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检索条件"机构=Dept. of Engineering and Computing"

共 3723 条记录，以下是41-50 订阅

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IoT-Driven Smart Car Parking System: Optimizing Urban Mobility and Reducing Environmental Impact 3

IoT-Driven Smart Car Parking System: Optimizing Urban Mobili...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tanvi, Y. Lakshmi Sowmya, B. Prashanth, B.N. Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid increase in urban vehicle numbers has significantly worsened traffic congestion, particularly in public parking spaces, where conventional parking systems often prove inefficient, leading to wasted time, excessive fuel consumption, and increased environmental pollution. A Smart Car Parking System is proposed to address these challenges by integrating advanced technologies, including the Internet of Things (IoT), to optimize parking operations. The system employs sensors embedded in parking spaces to detect vehicle presence and provide real-time data on availability. This enables drivers to locate free parking spaces efficiently, improving traffic flow. Additionally, the system offers valuable insights to city planners and facility managers, supporting optimal space allocation and dynamic pricing models. By reducing vehicle idling and unnecessary driving while searching for parking, the system helps minimize emissions, contributing to environmental sustainability. The study builds an automated smart car parking system for parking slot monitoring and gate control. The system ensures correct slot occupancy reading, status displayed in real time, and smooth operation of an opening and closing system. It manages additional features such as real-time sensor calibration and effective gate control with a high degree of accuracy and accountability. The proposed Smart Car Parking System represents a key innovation in urban mobility, offering a user-friendly, efficient, and ecofriendly solution to one of the most persistent challenges faced by modern cities. © 2025 IEEE.

关键词： Traffic congestion

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Enhancing Data Security through the Synergy of AI and AES Encryption: A Comprehensive Study and Implementation

Enhancing Data Security through the Synergy of AI and AES En...

引用

2024 MIT Art, Design and Technology School of computing International Conference, MITADTSoCiCon 2024

作者： Budhewar, Anupama Bhumgara, Sayonee Tekavade, Abhishek Nandkar, Jayditya Zanwar, Atharva MIT ADT University MIT School of Computing Dept. of Computer Science Engineering Pune India

ISBN: (纸本)9798350362879

In today's digital era, safeguarding data is crucial. This paper examines the combination of Artificial Intelligence (AI) and Advanced Encryption Standard (AES) for enhanced data security. We integrate the k-Nearest Neighbors (k-NN) algorithm with AES encryption to fortify data protection across various domains. This study highlights the strengths and limitations of both AI and AES, and demonstrates how k-NN's pattern recognition capabilities enhance AES's robustness against evolving threats. The practical implementation for secure file transmissions and confidential data storage is also discussed. © 2024 IEEE.

关键词： Cryptography

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Real Estate Price Prediction Using PySpark MLlib 1

Real Estate Price Prediction Using PySpark MLlib

引用

1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Dharmaraj, Kadam Prajwal Gupta, Pradeep Kumar Ajith, Keerthana Kolli, Mahi Khare, Sangita Panda, Niharika Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350377002

Prices of real estates in metropolitan landscapes harbour immense importance in steering the complexities of dynamic city environments. In India, the real estate sector contributes around 6-7% to India's GDP. Therefore, accurate forecasting of real estate prices is a factor in making informed decisions, affecting various stakeholders. Many techniques were used for this in past several years, like Hedonic models, Repeat-Sales models, Rule-Based Systems and Heuristics, etc. This study leverages the power of machine learning and big data to develop a robust framework for predicting housing prices in metropolitan landscapes. We employ PySpark, a powerful big data processing framework with built-in MLlib library (machine learning library), to analyse large-scale housing data encompassing various cities in India & to predict the prices for the same. By implementing a comparative analysis of prominent regression models - Random Forest, Linear Regression, Decision Tree, and Gradient-Boosted Tree - our approach identifies the most effective algorithms for real estate price prediction using MLlib library. Also, this study highlights the need for scalable solutions to manage an increasing number of data sources and emphasizes the PySpark library which will simplify big data handling and enable parallel computing. This study paves the way for utilizing advanced machine learning techniques and big data platforms to gain valuable insights into real estate markets. Our findings emphasize the critical role of combining these powerful tools to navigate the complex dynamics of urban housing and predict prices with greater accuracy. © 2024 IEEE.

