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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Lakshmi, M. Vidhya SivaPriya, M.S. Manivannan, Kirankumar Vivekanandan, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350379945

computer vision and the Internet of Things have led to significant advancements in student attendance monitoring. In this work, an innovative system has been designed to facilitate real-time, automated student attendance tracking in educational environments. Utilizing face recognition algorithms and IOT. The aim of the system is to optimize attendance management in educational institutions. IoT-connected, high-resolution cameras are strategically positioned across classrooms to capture visual data for analysis. By utilizing advanced computer vision algorithms, the system identifies and tracks students in real time, facilitating precise and efficient attendance monitoring. Student Faces are detected using a Haar cascade classifier and recognized using the LBPH (Local Binary Pattern Histogram) Algorithm. Then, Histogram data is checked against an established dataset. If a Match is found, the device automatically labels attendance. The Integration of IoT helps in seamless data transmission, storage and ensures accessibility and analysis of attendance records by administrators and educators. The primary objective is to offer an intelligent, automated solution for attendance management, that will significantly reduce manual workload and enhances accuracy. This system addresses the limitations of conventional attendance-taking methods, which are often laborintensive, error-prone, and time-consuming. © 2024 IEEE.

关键词： Local binary pattern

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Enhancing Agile Development in Tech Companies: Backlog Management, Tool Integration, and Stakeholder Collaboration 6

Enhancing Agile Development in Tech Companies: Backlog Manag...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Suganya, R. Banerjee, Dhiimayyi Mishra, Aarohi Subbulakshmi, T. Subramanian, Girish Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai India Penn State University Information Systems State College United States

ISBN: (纸本)9798350394702

This paper investigates the Agile development processes in tech companies, focusing on strategies for effective backlog management, tool utilization for progress tracking, Agile methodology integration, stakeholder involvement, and handling challenges in the tech domain. The study reveals the adaptability of Agile methodologies to diverse organizational needs and objectives, emphasizing their significance in the technical industry. Companies prioritize backlog tasks through subtask breakdown, Story Points, and priority tags, aligning with Agile principles. The integration of Agile involves Jira and Scrum, enhancing development efficiency. Stakeholders and team members engage through planning and design sessions, ensuring collaboration. Challenges are addressed through regular meetings, technical discussions, and transparency. Knowledge transfer sessions keep teams updated. Data security is maintained through NDA agreements and robust security measures. Metrics like story points and KPIs are tracked for Agile success evaluation. The paper concludes by highlighting the importance of aligning process models with organizational requirements. © 2023 IEEE.

关键词： Knowledge management

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An In-Depth Review of Machine Learning, Blockchain, and Deep Learning Models for Intelligent Security and Resilience Enhancement in Smart Cities

An In-Depth Review of Machine Learning, Blockchain, and Deep...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Lanjewar, Umesh Khekare, Ganesh Wahi, Amitabh Bhagwant University Department of Computer Science & Engineering Ajmer India School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331517953

A smart city is a fast-moving terrain that requires efficient and smart security mechanisms with resilience for solving the intricate challenges of modern urbanism. The current paper presents the critical review of machine learning, blockchain, and deep learning models in strengthening security and making the urban environment at smart cities more resilient. Not all the review articles heretofore successfully integrated these three pivotal domains, which lowered their practical applicability and insight depth. This paper reviews recent state-of-the-art models, including supervised and unsupervised machine learning algorithms, blockchain frameworks, and deep advanced learning architectures. The important machine learning models reviewed include Random Forest, Support Vector Machines, and K-means clustering. These were chosen for their already proved effectiveness in anomaly detection, predictive analytics, and classification tasks. On the other hand, blockchain models of Ethereum and Hyperledger Fabric would be evaluated for decentralized security features, immutability, and capability to improve data integrity and transparency. Deep learning models can handle large-scaled, unstructured data and extract complex patterns. Their integration, therefore, shows great promise in enhancing the security and resilience of smart cities. This paper contributes to a holistic, multidisciplinary viewpoint, filling literature gaps by providing a basic framework for further research and implementation of smart city initiatives. Such insights synthesized across these domains set a course for innovation in the formulation of solutions that fortify urban infrastructures against emerging threats and enhance overall urban resilience. (Abstract) © 2024 IEEE.

