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Modelling Complex Associations for Image Captioning Using Vision Transformers 9

Modelling Complex Associations for Image Captioning Using Vi...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Ritul, Chavda Sampath, Nalini Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350394474

Captioning an image is the method of writing a plain English the description of an image. This is a difficult assignment since it calls for the model to comprehend the image's information and to produce correct and fluid text. It has been demonstrated that a novel class of neural network called vision transformers is useful for picture captioning. Vision transformers are able to learn long-range dependencies in images, which is essential for generating accurate and fluent captions. In this project, will explore the use of vision transformers for modelling complex associations in images. The work will investigate how vision transformers can be used to generate captions that are more informative and comprehensive. The work will also explore how vision transformers can be used to generate captions that are more creative and *** work is based on Flickr8k Dataset, which is a large dataset of images and captions. The vision transformer model will be trained on this dataset. The model is then assessed using a held-out test set. Compared to conventional picture captioning models, the vision transformer model will be able to provide captions that are more detailed and insightful. It will also be able to generate more creative and engaging captions. This project has implications for the development of new image captioning models. Vision transformers are a promising new approach for image captioning, and they have the potential to generate more accurate, fluent, informative, creative, and engaging captions. © 2024 IEEE.

关键词： Computer vision

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License Plate Recognition-Based Automatic Gate Opening System

License Plate Recognition-Based Automatic Gate Opening Syste...

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Revanth, Yellanki Namitha, Kalakunnath Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350382693

This study proposes an automatic electronic gate system that uses three main modules for authenticity confirmation: vehicle make and model, validation of license plate, and facial recognition. When a vehicle arrives at a gate or entrance, the system detects the vehicle, recognizes the license plate, and make and model, cross-references it with the database, and validates the driver's face against stored data. When there is a match, the Raspberry Pi signals the servo motor to open the gate;if not, an alert system is triggered. The proposed system is intended to improve security and convenience while decreasing the need for traditional access control measures such as stickers or security guards. Experimental results show that the system is capable of successfully identifying vehicles and number plate readings, with high precision and recall. The integration of several deep learning models, such as YOLO v7, YOLO v7-Tiny, and ResNet, contributes to the overall efficiency and performance of the system. © 2024 IEEE.

关键词： Deep learning

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Strategic Mobility Insights: Optimizing Parking and Operational Efficiency through Density Analysis

Strategic Mobility Insights: Optimizing Parking and Operatio...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Praharsh, Payila Rama Tarun, Meka Venkata Sai Priya, Vanacharla Venkata Satya Reddy, A Anirudh Lalithamani, N. Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9798350375237

This research introduces a comprehensive approach to optimize parking and operational efficiency by employing density analysis based on entrance and exit camera footage in public spaces, such as malls or educational institutions. The study focuses on vehicle tracking to estimate crowd density within these locations, utilizing image processing techniques to count the number of vehicles entering and exiting the public space. The computed crowd density serves as a pivotal metric for proactive alert systems that trigger interventions when congestion levels are critical, redirecting incoming traffic to alternative parking areas. In addition to real-time density management, the research explores trends in crowd density variations across different days, offering insights for strategic resource planning and operational adjustments. The implemented alert system demonstrates its effectiveness in managing high-density situations, providing a dynamic solution for optimizing parking resources and overall crowd-related resources. This research contributes to the evolution of intelligent urban management systems by presenting a robust methodology that integrates both parking management and crowd density trends, setting the stage for future innovations in resource optimization. © 2024 IEEE.

关键词： Efficiency

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Enhanced Pomegranate Grading Using InceptionResNetV2 with Transformer Integration

Enhanced Pomegranate Grading Using InceptionResNetV2 with Tr...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and Computer engineering, WIECON-ECE 2024

作者： Kiran, V. Gopi Babu, T. Hemanth Vidula, N.A. Singh, Rimjhim Padam Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331535476

