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On - Device Translation of Dynamically Typed and Interpreted Languages 15

On - Device Translation of Dynamically Typed and Interpreted...

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

作者： Pranav, H. Sriharshini, Muppavarapu Tarun, Rachuri Belwal, Meena Amrita Viswa Vidyapeetham Amrita school of computing Dept of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350370249

On Device - translation of expression form languages with dynamic nature and using interpreted languages like MATLAB and Python requires a precise and automated form of processing the error-free conversion. With the help of the automation, the code in MATLAB is well-defined to Python so that translation is precise and the originality as well as functionality is ensured. This paper describes a robust and redundant methodology for converting maintaining the functionality and originality of the MATLAB code helping in smooth transition of code from MATLAB to python. The procedure being entirely local also ensures to protect privacy of the code. © 2024 IEEE.

关键词： Code Generator Dynamical Type Interpreting language MATLAB On - device Code Translation Parser Python Token Tokenizer

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Comparative Study on Synthetic and Natural Error Analysis with BART & MarianMT 9

Comparative Study on Synthetic and Natural Error Analysis wi...

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

作者： Rohit, R. Gandheesh, S.A. Sannala, Gayatri Sanjana Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350394474

Text is essential for communication, information sharing, knowledge acquisition, and analysis. It shapes public opinion, supports education, and drives online content, making it crucial in various domains. While there are various language models utilized for text analysis and text correction, there is little to no survey conducted on these model's behavior and limitations. This work deals with studying BART and MarianMT language models behavior to an input dataset consisting of two types of errors, Synthetic and Natural. Synthetic errors are efficient to create and test, whereas Natural errors are more common and close to real world occurring errors. The models were trained and tested with the generated data, the results highlighted that BART exhibited consistent outputs towards both Synthetic and Natural errors and hence revealing a break-even point at the vicinity of 26% Synthetic error introduction. Conversely, the performance of MarianMT was comparatively diminished for Synthetic errors in contrast to Natural errors. These findings provide valuable insights into the behavior and capabilities of the models. © 2024 IEEE.

关键词： Digital storage

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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...

引用

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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Deepfake Image Forgery Detection using Local Feature Descriptors 15

Deepfake Image Forgery Detection using Local Feature Descrip...

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

作者： Dienash, S. Swapna, T.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Deepfake Image Forgery,generates fake images after manipulating the original source images. This increases the risk of losing trust in images or videos. This paper proposes an efficient methodology to detect Deepfake images from real images. The methodology involves extracting Local Feature De scriptors(Local Binary Pattern (LBP),Frequency Decoded Local Binary Pattern (FDLBP),Binary Gabor Pattern (BGP)). The fusion of these features are fed to the DenseNet to classify the image as fake or real. The model uses Convolutional Neural Network (CNN) architecture that utilizes all three Local Feature Descriptors that extract features of the images. The model has an accuracy of 93.65% which is performing better than Deep learning models. © 2024 IEEE.

关键词： Binary Gabor Pattern (BGP) DenseNet Frequency Decoded Local Binary Pattern (FDLBP) Local Binary Pattern (LBP) Local Feature Descriptors (LFD)

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Preserving Data Integrity in Image Encryption: Comparing Deep Learning Models and integrating Gaussian Noise and Chaotic Baker Map 15

Preserving Data Integrity in Image Encryption: Comparing Dee...

引用

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

作者： Ramesh, Sreyas Sukanth, B.N. Jaswanth, Sathyavarapu Sri Singh, Rimjhim Padam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In the world of growing technology secure transformation and storage of visual data have become very important. Cryptographic techniques continue to play a vital role in various ways ensuring the confidentiality and integrity of sensitive and crucial information. This study talks about an image cryptosystem by using chaotic baker map and Gaussian noise. The chaotic baker map exhibits unpredictable characteristics making it very suitable for encryption processes. Instead of relying solely on chaotic maps the addition of Gaussian noise can impact the imagequality by adding unwanted artifacts or noise which simulates real-world flaws during image transmission. The main objective of the DL models is to reconstruct the original images from the noisy images. DL models can learn patterns from the noisy data as well as from the clean data based on the train and test dataset thereby improving the quality of reconstruction. This process ensures that only authorized users can recover the image accurately. This project has the potential to maintain the confidentiality and integrity of visual information. © 2024 IEEE.

