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Gaze Based Online Examination Cheat Detection with future integration of Deep Learning Models 11

Gaze Based Online Examination Cheat Detection with future in...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Padmavathi, A. Kumar, D. Prasanth Reddy, K. Nithish Kumar Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350350357

The popularity of online exams increasing rapidly because of this rapid change in education has resulted in both convenience and integrity concerns. but with this raise in popularity of online exams comes a big problem. how to maintain exam security and integrity in a connected and ever-evolving technology environment? the students also finding their own strategies over the time for cheating in online examinations. Online exams cheating has been developed into complex and difficult problem which includes techniques like false behavioral actions and accessing the unauthorized sites to find the solutions to the exam questions. © 2024 IEEE.

关键词： Deep learning

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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...

引用

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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IoT-Driven Intelligent Trash Bin (IntelliBIN) Enabled with Edge Segregation for Malls 3

IoT-Driven Intelligent Trash Bin (IntelliBIN) Enabled with E...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Reddy, D HarshaVardhan Gupta, T Subhash Reddy, C Gowthami Vishnu, S. Vishnuvarthan, R. Darshan, B Sree Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372120

Urban waste management, especially from commercial sources like shopping malls, presents a significant challenge. A large amount of packaging waste generated by retail outlets and food courts complicates the waste management process. The current method of segregating waste at centralized facilities leads to time-consuming processes, increased transportation costs, and increased labor demands. In response, this work proposes an innovative solution: an IoT-Driven Intelligent Trash Bin with Edge Segregation for shopping malls. By deploying a deep learning model on a Raspberry Pi board, waste segregation is optimized at the edge, reducing latency and improving efficiency. The system includes an intelligent bin named IntelliBIN, featuring four compartments, and utilizes the proposed deep learning model to analyze and segregate the wastes into paper, metal, food, and others via a servo mechanism. In addition, the bin is equipped with ultrasonic sensors to monitor the filled levels of each compartments and a Wi-Fi radio device to enable real-time data transmission and monitoring. The statistical data of waste disposal is presented, and automated notifications are sent to the mall management to take appropriate action. The system assures effective waste segregation and management with the proposed model by achieving a training accuracy of 94%, and it outperforms some of the existing works such as VGG 19 (92%), ResNet (93%), and Inception Net (87%). © 2024 IEEE.

关键词： Deep learning

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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...

引用

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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Unraveling IoT Network Security with Snort for Robust Intrusion Detection and Prevention 2

Unraveling IoT Network Security with Snort for Robust Intrus...

引用

2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Geetha, K. Sreedevi, A.G. Chadha, Angela Raj Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383652

The Internet of Things (IoT) has brought unprecedented connectivity, transforming technology across industries. This interconnected landscape, however, presents unprecedented network security challenges. This paper examines Snort's role as a powerful tool for Intrusion Detection in the network and the ways to prevent the intrusion detection and thoroughly investigates the security challenges posed by IoT networks. In light of the growing number of connected devices, the study focuses on specific vulnerabilities, emphasizing issues such as resource constraints, diverse communication protocols, and the need for tailored security measures. The paper provides a thorough examination of Snort's architecture and its application through the development of custom rules for detecting IoT threats. Practical implementation strategies such as network segmentation and continuous monitoring are investigated. The conclusion emphasizes how critical it is to understand threats unique to the Internet of Things and how to use Snort to fortify network defenses in this complex and ever-changing ecosystem. © 2024 IEEE.

关键词： Network intrusion

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Adding Constructs to 'C' Through Flex and Bison 8

Adding Constructs to 'C' Through Flex and Bison

引用

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

作者： Arroju, Nachiketh Vamsi, Nallabothula Reddy, N. Yeshwanth Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Developing a unique language for coding if and for loops involves using creatively designed markers such as 'ifff' and '4'. Meeting the three acceptance criteria requires coding loops using these markers, where 'ifff' signifies an if loop and '4' represents a for loop. Coding loops conventionally with '0' and '4' is considered unacceptable. Additionally, the language requires modifiers for loops and their respective lengths to be placed on one line. The language also encompasses formatting rules, such as combining multiple lowercase letters and ensuring that the letter 'c' appears three times for successful compilation. Repeated occurrence of certain letters is allowed and accepted within the language. Furthermore, specific responses like 'np' for 'no problem,' 'sry' for 'sorry,' and 'fq' for 'thank you' have been formulated based on input. © 2024 IEEE.

关键词： Codes (symbols)

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Deep Learning Based Fetal Brain Ventricle Segmentation and Size Estimation 15

Deep Learning Based Fetal Brain Ventricle Segmentation and S...