Sorting pomegranates based on quality grades is a crucial stage in their export preparation and packing process. Traditionally reliant on manual sorting methods which are time-consuming, prone to inaccuracies and incur very high costs due to the need for a large workforce prove to be very inefficient. Moreover, manual grading of export-quality of foods poses additional challenges. This paper addresses such issues by proposing an automated system employing modern digital image processing and deep learning techniques for pomegranate quality grading and classification. Initially, pomegranate images are subjected to several augmentation techniques like extracting HSV color features, Local Binary Pattern features, horizontal and vertical transformations, etc. Then a novel classification model having InceptionResNetv2 convolutional network as its backbone and integrated with optimally placed transformer blocks is proposed. The augmented dataset is utilized to train six state-of-art deep learning models namely, Vision transformers, XceptionNet, EfficientNetV2B0, DenseNet121, InceptionResNetV2 and NasNet-Large. It is observed that the proposed InceptionResNetV2 model containing transformer blocks exhibited superior performance, achieving an accuracy of 90.56%. This underscores the ability of the proposed deep learning model in automating quality evaluation and sorting procedures for pomegranates. © 2024 IEEE.

关键词： Deep learning

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Design of an Automatic Washing Machine Control System using Deterministic Finite Automata 5

Design of an Automatic Washing Machine Control System using ...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Madhuja, C. Bhanot, Shreya Srinidhi, M. Radha, D. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350376685

A Deterministic Finite Automaton (DFA) is a theoretical model of computation used to design and analyze the behavior of systems with a finite number of states, where each state transitions deterministically based on input symbols. This model ensures a clear and predictable progression from one state to another. The integration of Deterministic Finite Automata (DFA) into the control system of new-age automatic washing machines marks a significant leap forward in efficiency and user-friendliness. A logical and well-organized model for the machine's behavior by detailing every step of the washing process as separate states inside the DFA framework has been created in this project. Based on variables like stain intensity, load size, and user preference the proposed model dynamically modifies its cleaning strategy to ensure best performance while consuming the least amount of water and energy. Furthermore, by incorporating all possible states and transitions, the DFA structure allows the project to resolve possible problems and ensure continuous operation for the duration of the wash cycle. The comprehensive incorporation of all possible states and transitions within the DFA allows for resolving potential issues, ensuring uninterrupted operation throughout the wash cycle. This organized depiction of control logic improves efficiency and reliability and provides users with a clearer understanding of their washing machine's functionality, enabling seamless operation and maintenance. In conclusion, utilizing DFA in the control system of washing machines facilitates the creation of an error-free machine, enhancing both performance and user satisfaction. © 2024 IEEE.

关键词： Washing machines

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Design of a Secured and Trusted E-Voting System Using Blockchain Technology

Design of a Secured and Trusted E-Voting System Using Blockc...

引用

2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Elegba Jeremiah, O. Agbelusi, O. Olutayo Victor, A. Akintola Kolawole, G. School of Computing The Federal University of Technology Department of Software Engineering Akure Nigeria School of Computing The Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9798350376838

The state of election in Nigeria is worrisome. Electoral malpractices have been a major challenge to the Nigerian government in recent times. Government has made frantic efforts to tackle these electoral challenges but the recent event in year 2023 presidential election is worrisome suggests that the solution is not near. Recently, the percentage of eligible voters who vote on election days is declining. Voters turnout went up from 52.3% in 1999 - the first general election since 1993 - to 69% in 2003. But, it has been on the decline nearly ever since - 57.5% in 2007, 53.7% in 2011, 43.7% in 2015 and 34.8% in 2019. In 2023, it is 28.63%. With the way things are going, if urgent action is not taken, this could signal the end of democracy in Nigeria. This research presents the design and implementation of a trusted and secured e-voting based on blockchain technology that will increase voters confidence and trust. The system utilizes *** for the frontend and Solidity on the Polygon blockchain for the backend, ensuring transparency, tamper-resistance, and heightened security. This innovative approach addresses trust and reliability concerns in traditional voting systems, offering a modernized and trustworthy electoral process. © 2024 IEEE.

关键词： Blockchain

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A Comparative Study of Machine Learning Algorithms and Explainable AI for Fitness Club Attendance Classification 6

A Comparative Study of Machine Learning Algorithms and Expla...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Divyasri, S. Gupta, Deepa Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798350381689

The growing interest in health and fitness has resulted in an increase in the number of people joining fitness clubs. However, maintaining member engagement remains a challenge for fitness club operators. The goal of this study is to create a binary classification model that predicts whether a member is likely to attend a fitness club or not. In this paper, we propose a framework to explain how good the fitness club attendance classification performance will be under various Machine learning models by understanding its metrics. In this study by employing several classification models to determine Fitness Club attendance Classification using machine learning algorithms such as Random Forest, Gradient Boosting, Light GBM, etc. The Random Forest classifier performs with a higher accuracy of 0.87, and Explainable AI has been used to reveal the most significant feature. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Emotion Analysis: 'Enhancing Emotion classification with Transformer Model' 15

Deep Emotion Analysis: 'Enhancing Emotion classification wit...

