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

作者： Sudalaimuthu, T. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

Optimization plays a vital role in cloud computing. The process of optimization can be effectively implemented using replica replacement and scheduling policy. The major challenge in virtualization is equal sharing of resources and jobs across cloud environments. The challenges have been addressed by scheduling policies. There exist various scheduling policies under various strategies. The proposed approach implements the scheduling policy known as Weight Based Scheduling (WiBaS). The WiBaS invokes the weight value for various parameters. The performance of the WiBaS was compared with the legacy scheduling methods in terms of waiting time, response time, turnaround time, Computing Element etc. The WiBas yields a throughput of 1900 microsecond and 23% less computational element compared to traditional scheduling policy. © 2024 IEEE.

关键词： Computational Element Throughput Turnaround Virtualization Weight Based Scheduling

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Efficient Application Deployment: GitOps for Faster and Secure CI/CD Cycles

Efficient Application Deployment: GitOps for Faster and Secu...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Saikiran Reddy, J. Durga Prashanth Padal, S. Albert Mayan, J. Catharine, A. Shanthamalar, J. Jeslin Sathyabama Institute Of Science And Technology Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350371567

In the rapidly evolving landscape of technology, the need for swift and efficient deployment of software and applications has never been more critical. Due to the rapid and constant evolution of innovation, there is a consistent demand for quicker, seamless deployment of software and apps. Currently, most of the programming and applications are designed in such a manner that they may encounter diverse operational scenarios. The current study explores the crucial significance of agile approaches, with a particular emphasis on GitOps integration as a paradigm-shifting strategy in the modern software development environment. Leveraging DevOps best practices to underpin automation, GitOps is a useful solution that provides a way to accomplish safer and more efficient system cycles. Teams may improve security and viability while accelerating arrangement times by using Git as the one source of truth for foundation and application code through the use of GitOps. In order to improve the CI/CD pipeline and enable a more automated, straightforward, and simple exporting technique, this paper explores the potential real-world applications of GitOps concepts in AI application design. It looks at how agile approaches and GitOps practices work together to address the various and multifaceted problems that modern programming development encounters. The main emphasis is on how these approaches may guarantee a high level of adaptability and readiness in a technological environment that is always changing, in addition to being able to react quickly to changes. The aim is to explore innovative strategies and methods to enhance the speed and efficiency of software and application delivery, while examining the complexities of a constantly evolving technological landscape. © 2024 IEEE.

关键词： Machine learning

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Experimental Study on Designing Advanced Driver Assistance System for Two-Wheelers Using Mobility and Physiological Sensors 18

Experimental Study on Designing Advanced Driver Assistance S...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Vyas, Daiwat Amit Chaturvedi, Manish Kumar, Naveen Kamath, Karan Gupta, Piyusha Dwivedi, Aryan Department of Computer Science and Engineering India Institute of Technology Nirma University India IITRAM Department of Computer Science and Engineering India Vadodara India SRM Institute of Science and Technology India

ISBN: (纸本)9798350387278

Advanced Driver Assistance Systems (ADAS) enhance driver safety through various technologies. In high-end cars, ADAS typically uses computer vision-based solutions, such as eye-tracking, to monitor driver attention, mood, and drowsiness. However, these ADAS solutions are challenging to implement for two-wheeler (2W) drivers, who constitute about 80% of drivers population in India, mainly due to helmet usage and the unique dynamics of 2W vehicles. This paper investigates improving 2W driver safety through integrating mobility and physiological sensors for ADAS. Experimental studies evaluated a novel ADAS framework combining mobility sensor data (e.g., speed, acceleration, position) with physiological parameters (e.g., heart rate, etc.). This fusion of sensors data approach enables monitoring vehicle dynamics and driver conditions for providing adaptive safety interventions by identifying aggressive/non-aggressive and stress/non-stress patterns. To facilitate the collection of mobility and physiological sensors data a mobile application was developed using Flutter. The physiological parameters were captured from the smart-watch paired with the smart-phone and the mobility parameters were captured from the mobility sensors that were in-built in the smart-phone. The data was collected in Navi-Mumbai area of India. Preliminary experiments conducted using these integrated sensors data show promising results. To incorporate supervised approach for Driver Behaviour Analysis (DBA) requires a tedious task of manual tagging and each driver reacts differently to the different situations that arise while driving. So, an un-supervised learning approach has been incorporated and clustering techniques like k-Means, Gaussian Mixture Model (GMM), Hierarchical Clustering, and k-Means with Dynamic Time Wrapping (DTW) analysed the sensor data to identify patterns indicating potential hazards or driver impairment. The findings based on the preliminary experimental studies done indicate t

关键词： Advanced driver assistance systems

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An Assistive Vision for a Visually Challenged Person using Alexa 4

An Assistive Vision for a Visually Challenged Person using A...

