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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kaviyaraj, R. Sathya, P. Nandhini, P. Vinoth Kumar, P. Chelliah, Sureshkumar Ezhilmathi, S. Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Shanmuga Collage of Engineering and Technology Department of Computer Science and Engineering Sankari India St. Joseph's Institute of Technology Omr Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350361186

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion. © 2024 IEEE.

关键词： Augmented reality

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Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI data 5

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses. © 2024 IEEE.

关键词： Convolutional neural networks

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A Traffic Sign Recognition qnd Speech-BasedFeedback Generation System to Assist Drivers 5

A Traffic Sign Recognition qnd Speech-BasedFeedback Generati...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Sarma, Himangshu Saharia, Navanath IIIT Department of Computer Science & Engineering Andhra PradeshSri City India IIIT Data Engineering Lab CSE Senapati Manipur India

ISBN: (纸本)9798350338287

According to the World Health Organization (WHO), 1.3 million people loss their life annually as a result of road accidents. By alerting or assisting the driver while they are driving, information technology such as computer vision can play a significant part in reducing the accident rate. Keeping that in mind, in this article we presents one traffic sign recognition tool using deep neural network (DNN) models with speech-based feedback for vehicle drivers. The proposed system can give voice-based feedback to driver to direct or inform them to take the appropriate action while driving, such as 'Drive slow, speed breaker ahead', 'Maximum Speed Limit is 80km/hr', 'School Ahead, drive slow', 'Don't take overtake' etc. © 2023 IEEE.

关键词： Deep neural networks

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VeRB: Revolutionizing Vehicle Registration with Blockchain for Enhanced Security and Efficiency in RTO Processes 3

VeRB: Revolutionizing Vehicle Registration with Blockchain f...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Ramalingam, Gautham Praveen Pandian, Deepika Rajan, Deepak Syed Ammal Engineering College Department of Computer Science and Business Systems Tamilnadu Ramanathapuram India Syed Ammal Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Ramanathapuram India Syed Ammal Engineering College Tamilnadu Ramanathapuram India

ISBN: (纸本)9798350354348

The Vehicle-RTO-BlockChain (VeRB) system presents a pioneering solution to modernize the vehicle registration and re-registration process within Regional Transport Offices (RTOs) in India. Our work focuses on implementing blockchain technology to address challenges related to data security, administrative efficiency, and transparency in managing vehicle registration records. The VeRB system encompasses a user-friendly registration portal integrated with robust data verification modules and a permissioned blockchain infrastructure tailored to our specific needs. Through dynamic updating mechanisms and secure historical data storage, our system ensures the accuracy, integrity, and accessibility of registration records while mitigating risks associated with fraud and duplication. This abstract provides an overview of our research, highlighting the key components, benefits, and implications of the VeRB system in revolutionizing automotive registration practices and fostering safer, more streamlined transportation systems tailored to our organizational goals. © 2024 IEEE.

关键词： Blockchain

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Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9899-9903页

作者： Zhang, Yichi Zhang, Yuchen Zhu, Lipeng Xiao, Sa Tang, Wanbin Eldar, Yonina C. Zhang, Rui University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu611731 China Kash Institute of Electronics and Information Industry Kash844000 China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Chinese University of Hon g Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this correspondence, we propose a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. To maximize the system sum rate, we jointly optimize the digital beamformer, analog beamformer, and positions of sub-arrays, under the constraints of unit modulus, finite movable regions, and power budget. Due to the non-concave/non-convex objective function/constraints, as well as the highly coupled variables, the formulated problem is challenging to solve. By employing fractional programming, we develop an alternating optimization framework to solve the problem via a combination of Lagrange multipliers, penalty method, and gradient descent. Numerical results reveal that the proposed MA-aided hybrid beamforming scheme significantly improves the sum rate compared to its fixed-position antenna (FPA) counterpart. Moreover, with sufficiently large movable regions, the proposed scheme with sub-connected MA arrays even outperforms the fully-connected FPA array. © 1967-2012 IEEE.

关键词： Budget control

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An Auto Encoder-Decoder Approach to Classify the Bird Sounds Using Deep Learning Techniques

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SN computer science 2023年第3期4卷 1-13页

作者： Vamsi, Bandi Mahanty, Mohan Doppala, Bhanu Prakash Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology and Science Madanapalle 517326 India Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A) Visakhapatnam 530046 India Data Analytics Generation Australia 88 Phillip St Sydney 2000 NSW Australia

Nowadays identification of bird species is the main problem for ornithologists and ecologists. The increase in volumes of data labelling, still there is a scope to implement an automated bird classification through deep learning techniques. Identifying the nature of a bird and its flock aids in analyzing different other species in the natural environment. This is considered as the main role in the problem of ecology. The species of birds act accordingly to the changes in ecology and this became a challenging task to identify the growth of the bird species with advanced technologies. In this work, our aim is to classify the different bird species based on voice recordings across various locations using autoencoders and decoders. This proposed work is a four-step process: data preprocessing of bird acoustic, feature extraction using a deep network, classification by random forest (RF) and support vector machine (SVM) and identifying the pitch intensities of a bird in low, medium and high ranges. In data pre-processing, the recorded audio is converted into spectrograms through frequency waves. The Mel Spectrogram filter is used to remove the noise from the recorded bird audio. For extracting the features in the spectrogram image, encoders and decoder convolutions are used to construct a fully connected vector. These features are given to RF and SVM for classifying the bird species to obtain standard prediction accuracy. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bird classification CNN Decoder Encoder Mel spectrum

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Single Shot Detector Based MobileNet for Automatic Detection and Recognition of License Plates

