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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Sameer, Mohammed Sudharsan, K. Benazir Begum, A. Hindustan Institute Of Technology And Science Dept.of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

In response to the challenges posed by complex and hard-to-remember passwords in traditional authentication systems, this research introduces an innovative approach leveraging Low Rank Adaption (LoRA) fine-tuned Large Language Models (LLMs) to generate natural language-like strong passwords. Focused on striking a balance between usability and security, proposed system methodology employs smaller-sized pre-trained LLMs, adapted using LoRA fine-tuning, to produce passwords that are both strong and memorable. By addressing the limitations of current password generation practices, proposed system framework aims to encourage user adoption of secure practices, contributing to a positive societal impact through improved cybersecurity hygiene. The proposed solution involves deploying a fine-tuned LLM and serving them through an frontend web UI which emphasizes resource efficiency scalability of deployment of such model, and ease of using the generated passwords in real life authentication purposes fostering good cybersecurity practices among non-technical users. © 2024 IEEE.

关键词： Authentication

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BlogGen- A Blog Generation Application Using Llama-2

BlogGen- A Blog Generation Application Using Llama-2

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Verma, Amitasha Anand Kurupudi, Deshik Sathyalakshmi, S. Hindustan Institute Of Technology And Science Dept.of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

In recent years, Large Language Models (LLMs) have emerged as transformative tools in natural language processing (NLP) and artificial intelligence (AI) *** excel in generating coherent and contextually relevant text across a wide range of applications one such segment focusing on content generation for websites, blogs and creative writing platforms. In such cases,with an open-source LLM like Llama-2, any person or business can use it for their means without having to pay licensing fees. This includes deploying the LLM to their own infrastructure and fine-tuning it to fit their own needs eliminating the exposure of data to third party interfaces, also making it task specific. This paper introduces a unique model to generate customised blogs by fine-tuning Llama-2 model and perform a comparison with GPT-3.5 and BERT. © 2024 IEEE.

关键词： Blogs

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Analysis of Pattern Matching Techniques used for Information Leakage 15

Analysis of Pattern Matching Techniques used for Information...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kumar, Abhishek Senapati, K.K. Patnaik, K.S. Dept.of Computer Science & Engineering Birla Institute of Technology Mesra Ranchi India

ISBN: (纸本)9798331300579

In the rapidly evolving digital landscape, ensuring the utmost security of sensitive information against the pervasive risks of information leakage has become a paramount *** study dives deep into a critical facet of information security-the intricate world of pattern matching algorithms-vital tools in the ongoing battle against Information *** research embarks on a comprehensive exploration of a diverse array of pattern matching algorithms, shedding light on their pivotal roles in the identification and effective mitigation of potential information *** meticulously analyze their inherent strengths, nuanced limitations, and practical applications across real-world contexts, understanding their multifaceted ***, we delve into the complexities of commonly utilized datasets for training and rigorous testing, strategically accentuating essential evaluation criteria that guide algorithmic *** this extensive exploration, we seek to elucidate the landscape of pattern matching algorithms, placing emphasis on their dynamic nature and promising potential in advancing information ***, we put forth insightful proposals for pective research directions within this vital domain, aiming to shape the future of information protection and fortification. © Grenze Scientific Society, 2024.

关键词： Information leakage

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E-ATM For Card Less Transactions Using Machine Learning 1

E-ATM For Card Less Transactions Using Machine Learning

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Aishwarya, D. Muralikumar, A. Mohammed Inthiyaz, M. Sathyabama Institute of Science and Technology Dept.of Computer Science and Engineering Tamilnadu Chennai600119 India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371802

