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Passive RFID Encryption and Decryption using CLI 7

Passive RFID Encryption and Decryption using CLI

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Sailesh Kumar, K.L. Srivatsan, M. Suhas Kumar, S.J. Satyaa, Sudarshan Vishwamalyan, J.S. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350341485

The implementation of the concept of RFID encryption to make a system that prevents the attack on the data of any user or organization on the cloud. The built application has a command line interface. The command line interface is used here instead of a web server to prevent data leakage during data transmission and reduce the risk of a rogue web server. The command line interface takes in an input image and encrypts it, producing an encrypted image and a key file. The CLI also decrypts the previously encrypted image using the key file. This app as a whole solves the problem of users and organizations that store images on the cloud, using this app the user can store encrypted images on the web while the low storage key file is on the user/organization's on-site server. This provides a 2-tier security mechanism via which an adversary will need to hack both the organization server and the cloud server to compromise the data, this way the image data is secure without any additional cost or overhead to the organization. an RFID encryption algorithm is developed along with a non-network-based CLI that secures the organization's RFID data and helps implement a secure cloud-bursting architecture. In this project, an algorithm is developed by combining both the XOR-based encryption algorithm and non-network-based CLI to secure the RFID of the user/organization. Most encryption algorithms nowadays use web-based servers to encrypt the RFID and produce the key file. This makes web-based encryption insecure and vulnerable to a man-in-the-middle attack. Therefore, this app comes to the rescue as it provides a non-network-based CLI by which there is no need for the actual RFID to go outside of the local machine, thus making this app and its architecture secure and robust. © 2023 IEEE.

关键词： Radio frequency identification (RFID)

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A Comparative Analysis of Transformers for Multilingual Neural Machine Translation 7

A Comparative Analysis of Transformers for Multilingual Neur...

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7th IEEE Conference on Information and Communication technology, CICT 2023

作者： Prasanna Kumar, R. Sudharson, S. Reddy, Avuthu Avinash Siva Jyothi Natha Reddy, B. Anvitha, Vemireddy Amrita School of Computing Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350305173

Multilingual neural machine translation (NMT) has emerged as a promising solution to break down language barriers and promote cross-lingual communication. Transformer-based NMT models have gained significant attention due to their ability to learn contextual information and their effectiveness in handling long-range dependencies. The transformer model, with its self-attention and cross-attention mechanisms, fundamentally alters machine translation when compared to other neural machine translation models and traditional machine translation models. Token alignments between source and target sentences are successfully modelled by these mechanisms. This paper deals with the performance of a transformer-based multilingual NMT model that uses a shared encoder and decoder. We conducted experiments on the publicly available Multi30k dataset for translation jobs involving German, French, and Czech into English. In comparison of models, the Transformer model has performed better against the Convolutional SeqtoSeq and Attention-Based Seq2Seq models. We have evaluated our model on the metric Bilingual Evaluation Understudy (BLEU), where the transformer achieved 36.67, 48.47, and 33.66 for DE, FR, and CZ to EN translations. © 2023 IEEE.

关键词： Neural machine translation

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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AutoML Framework for Physical Activities Recognition 3

AutoML Framework for Physical Activities Recognition

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Kovalevsky, Vladislav Delhibabu, Radhakrishnan Zhukova, Nataly St. Petersburg Federal Research Center The Russian Academy of Sciences St. Petersburg Russia Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350372120

The rise of measuring, computing, and storage capabilities in modern information systems has led to vast amounts of data in various fields of human activities. Various algorithms for machine learning have been developed to get valuable insights from this raw data. Tuning the hyperparameters of each algorithm is necessary. Finding the right algorithm and manually tuning its hyperparameters for a given task can be very time-consuming. The methods for automating this search are within the scope of AutoML (automated machine learning). The wide use of wearable devices such as smartwatches and fitness trackers enables the gathering of various information about the physical activities of the wearer. This information, in turn, can be used to produce useful insights, such as recognizing activities being performed. AutoML can automate the process of building models for this task, but the performance of the resulting models can be very different depending on the AutoML library that performed the task. In this work, we develop an AutoML framework that unites several commonly used AutoML libraries and enables the use of common constraints and metrics. The developed framework is then used to build models for the task of human activity recognition by processing a dataset containing information collected from smart devices worn by users. © 2024 IEEE.

关键词： Machine learning

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Classification of Pavements into Bleeding and Non-Bleeding using Deep Architectures: A Comparative Study 3

Classification of Pavements into Bleeding and Non-Bleeding u...

