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检索条件"机构=School of Engineering Computing and Science"

共 33459 条记录，以下是161-170 订阅

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A Graph-Based Approach to Automatic Car Reliability System Prediction with Probabilistic Modeling and Machine Learning 3

A Graph-Based Approach to Automatic Car Reliability System P...

引用

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

作者： Krishna, Kaivaram Sivarama Kumar, Bommala Deepak Reddy, Morumpalli Deekshith Varun, Bheema Sai Murali, K. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350369083

Reliability prediction in automotive systems undoubted represents a substantial part of safety and customer satisfaction. a new graph-based probabilistic method and machine learning algorithm for the automotive system reliability prognosis. By breaking complex dependency among vehicles' components into a hierarchy and using past failure data, the method improves on prediction error. Real-world automotive datasets show excellent results with better resolution and higher accuracy for the enhanced components compared to existing techniques while at the same time providing interpretability by visualizing the dependency of the components. © 2024 IEEE.

关键词： Prediction models

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Comprehensive Approach to Motion Planning for Self-Driving Vehicles: Trajectory Roll Out, Route Planning, and Safe Maneuver Execution 3rd

Comprehensive Approach to Motion Planning for Self-Driving V...

引用

3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Adhikari, Vishal Singh Arora, Gautam Koli, Vrushabh Namdeo Dagur, Arvind School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9789819725076

In this paper, a comprehensive strategy for motion planning in self-driving vehicles is presented. The suggested approach, called trajectory roll out motion planning, allows for the computation of time to collision with both stationary and constantly-moving obstacles, facilitating the creation of routes in intricate road networks. The method includes the definition of high-level vehicle behaviors and transitions, which enable the autonomous vehicle to navigate intersections, pass parked vehicles, and merge with other vehicles. Additionally, kinematically feasible paths are calculated in the presence of static barriers. Velocity profiles are also generated to fulfill speed, curvature, and motion planning constraints of moving objects. The efficacy of the approach is demonstrated by testing and debugging in the CARLA simulator, with successful execution of planned maneuvers in the real world. The research findings offer valuable insights and techniques for motion planning in autonomous vehicles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Trajectories

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Hybrid Classification Framework Based on CNN and Random Forest Method to Predict Brain Tumor 5

Hybrid Classification Framework Based on CNN and Random Fore...

引用

5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Soni, Anmol Kumar Boobalan, A. Rajesh, E. Janakiraman, V. Kesarwani, Vartika Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

This paper proposes a combined framework of CNN+RFC to brain tumor categorization/classification using MRI (Magnetic-Resonance Imaging) images, which combines both CNN (Convolution Neural Networks) and RFC (Random Forest Classification). Preprocessing, Feature bring-out, and Categorization are the three phases of the proposed framework. In the first step, we use the Gaussian Filter Method on the data set then we combine the original data set with processed data in parallel. The feature extraction of magnetic resonance imaging was performed automatically by CNN in the second step. We also called such a type of process in this paper as non-hand-crafted feature extraction. Several classification algorithms, including RFC (Random Forest Classifier), KNN (K-Nearest Neighbor Classifier), DT (Decision Tree Classifier), SVM (Support Vector Machine Classifier), and NB (Naïve Bayes Classifier), are used in the final step. The extracted features from the CNN model are then given to the classifier algorithms, which predict Glioma tumor, Pituitary tumor, Meningioma tumor, and no tumor as a result of the testing data set. Experiments are carried out on an open data set of images selected for classification from the Kaggle databases. This data set is very complex since it contains images of brain tumor with different angles and different depths. We don't alter this data set at all. We make a separate CSV file that contains testing images' name and their specification. Using this proposed approach, we were able to achieve 99.61% accuracy on the training data set, 92.16% on the validation data, and 71.2% on the CSV/testing data. © 2024 IEEE.

