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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Prasanna Kumaar, D. Abisha, D. Ida Christy, J. Navedha Evanjalin, R. Rajesh, P. Mohamed Haris, K. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

The traditional retail experience for purchasing watches lacks the interactivity and personalization that modern consumers seek. With the increasing shift towards online shopping platforms, there is a growing demand for an engaging and immersive virtual experience that allows customers to explore and try on watches. The absence of expert guidance during the online shopping process often results in less informed decision-making. To address these challenges, WRISTVIEW presents an innovative solution that integrates augmented reality (AR) and a conversational Generative AI (GENAI) chatbot. The AR component enables users to virtually try on watches, providing a realistic and interactive experience. The GENAI chatbot enhances this experience by offering expert advice, answering queries, and guiding users through the watch shopping journey, thereby creating a more personalized and informative shopping process. The objective of this research is to bridge the gap between the traditional in-store watch shopping experience and the online environment, ensuring that customers can make well-informed and satisfying purchase decisions in a virtual setting. The development, implementation, and potential impact of combining AR and GENAI technologies to transform the online watch shopping experience are discussed. © 2024 IEEE.

关键词： Virtual environments

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Comparative Analysis of CNN and Transformers on Malicious Intent Detection in HTTP 5th

Comparative Analysis of CNN and Transformers on Malicious In...

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5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Tiwari, Kanishka Bhatia, Anmolpreet Singh Garg, Nibhrit Arora, Ishaan Saini, Poonam Department of Computer Science and Engineering Punjab Engineering College Chandigarh India

ISBN: (纸本)9783031609343

The utilization of web-based applications has greatly increased in recent years. This, in turn, has raised security concerns and data breaches leading to losses of millions of dollars. A large part of these attacks is carried out through modifications in HTTP requests and are often too complex to be recognised through simple rule-based checks. Thus, it is vital to utilize machine learning approaches in order to protect the applications against such attack vectors. Additionally, given the pace at which web servers need to operate, these machine learning methods need to be extremely fast at processing the requests. The paper presents a comparison of two recent deep learning models to recognize the web application attacks namely, a convolution neural network (CNN) and a Transformer-based model. We compare the prediction accuracies of both the models as well as their prediction time to identify the best model suited for being deployed in a real-life scenario. Based on implementation and results, it is observed that for our dataset, CNN tends to perform better in terms of both, accuracy and prediction time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Cognitive Vision Companion: An AI-Enhanced Support System for the Visually Impaired 2

Cognitive Vision Companion: An AI-Enhanced Support System fo...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gowthami, M. Shreya, P. Sripriya, T. Yazhini, B. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

In a world where a major portion of the populace suffer with daily challenges of blindness, this pioneering project introduces a comprehensive smart support system designed to aid the needs of people who are visually impaired. This advanced system is ingeniously integrated into a pair of headphones, combining various state-of-the-art technologies such as cameras, ultrasonic sensors, and cutting-edge machine learning algorithms. Its primary goals encompass recognizing objects, accurately estimating distances between objects and users, deciphering captured signs and indications, and offering real-time location-based navigation and directions. This multifunctional blind assistance device is designed to empower users by enhancing their spatial awareness and providing them the tools they need to navigate travel securely and on their own. It represents a remarkable leap forward in refining the overall quality of life and mobility for the blind people. © 2024 IEEE.

关键词： Ultrasonic cameras

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Automated Fraud Detection in Online Transaction Using Representation Learning

Automated Fraud Detection in Online Transaction Using Repres...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Farjana, U. Sitaram, M. Sathish Kumar, J. Rajalakshmi Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350352689

