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检索条件"机构=Dept. Computer Technology Engineering"

共 17201 条记录，以下是11-20 订阅

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Building Resilient CICD Pipelines: A DevOps Security-First Framework

Building Resilient CICD Pipelines: A DevOps Security-First F...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Ashtagi, Rashmi Belani, Chirag Bhalerao, Prasanna Bhalerao, Yash Chawle, Atharva Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331512965

Continuous Integration and Continuous Deployment are now integrated in automated systems that enhance code stability, and accelerate the deployment process as well as improving monitoring of the whole system in the software development field. This document deals with setting up of a strong CI/CD process involving AWS cloud services and DevOps. The infrastructure is built with the help of Terraform which ensures the scalability and repeatability applying the Infrastructure as Code (IaC) approach. A safe Virtual Private Cloud (VPC) is designed consisting of public and private subnets, load balancing for traffic distribution, and CloudFront CDN to improve geographical distribution. Jenkins is used in CI process to perform disk analysis, OWASP and Trivy vulnerability scanning, and making Docker images. The CD phase use ArgoCD for updates on Kubernetes and integrates Monitoring and observability using Prometheus and Grafana. Notifications are produced to JIRA and email communication for improvement. With the help of the offered cloud services and DevOps tool it is possible to consolidate the SDLC and bring effects in code safety, testing and utilization. This document also provides an overview of methods, instruments, and outcomes, demonstrating how the CI/CD automation influences the development and operation positively. © 2025 IEEE.

关键词： Observability

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An Overview on IoT in Electric Vehicles: Efficiency and Sustainability 2

An Overview on IoT in Electric Vehicles: Efficiency and Sust...

引用

2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Gupta, Shashank Sounkhla, Indu Dubey, Ranjeet Kumar Yadav, Mithilesh Kumar Singh Yadav, Sandeep NIT Dept. of Computer Science and Engineering Jalandhar India KIPM Dept. of Computer Science and Engineering Gorakhpur India Buddha Institute of Technology Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Internal combustion engine (ICE) vehicles are very polluting and release high nitrogen oxides, carcinogens, and soot into the environment. ICE vehicles are the best choice in this era, but replacing ICE vehicles with electric vehicles (EVs) and automated vehicle control is a potential transition process. The most important turning point in the history of the automobile occurred with the entry of EVs into the vehicle market. The rise of the Internet of Things (IoT) paradigm in electric vehicles (EVs) has changed the way we look at large-scale transportation by making it intelligent and more sustainable. Recently, with the advent of connectivity through IoT platforms, IoT in EVs has improved performance and reduced environmental impact: from any on-off switch and adoption of IoT through our smartphones placed on the charging base. We can implement smart charging solutions for better energy consumption management, reduction of grid load, and battery life enhancement. IoT allows electric vehicles to communicate directly with charging stations and even the power grid, providing a better way to create balanced transportation networks. But implementing IoT in electric vehicles also raises some issues, including data security and user privacy. With all of these issues, there is huge potential in IoT usage with electric vehicles to help produce cleaner transportation and create a more sustainable future. © 2024 IEEE.

关键词： Electric Vehicles Internal Combustion engine Internet of Things

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TurfIt: An Instant Turf Booking Application

TurfIt: An Instant Turf Booking Application

引用

2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Rajeswari, A.M. Tamilselvan, V. Abishek, M.R.P. Dept. of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331512965

As sports grow in popularity need for effective online turf booking applications has also increased. Although some existing applications are faced with numerous challenges such as;restrictions on access, ineffectiveness in scheduling, and over-bookings, leading to frustrations and operational problems. This paper focuses on those problems by the presentation of an online turf booking application to streamline the booking process. The solution offers real-time updates, automatic payment, and safe transactions based on QR code. The more user-friendly the application is, the more efficient it will be. Firebase would be used for real-time data synchronization and the responsiveness of React interface ensures the smooth and conflict-free bookings. This solution would lead to increasing customer satisfaction as well as increasing the booking rates and effective utilization of sports facilities. © 2025 IEEE.