关键词： brain tumor classification CNN (Convolution Neural Network) hybrid model CNN-RFC MRI (Magnetic-Resonance Imaging) RFC (Random Forest Classifier)

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Object Detection in Shelf Image of the Retail store using Single Stage Detector 2

Object Detection in Shelf Image of the Retail store using Si...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Bagyammal, T. Mahesh Madhav, K. Dhiraj Kumar, Y. Arthi Sri, N. Siva Srinivas, V. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354461

The primary objective is to develop a robust system for precise object identification on retail shelves, accurate item counting, and product categorization through class detection. This multifaceted approach directly tackles critical issues in retail industry, encompassing inventory management and optimized product placement. The ultimate goal is to boost operational efficiency and elevate customer satisfaction within the retail sector. The proposed methodology involves comprehensive steps, including data collection, annotation, pre-processing, and the deployment of advanced object detection models based on YOLOv5, with a particular emphasis on YOLO due to its real-time capabilities and being known as a single stage detector. By achieving these milestones, the proposed model promises to provide valuable solutions for the retail industry, reducing out-of-stock situations, minimizing manual labour, and delivering enhanced product organization. Thus, ensuring good performance compared to its other object detectors of the same use case. We have modified the architecture at different levels of the State-of-the-art YOLOv5 (Backbone, Head) and compared the models implemented for inference. We found out that Soft-IoU layer added in the head has given the best performance in detecting object with mean average precision of 79.7 © 2024 IEEE.

关键词： Customer satisfaction

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Enhancing Code Insights through Semantic Change Impact Evaluation 15

Enhancing Code Insights through Semantic Change Impact Evalu...

引用

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

作者： Vyshnavi, Chellu Raj, Chukka Mishal Gopakumar, Gokul Prasanna, Guddeti Bindu Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In software development, system integrity is a measure of the impact code changes have on them. It is determined by the team's comprehension. However, rapid evolution of change commits and interaction in complex code bases create problems. By evaluating the effect of reflection changes, this study contributes to the improvement of the comprehension of code, a critical success factor for quality and efficiency. This addresses issues by using semantic analysis, change detection, refactoring detection, and semantic change detection and diffing. Our approach documents the semantic differences between the original and changed source code and gives pragmatic conclusions on how to enhance code quality and maintainability. Specifically, we divide the code into AST files using simple python scripts for semantic analysis and compare them for differences to find implications. It executes the method through the focus on development workflows and major projects as it includes the past information while still simplifying argument parsing and file processing. Based on our study findings, a better understanding of code can help developers to improve their decision-making capacity and protect the performance during the enhancement activity of the program. © 2024 IEEE.

关键词： Abstract Syntax Trees (ASTs) change detection refactoring detection semantic analysis semantic changes

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MAESTRO-EP: Multi-Architecture Ensemble System for Temporal Reasoning and Outcome Prediction in Event Processing 8

MAESTRO-EP: Multi-Architecture Ensemble System for Temporal ...

引用

8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Shekar, Vaibhav Devaraja, G. Mahadevan, Anbazhagan Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331531539

MAESTRO-EP (Multi-Architecture Ensemble System for Temporal Reasoning and Outcome Prediction in Event Processing) is an innovative deep learning framework designed to model and predict outcomes in complex event-driven systems. This paper introduces a novel multi-head ensemble architecture that leverages diverse neural network models, including RNNs, LSTMs, CNNs, Transformers, and GRUs, to capture intricate temporal patterns in event sequences. Our approach utilizes a minimal event schema, allowing the model to organically learn complex behaviors across various domains, from supply chain networks to game environments. We evaluate MAESTRO-EP on multiple datasets, including supply chain simulations, Tic-Tac-Toe, and pathfinding algorithms, demonstrating its superior performance in context output accuracy. The system's highly scalable and adaptable architecture provides a robust framework for future research in adaptive AI systems and digital twin simulations, with the potential to significantly advance event sequence modeling capabilities in dynamic multi-agent environments, particularly in complex supply chain networks. © 2024 IEEE.

关键词： Multi agent systems

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Revolutionizing Brain Tumor Analysis: Unleashing the Potential of Residual UNet for Enhanced Segmentation Accuracy 4

Revolutionizing Brain Tumor Analysis: Unleashing the Potenti...