Automated fraud detection in online transactions is a challenging task, as fraudsters are constantly developing new and sophisticated methods. Representation learning is a machine learning technique that can be used to learn patterns in data by transforming the data into a lower-dimensional representation that is more informative and easier to learn from. Two popular representation learning techniques are autoencoders and graph neural networks (GNNs).Auto encoders are a type of neural network that is trained to reconstruct its input. This means that an auto encoder learns to represent the input data in a compressed form, and can then be used to detect anomalies in the data. GNNs are a type of neural network that can be used to learn from data that is represented as graph. A graph is a data structure which in form of nodes and edges. This paper proposes a novel fraud detection system using auto encoders and GNNs. The proposed system is evaluated on a real-world dataset of online transactions, and is shown to achieve state-of-the-art results. We aim to capture complex relationships among accounts, transactions, and entities within the transaction network. The implementation of automated fraud detection using representation learning stands to enhance customer trust, reduce operational costs, and safeguard online transactions from the ever-evolving threat landscape. © 2024 IEEE.

关键词： Metadata

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LeNet - IoT Security Aware Intrusion Detection Framework Using Deep Learning

LeNet - IoT Security Aware Intrusion Detection Framework Usi...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ananthajothi, K. Adharshini, C. Amirdha, M. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

The rising complexity of cyberthreats created the demand for compelling cybersecurity solutions that along with expanding Internet of Things (IoT) devices has made them indispensable. In the work our proposal is a Deep Learning-Assisted Intrusion Detection System (IDS), based on the LeNet architecture, that can be used for the identification and prevention of potential intrusions in Internet of Things environments. To increase the detection accuracy, we developed our method based on a properly structured preprocessing step comprising normalisation and data augmentation using image-based and QR-based datasets. We take a look at the architecture of LeNet including the pooling and convolutional layers that help to identify important features of the input dataset. The final parts of the model are fully linked layers and a Softmax classifier that predicts the type of anomaly. Performance metrics measure the efficacy of the IDS including precision, recall, accuracy, and F1 score that reveal our method surpass the classical methods with a high margin. We shall describe in this paper how one can train the LeNet model using IoT-specific attack data so that it could be used to detect intrusion in surrounding network space and inject the walled-off attack, thereby facilitating threat prevention. Since the model is GPU accelerated, it converged close to what was able to protect the IoT network effectively from the evolving cyber-attacks and was quite close to its high accuracy and real time efficient performance in terms of time. © 2024 IEEE.

关键词： Network intrusion

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MedConnect - A Comprehensive AI-driven Platform for Seamless Patient care and Optimization of Medical Services 8

MedConnect - A Comprehensive AI-driven Platform for Seamless...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Ida Christy, J. Abisha, D. Navedha Evanjalin, R. Sriranjani, K. Dimple Abirami, S. Guru Varshini, B. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

In the rapidly evolving landscape of healthcare, technological advancements play a pivotal role in enhancing patient care and optimizing medical services. New solutions are being developed and implemented, both those catered for treatment of organ-specific diseases, MedConnect's aim is to improve the process of treatment delivery. It helps the user streamline an 'in-matching' selection process with ways of overcoming the challenges posed by manual processes, having to wait over long periods, and more. Modifying a GPT-3 API within chatbot components increased the applications power and responsiveness as well as made use of the turbo version. The implementation offered users a straightforward health information retrieval system when patients could retrieve their medical records, look for relevant diseases and information on their prevention. Results also demonstrated a substantial enhancement in user satisfaction and usability of operations. Such results are of great importance, indicating clearly how transformational technology that incorporates AI can be in healthcare. MedConnect seeks to enhance the use of available medical facilities in as much as it improves the system of medical services provision;it has proved to be a success story in the application of technology in fighting for patients' needs. © 2024 IEEE.