A fingerprint recognition technique in the ATM system is the foundation of this study. We chose this to improve client security and facilitate simple transactions. Since every person has unique fingerprint details, this method will be more widely applicable. It is not necessary to keep your ATM card on hand at all times, nor is there any concern that your card will be lost. It is shown that fingerprint recognition technology outperforms and is safer than other technologies when compared to other ATM security systems already in use. This facilitates safe and simple transactions while maintaining a user-friendly environment. Without a doubt, service providers should also consider security when designing their ATM products, in order to protect financial transactions against security threats. And a lot about security holes, identity theft, credit card fraud, and ways for hackers and criminals to get any sensible passwords or pin numbers. Similar to a username, identification is employed in the present architecture to provide access to the system. To ensure that the intended user is indeed who say they are, since passwords may be stolen or lost. Biometric identity and authentication technologies are the most modern answers to security and privacy problems. The study of traits that may be used to identify a person is known as face recognition. These bodily attributes include, among other things, faces, voices, fingerprints, and hand shapes. The different security system authentication techniques are surveyed in this paper. This face biometric with QR code verification offers the highest degree of security in authentication. One of the most promising technologies for electronic money transactions is this procedure. The need for quick and precise user identification and authentication has increased due to the expansion of the electronic transaction industry. The Automated Teller Machine (ATM) is a fantastic convenience for people to satisfy their financial requirements. Not

关键词： Authentication

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A Real-Time Worker's Monitoring System using Deep Learning 15

A Real-Time Worker's Monitoring System using Deep Learning

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Harshavardhan, L. Kumar, Girish T.P. Gowda, Jayanth B.R. Keerthan, B.K. Bevinatti, Mohan K.S. Dept.of Computer Science and Engineering BGS Institute of Technology Karnataka BG Nagara India

ISBN: (纸本)9798331300579

The Real-Time Worker Monitoring System employing deep learning and web integration represents a cutting-edge solution for enhancing workplace safety and *** advanced deep learning algorithms, this system continuously analyzes real-time data from various sensors, such as cameras and wearable devices, to monitor workers' activities and *** integration with web platforms enables instant access to the monitored information, facilitating seamless communication and *** providing timely insights into worker behavior and environmental factors, this innovative system not only promotes a safer work environment but also optimizes productivity through informed management *** the convergence of deep learning technology and web integration, this monitoring system stands at the forefront of workplace safety solutions, offering a robust framework for real-time insights and proactive intervention. © Grenze Scientific Society, 2024.

关键词： Decision making

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YOLO Dietician 3

YOLO Dietician

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Vaishnavi, Sirikonda Shetty, Sanne Hruthika Maheswari, M. Jany Shabu, J.L. Refonaa, J. Sathyabama Institute of Science and Technology Department of Cse Chennai India Sathyabama Institute of Science and Technology Dept.of Computer Science and Engineering India

ISBN: (纸本)9798350375190

In recent years, the integration of Artificial Intelligence (AI) in healthcare has attracted a lot of attention, particularly in the domain of personalized nutrition. With the rising prevalence of diet-related health issues such as obesity, diabetes, and cardiovascular diseases, the primary objective of this research study is to design and implement a platform capable of analyzing individual preferences, nutritional needs, health goals, and lifestyle factors to offer customized dietary guidance and management. This research study proposes a AI Dietician by utilizing AI algorithms to provide personalized dietary recommendations. This study also covers the technical aspects and methods used in creating a AI dietician, including feature extraction, algorithm selection, data gathering strategies, and model training by ultimately contributing to the prevention of diet-related health issues and promoting long-term health outcomes. © 2024 IEEE.

关键词： Nutrition

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Human Activity Recognition using ShuffleNetV2 Model

Human Activity Recognition using ShuffleNetV2 Model

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Shanufa Nazrin, P.S. Benifa, J.V. Bibal Anu, K.P. Vijayakumar, Aiswarya Dept. of Computer Science Kerala Thrissur India Indian Institute of Information Technology Dept.of Computer Science and Engineering Kerala kottayam India

ISBN: (纸本)9798350394580

HAR is specified as the crucial task within the field of computer vision and artificial intelligence. Main aim of HAR is to observing and labelling different human activity images of distinct data sources by machines such as computer. HAR has applications in various fields. CNN model is an appropriate model for HAR task, due to their ability to automaticaly acquire relevant spatial features, capture hierarchical representations, and handle input of various sizes and types. So it is utilized for developing HAR model. This model can detect objects and also can classify images. The model contains wide ranging catogories. ShuffleNetV2 is a particular architecture within it, mainly for efficient and lightweight deep learning task. This paper develops an intelligent HAR using ShuffleNetV2 model which acquired an accuracy about 69.52 percentage. © 2023 IEEE.

