts inside electronic health information, an IoT-based totally Artificial Deep Neural Network (IoT-ADNN) is offered in this studies. There are a huge style of revolutionary uses for actual-time (AI-ML) in healthcare administration. These gear provide healthcare carriers with actual-time information, which in flip results in better resource allocation and a more gratifying affected person revel in. In addition, they enhance information security and accessibility, making sure privateness and ease of get entry to to touchy healthcare facts. Data transmission and shipping quotes, postpone estimation accuracy, energy performance, and throughput have all been dramatically improved. The importance and usefulness of actual-time AI and ML systems in healthcare management are emphasised by means of these measurements. © 2023 IEEE.

关键词： Deep neural networks

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A vision-based litter detection and classification using SSD MobileNetv2 10

A vision-based litter detection and classification using SSD...

引用

10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Balmik, Archana Barik, Subhasish Jha, Mrityunjay Nandy, Anup National Institute of Technology Rourkela Department of Computer Science & Engineering Rourkela India

ISBN: (纸本)9781665490993

Municipal solid litter generation has steadily increased significantly in recent years. To prevent waste from collecting in the environment, it is beneficial to clean up wastes and rubbish using autonomous cleaning equipment such as unmanned surface vehicles. Cleaning efficiency requires a high-accuracy and reliable object detection system. Many machine learning techniques are explored so far to produce litter recognition systems. There are few studies on the effectiveness of cuttingedge deep learning object detection algorithms in the domain of litter localization and detection. We propose a detection and classification module for five different waste objects in this research. The dataset is created by gathering photos from various sources and under various environmental conditions at our institute. In order to enhance the quantity of data, data augmentation techniques are used. Image processing and feature extraction steps are applied to enhance the performance of the proposed model. The SSD MobileNetv2 model is employed for litter recognition which detects and identifies distinct contaminants and hazardous waste materials with a mean Average Precision (mAP) of 0.84. The proposed module aids in environmental cleanup by efficiently detecting the waste objects present in the surrounding environment. © 2023 IEEE.

关键词： Object detection

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TruceNet: A CNN-Based Model for Accurate Classification of DeepFake Images

TruceNet: A CNN-Based Model for Accurate Classification of D...

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2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Rao, Darshan Utturwar, Kaustubh Shelke, Tejas Patil, Anuj Sarda, Ekta Ramrao Adik Institute of Technology Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350301595

With the rise of GANs, the prevalence of Deepfakes has increased, resulting in significant privacy, security, and social concerns. To tackle this issue, we propose TruceNet, a novel approach that utilizes a CNN architecture to detect Deepfakes in images. Our method analyzes various image features to determine if manipulation has occurred. Through experiments on a comprehensive dataset of Deepfakes and non-Deepfakes, we evaluate TruceNet's effectiveness. Our CNN architecture achieves high detection accuracy, outperforming state-of-the-art methods. TruceNet exhibits robustness against common Deepfake manipulations such as face swapping, expression modification, and Neural Texture Synthesis. It consistently achieves an average accuracy of 94.2% in correctly classifying manipulated images, maintaining a precision of 95% across different manipulation scenarios. The research findings highlight TruceNet's significant contribution in mitigating Deepfake risks and enhancing digital media security and trustworthiness. By accurately detecting Deepfakes, TruceNet offers a reliable solution to combat the growing threat posed by manipulated images. © 2023 IEEE.

关键词： Digital storage

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Hand Detection and Morse Code Translation for Alternative Communication

Hand Detection and Morse Code Translation for Alternative Co...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Seshadri, Prithvi Dananjayan, Sathian Ahmed, Hasmath Farhana Thariq Vellore Institute of Technology Department of Computer Science and Engineering Chennai India Malaysia

ISBN: (纸本)9798350315127

This paper focuses on developing a real-time Morse code translation system using finger movements captured by a camera and processed with the MediaPipe framework. To enable this, the paper compares the performance of three hand detection algorithms: Yolov5, RetinaNet, and Detectron 2, and identifies MediaPipe as the most effective method for hand tracking. The proposed approach provides a practical and accessible communication method for individuals with speech disorders and opens up new opportunities for improved communication in various settings. The novelty of this work lies in the combination of hand detection algorithms and Morse code translation to develop a robust and efficient communication system for those with speech disorders. Overall, this study highlights the potential of Assistive Speech Technologies in improving communication and promoting inclusion for individuals with speech disorders. © 2023 IEEE.

关键词： Palmprint recognition

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A novel Recommendation Algorithm Based on Knowledge Graph Convolution Networks and LDA 8

A novel Recommendation Algorithm Based on Knowledge Graph Co...

引用

8th International Conference on Intelligent informatics and Biomedical Sciences, ICIIBMS 2023

作者： Chen, Liqiong Xu, Zhengyu Shanghai Institute of Technology Department of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350369564

Recommender systems exist with the aim of helping users discover personalized and useful content and products in a sea of information. Recommender systems have long faced challenges such as data sparsity and cold start. In this study, we propose a recommendation model based on Knowledge Graph Convolutional Network and LDA (KGCN-LDA), which first constructs a collaborative knowledge graph, and deep mines the auxiliary entity information in the collaborative knowledge graph using graph convolutional network(GCN). In the knowledge graph, GCN is used to deep mine the auxiliary entity information in the collaborative knowledge graph, while LDA mines the potential features in the semantic information, and finally, the user and entry embeddings obtained from the two parts are concatenated to generate the final recommendation results. In order to evaluate the effectiveness of the proposed model, we conducted experiments on a public dataset, and the results show that the proposed method significantly outperforms several existing methods in terms of precision, recall and F1. © 2023 IEEE.

关键词： Recommender systems

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Tampering Detection and Localization on Copy-Move Images Using Deep Learning Approaches

Tampering Detection and Localization on Copy-Move Images Usi...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Rao, Kalabarige Lakshmana Tejaswari, Sanapathi Meghana, Patnana Juveria, Shaik Sathwika, Sankili Gmr Institute of Technology Rajam Department of Computer Science & Engineering Vizianagaram India

ISBN: (纸本)9798350300857

Images can be used as legal evidence in forensics, journalism, and other fields. Image tampering is modifying images using modern technologies, which might create false evidence. There are many tampering with images like copy-move image forging, image slicing, and recoloring. Now the detection of those types of images whether tampered with or not and the process of recognizing copied and pasted portions for the copy-move tampered images is a challenging task. In this connection, this work employed CNN, ResNet-50, and VGG-16 for the detection of tampering images. These models were trained on CASIAv2 and COMOFOD datasets. Moreover, error-level analysis techniques and Resizing are used to preprocess for detection. SIFT, and Gray Scaling were applied to preprocess the Copy-Move images to identify similar features in the image. Furthermore, preprocessed images are fed to the DBSCAN algorithm to locate the portions of the image where it is copied and pasted in copy-move tampered images. From the results, it was observed that the CNN model trained on the CASIAv2 dataset outperformed all other models. Hence, it was tested on the MICC-F220 dataset followed by DBSCAN for localization of tampered images. © 2023 IEEE.

关键词： Convolutional neural networks

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