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

作者： Mokshit, P. Prasad, P. Syam Reddy, N. Sumanth Singh, Tripty Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In the era of digital technology, it becomes very critical to perceive human emotions in textual data from the point of view of many applications - sentiment analysis, mental health monitoring and user experience optimization. The investigation is on the incorporation of transformer-based models and ML techniques in order to enhance the accuracy for emotion classification. This work employs a variant of Bidirectional Encoder Representations from Transformers (BERT) that is able to preserve complex linguistic constructs and contexts. Along with this, logistic regression, decision tree, support vector machine (SVM), random forest, and gradient boosting algorithms are utilized to classify emotional aspects which are then compared for their accuracy in expressing subtleties present in text data. Fundamentally, eXplainable AI (XAI) tools such as ELI5 for random forest and LIME for BERT are employed to improve the interpretability and transparency of the emotion categorization process. These XAI tools enable users to see the feature importance and decision-making process of the model which helps in improving their understanding of the critical features that influence emotion classification. This work proposes the integration of transformer models and traditional machine learning algorithms with explanation of sentiments (XAI) to boost text-based emotion analysis. © 2024 IEEE.

关键词： Random forests

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Evaluating the Effectiveness of Google Cloud Platform Against a Web Server 14

Evaluating the Effectiveness of Google Cloud Platform Agains...

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14th International Conference on Cloud computing, Data science and engineering, Confluence 2024

作者： Abirami, K. Harini, N. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350344837

Containers are serverless and wrap an application as a solitary executable programming bundle that groups application code along with all the connected arrangement records, libraries, and conditions expected to run. The popularity of containers is growing over traditional infrastructure because they are inherently efficient. Lacking a guest operating system and being agnostic to the underlying platform, containers promote modern development practices and architecture, enhancing scalability by allowing individual components to be scaled independently. Peak load on servers makes services inaccessible when an unforeseen responsibility hits the server. To improve performance, peak loads on the servers need to balance the user-generated requests. In this work, a comparison between the web servers and serverless containers is carried out. The results show clearly that the serverless containers perform better than the webservers. In our work, the Firefox GUI is successfully hosted in the web server and Google Container. The requests from the clients are monitored, and the metric Response Time is measured. © 2024 IEEE.

关键词： Containers

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Cloud-Native CDN Monitoring Using CI/CD 15

Cloud-Native CDN Monitoring Using CI/CD

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

作者： Savant, Richa Vivek Sunder, S Navin Seshadri, Samarth Panda, Niharika Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

In a time where digital content consumption reigns, it is essential to efficiently deliver multimedia resources. This research seeks to transform how content is delivered by implementing and overseeing a secure, monitored Content Delivery Network (CDN) on a scalable Kubernetes cluster, managed through a secure CI/CD pipeline built on Jenkins, using the Amazon Web Services (AWS) and essential DevOps tools like Docker, SonarQube, Trivy, Prometheus, Grafana, Argo CD and Helm. The results obtained in this study are derived from an emulation of a CDN. The SonarQube analysis confirms whether the system meets all code quality gates, Trivy identifies and resolves critical container vulnerabilities and OWASP vulnerability checks identify and mitigate security risks in software dependencies. The system is monitored by analyzing the performance metrics visualized in Grafana dashboards, which offer detailed insights into metrics including and not limited to: active jobs, job queue duration, queued rate, memory usage, CPU usage, executor health metrics, system load, RAM usage, root filesystem usage, disk space usage, disk I/O activities, time synchronized drift and time synchronized status. Additionally, SMTP e-mail notifications are configured to improve responsiveness to anomalies identified via monitoring. This study emphasizes making content delivery mechanisms more robust, providing advanced tools for developers, and enabling easy access to digital resources for the community. © 2024 IEEE.

关键词： Program debugging

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