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Exploring the Essence of Prompt Tone: A Comparative Study of Large Language Model Security 3

Exploring the Essence of Prompt Tone: A Comparative Study of...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Nair, Aiswarya Vithayathil, Jose Nambiar, Sandeep Prem Snehasree, S. Murali, Shalu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350369083

Chatbots powered by Large Language Model(LLM) can be manipulated by malicious prompts, generating harmful content and biased responses which would raise security concerns. Growing dependence on chatbots demands robust security for ethical development and user trust, which makes the work relevant in today's world. The motivation behind the work is to let the user have a safe experience with no negative responses being displayed while using the chatbot, which paved the way to arrive at the goal of developing a security filter that could be integrated into any LLM feature integrated application to mitigate the risk of having security vulnerabilities like prompt injection and jailbreaking, which could be achieved by converting malicious prompt into safer prompts by the method of eliminating negative sentiment phrases. The work focuses on building and implementing the security filters to popular in-production LLMs like Large Language Model Meta AI-2 (LLaMA2) and Generative Pre-trained Transformer - 3.5 turbo (GPT-3.5) to see how they handle against prompt injection and jailbreaking before and after the security filter being integrated. A large database of 200,000 prompts has been collected and preprocessed to train on a machine learning model using binary classification algorithm having 99.7% accuracy for classification of prompts into malicious or non-malicious and further checks are being done by breaking the prompt into smaller phrases and individually analyzing their compound sentiment score using Natural Language Toolkit (NLTK) Valence Aware Dictionary for Sentiment Reasoning (VADER) algorithm to detect and drop the negative sentiment phrases for the modification of the user prompt to eliminate the possibility of malicious prompt being passed to LLM. It is difficult to determine the sentiment of prompts in a detailed way and convert it into an efficient design that will perform well with models. Once this hurdle is overcome, chatbots will become even more reliable,

关键词： Economic and social effects

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Secure Authentication Scheme for Internet of Battlefield Things 15

Secure Authentication Scheme for Internet of Battlefield Thi...

引用

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

作者： Ashutosh, Devu Pawan Akhtar, Shoaib Yeshwanth Reddy, G. Singh, Hardik K. Nimmy, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Future military operations will increasingly depend on interconnected technology, using advanced embedded systems and AI to improve defense capabilities and technology. The Internet of Battlefield Things (IoBT) will link soldiers with smart battlefield equipment such as armor, radios, and weapons, reducing decision cycle times, improving tactical analytics, and delivering precise AI support where needed. Authentication plays an important role in IoBT, as confidential information generated from battlefield devices needs to be secured from enemies. We propose a novel authentication approach designed to address the particular constraints of the IoBT, utilizing fuzzy extractor and geometric secret sharing techniques. This approach overcomes the weaknesses caused by dynamic and hostile combat situations, in which standard authentication methods may fail. The proposed scheme is based on fingerprint biometrics and fuzzy extractors, enabling reliable authentication even when dealing with noisy biometric data. Moreover, geometric secret key sharing improves security by providing secure key establishment. Also, the suggested methodology is ideal for resource-sparse Internet of Things (IoT) devices which are commonly found in military situations. The effectiveness and robustness of the proposed authentication design are shown by exhaustive analysis employing Scyther tool and simulations, illustrating its ability to resist numerous security attacks prevalent in IoBT environments. © 2024 IEEE.

关键词： Authentication

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Wearable Security-Authentication using Smartwatches 2

Wearable Security-Authentication using Smartwatches

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Bhardwaj, Sanyam Dhaksana, R. Varshini, Katari Amrutha Harini, N. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350371314

Multi-factor authentication has become a widely employed method for verifying users and safeguarding sensitive information. This approach typically combines a password with an additional authentication factor, such as something the user knows, possesses, or uniquely possesses (e.g., fingerprint or iris recognition). This paper introduces a novel method to augment user authentication by integrating smart- watches/fitness bands, categorizing it under the category of the user possessions within multi-factor authentication (MFA). A noteworthy aspect of this approach is its ability to enhance the authentication process without the additional need of finances. The results from the prototype and survey revealed that most of the users find this type of scheme to be comfortable and reliable. The challenges from the user and the service provider perspective have been thoroughly reviewed for the proposed method and found to be usable while still enhancing security. © 2024 IEEE.