关键词： Electronic health record

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Machine Learning Prognosis for Smartphone Dependency 5

Machine Learning Prognosis for Smartphone Dependency

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

作者： Julian, Anitha Prathima, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Concern over smartphone addiction has grown recently as more and more people show signs of addiction, including compulsive phone use, decreased productivity, and even physical and mental health issues. Consequently, a growing necessity exists for the creation of efficient tools to forecast smartphone addiction and pinpoint individuals susceptible to this behavior. The proposed model is crafted to predict smartphone addiction using data collected from a survey of individuals who use smartphones. The survey encompassed inquiries related to demographics, phone use patterns, and various psychological aspects like anxiety, depression, and stress. a popular and effective machine learning method, to build our model. After pre-processing the data, the model is trained and its performance assessed by its accuracy, to see whether the model could be learned efficiently. Our findings demonstrated how effective the recommended treatment was at preventing smartphone addiction. Key features crucial for predicting addiction included patterns of phone use, such as the frequency of checking notifications, daily hours spent on the phone, and the predominant types of frequently used apps. Other important factors included age, gender, and stress levels. Our designed model has multiple possible uses. The solution finds its usage by healthcare professionals to identify individuals prone to potential risks of developing smartphone addiction and provide appropriate interventions. It could also be used by app developers to design apps that are less addictive and promote healthier phone use habits. © 2024 IEEE.

关键词： addiction healthcare machine learning prediction smart phone

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AI Based Native Food Nutrition Prescribing Chatbot

AI Based Native Food Nutrition Prescribing Chatbot

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Kumar, P. Ananthajothi, K. Duraimurugan, N. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

Several studies on the advantage of eating mindfully have concentrated on the idea that maintaining a healthy diet can aid people in preventing the onset of chronic illness including cancer, diabetes, and obesity. It is time-consuming to learn the nutritional worth and contents of each item we eat on a regular basis. So, we require a "Intelligent Chat Bot"that uses artificial intelligence to respond to queries about food's nutritional information and other healthier substitutes. It makes use of algorithms and information databases to comprehend user input and produce responses about diet and nutrition. Natural language comprehension techniques form the basis of the chatbot, but voice synthesis and recognition are also crucial for the achievement of the agent's interactions. This Chabot offers the user the nutritional information for the foods we consume every day, together with recommendations for local food options from the closest market, and also safe chat history storage for future users' input. © 2024 IEEE.

关键词： Bot (Internet)

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Leveraging genre classification with RNN for Book recommendation

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International Journal of Information Technology (Singapore) 2022年第7期14卷 3751-3756页

作者： Saraswat, Mala Srishti ABES Engineering College U.P Ghaziabad India Department of Computer Science and Engineering The NorthCap University Gurugram India

Reviews of books and movies act as valuable source of user generated content that can be used for recommending books and movies respectively. Traditional recommendation algorithms have limitations as they ignore the combination factor of reviews and genre for book. Algorithms used in area of Natural Language Processing are based on similarity between word vectors in the two texts or documents. Often context between these words are missing leading to poor recommendation as it requires semantics to be analyzed between text from the given reviews. In this paper we use Recurrent Neural Networks (RNN) as a deep learning approach to classify books plots and reviews among various categories and recommend to the users or customers the books based on these categories. RNN is an improvement over traditional models as instead of each input unit being independent as in logistic regression and other neural networks each neuron can use its internal memory to maintain information about the previous units. Hence the context between reviews is maintained by memory units to provide more accurate classification leading to better recommendation. Our approach using RNN provides accuracy of 84% and F-measure of 0.80. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Deep learning. Recurrent neural network Genres Recommender Systems Reviews

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Bus Route Tracking System 7th

Bus Route Tracking System

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Rubesh Muthuvel, R. Nithish Sankar, B. Kasirajan, P. Dheenathayalan, S. Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9783031699856

This research paper explores the challenges faced by daily bus commuters and presents a transformative live route monitoring system designed to address these issues. The project focuses on local buses and utilizes a user-friendly web application integrated with Google Maps. Key features include personalized notifications, precise real-time tracking, and historical performance data for efficient journey planning. The integration of crowd-sourced insights and multimodal functionality enriches user decision-making, while emergency communication, multilingual support, and energy-efficient tracking contribute to an enhanced overall experience. The project also includes a community engagement platform to encourage collaboration and feedback. Accessibility features and offline functionality ensure inclusivity and convenience, while strategic partnerships with local transport authorities bolster system accuracy. © IFIP International Federation for Information Processing 2024

关键词： Decision making

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