关键词： HAR Human activity ShuffleNetV2

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Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning

Improving Multi-Domain Task-Oriented Dialogue System with Of...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Prajapat, Dharmendra Toshniwal, Durga Indian Institute of Technology Dept of Computer Science and Engineering Roorkee India

ISBN: (纸本)9798350362480

Task-oriented dialogue (TOD) system is designed to accomplish user-defined tasks through dialogues. The TOD system has progressed towards end-to-end modeling by leveraging pre-trained large language models. Fine-tuning the pre-trained language models using only supervised learning leads to the exposure bias and token loss problem and it deviates the models from completing the user's task. To address these issues, we propose a TOD system that leverages a unified pre-trained language model, GPT-2, as a base model. It is optimized using supervised learning and offline reinforcement learning (RL). The issues in the TOD system are mitigated using a non-differentiable reward function. The reward is calculated using the weighted sum of the success rate and BLEU evaluation metrics. The success rate and BLEU metrics in reward calculation guide the language model for user task completion while ensuring a coherent and fluent response. Our model is acquired by fine-tuning a pre-trained model on the dialogue-session level which comprises user utterance, belief state, system act, and system response. Experimental results on MultiWOZ2.1 demonstrate that our model increases the inform rate by 1.60% and the success rate by 3.17% compared to the baseline. © 2024 IEEE.

关键词： Supervised learning

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Moth flame optimization algorithm for efficient feature selection and hybrid classification technique for intrusion detection system

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Multimedia Tools and Applications 2025年 1-21页

作者： Keerthanarani, U. Selvakumar, V. Singaravelan, S. Raja, S. Edwin Thenmozhi, K. Shunmugam, D. Arun Dept. of Computer Science and Engineering P.S.R Engineering College Sivakasi India Dept.Of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai Avadi India Dept. Of Computer Science and Engineering Coimbatore Institute of Engineering and Technology Coimbatore India Dept. Of Electronics and Communication Engineering P.S.R Engineering College Sivakasi India

The rapid expansion of the Internet of Things (IoT) brings numerous benefits but further presents fresh difficulties, especially in terms of security. The distributed and interconnected nature of IoT devices makes them susceptible to various security threats. An Intrusion Detection System (IDS) plays a crucial role in safeguarding IoT networks by monitoring, detecting, and responding to potential security incidents. The Internet of Things (IoT) connects billions of devices, sensors, and systems, transforming industries and everyday life. However, this extensive interconnectivity also introduces significant security risks, making Intrusion Detection Systems (IDS) a critical component for safeguarding IoT environments. Feature selection is a crucial aspect of building effective Intrusion Detection Systems (IDS). Selecting relevant features not only helps in achieving better classification accuracy but also contributes to reducing false alarm rates. In the proposed system a moth flame optimizer is utilized to select the efficient features from the dataset, overall accuracy of over 97%. Further it is classified employing Random Forest and J48 classifier which attains high classification accuracy and low false alarm rate. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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Twitter Bot For Consumer Feedback Analysis 5

Twitter Bot For Consumer Feedback Analysis

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Dwivedi, Ashish Sharma, Santosh Mishra, Abhinav Sahu, Ashutosh Gupta, Himanshu United Institute Of Technology Dept.Of Computer Science & Engineering Prayagraj India

ISBN: (纸本)9798350330861

This research presents the development of a Twitter bot for consumer feedback analysis using natural language processing (NLP) techniques. The bot is designed to collect and analyze tweets related to specific products or services, and provide businesses with real-time insights on consumer sentiment and feedback. The bot uses a combination of NLP techniques, including sentiment analysis, topic modeling, and keyword extraction, to identify trends and insights from the tweets. The resulting analysis can be used by businesses to improve their products or services, as well as their customer engagement strategies. The paper also discusses the challenges and opportunities associated with developing and deploying such a bot, including ethical considerations related to privacy and data protection. © 2023 IEEE.

关键词： Sentiment analysis

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