关键词： Decision trees

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IoT-Based Health Monitoring System for Forbidden Patients 10

IoT-Based Health Monitoring System for Forbidden Patients

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Thangam, S. Niranjan, D.K. Shyam, Katragadda Megha Reddy, Malireddy Charan Kumar Sai, Kandlapalli Aravind Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350384369

The device is wearable and user-friendly;the patient's health monitoring system may be built using cutting-edge technology like wearable sensors and the IoT. patient's blood pressure, heart rate, temperature, pulse, ECG. The other physiological data may be continually monitored and recorded by the system. The healthcare system may become independent and less dependent on physical intervention by using sensors to analyze and diagnose patient activities in real time. By making the state may be continually checked. Furthermore, the system might be designed to be wireless and cloud-based, connecting several therapeutic devices for patient care and health tracking. To protect patient data, however, security and privacy concerns with implanted medical devices must be addressed. © 2024 IEEE.

关键词： Blood pressure

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Cyberbullying Detection on Multiclass Data Using Machine Learning and A Hybrid CNN-BiLSTM Architecture

Cyberbullying Detection on Multiclass Data Using Machine Lea...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Balaji, Peddi Gowtham Katariya, Priyanka Prakash Sruthi, S. Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Cyberbullying has emerged as a significantly concerning online societal issue in the past few years with the proliferation of social media platforms. To address and overcome this pressing communal concern and successfully detect cyberbullying across various categories, this study proposes a novel approach that integrates Convolutional Neural Networks and Bidirectional Long Short-Term Memory Networks, alongside six traditional machine learning techniques, driven by the purpose of enhancing the accuracy and robustness of cyberbullying detection across diverse cultural and textual landscapes. To highlight the multiclass aspect, the model is trained and tested on a dataset containing instances categorized into Age, Religion, Ethnicity, Gender, Other Cyberbullying, and Not Cyberbullying. Through rigorous experimentation, the proposed deep learning model's proficiency results in a 0.83 accuracy and F measure, showcasing its efficacy in capturing the nuances of various categories. Meanwhile, among the six machine learning models employed, Random Forest emerges as the top performer with values 0.94 and 0.93 for the accuracy and F measure respectively. This comprehensive methodology underscores its ability to effectively discern between the different classes and offers a promising solution to combat cyberbullying across diverse demographics and thematic dimensions. © 2024 IEEE.

关键词： Adversarial machine learning

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License Plate Detection using YOLO v8 and Performance Evaluation of EasyOCR, PaddleOCR and Tesseract 15

License Plate Detection using YOLO v8 and Performance Evalua...

引用

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

作者： Reddy, P Praneeth Shruthi, P Sai Himanshu, P. Singh, Tripty Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

License plate recognition is critical to the functioning of the control and surveillance systems. Vehicle identification is based on number plates, which are made up of a certain arrangement of letters and digits. However, manually identifying every number plate is a laborious and difficult task. The majority of earlier automatic number plate recognition (ANPR) systems were restricted in how they could operate. These conditions included being limited to fixed illumination, background noise in the vehicle image. The primary goal of this project is to develop a reliable number plate recognition model that functions with the help of YOLO v8 for license plate segmentation followed with a comparison model of EasyOCR, PaddleOCR, Tesseract models based on best model for character recognition of license plate. The steps involved include processing of vehicle image, segmentation of license plate from the image, followed by internal segmentation of characters on the plate and pattern matching. Data pre-processing is finally performed in order to read and process the license plate. Problems like background noise in image like characters apart from the license plate and number-based characters being wrongly predicted as alphabets have been tackled. © 2024 IEEE.

关键词： Image segmentation

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A Hybrid Prediction Model for Disseminating the Factors Related to Anomaly Detection in Software Defined Networks 15

A Hybrid Prediction Model for Disseminating the Factors Rela...

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

作者： Darshan, S. Radhika, N. Radhika, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

The domain of network traffic and anomaly reporting has its highlights in recent days towards real-time applications. The utilization of network data with its correlating attributes and its measures must be considered before deriving a pattern or decision making process. SDN(Software Define Networks) in turn has been facilitating more towards the efficient and reliable mechanism for data control and network management system. This research focus on the development of a hybrid prediction model for disseminating the factors that contribute towards anomaly detection in SDN. The experimentation can be made by hybridizing different classification schemes with tuned parametric components upon statistical modelling and evaluation. The proposed hybrid model is suggested up with the hybridization of SVM, NN along with Ada Boost algorithm with tuned parameter metrics. From the proposed model it is evident that the selected features will play a significant key role in anomaly prediction in a given network. © 2024 IEEE.