关键词： Customer satisfaction

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Scraping Data from Google Maps: Comparisons and Experimentations 15th

Scraping Data from Google Maps: Comparisons and Experimentat...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Thakur, Shivam Jha, Rajan Dalawat, Monika Jha, Prashant Khandare, Anand Dept. of Computer Engineering Thakur College of Engineering and Technology Autonomus Mumbai India

ISBN: (纸本)9789819786305

Web scraping, an automated process that extracts data from websites, is a powerful tool that faces several challenges. Existing scrapers often entail drawbacks, such as subscription fees and limited accessibility for small businesses. Concerns regarding the reliability and accuracy of data arise, especially with respect to real-time content. This study scrutinizes web scraping tools, delving into operational principles, advantages, and limitations, addressing applications in data mining, research, and social media marketing. Two scraping approaches, manual extraction, and automated methods, such as HTML parsing, were explored. Popular tools such as ParseHub, WebHarvy, and Octoparse have been discussed, but concerns about accessibility and accuracy persist. Libraries such as BeautifulSoup, Selenium, and Scrapy offer alternatives to those with programming skills. Although web scraping accelerates data collection, its limitations and potential drawbacks must be carefully considered. This paper focuses on web scraping, a potent tool for extracting data from websites. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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Disease Detection And Severity Prediction Of Pomegranate Fruit 15

Disease Detection And Severity Prediction Of Pomegranate Fru...

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

作者： Paul, Poulin Manishankar, S. Satheesh Kumar, R. Sahrdaya College of Engineering and Technology Dept. of Computer Science and Engineering Kerala Thrissur India Jawaharlal College of Engineering and Technology Dept. of Computer Science and Engineering Kerala Palakkad India

ISBN: (纸本)9798350370249

This paper presents an integrated framework for the comprehensive analysis of diseases affecting pomegranate fruit. The suggested system, which makes use of deep learning techniques, includes semantic segmentation for diseased region localization, multiclass illness classification, and image processing-based severity assessment. Initially, four common diseases - Alternaria, Anthracnose, Bacterial Blight, and Cer-cospora - are successfully identified through the use of a Convolutional Neural Network (CNN) for multiclass classification. Then, annotated photos are used as training data for Semantic Segmentation, which uses the UNet architecture to identify the unhealthy spots within the fruit. The area and percentage of the segmented region are determined, and thresholds for high, medium, and low severity levels are then defined. The segmentation results are then used as inputs for severity estimate. Early identification and intervention tactics are made easier by the severity level prediction, which is based on the proportion of the segmented region. In order to improve pomegranate fruit management methods, the suggested framework provides a thorough method of disease analysis by combining categorization, semantic segmentation, and severity prediction. © 2024 IEEE.

关键词： Annotation Convolutional Neural Networks Deep learning Semantic segmentation Unet

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Towards Automated Lip Reading: Developing Marathi Lip Reading Datasets and Neural Network Frameworks 4

Towards Automated Lip Reading: Developing Marathi Lip Readin...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Kulkarni, Apurva Kirange, Dnyaneshwar Khemachandra Ssbt College of Engineering and Technology Dept. of Computer Engineering Jalgaon India

ISBN: (纸本)9798350349900

This paper introduces an innovative method for automating lip-reading, with a specific focus on the Marathi language. Lip-reading plays a crucial role in aiding those with hearing impairments, but automating it presents significant challenges, especially for languages like Marathi lacking sufficient datasets. To tackle this, we propose a novel approach to automatic lip-reading, accompanied by the development of a specialized Marathi dataset. Leveraging advancements in computer vision and deep learning, our model deciphers linguistic content from lip movements, trained on this dataset. We employ various neural network architectures, including feed-forward, recurrent, and convolutional networks, to extract vital visual features crucial for accurate language interpretation Our primary goal is to provide a robust solution for automated lip-reading tailored specifically to regional languages like Marathi, aiming to enhance accessibility for individuals with hearing impairments, particularly in linguistically diverse contexts. This paper primarily discusses detailed exploration of dataset creation, neural network architectures for lip-reading system, we demonstrate the feasibility and potential impact of our approach. Our study underscores the significance of giving priority to regional languages in technological advancements to promote inclusivity for all individuals. © 2024 IEEE.