关键词： Adversarial machine learning

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Transfer Learning-Based Method for Classifying Yoga Poses using Deep Convolutional Neural Networks

Transfer Learning-Based Method for Classifying Yoga Poses us...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Vallabhaneni, Nagalakshmi Panneer, Prabhavathy Sundaram, Venkatesan Meenakshi School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Computer Science and Engineering National Institute of Technology Puducherry India

ISBN: (纸本)9798350325188

Yoga is a popular practice that aims to improve one's health and well-being through physical postures, breathing exercises, and meditation. The growing popularity of yoga has prompted researchers to focus on automating the process of classifying yoga poses. Using deep convolutional neural networks (CNNs) and transfer learning, we present a deep learning-based method for correctly categorizing yoga postures in this research. The primary goal of this study is to develop an efficient method for automatically recognizing and categorizing yoga poses in given photographs. The impressive success of deep CNNs in picture classification tasks is exploited here to accomplish this goal. We also use transfer learning, a method for customizing pre-trained CNN models for our unique yoga posture categorization task. This paper presents a Transfer Learning based Method for Yoga Poses classification using Deep Convolutional Neural Networks (TLMYPC-DCNN). The proposed model uses the Yoga Posture Dataset for classification of images. Initially, this model starts with pre-processing the input images by splitting input data into three groups such as training set, validation set and test data. The deep learning CNN model will be trained using the training data, and then its parameters will be tweaked using the validation data. Finally, the test data will be used to assess how well the data performs. A convolutional neural network (CNN) model called MobileNetV2 will be applied to the model photos. The experimental results show that the suggested TLMYPC-DCNN based technique with transfer learning is effective at accurately and reliably classifying yoga poses. © 2023 IEEE.

关键词： Deep Convolutional Neural Networks MobileNetV2 Transfer Learning Yoga Pose classification

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Context-Aware Summarization for PDF Documents using Large Language Models 4

Context-Aware Summarization for PDF Documents using Large La...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Ramprasad, Akshara Sivakumar, P. School of Computer Science and Engineering Vellore Institute of Technology B. Tech Computer Science with specialization in AI & ML Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385793

In an era characterized by the overflow of textual information, the demand for effective text summarization techniques has become increasingly evident. This research study presents a novel solution to address this demand: the development of a PDF Summarizer Generative AI Platform for context-aware text and document summarization. The developed platform empowers users to generate informative summaries from lengthy textual documents. It features an interface powered by Gradio for enabling user-friendly accessibility. Equipped with essential components such as OpenAI's language models for text comprehension, Chroma vector enables document embedding and conversational retrieval chains for contextual understanding, where the platform offers a comprehensive solution. Users can effortlessly upload documents in various formats, including PDFs, and interactively engage with the system to obtain concise summaries tailored to their specific requirements. © 2024 IEEE.

关键词： Chroma Database (ChromaDB) Gradio LangChain Large Language Models (LLM) OpenAI

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Applications of Machine Learning in Cloud Forensics

Applications of Machine Learning in Cloud Forensics

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2023 International Conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Theertani, Malluri Reddy Valeti, Neha Solanki, Darshini Kumar, Kakelli Anil Vellore Institute of Technology Vellore Computer Science and Engineering Vellore India Vellore Institute of Technology Vellore Sr. School of Computer Science Engineering Vellore India

ISBN: (纸本)9798350333602

Cloud computing has recently experienced rapid expansion, making it a major target for cybercrimes. Though cloud computing has many advantages, there are also substantial security issues related to confidentiality, integrity, privacy, and availability. Being a relatively new discipline, cloud forensics faces numerous challenges and issues with interpreting and analyzing data. Forensic investigators and law enforcement confront numerous difficulties in data collection, data protection, and evidence access. Several sophisticated models have been proposed in recent years aiming to accelerate the entire investigation process or address several issues that arise frequently in forensic investigations. This review paper seeks to comprehend the significance of various machine learning models which would help cloud forensics advance significantly. It also provides a detail information on cloud forensics as well as the research trends that have been going on in this field over the past few years. A detailed comparative analysis of different approaches of machine learning on development of cloud forensics has been presented in this research study. © 2023 IEEE.

关键词： Data acquisition

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Optimization of Traffic System using Adaptive Response First Come First Serve

Optimization of Traffic System using Adaptive Response First...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Amutha, S. Singh, Dhruv Singh, Shashank Shekhar Jhandial, Anant Gupta Krishna, Kunal School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics and Communication Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

In this period of urbanization and adding vehicular traffic, the optimization of business operation systems is consummate to insure both the effectiveness of transportation networks and the safety of commuters. This exploration paper explores the operation of deep literacy ways to revise the field of business operation. By employing the power of deep learning, sensors and timers, our study aims to develop innovative results that ameliorate the delicacy, rigidity, and real- time responsiveness of business control systems. The primary focus of this exploration is to design a comprehensive deep literacy-grounded business operation system that incorporates colorful data sources. By assaying vast quantities of miscellaneous data, the system will acclimatize to dynamic business conditions and offer real- time perceptivity for effective business inflow operation, accident discovery, and traffic relief. The exploration will also explore the integration of machine literacy algorithms for intelligent business signal control, prioritizing green light allocation grounded on real- time demand and business viscosity. The proposed deep literacy result aims to significantly reduce business traffic, minimize trip times, and enhance overall road safety by furnishing optimized business operation strategies. The exploration will concentrate on the practical perpetration of the system in real- world business scripts, with the eventuality to gauge and acclimatize to colorful civic surroundings. This exploration bid seeks to lay the foundation for a future in which business systems seamlessly acclimatize to evolving business patterns, creating safer, more effective transportation networks, and contributing to a sustainable and intelligent civic ecosystem. The proposed dynamic signal control logic is 83.33 % more efficient in utilizing green time compared to the traditional fixed-time system. Estimated reduction in wasted green time is 25%. © 2024 IEEE.