关键词： Chaotic Baker Map Confidentiality Cryptography Data Security Decryption Deep Learning Encryption Gaussian Noise Image Cryptosystem Key Generation Visual Data

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Comparative Analysis of Neural Network Models for Spam E-mail Detection 4

Comparative Analysis of Neural Network Models for Spam E-mai...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Mohan, G Bharathi Prasanna Kumar, R. Sarrvesh, N. Poorani Ayswariya, P.S. Yagnitha, P. Pranav Raj, S. Boopalamani, J. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

With increasing number of internet users and the fast growth of digital communication have made email one of the most important ways of communication. Along with the growing number of users, spam emails also start rising. It has become one of the common problems on the internet. Spam emails have a huge impact on users as spammers' deceptive emails can trick people into sharing personal information or unknowingly downloading harmful software by pretending to be genuine messages. The consequences of falling for scams can vary from losses to the exposure of personal data. To address this issue, numerous types of machine learning and deep learning models have been proposed in various research papers. This paper presents a comparative study of Neural network models such as [deep learning models] Capsule Networks (CAPSNET), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and [Transformer model] RoBERTa to determine the best model. The capacity of each model was evaluated by assessing various performance metrics, including F1 score, recall, precision, accuracy, and support. © 2024 IEEE.

关键词： CAPSNET CNN Neural Network RNN RoBERTa Spam

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Personalized Travel Itinerary Recommender System using Tkinter and SQLite 15

Personalized Travel Itinerary Recommender System using Tkint...

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

作者： Narendran, Advik Hothri, Anantha Yashaswini, Manyam Sundaram, Srinidhi Aiswariya Milan, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

In the ever-evolving landscape of travel planning, the 'Trip Planner' application is highly recommended. Using Python, Tkinter, and SQLite managed through pandas, this tool enhances user interaction with technology, offering a user-friendly platform for selecting hotels, generating itineraries, and preserving travel plans. Its design emphasizes simplicity and efficiency, making it ideal for users looking to redefine their travel organization. The application integrates advanced technological features, positioning it as a top choice for those interested in an effective travel planning tool. This recommendation suits anyone seeking to improve their travel planning processes with innovative software solutions, enhancing the overall experience and convenience of organizing travel adventures. © 2024 IEEE.

关键词： Tourism

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A Quantum Computation Language-based Quantum Compiler 15

A Quantum Computation Language-based Quantum Compiler

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

作者： Amruth, S. Jaya Nigelesh, T.M. Shruthik, V. Sai Reddy, Valluru Sateesh Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Quantum computing is a revolution in computational theory that uses the principles of quantum mechanics to perform computations that are beyond the capacity of classical computers. It works with the special features of the quantum bits or qubits to encode and transform the information in manners conventional bits cannot imagine. A quantum compiler is a software tool designed to translate high-level quantum language code into executable quantum instructions. This paper aims to develop a quantum compiler for translating Quantum Computation Language source codes into quantum bytecodes which can later be executed on a Quantum Virtual Machine. This compiler will have a combinational structure that includes a frontend for interpreting the QCL code, an intermediate representation for optimizing quantum algorithms, and a backend for generating efficient quantum bytecode. Optimization techniques will be applied to improve the functioning of the quantum circuits. The compiler will also include an error handling module to handle the errors. The successful deployment of a quantum compiler for QCL has many implications and unlocks opportunities for advancements in quantum technologies. © 2024 IEEE.

关键词： Quantum Compiler Quantum Computation Language (QCL) Quantum Virtual Machine (QVM)

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YoloV8-Powered Surveillance: Safeguarding Crops from Wildlife and Fire Hazards 15

YoloV8-Powered Surveillance: Safeguarding Crops from Wildlif...

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

作者： Nishanth, K. Pranav, Shri S. Jayasuriyaa, E. Lalithamani, N. Prasand, Ajey M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350370249

Agriculture is the backbone of India's economy. However, the farmers are facing crop damage due to wild animals encounter and fire. The study has mainly intended to provide a solution for the prevention of wildlife encounters into agricultural field and fire using Object Detection. This study mainly focuses on detection of fire and four distinct animals: Elephant, Wild Boar, Deer and Cow. The dataset contains 7000 images with annotated five categories of Object. We trained five YOLOv8 models (nano - YOLOv8n, small - YOLOv8s, medium - YOLOv8m, large - YOLOv8l, extra-large – YOLOv8x) on our dataset. Raspberry Pi equipped with a Camera captures video of the crop field and applies Yolov8 (Object Detection Algorithm) to detect fire and wildlife encounters and plays some loud noises to deter wild animals. The Farmer is notified with a photograph of Farm and class name of the animal detection made through a Telegram Bot. The trained medium YOLOv8m model has attained a mAP of 92.2%, proving its efficacy in identifying fire and wild animals. By integrating this device across farm field, real-time automated monitoring and alerts can be provided to farmers, minimizing crop losses. © 2024 IEEE.

关键词： Fire hazards

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