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

作者： Dheeraj, Chilakapati Reddy, Kothapalli Sujith Rajalakshmi, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360165

It has been associated with converters and inverters. The system has been found to be feasible in efficiently utilizing Photovoltaic energy and integrating it with the electrical grid without any disturbances. The successful integration of this two-stage system assures not only that the power generated comes from the photovoltaic panels but also that grid stability and power quality are assured. This project brings in a new control methodology for active power regulation and is the main constituent for this system. Based on real-time monitoring and advanced control algorithms, it assures very accurate matching of power generation with grid demand in dynamic power adjustment capabilities. This feature maximizes the consumed power but also improves the grid stability through voltage and frequency control, thus minimizing the impact of variable Photovoltaic irradiance. Alongside, the converters and inverters applied in the system provide an essential interface between the D.C. output from the Photovoltaic panels and the A.C. grid;thus, it is possible to provide bidirectional power flow seamlessly into the grid. The active power regulation control approach to be adopted in this double-stage single-phase gridconnected P.V. system, with the converter and inverter facilities, is a sustainable and reliable solution for Photovoltaic power generation and grid integration. Such development and deployment will not only become a part and parcel of the global shift to renewable energy but also provide the current grid integration, power quality, and efficiency challenges with a feasible and promising solution. Considering the huge role that Photovoltaic energy can play in our profile, this project actually becomes a contribution to furthering renewable energy systems. © 2024 IEEE.

关键词： Recurrent neural networks

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Enhancing Accessibility for Visually Impaired Users: A BLIP2-Powered Image Description System in Tamil

Enhancing Accessibility for Visually Impaired Users: A BLIP2...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Roslyn, Smrithika Antonette Negha, A. Vishnu Sekhar, R. Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

Image captioning is the task of analyzing an image and expressing its content using natural language. It involves computer vision, image analytics, object recognition and natural language processing. This field has been attracting greater popularity recently resulting from its possible applications in numerous domains, including accessibility for those with visual impairments, which lacks in the Tamil community due to limited availability of reliable and accurate tools for creating context-aware image captions in Tamil. The primary objective of this research is to create a context-aware image captioning system capable of giving a thorough explanation of an image in Tamil to assist in the lives of the visually impaired. The proposed technique will leverage Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models (BLIP-2) technology and generate image descriptions in Tamil and convert them to audio format. This will extract visual features using a frozen image encoder, and process them using a large language model to produce natural language descriptions in Tamil. This research is based on BLIP's ability to process linguistic as well as visual data efficiently along with being able to provide accurate, natural-sounding image descriptions in the target language, making them a best fit for the task. © 2024 IEEE.

关键词： Object recognition

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Spotting simulated commodity welding metamask technology 2

Spotting simulated commodity welding metamask technology

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Mandal, Rishita Siriporam, Rohit Naidu Gopika, G.S. Sreekrishna, M. Supriya, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349221

Supply chain management Constantly encounters challenges for instance service duplication, Indigent Collaboration among various Divisions, and absence of formal procedures, leading to lucidity issues. The prevalence of product counterfeiting poses a significant threat, and differentiate a counterfeit product through visual check has become nearly impossible. Counterfeit activities present dire obstacles for legal businesses, yet many individuals remain unaware of the full extent of their impact on brands. Various methods have been proposed in the past to address the issue of product counterfeiting, including the usage of QR code-based systems, Artificial Intelligence and RFID tags as such, etc. However, every method has its snags;for instance, replicas of QR codes from legit real products then affixation to fake ones, and employment of machine learning and CNN requests by AI takes considerable computation power. © 2024 IEEE.

关键词： Blockchain

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An Anchor-Aware Graph Autoencoder Fused with Gini Index Model for Link Prediction

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SN computer science 2024年第6期5卷 745页

作者： Kumar, Shambhu Bisht, Dinesh Jain, Arti Computer Science & amp Engineering and IT Jaypee Institute of Information Technology Department of Mathematics Jaypee Institute of Information Technology