关键词： Traffic signals

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A novel and secured email classification and emotion detection using hybrid deep neural network

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 44-57页

作者： Krishnamoorthy, Parthiban Sathiyanarayanan, Mithileysh Proença, Hugo Pedro School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India MIT Square London United Kingdom University of Beira Interior Covilha Portugal

Compared to other social media data, email data differs from it in various topic-specific ways, including extensive replies, formal language, significant length disparities, high levels of anomalies, and indirect linkages. In this paper, the creation of a potent and computationally effective classifier to categorize spam and ham email documents is proposed. To assess and validate spam texts, this paper employs a variety of data mining-based classification approaches. On the benchmark Enron dataset, which is open to the public, tests were run. The final 7 Enron datasets were created by combining the six different types of Enron datasets that we had acquired. We preprocess the dataset at an early stage to exclude any useless phrases. This method falls under several categories, including Logistic Regression (LR), Convolutional Neural Networks (CNN), Random Forests (RF), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and suggested Deep Neural Networks (DNN). Using Bidirectional Long Short-Term Memory (BiLSTM), email documents may be screened for spam and labeled as such. In performance comparisons, DNN-BiLSTM outperforms other classifiers in terms of accuracy on all seven Enron datasets. In comparison to other machine learning classifiers, the findings demonstrate that DNN-BiLSTM and Convolutional Neural Networks can categorize spam with 96.39 % and 98.69 % accuracy, respectively. The report also covers the dangers of managing cloud data and the security problems that might occur. To safeguard data in the cloud while maintaining privacy, hybrid encryption is examined in this white paper. In the AES-Rabit hybrid encryption system, the symmetric session key exchange-based Rabit technique is combined with the benefits of the AES algorithm for faster data encryption. © 2024

关键词： Long short-term memory

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Lung-Retinanet Lung Cancer Detection Using A Retinanet With Multi-Scale Feature Fusion And Context Module 15

Lung-Retinanet Lung Cancer Detection Using A Retinanet With ...

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

作者： Gedela, Dattatreya Krishna, S. Venakata Satya Kumar, Dinesh R. Keerthika, P. CSE Department Siddhartha Institute of Technology & Sciences Hyderabad India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798350370249

Lung cancer identification is troublesome, on the grounds that blood tests and CT filters take time and require human mediation. Lung-RetinaNet, a lung tumour detection arrangement, resolves these issues. The RetinaNet model is increased with multi-scale feature fusion and a setting module. The Lung-RetinaNet model purposes a multi-scale highlight combination module to join neural network layers. This strategy further develops the model's semantic data assortment, which is fundamental for lung tumor identification. Notwithstanding multi-scale feature fusion, Lung-RetinaNet utilizes an enlarged and lightweight setting module approach. This module utilizes context oriented data with each neural network layer to further develop feature extraction and lung imaging little cancer area. The task's lung cancer detection technique is more proficient and precise utilizing these parts. Adding the Xception model boosts lung cancer classification accuracy to almost 100%. Likewise, YOLOv5 and YOLOv8 further develop lung cancer detection in photos. This complex strategy ensures a total lung cancer analysis, coordinating accurate classification and exact object recognition. ©2024 IEEE.

关键词： Lung cancer

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A Deep Learning based Model for Predicting the future prices of Bitcoin 2

A Deep Learning based Model for Predicting the future prices...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Singathala, Harshith Malla, Jyotsna Jayashree, J. Vijayashree, J. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Vellore Institute of Technology Department of Computer Science Vellore India

ISBN: (纸本)9798350347982

Bitcoin was introduced in 2009 and is the earliest cryp- tocurrency in the world. It has gained immense popularity and has attracted a huge consumer base owing to its ever-increasing market capitalization. This has led to many traders and investors being interested in knowing the future prices of these cryptocurrencies to gain profits. Researchers have contributed several works in the field of predicting the future cryptocurrency but with very low accuracy. The aim of this paper is to propose a bitcoin price prediction model which will help predict the future prices of bitcoin. Different deep-learning models are involved in the proposed prediction model namely Gated Recurrent Unit(GRU), Long Short-Term Memory(LSTM), Bidirectional GRU (BiGRU) and Bidirectional LSTM (BiLSTM). The performance analysis of the different models shows that BiGRU is able to predict the future bitcoin prices with the lowest Mean Absolute Error Percentage(MAPE) score of 3.41 © 2023 IEEE.

关键词： Forecasting

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