引用

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

作者： Prakhalbh Santhosh, M. Surendran, Simi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Medical diagnostics with the segmentation and analysis of fetal brain ventricles assumes paramount importance in the prediction and management of neurological disorders. This paper focus on the segmentation and analysis of fetal brain ventricles using four deep-learning models: U-Net, VNet, ResNet, and DeepMedic, on the Feta 2022 dataset of T2weighted MRI scans. Saliency maps were used to visualize key areas in the decision-making processes for these models, which helped clinicians verify the AI output to ensure that it focused on the relevant anatomical features. The results showed very distinct strengths and trade-offs among the models, where ResNet and V-Net generally provided the best segmentation accuracy. This work not only enhances the fetal brain imaging analysis through the integration of and underlines their potential to improve diagnostic workflows for the early detection of fetal brain abnormalities. © 2024 IEEE.

关键词： Deep Learning Medical Image Analysis Ventricle Segmentation

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Comprehensive Study: Advancements in Parkinson's Disease Diagnosis with Data-Driven Insights and Machine Learning

Comprehensive Study: Advancements in Parkinson's Disease Dia...

引用

2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Subhashini, S.M. Sriram, Suthir Nivethitha, V. Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Chennai India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366846

The authors have a unique strategy that has never been used before, suggesting that a wearable device that records data is a combination of data from patients with Parkinson's disease and healthy individuals. Thus, it can accurately record motor features that are especially prevalent in Parkinson's disease, allowing for the actual identification of motor patterns peculiar and symptom intensity assessments. To further innovate in the field of self-assessment and individual evaluation of a patient's Parkinson's disease progression, the study points a significant relationship between clinical tests and quantitative symptom scores. Finally, a planned second opinion system using sensors and machine learning would help reduce the number of people misdiagnosed by comparing individuals with Parkinson's disease and those with similar phenotypic diseases. The differential diagnosis of tremor utilizing machine learning algorithms has been validated in various patient cohorts, significantly enhancing diagnostic specificity. In addition, the current study underscores the game-changing aspect of wearable technology and machine learning for medical care, eliminating resource constraints and allowing for the early identification and treatment of diseases. The experiment aims to improve remedy results and PD sufferers lives by recognizing barriers and encouraging ongoing research and innovation. It features the importance of patient-oriented monitoring methods, which are among the most valuable for PD patients with advanced disease. These techniques integrate quantitative sensor data analysis with handwritten comments and remote video monitoring. Finally, the investigation promotes tailored approaches to diverse patient demands, achieving better PD intervention and care capabilities. © 2024 IEEE.

关键词： Diagnosis

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Automatic Braking System and Driver Safety Using Arduino Uno Atmega 328p 15

Automatic Braking System and Driver Safety Using Arduino Uno...

引用

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

作者： Sunil, M. Sai, Thippaluru Tharun Sanjay, Thokali Dhanush, Uppalapati Vishwas, H.N. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This project addresses one of the primary causes of vehicle accidents: the sudden appearance of obstacles while driving. Drivers' varying response times often lead to accidents due to delayed braking or inability to stop in time. To mitigate this, an Autonomous Braking system is proposed, leveraging intelligent mechatronic technology. The system employs an Ultrasonic wave emitter at the front of the vehicle to emit Ultrasonic waves, coupled with a corresponding Ultrasonic receiver that detects reflected signals. By analyzing these signals, the system calculates the distance between the vehicle and any obstacles, enabling the microprocessor to adjust the vehicle's speed accordingly. In emergencies, the microcontroller activates the braking system to apply brakes swiftly and decisively, enhancing safety. A significant upgrade to the system is the integration of a Python-based application using Flask, which dynamically determines the incident's location and communicates with the nearest emergency services based on real-time geographical data. This is facilitated through GSM technology, which transmits alarm signals and the vehicle's GPS location to the appropriate authorities, ensuring prompt response and assistance. Additionally, the system's potential expansion includes inter-vehicle communication capabilities, allowing cars to share information about their locations and speeds, thus enhancing road cooperation and awareness. This not only leads to fewer collisions but also smoother traffic flow. The models used in this project, including the ultrasonic sensor and GPS modules, have demonstrated an accuracy rate of over 90% in obstacle detection and emergency alert transmission. Overall, the Autonomous Braking system aims to enhance road safety by reducing response times and stopping distances, thereby preventing accidents and significantly improving emergency response capabilities. © 2024 IEEE.

关键词： Autonomous Braking System Emergency Braking GPS Location Tracking Ultrasonic Technology GSM Technology Obstacle Detection

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