Link prediction stands as a crucial network challenge, garnering attention over the past decade, with its significance heightened by the escalating volume of network data. In response to the pressing need for swift research focus, this study introduces an innovative approach—the Anchor-aware Graph Autoencoder integrated with the Gini Index (AGA-GI)—aimed at gathering data on the global placements of link nodes within the link prediction framework. The proposed methodology encompasses three key components: anchor points, node-to-anchor paths, and node embedding. Anchor points within the network are identified by leveraging the graph structure as an input. The determination of anchor positions involves computing the Gini indexes (GI) of nodes, leading to the generation of a candidate set of anchors. Typically, these anchor points are distributed across the network structure, facilitating substantial informational exchanges with other nodes. The location-based similarity approach computes the paths between anchor points and nodes. It identifies the shortest path, creating a node path information function that incorporates feature details and location similarity. The ultimate embedding representation of the node is then formed by amalgamating attributes, global location data, and neighbourhood structure through an auto-encoder learning methodology. The Residual Capsule Network (RCN) model acquires these node embeddings as input to learn the feature representation of nodes and transforms the link prediction problem into a classification task. The suggested (AGA-GI) model undergoes comparison with various existing models in the realm of link prediction. These models include Attributes for Link Prediction (SEAL), Embeddings, Subgraphs, Dual-Encoder graph embedding with Alignment (DEAL), Embeddings and Spectral Clustering (SC), Deep Walk (DW), Graph Auto-encoder (GAE), Variational Graph Autoencoders (VGAE), Graph Attention Network (GAT), and Graph Conversion Capsule Link (G

关键词： Capsule network Ideal Anchor Point Link prediction Path Estimation Portion Path information function Residual module Shortest path distance

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Document-Based Text Summarization using T5 small and gTTS

Document-Based Text Summarization using T5 small and gTTS

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Raj, Ankit Raj, Mehul Umasankari, N. Geethanjali, D. Sathyabama Institute Of Science And Technology Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The research project on 'Deep Learning-Based Text Summarization System using T5 small and gTTS' introduces a method to automatically extract and understand information from PDFs. The first step is to extract text from PDF files accurately. Following this, advanced natural language processing techniques are applied, utilizing a BERT-based model for sentiment analysis to identify emotional nuances in the text. Additionally, the integration of the T5 model streamlines the text summarization process, condensing extensive information into a clear and concise summary. The sophistication of the project is enhanced through the inclusion of Google Text-to-Speech (GTTS) capabilities, converting the written summary into an audio file. This feature accommodates various user preferences, improving overall accessibility. The research establishes a multimodal strategy for information dissemination, delivering the summary in both written and audio formats for a concise yet inclusive presentation. Beyond its technical contributions, the document summarization system has applications in education, content curation, and information retrieval. This system can assist educators in creating concise educational materials, support content curators in efficiently summarizing articles, and aid users in quickly extracting relevant information from large datasets, showcasing its versatility and potential impact across different fields. The integration of advanced natural language processing techniques underscores their adaptability and efficiency in handling textual data, ultimately enhancing the overall user experience and accommodating a broader spectrum of use. © 2024 IEEE.

关键词： Sentiment analysis

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A Hybrid Deep Learning Solution for Real-Time DDoS Mitigation in IIoT Environments 5

A Hybrid Deep Learning Solution for Real-Time DDoS Mitigatio...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kamaleswari, S. Suganthi, N. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram India

ISBN: (纸本)9798331530013

The Industrial Internet of Things (IIoT) has revolutionized industrial operations by enhancing connectivity and automation. However, this interconnectivity also introduces significant vulnerabilities, particularly to Distributed Denial of Service (DDoS) attacks, which can disrupt critical industrial processes. This proposed model designed to fortify IIoT networks against DDoS threats. The proposed approach employs a hybrid model that integrates CNN and Bi-LSTMs networks to effectively capture spatial and temporal patterns in network traffic data. The framework encompasses including data collection, preprocessing, DDoS detection, and automated strategies. The framework using real-world IIoT traffic datasets, preliminary results prove that the work expressively improves DDoS detection capabilities while maintaining low false positive rates. This work ensuring the reliability and availability of industrial operations. Future research will explore advanced deep learning architectures and the integration of this framework with cutting-edge networking technologies for dynamic threat response. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Sentimental Analysis for Trump Tweets Using R 8th

Sentimental Analysis for Trump Tweets Using R

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Nishok, V.G. Bharathi, N. Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9789819786657

Twitter is the best source for sentiment analysis, product reviews, current issues, etc. Sentiment analysis extracts positive and negative opinions from the Twitter data set, and R studio provides the best environment for this Twitter sentiment analysis. Find if Trump’s phone Android is fake or not by comparing his iPhone and Android tweets using R with datasets. Detailed analysis is not there. The Twitter data collected might not be representative of the entire population. By using filtering of tweets as we need Android and iPhone tweets here and usage of tweets per day, no of tweets with and without quotes and pictures and links, taking common words from Android and iPhone and trying with log ratio that is Android /iPhone and with log ratio and common word find sentiment. We got good results by the usage of Twitter of each device, no of tweets quoted and non-quoted, no picture/link and picture/link, most common words, words compare with Android and iPhone and log ratio, finding sentiment of it which type of word it is. My work helps his account is a trump or not for Android as it contains negative tweets by common words, Android and iPhone words and log odd ratio with that it detects sentiment as it is a positive, negative or neutral by showing anger, sad, happy, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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