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Sowmiyalakshmi, G. Jegadish, Subiksha Kothai, R. Srinivasa Murthy, Y.V. Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Dept of Information Technology Manipal India

ISBN: (纸本)9798350370867

Bleeding or flushing refers to a reflective, dark layer of asphalt on the road caused by the upward movement of asphalt within the pavement. This issue is often caused by excess asphalt in the asphalt concrete, high temperatures, low air void content, and poor asphalt quality. The presence of bleeding poses safety risks as it creates an excessively smooth surface, lacking the necessary texture to prevent hydroplaning. This study presents a comparative analysis of various image classification models for the detection of bleeding and non-bleeding roads. The objective is to develop an automated system that accurately identifies road conditions related to bleeding, which is crucial for effective road maintenance and safety measures. In this regard, four pre-trained deep learning models such as VGG16, InceptionV3, EfficientNet, and ResNet50, to classify road images. The advantages of this work lie in the exploration of multiple state-of-the-art models, allowing for a comprehensive evaluation of their performance. The features and approach of the proposed work involve training the models on a labeled dataset of bleeding and non-bleeding road images. Each model is fine-tuned using transfer learning techniques, leveraging the pre-trained weights from large-scale image datasets. This study highlights several notable contributions about pre-trained deep learning models. In addition, the study identifies the strengths and weaknesses of each model, helping to guide future research and development efforts. The findings of this comparative study contribute to the field of road condition analysis and maintenance by providing valuable insights into the performance and limitations of different image classification models. The better performance is identified with InceptionV3 with an accuracy of 70.00% and least performance is given by ResNet50 with 31.66%. © 2023 IEEE.

关键词： Deep learning

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Video Chat using WebRTC with In-built Sign Language Detection 3

Video Chat using WebRTC with In-built Sign Language Detectio...

引用

3rd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Sailesh Kumar, K.L. Srivatsan, M. Sendhan, A. Saran, J.U. Vidhya Sagar, P.V. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350306989

Many research efforts into video conferencing technology have focused on audio-only interactions. Text chat, on the other hand, has many uses and is often used in conjunction with the audio channel in multimedia conferencing systems because of the many benefits it offers to users in a variety of contexts. Video chat on the other hand has been providing participants to participate not only through voice but also through visual clues, creating a richer and more immersive communication experience. With the development of technology, video chat has transformed how people cooperate and communicate across distances by becoming more accessible and user-friendly. This study details a prototype program that uses video chat with in-built sign language identification to help the hearing-impaired and the speech-impaired. The prototype is constructed from WebRTC to provide realtime communication for the application, on top of which the object detection model from Tensor Flow is integrated to provide hand sign detection and recognition for the user. We focus our testing on the bandwidth and sign language detection needs of customers in order to provide improved support for video chat. We then talk about how this evaluation's findings can inform the evolution of video chatting software. © 2023 IEEE.

关键词： Object detection

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ASEGAA: Leveraging Attention in Emotion, Gender and Age Analysis from Speech 15

ASEGAA: Leveraging Attention in Emotion, Gender and Age Anal...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sigicharla, Indra Ritesh, Aron Tiwari, Sourabh Syed Ibrahim, S.P. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Samsung R&d Institute Voice Intelligence Group Bengaluru India

ISBN: (纸本)9798350370249

This paper investigates the effectiveness of attention mechanisms in speech classification tasks for predicting age, gender, and emotion. Despite the success of attention in multimodal systems, its applicability to speech context preservation remains underexplored. We examine this using the SEGAA model alongside our ASEGAA (Attention in Speech Emotion Gender and Age Analysis) models. Experiments on CREMAD and EMO-DB datasets reveal challenges in capturing speech context due to limited MFCC correlation. Speech attention models struggle to maintain context, leading to reduced accuracy compared to non-attention speech models. The results highlight the significance of feature selection and advocate for further exploration of alternative mechanisms. In terms of F1 scores, the multi-output SEGAA model achieves relatively high scores across age, emotion, and gender prediction tasks. However, attention mechanisms such as ASEGAA attention at end and ASEGAA Interspersed Attention exhibit fluctuating F1 scores, suggesting differing impacts on context preservation across the prediction tasks. © 2024 IEEE.