关键词： Convolutional neural networks

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A Machine Learning Approach to Identifying Employees at Risk of Workplace Injury 2

A Machine Learning Approach to Identifying Employees at Risk...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Pramanik, Bablu Modak, Santanu Brainware University Dept. of Computer Science & Engineering India Bengal College of Engineering and Technology Dept. of Information Technology India

ISBN: (纸本)9798350360523

Workplace injuries are a critical concern, with millions occurring annually, leading to substantial human and economic costs. This study focuses on a qualitative analysis based on statistics from 2013 to 2017, with a specific emphasis on fatal and non-fatal workplace accidents within the European Union (EU) and Romania. The primary objective is to assess Romania's workplace safety status in the context of the EU and to propose strategic measures for improvement. To achieve this goal, four key indicators and statistical datasets are utilized, sourced from the National Institute of Statistics (NIS) of Romania, the Eurostat database of the European Commission, and the Romanian Labor Inspection. These indicators include the rate of incidence frequency index for occupational accidents, the average duration index, the frequency index for fatal accidents, and the severity index, enabling a comprehensive evaluation of accident frequency and severity. The rate of incidence, measuring injuries per 100,000 workers, is a pivotal indicator. Additionally, the study calculates the frequency index of non-fatal accidents (injuries per 1,000 employees) and the fatal accident frequency index (injuries per 1,000 workers). Statistical findings are rigorously validated through ANOVA analysis and T-tests. Following data evaluation, the study offers strategic recommendations informed by national and European strategies, including the National Occupational Safety and Health (OSH) Strategy for 2017-2020 and the "EU Strategic Framework on Health and Safety at Work"spanning 2014 to 2020. These recommendations aim to guide efforts for workplace accident control and prevention in Romania and the broader European *** summary, this study's core objective is to comprehensively analyze workplace accidents in Romania and the EU, employing various indicators and statistical data. Its primary aim is to assess the current status and propose evidence-based strategies for improvement, aligning with

关键词： Machine learning

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Face Generation and Recognition in Forensic Science 11

Face Generation and Recognition in Forensic Science

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Ravi, Gayathri Joy, Heynes Jitto, Jeffin Joshy, Jocelyn Jose, Jisha Mary Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350379136

Suspect identification can be challenging for forensic investigations since standard procedures are time-consuming and prone to mistakes. This calls for the creation of novel approaches utilizing developments in machine learning (ML) and artificial intelligence (AI). In order to overcome these obstacles, the proposed Face Generation and Recognition in Forensic Science will make use of sophisticated recognition algorithms and AI-based face generation models. Fully trained Stable Diffusion model is applied to generate high-quality face images from textual descriptions. Image Generation, Text Guided Image Manipulation using Denoising Diffusion Probabilistic Models (DDPMs), and Dataset Matching are the three primary components of the process. Using a stable diffusion model, Image Generation quickly creates high-resolution images from word prompts by combining an autoencoder (VAE), U-Net, and text encoder. With the introduction of an alternate noise space for DDPMs, Text Guided picture Manipulation makes it possible to do meaningful picture altering tasks in response to text prompts. VGG-16 , a convolutional neural network architecture is used in dataset matching to extract features and calculate similarity, which makes dataset alignment and comparison easier. The suggested methodology gives law enforcement authorities effective tools for identifying suspects, which represents a substantial development in forensic investigations. The project intends to increase the efficiency of criminal investigations, accelerate the matching process with large datasets, and enhance the accuracy of facial sketches by utilizing AI and ML approaches. The approach's ability to produce coherent and contextually relevant face images is validated by experimental results, which also show the approach's potential for speeding up the conclusion of criminal cases, particularly unsolved cold cases. All things considered, Face Generation and Recognition in Forensic Science is a promising step in st

关键词： Convolutional neural networks

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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Enhancing VANET Efficiency with ACRR Technique for Improved ...

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2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Karthick, K. Talari, Praveen Nishadevi, V. Krishna Prakash, R. Asokan, R. Velusamy, A. Dept. of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Computer Science and Engineering Vignan Bharathi institute of technology Telangana Hyderabad India Dept. of Computer Science and Engineering Mahendra College of Engineering Tamilnadu Salem India Dept. of Information Technology Sona College of Technology Salem India Dept. of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Information Technology Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9798350376913

The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p

关键词： Traffic congestion

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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

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

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Sampathkumar, Balaji Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Nagarjuna College of Engineering and Technology Dept. of Information Science and Engineering Bangalore India Sri Venkateswaraa College of Technology Dept. of Computer Science and Engineering Chennai India Akshaya College of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359688

Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of 95%, low amount of false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of the study reveal that a detection accuracy of 93% was achieved by CNN, while a higher accuracy of 96% in threat detection was exhibited by RNN. © 2024 IEEE.

关键词： Zero-day attack

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