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A Next-Gen Approach to Streamlined Canteen Management Systems using Mesa Framework 8

A Next-Gen Approach to Streamlined Canteen Management System...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Narendran, Advik Inuguri, Anantha Hothri Yashaswini, Manyam Sundaram, Srinidhi Khanna, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

A next-generation canteen management system is proposed that employs intelligent software agents to streamline various tasks, such as order processing and customer service, within canteens. The system automates verification through ID card scanning and OTP authentication, ensuring secure and efficient operations. By integrating agents to manage orders and provide real-time updates, the system enhances the dining experience by reducing manual effort and improving service speed. This smart solution, designed to cater to institutional needs, provides seamless control over canteen processes while enhancing user satisfaction through a modern, technology-driven approach. © 2024 IEEE.

关键词： Intelligent agents

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Adapting Optimal Solutions to Travelling Salesman Problem 15

Adapting Optimal Solutions to Travelling Salesman Problem

引用

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

作者： Madhusoodanan, Devika Vismaya, R. Hridyalakshmi Sahay, Apurvanand Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

A comparative analysis of algorithms for the Traveling Salesman Problem (TSP) is proposed that evaluates four distinct approaches: Nearest neighbor (NN), 2-opt algorithm, Genetic Algorithm (GA), Simulated Annealing (SA) and a hybrid method of NN and 2-opt to determine its minimum distance achieved with minimum execution time. These algorithms are evaluated by their capability to find the shortest route and the total distance travelled in the given dataset. The evaluation process consists of execution time and route rendering. The results claim that the hybrid approach outsmarts all the other methods in terms of their execution time as it takes as low as 0.308 seconds as compared to other methods on the given dataset. This hybrid approach has the potential to improve the shortest distance in an iterative manner which balances the time versus distance objective criteria in a scalable manner. © 2024 IEEE.

关键词： 2-opt Compare Approaches Genetic Algorithm Hybrid Method Nearest neighbor Optimization Algorithms Simulated Cooling TSP

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A Speech2Text based Recognizer and Probabilistic Parser Application for TeluguSentences 15

A Speech2Text based Recognizer and Probabilistic Parser Appl...

引用

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

作者： Varshini, Surisetty Hima Varma, Gottimukkala Sarayu Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Telugu, the largest Dravidian language, is one of the Indian languages that is predominantly spoken in Andhra Pradesh, Telangana and a few parts of southern India. The proposed work presents a speech-to-text based recognizer cum parser for some simple sentences in Telugu which are commonly used in day-to-day conversations. The Probabilistic Cocke-Younger-Kasami (P-CYK) algorithm has been employed in order to recognize these simple sentences and generate the corresponding probabilistic parse for these sentences. These parses will be useful in gaining a better understanding about the grammatical structure of a given sentence. At present, there are not many linguistic processing methodologies which are integrated with speech-based inputs for regional languages like Telugu. Hence, an effort has been made in order to efficiently use speech as the form of input and obtain the parse for some basic sentences with the corresponding probability of that parse. The probabilistic parser that has been developed in this work takes speech in Telugu as the input and identifies both grammatically correct and incorrect sentences and produce the probabilistic parse of the grammatically correct sentences. This system was then assessed by making use of the metrics like accuracy score, precision score and recall score. Also, a user interface was developed using Streamlit for the same. © 2024 IEEE.

关键词： accuracy score parser precision score Probabilistic Cocke-Younger-Kasami algorithm probabilistic parse recall score recognizer speech-to-text Streamlit user interface

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Comparative Analysis of Eminent Algorithms for Detecting Ham and Spam Contents in OSN 4

Comparative Analysis of Eminent Algorithms for Detecting Ham...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ponnuchamy, Kardeepa Subbulakshmi, N. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India

ISBN: (纸本)9798331540364

Social or Communal media platforms is a lay of exchanging the thoughts or ideas of online users through various data formats like text content, image content or through audio, video, Graphics Interchange Format (GIF) etc and user can dynamically share real-time interactive events in a cost effective manner. Nowadays, with emerging fast-growing tools and technology, online socio-communal networks have shattered popularity as the central thought is to offer a framework for virtual users to link with their associative friends, subordinates and family members that blowouts their messages instantly which in turn paves a way for attackers to gain information or hack online user's data by unauthorized access. Extraction of contents is not accurate due to inflexible rule sets and some contents which are furnished in short texts are more difficult to correlate with their semantics and thereby it is difficult to prevent the display of undesired data in the existing system. Hence, the trained model system needs architecture with Enhanced Adaptive Rule-sets to overcome the inflexibility. Adaptation rule is employed by incorporating the blacklist mechanism along with eminent Machine learning algorithms to predict and prevent the undesired data. Information filtering system and Content filter removes redundant, irrelevant or unwanted information using user feedbacks with multiclass data labels. Furthermore, self-protective approaches are emphasized to achieve fidelity and ensure the trustworthiness in any micro blogging services or in online social networks. © 2024 IEEE.