关键词： Prediction models

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Comparative Analysis of Bellman-Ford and Dijkstra Algorithms in Software Defined Networking 5

Comparative Analysis of Bellman-Ford and Dijkstra Algorithms...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Tammanashastri, Prabhav Ravi Rajagopal, Shinu M. Bananjee, Sahen Shivadutt Amrita Vishwa Vidyapeetam Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350389609

One of the key functions of the internet is packet routing of data. A routing protocol outlines the channel of communication between routers that is utilized to connect networks. Numerous routing protocols, including OSPF and RIP are used on the internet. Each protocol has a unique packet routing function. Bellman-Ford and Dijkstra are SDN routing algorithms;the former accommodates negative weights, suiting more complex network topologies, while the latter works efficiently for non-negative weights, ensuring faster convergence. RIP's reliance on the Bellman-Ford algorithm leads to slower convergence and potential routing cycles in bigger networks. OSPF, while stronger with Dijkstra's algorithm, can introduce high communication overhead due to maintaining complete network topology data. Bellman-Ford employs a dynamic programming approach, handling diverse network conditions, whereas Dijkstra's greedy strategy excels in finding the shortest path in positively weighted networks. respectively. © 2024 IEEE.

关键词： Network topology

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Seamless Attendance Logging Application: A Technological Revolution in Logbook Capture 9

Seamless Attendance Logging Application: A Technological Rev...

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

作者： Raj, Bhupathi Shwejan Sujith, Bolisetty Krishna, Janagama Vamshi Anjali Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India

ISBN: (纸本)9798350394474

In this age of swift technological progress, Most educational establishments seek novel approaches that may further boost productivity. One such challenge has been the manual attendance tracking system in colleges. This research paper presents the development and implementation of an automated college attendance system powered by deep learning-based text extraction models. The primary objective of this system is to remove the labor-intensive process of taking attendance, improve accuracy, and provide valuable insights into student attendance patterns. The research begins by utilizing state-of-the-art deep learning techniques to extract roll numbers from images of attendance sheets. A Convolutional neural network is utilized for situating and identifying text within images. The system then processes this data and marks students as present or absent based on predefined criteria. The findings demonstrate that the automated attendance system, with its high degree of precision, makes a major dent in the time and energy formerly needed for keeping attendance logs, streamlining the whole process significantly. Furthermore, by enabling the ongoing monitoring and analysis of data in real time, educators and administrators are empowered to make evidence-informed decisions about student attendance through insights continuously gained from observant assessment and evaluation. © 2024 IEEE.

关键词： Students

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Shellcode Generation: A Resource Efficient Approach using Fine-tuned LLMs 15

Shellcode Generation: A Resource Efficient Approach using Fi...

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

作者： Ram, Madhav Nair, Priyanka C. Krishna, Mohith V. Gupta, Deepa Pranavkrishnan, M. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350370249

In order to create better shellcode for offensive cybersecurity, this study investigates the use of large language models (LLMs) such as Mistral and Llama. It focuses on LLM optimizations to improve shellcode accuracy and efficiency with the goal of quickly locating and taking advantage of software vulnerabilities. To optimize shellcode generation, several model parameters are tuned through controlled experiments, with the BLEU score serving as the primary criterion for impartial evaluation. This study achieved a BLEU-1 score of 0.8506, highlighting the effectiveness of the optimization and finetuning strategies. The study explores the subtle aspects of LLMs that influence the creation of shellcode, such as their capacity for learning, their ability to adjust to the peculiarities of cybersecurity, and their ability to replicate complex patterns into effective shellcode. This project intends to expand knowledge of using LLMs for shellcode development by submitting significant experimentation and research, providing insights into more efficient vulnerability exploitation strategies in cybersecurity. ©2024 IEEE.

关键词： cybersecurity large language models Llama LLM finetuning LoRA Mistral Shellcode generation

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