引用

4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Tiwari, Pradeep Kumar Johri, Prashant Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350360165

Advances in diagnosis have been made, leading to significant changes in the approach to mental health diagnosis. This update delivers the powerful segmentation algorithms needed to provide accurate diagnosis and treatment planning. Within the scope of this research, a new technology that uses the Residual UNet deep learning architecture is proposed to increase segmentation accuracy. Our method solves the problem of incompleteness while ensuring that the model captures the correct data. This is done by taking advantage of the fixed connection available in the UNet design. The results show that this method is more efficient than other methods. This is done through long-term experiments on large datasets, including multimodal neuroimaging. An unlikely oncology surrogate is Residual UNet, which has proven to be accurate and useful in most cases. Specifically, our strategy achieved 0.9897 accuracy, 0.9726 accuracy, 0.9985 accuracy, and 0.8665 accuracy. BraST 2020 profile is used for this purpose. Additionally, our study proves that Residual UNet exceeds standards for brain tumors. This shows that it can provide superior segmentation for existing and specific technologies. © 2024 IEEE.

关键词： Brain Tumor Medical Imaging Residual UNet Segmentation

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A Comprehensive Survey: Exploring Current Trends and Challenges in Intrusion Detection and Prevention Systems in the Cloud computing Paradigm 2

A Comprehensive Survey: Exploring Current Trends and Challen...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Prabu, K. Sudhakar, P. School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350327533

The rapid evolution of internet technologies has led to a significant proliferation of connected devices, expanding the potential attack surface. This necessitates the implementation of effective countermeasures to safeguard network infrastructures against the detrimental impacts of cyber-Attacks. Hence, it is crucial to differentiate personal information and cloud computing worldwide, and to implement specific policies and regulations for information security. The main aim of the security policy and framework in cloud computing is safeguarding users and data, optimizing task operations, ensuring compliance, and setting user behavior standards. Additionally, IDPS-Intrusion Detection and Prevention Systems have gained widespread adoption as solutions for monitoring and analyzing network traffic, identifying anomalies, thereby detecting and preventing the ongoing adversarial activities, which play a crucial role in triggering alerts and autonomously blocking traffic from malicious sources. This study provides a comprehensive outline for a security policy in cloud computing with a focus on critical security tools, policies, and services, placing a particular emphasis on cloud environments to meet the organizational adoption requirements. By establishing vital connections between prerequisites, legal considerations, analytical techniques, and IDPS to mitigate threats, this research study provides strategic recommendations for cloud computing security policies. © 2024 IEEE.

关键词： Cloud computing Cloud Security Information Privacy Intrusion Detection System (IDS) Intrusion Prevention System (IPS) Network Security Security Policies

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Empowering data structure visualization through modification in Python 15

Empowering data structure visualization through modification...

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

作者： Puvvadi, Harsha Vardhan Angala, Hari Prasad Swaroop, Bandi Vishnu Reddy, Busa Thanuj Sathwik Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This article introduces a novel approach to data structure visualization through the development of a new programming language, utilizing Python's Lex-YACC library for lexical analysis and parsing, and the Turtle module for graphics in the backend. The proposed language enables users to visualize various data structures such as arrays, stacks, and queues, along with their operations, facilitating a deeper understanding of these fundamental concepts in computer science. By combining the power of a custom scripting language with Python's versatile libraries, this research aims to empower users to interactively explore and comprehend complex data structures through intuitive visualizations. © 2024 IEEE.

关键词： Python

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Cyber Chronicles: Tracking Behavior Patterns for Detecting Threats in Large Networks 3

Cyber Chronicles: Tracking Behavior Patterns for Detecting T...

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

作者： Ramasubbu, Senthilnathan Thangavel, Senthil Kumar Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372120

One of the primary challenges in cybersecurity is that even one un-detected, appropriately unanalyzed malicious security event can hide the attack vectors of a potential hacker. It is essential to detect the data breach at the earliest stage to reduce the impact on the business. The malicious actor's activities will have higher visibility during the initial attacks to compromises or exploitation at the beginning of the attack cycle. After the initial compromise, the attacker studies the environment and establishes the persistence and covert channel communication. At this successive phase of the attack cycle, the attacker camouflages with the production data flow, access, and noises in the network and application logs. It is essential to detect the attacks during the reconnaissance and initial attacks and should not miss any malicious activities. Most of our security tools focus on reducing false positives;however, reducing false negatives is critical to cyber security. Hence, this paper is focused on suitable deep learning to reduce the False Negatives (FNs) with minimal Type II error to detect the attacks well in advance. © 2024 IEEE.

关键词： Cybersecurity

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