关键词： Authentication

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Forecasting India's Air Quality: A Machine Learning Approach for Comprehensive Analysis and Prediction 4

Forecasting India's Air Quality: A Machine Learning Approach...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Kothari, Osho Sah, Nabin Kumar Kumar, Konda V S Harshith Nair, Priyanka C Sampath, Nalini Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

With rising industrialization, India confronts increasing difficulties in maintaining air quality regulations. This research proposes a comprehensive analysis and prediction framework based on machine learning approaches for assessing and forecasting the Air Quality Index (AQI) in different place of India. This model takes into account a wide variety of pollutant concentrations that are monitored at regular intervals. This approach, which uses historical data from numerous Indian cities, employs a variety of machine learning methods, including k-Neighbour regression, decision tree regressor, linear regression, and random forest regressor, in addition to support vector regressor. Every model had performed well but the Decision Tree Regression and Random forest has performed best achieving RMSE value as 0.0016 and 0.00095 respectivelly whereas R-Squared value as 0.99 for both the model, indicating strong performance and a substantial progress in air quality prediction. The environmental health risks associated with air pollution have been mitigated by the addition of important knowledge. © 2024 IEEE.

关键词： Support vector regression

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A Comparative Analysis of Deep Learning Models for Detection of Diabetic Foot Ulcer using Foot Thermography Images 15

A Comparative Analysis of Deep Learning Models for Detection...

引用

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

作者： Chowdary, Chava Sriya Ruthwik, Dovala Sai, Kancharla Nitish Rishik, Suthapalli Sampath, Nalini Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Diabetic foot ulcers (DFUs) are a serious consequence for diabetes individuals, frequently resulting in amputation. Early identification is critical in averting such results. This study looks at the effectiveness of deep learning models in recognizing DFUs from foot thermography photos. We evaluate the performance of several deep learning architectures against more contemporary models. The models will be trained and assessed using a publicly accessible foot thermography picture collection that includes both healthy and DFU patients. Each model's performance in DFU identification will be evaluated based on accuracy, precision, recall, and f 1 score. We intend to find the deep learning model with the greatest accuracy,f1 score, and robustness in DFU classification, which would pave the way for a computer-aided diagnostic tool that uses thermography to detect DFU in diabetes patients early. This research shows potential for enhancing patient care and lowering amputation rates. © 2024 IEEE.

关键词： Convolutional Neural Networks (CNNs) Deep learning models Diabetic foot ulcer (DFU) Foot thermography images Transformers

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Leveraging a Custom Multi Modal OCR System for Marksheet Data Extraction 15

Leveraging a Custom Multi Modal OCR System for Marksheet Dat...

引用

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

作者： Kalra, Bhavvuk D. Suresh, Sreedath Sanjay Adithya, M. Shanmuga Priya, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

In the current educational landscape, the transition towards digitalization has become crucial. However, the manual entry of data from traditional physical marksheets into digital systems remains a significant bottleneck in the process. Educators often spend significant amount of time and effort entering data into electronic systems, which could be better utilized for more productive *** this paper, we offer a solution to this problem. We propose an automated system that leverages Optical Character Recognition (OCR) Technology to streamline the extraction of essential information from marksheets. We discuss about the Dataset, preprocessing on the input image, Multi Modal OCR system Structure and other implementation details. We conclude the paper by a comparative study of our Multi Modal OCR system with other Commercially available OCRs like EasyOCR, PaddleOCR and PyTesseract. © 2024 IEEE.

关键词： Character Isolation CNN Data Extraction Digits Handwritten Letters Marksheets Multi Modal Neural Network Optical Character Recognition (OCR) Preprocessing Region of Interest Template Image Text Recognition

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AcuLearn - Precision Based Learning Analytics Platform Using Machine Learning Algorithms 4

AcuLearn - Precision Based Learning Analytics Platform Using...

