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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Priyadharshini, B. Apoorva, N. Alekhya, Arvapalli Sagar, Gajula Sujan Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

Recommendation systems are the subset of data filtering techniques and focus on providing personalized suggestions to the users. The systems rely on the data to provide insightful suggestions. Over the years, recommendation system has gained huge prominence in boosting e-commerce platforms, and online personalized services to users. Along with their popularity, these systems also face challenges such as data sparsity, time constraints, and scalability. As a result, various approaches are opted along with the traditional techniques to optimize the process. This paper reviews the various approaches followed in engineering the recommendation systems and analyzes their efficiencies. The approaches focus primarily on understanding the involvement of neural networks and evolutionary algorithms and their performance compared to other systems involved with approaches like clustering techniques, linear-based techniques, probabilistic models, etc. This paper also provides insights into the various hybrid approaches followed by the different recommender systems involving neural networks and evolutionary algorithms. © 2024 IEEE.

关键词： Recommender systems

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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...

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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...

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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...

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

Revolutionizing Brain Tumor Analysis: Unleashing the Potenti...

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

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

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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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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...

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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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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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