关键词： Tweets

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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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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...

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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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MazeSolver: Exploring Algorithmic Solutions for Maze Navigation 15

MazeSolver: Exploring Algorithmic Solutions for Maze Navigat...

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

作者： Narendran, Advik Hothri, Anantha Saga, Hemanth Sahay, Apurvanand Bangalore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350370249

The initiative examines the efficiency of various maze-solving algorithms, including breadth-first search (BFS), depth-first search (DFS), A∗ search, Markov decision processes with policy iteration (MDP PI), value iteration (MDP VI), and an enhanced wall-follower algorithm. The study seeks to discover the best algorithm for the autonomy of navigation in harsh and non-obvious environments, which is a crucial step for robotics applications in logistics, exploration, and rescue operations. In a comparative analysis within a controlled maze environment, each algorithm's performance is evaluated based on path length, execution time, and memory usage. The study highlights the benefits of sophisticated decision-making and optimized pathfinding, particularly in MDP implementations and heuristic-based A∗ searches, addressing traditional limitations such as high computational costs and susceptibility to specific maze configurations. Results from this study are anticipated to enhance autonomous navigation systems, provide valuable educational insights, and improve operational efficiencies in critical applications. © 2024 IEEE.

关键词： Markov processes

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Generating Event Descriptions from Legal Documents with Bidirectional and Auto-Regressive Transformers 4

Generating Event Descriptions from Legal Documents with Bidi...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Kumar, Aparna Ashok Pati, Peeta Basa Prabhakar, Priyanka Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Benagluru India

ISBN: (纸本)9798350354218

Legal document analysis often requires understanding complex sequences of events, their relationships, and their implications within a legal context. This paper introduces a novel method for event generation by extracting segments from legal documents and generating events based on these chunks. We employ two models, T5 and BART, leveraging their text-to-text transformer capabilities for event generation tasks. Our approach utilizes deep learning methods to handle the complexities and variations inherent in legal texts effectively. The dataset, comprising 246 event descriptions was manually curated to ensure high-quality training and testing data. A comparative study of both models was conducted, and evaluation of results using ROUGE scores demonstrated their proficiency in capturing relevant information. Notably, both T5 and BART achieved the same BERT Similarity score of 0.85, indicating strong semantic understanding and consistency in event generation from legal texts. This study highlights the potential of transformer models in automating and enhancing legal document analysis. © 2024 IEEE.

关键词： Semantics

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Empowering IoT Security: Leveraging Deep Neural Networks Against Ransomware and DDoS Attacks 15

Empowering IoT Security: Leveraging Deep Neural Networks Aga...

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

作者： Geetha, K. Chaudhary, Dharmindar Shrivarshini, K. Amrita Vishwa Vidhyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Using Internet of Things (IoT) devices has become more efficient and convenient, but it has also increased the potential of security breaches, particularly from ransomware and Distributed Denial of Service (DDoS) assaults. This study looks into the ever-changing threats posed by ransomware on the Internet of Things, emphasizing human-centered detection and mitigation techniques. It highlights attack methods like supply chain breaches and device exploitation that jeopardize privacy and service continuity. To enhance IoT security, a deep neural network-based approach is proposed for automated reaction and real-time monitoring. The superior precision and efficacy of the suggested approach in mitigating ransomware and DDoS assaults in contrast to existing techniques underscores the significance of robust security protocols and active stakeholder engagement. This paper's goal is to provide a thorough study of these dangers along with a novel solution that will improve security protocols in IoT ecosystems. This study seeks to close the gap between sophisticated technological protections and workable, user-oriented tactics by highlighting the need of human-centered approaches, guaranteeing that IoT devices continue to be dependable and safe in an increasingly interconnected world. This study emphasizes the vital need for an integrated strategy to IoT security that incorporates both cutting-edge technology and proactive human engagement through thorough evaluation and suggested solutions. © 2024 IEEE.

关键词： and IoT security automated reaction device exploitation Distributed Denial of Service (DDoS) human-centric detection Internet of Things (IoT) mitigation techniques privacy violations ransomware service disruptions supply chain breaches

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Comparative Analysis of Legal Outcome Prediction with Detailed and Summarized Text 9

Comparative Analysis of Legal Outcome Prediction with Detail...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Varshini, Surisetty Hima Varma, Gottimukkala Sarayu Prabhakar, Priyanka Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350394474

Legal Outcome Prediction(LOP) is the process of predicting the possible outcome of a court case based on the contents in the case *** aim of this work is to perform a comparative analysis to assess the effectiveness of popular language models for outcome prediction with the case file as well as its summary *** models of BERT, LegalBERT, DistilBERT and RoBERTa are enhanced with transfer learning on the legal data files and the models are evaluated with accuracy and F-score. It is observed that RoBERTa performs best in LOP for both complete case file text as well as for summary text. © 2024 IEEE.

关键词： Forecasting

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