引用

4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Manaswini, Poluri Shaik, Sajjad Kartik, Rejeti Khare, Sangita Amrita School Of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350387063

In today's world, the use of data analytics with precision has been extremely important and crucial with the changing times. The creation of virtual environments to facilitate communication and education among the students and teach-ers alike. It allows the creation of personalized education for students. Machine Learning algorithms, hence can be helpful to recommend students to what courses that can be taken and their likely improvement of the system. This study shows the creation of an website for creating better personalized education for students including course completion predictors and score predictors to help them make decisions proactively. The system shows the random forest regression for score prediction and random forest classifier for course completion to perform the best among all such algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Automated System for Efficient Cervical Cancer Screening Using Cervicogram Images

Automated System for Efficient Cervical Cancer Screening Usi...

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2024 International Conference on Intelligent computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Gireesh, N. Nair, Rahul P. Nemmini, Sanghamitra Vani, R. Vinitha Panicker, J. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science Engineering Amritapuri India

ISBN: (纸本)9798350369175

Cervicography, a diagnostic technique that checks and identifies problems in the cervix area with its major aim being cervical cancer, is used. This involves application of 5% acetic acid to the cervix followed by capturing detailed images of it through special imaging technology. The proposed method offers an alternative to conventional ones such as Pap smears that are both time-consuming and call for well trained staff. Cervicography is widely used in community screening programmes due to its ability to be completed quickly and efficiently on a larger number of women. Furthermore, it has improved sensitivity for detecting abnormalities in the cervical canal thus providing clearer pictures of the cervix while also increasing possibilities for finding premalignant or malignant growths. The purpose of this study is to use cervicogram images for improvement of the cervical cancer screening techniques. This study proposes a Dirichlet Ensemble Model with RoI extraction of cervix using You Only Look Once (YOLO) framework. The classification model uses Kaggle Intel and MobileODT dataset for training, validation and testing. The testing accuracy obtained as 80%, F1 score of 0.79 and ROC AUC score as 0.8327. © 2024 IEEE.

关键词： Lung cancer

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Vision Based Gesture Recognition 2

Vision Based Gesture Recognition

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Hrishikesh, P. Akshay, V. Anugraha, K. Subramaniam, T.R. Hari Nair, Jyothisha J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

The use of computers has become an integral part of our daily lives, and they are utilized in various fields. However, traditional input devices such as a mouse and keyboard can sometimes be limiting. Hand gestures can provide a more natural and efficient way of interacting with computers. However, since gestures can vary in orientation and shape from person to person, this creates a non-linear problem. Hence we propose an image-based gesture recognition method that leverages well-established models. The models were trained on a diverse dataset encompassing a wide range of gestures performed by various individuals under different environmental conditions. The experiments exhibit remarkable adaptability to different gestures resulting in a robust and accurate recognition in real-world scenarios. This research will help in creating better human-computer interaction interfaces offering users more natural and efficient ways to interact with computers. The improved gesture recognition systems elevate overall user experience and productivity. © 2024 Elsevier B.V.. All rights reserved.

关键词： Gesture recognition

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Deep Fake Image Detection using Xception Architecture 5

Deep Fake Image Detection using Xception Architecture

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Joshi, Paritosh Nivethitha, V. Amrita School Of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350351378

Deep Fake technology has become increasingly sophisticated, posing a significant challenge to the integrity of digital content in today's information age. This research paper introduces a novel approach in detecting deep fake images and videos utilizing the Xception model, a deep CNN known for its exceptional image classification capabilities. The study begins by collecting a diverse dataset containing real and deep fake images and videos across various domains. The Xception model is fine-tuned for deep fake detection through transfer learning. To improve model robustness, a range of data augmentation strategies and regularization techniques are incorporated during training. The results of the proposed model demonstrate an accuracy of 93.01% on the test dataset. The proposed approach exhibits promising potential in combating the spread of deceptive and malicious deep fake content across the internet and social media platforms. In conclusion, this research paper presents a reliable and efficient method for deep fake image and video detection, leveraging the Xception model's capabilities. © 2024 IEEE.

关键词： Fake detection

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