关键词： Attention CNN Deep Learning Speech Processing

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Intelligent Stock Trading using LSTM Approach 3

Intelligent Stock Trading using LSTM Approach

引用

3rd IEEE India Council International Subsections Conference, INDISCON 2022

作者： Saraswathi, Pedada Rao, Patruni Muralidhara Rajkumar, G V S GITAM Institute of Technology Department of Computer Science and Engineering Visakhapatnam530045 India Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India

ISBN: (数字)9781665466011

ISBN: (纸本)9781665466011

Everyone deals with money daily. As the famous saying goes, it's not what you earn but what you save. For the past few generations, people in India have believed that putting all the surplus money in FDs was the best way to invest. But considering the tax reductions and return rates recently, people have started to switch to better ways of investment. One of these ways is pumping money into direct equity and Mutual Funds/SIPs. For this, Indians rely heavily on CAs and financial advisors that charge a lot of compensation for their consultation and advice. We propose a system that replaces these advisors by using state-of-The-Art Machine Learning approaches. We implement a Long Short-Term Memory (LSTM) network that takes input of historic prices of a pre-specified company and predict a future price which will enable the user to make smart decision of investing in the company or to choose a different approach of utilizing their surplus income. © 2022 IEEE.

关键词： Sentiment analysis

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Middle School Architect: Programmatic Modeling and Construction via a 3D Graphics Platform in Elm 2

Middle School Architect: Programmatic Modeling and Construct...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Parcha, Anurag Mishra, Sanjana Anand, Christopher Kumar Pushparaj, Jagalingam Simon Fraser University BurnabyBC Canada McMaster University Computing and Software HamiltonON Canada School of Computer Science and Engineering Vellore Institute of Technology TN Vellore India

ISBN: (纸本)9798350328202

Today, school students are first introduced to geom-etry and advanced mathematical concepts by plotting a graph in 2D and they struggle with 3D concepts since current technologies are either too difficult to grasp or too simple with only drag-drop functionalities disconnected from coordinates. This article presents a 3D graphics library and an interactive, educational system for visualization of a 3D environment where planks of Cross Laminated Timber (CLT) can be manipulated by geometric and mathematical functions to produce a 3D building. Throughout, there is a focus on functions, 3D geometry and other underlying mathematics, and links are made with the world of computing, specifically computer graphics, to increase students' interest and confidence. Activities built on this library simultaneously meet multiple curricular objectives: mathematics (including 3D geometry), programming (in Elm, a web program-ming language used commercially), and sustainable development (using a negative embodied carbon building material). © 2024 IEEE.

关键词： Sustainable development

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Deep Learning Algorithm for Brain Tumor Detection and Classification using MRI Images 1

Deep Learning Algorithm for Brain Tumor Detection and Classi...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Harshavardhan, A. Maheswari, N. Uma Prakash, M. Sammeta, Naresh VNR Vignana Jyothi Institute of Engineering and Technology Department of CSE Hyderabad India Panimalar Engineering College Department of Computer Science and Business Systems India Vellore Institute of Technology School of Computing Science and Engineering Vellore India R.M.K College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350323795

Brain tumor classification is beneficial for identifying and diagnosing the tumor's specific location. According to the medical imaging system, early diagnosis and categorization of a tumor extend a person's life. Clinical specialists rely heavily on magnetic resonance imaging (MRI) among numerous imaging modalities since it provides contrast information on brain malignancies. The primary purpose of this project is to use a competent automated approach that improves tumour identification accuracy. Several segmentation strategies have been developed throughout the years to achieve and improve the categorization precision of brain tumours. Brain picture segmentation has long been recognised as a difficult and time-consuming aspect of medical image processing. This method for detecting brain tumors Brain pictures are classified using the Full Resolution Convolutional Network (FRCN) classification architecture after pre-processing and segmentation. This study presents a Full Resolution Convolutional Network (FRCN) with Support Vector Machine (SVM) approach for detecting tumors on MRI scans. The procedure is broken down into four steps. In the first phase, the anisotropic filter is utilized to pre-process raw MRI images, followed by segmentation using the Support vector machine (SVM) and skull classification. The singular value decomposition and primary component analysis operations are performed in the third step. Tumors are then detected and classified using the Full Resolution Convolutional Network (FRCN) approach. Simultaneously, the Support Vector Machine (SVM) technique is employed to improve the classification precision of the study model. The experimental results showed an amazing accuracy rate of nearly 100% in detecting both normal and diseased tissues from brain MR images, confirming the efficacy of the suggested technique. © 2023 IEEE.

关键词： Support vector machines

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