Single Shot Detector Based MobileNet for Automatic Detection...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Kiruthuka, D.C. Blessy, M. Prasanna Premalatha, M. Pooja, S. Artificial Intelligence & Data Science Dr.Mahalingam College Of Engg. & Tech Pollachi India Dr.Mahalingam College Of Engg.&Tech Pollachi India Artificial Intelligence & Data Science Kathir College Of Engineering Coimbatore India Computer Science & Engineering Sri Raganathar Institute Of Engg.& Tech Pollachi India

ISBN: (纸本)9798350371567

An Automatic motor vehicle Registration Number Recognition and Monitoring Solution is crucial for the shift towards technological advances in numerous everyday life features such as the management of parking spaces, tollbooth facilities, and roadway control situations. The proposed research concentrates on the databases for automobile registration numbers, regions, and forward images. The suggested approach concentrates on collecting deformed registration number plates caused by angled perspectives. The Single Shot Detector (SSD) is used to recognize as well as distinguish distorted registration plate numbers in frame by frame. Following license plate recognition, distortion on the registration plate is reduced using computerized processing methods. Additionally, it crops the text after retrieving them from the registration plate utilizing a sort outline technique. With the use of a lightweight in nature neural network such as MobileNets, the characters are subsequently turned into electronic characters through Optical Character Recognition (OCR). The suggested model also uses an evaluation measure, like stopping training when the value of the loss does not decrease after five epochs. As a result, it achieves 99.54% overall precision throughout the validation dataset and a precision of 98.26% after 23 training epochs. In order to prevent unauthorized entrance into susceptible sites, the suggested solution uses automated automotive vehicle identification validation through vehicle numbers. © 2024 IEEE.

关键词： Optical character recognition

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A Robust Framework for Epileptic Seizure Diagnosis: Utilizing GRU-CNN Architectures in EEG Signal Analysis 20

A Robust Framework for Epileptic Seizure Diagnosis: Utilizin...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Nikoupour, Maryam Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran Iranian Research Organization for Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350383942

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository's epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods. © 2024 IEEE.

关键词： Electroencephalography

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ASAnalyzer: Attention based Sentiment analyzer for Real-world Sentiment Analysis 20

ASAnalyzer: Attention based Sentiment analyzer for Real-worl...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Hussain, Khadim Azhar, Muhammad Lee, Bumshik Iqbal, Asma Affan, Muhammad Ullah Khan, Sajid Comsats University Islamabad Department of Computer Science Sahiwal Pakistan Hong Kong Shue Yan University Department of Applied Data Science Hong Kong Hong Kong Chosun University Department of Computer Science and Software Engineering Korea Republic of Tsinghua University Department of Computer Science China University of Lakki Marwat Department of Computer Science Pakistan

ISBN: (纸本)9798350395785

In this era of big data, a lot of data is produced in various forms every second through various sources. Text data is one of those types that is produced mainly through social media like Twitter, Facebook, YouTube comments, WhatsApp, etc. To know the public's point of view on a specific issue, we can perform sentiment analysis on the text data collected from the above sites. Even though various algorithms have been proposed for sentiment analysis, these algorithms have issues of high time complexity and low context awareness, leading to low classification accuracy. To resolve the above issues, we propose an attention-based sentiment analyzer for real-world sentiment analysis named 'ASAnalyzer', which uses CNN+Attention-Based BiGRU. CNN is used to extract local features from the tweets, and then these features are used by Attention-Based BiGRU to learn the contextual information of the tweets and the long-term dependencies in both directions of the text, which helps to improve accuracy. To validate our algorithm, we used tweet data about the anti-COVID-19 vaccine from Twitter, and the results have shown that our method outperformed other state-of-the-art methods. © 2023 IEEE.

关键词： Classification (of information)

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Diagnosis of Plant Infection for Optimised Authomatic Health Monitoring System Using Vision Transformer Models 5

Diagnosis of Plant Infection for Optimised Authomatic Health...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thapliyal, Nitin Aeri, Manisha Satyarthi, Abhishek Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Artificial Intelligence & Data Science Iimt College of Engineering Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

Mosaic Virus is highly responsible for the crop loss, especially the pepper, which is considered part of food crop, an essential aspect of world's agriculture. This work aims at automating the classification of disease severity level-related the Pepper Leaf Mosaic Virus Disease threshold through vision transformer (ViT) model to ensure a critical job. The classification of the severity is more often accompanied by a fairly significant amount of workload and arbitrariness, resulting in equipment innovation in most of the locations. The work applies ViT-based approaches with sufficient precision to appropriately classify a dataset, which contains 10,800 meticulously selected photographs. The present model obtained a commendable average accuracy of 97.25%, which shows that the model was able to detect even smaller differences between the classes of severity. The operational criteria that include the tale of the precision, recall, and F1-score highlight the model's ability to minimize to Feggsor false Positives with its ability to discriminate cases of viruses at different levels of the spectrum. The more advanced comparison charts made hand methodologies stand at the pinnacle in plant pathology studies. This study is not only beneficial in terms of illuminating the understanding about the Pepper Leaf Mosaic Virus but also gives farmers and researchers a transformative tool that could change the way disease control methods are used. Technologies AI that power ViT model are helping the agriculture industries throughout the world to implement sustainability and resiliency of the systems by providing responses in the right time and place in addressing viral infections. © 2024 IEEE.

关键词： Agricultural Automation Agricultural Technology Artificial Intelligence Automated Diagnosis Crop Health Disease Management Global Food Security Image Classification Machine Learning Pepper Leaf Mosaic Virus Plant Pathology Precision Agriculture Severity Classification Vision Transformer ViT Model

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