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Augmenting Sentiments into Chat-GPT Using FacialEmotion Recognition 10

Augmenting Sentiments into Chat-GPT Using FacialEmotion Reco...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Iyer, Aditya A. Vojjala, Saipranav Andrew, J. Manipal Institute of Technology Dept. of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350384369

This research initiative addresses the task of enhancing Chat Generative Pre-trained Transformer's (ChatGPT's) conversational capabilities by integrating the comprehension and response to user emotions conveyed through facial expressions. The central challenge lies in refining the AI system's proficiency to tailor responses according to users' detected emotional states. In this pursuit, our study adopts a comprehensive approach, aiming to seamlessly incorporate emotional intelligence into AI-driven interactions. To achieve this objective, the methodology involves integrating real-time sentiment analysis based on facial expressions into the ChatGPT framework. This is carried out through the utilization of a deep convolutional neural network (DCNN) architecture, designed to recognize and interpret various emotions exhibited in facial expressions. The primary goal is to enable ChatGPT to dynamically adjust its responses, fostering a more empathetic and contextually relevant interaction with users. In terms of evaluation metrics for facial expression recog- nition, our assessment employs a confusion matrix to quantify the model's performance across different emotional categories. Additionally, a heuristic approach is implemented, wherein the sum total probability of each detected emotion is calculated over the duration the user enters the prompt. These evaluation methodologies aim to provide a comprehensive understanding of the model's accuracy and effectiveness in discerning and responding to user emotions. Overall, this research contributes to the ongoing endeavor of imbuing AI systems with emotional intelligence, paving the way for more nuanced and human-like interactions. © 2024 IEEE.

关键词： Chatbots

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Agritech Harmony: Real-time ESP32 Automation and Cloud Computing for Socially-Informed Crop Suggestions 5

Agritech Harmony: Real-time ESP32 Automation and Cloud Compu...

引用

5th IEEE International Conference for Emerging technology, INCET 2024

作者： Bawadkar, Vishal Verma, Aman Pawar, Aditya Phatale, Utkarsh Sadaphule, Prashant Dept. of Computer Engineering AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9798350361155

Accurately predicting long-term crop yield trends remains a crucial challenge in optimizing agricultural practices and ensuring food security. This paper proposes a novel framework that merges real-time data acquired from Internet of Things (IoT) sensors with a Bi-directional LSTM model with an attention mechanism to achieve this objective. This architecture extracts insights from historical and future environmental conditions and prioritizes critical factors influencing yield through attention weighting. The framework incorporates an ESP32-managed sensor network measuring humidity, temperature, moisture, rainfall, and NPK levels, providing a continuous stream of real-time field data. Evaluation of real-world datasets demonstrates the framework's superiority over traditional methods, yielding significantly higher prediction accuracy and exposing key drivers of yield fluctuations through attention analysis. An accessible API facilitates seamless integration with farm management systems, empowering farmers with data-driven decision-making capabilities. This paper highlights the transformative potential of integrating deep learning, real-time IoT data, and APIs for revolutionizing agriculture, paving the way for a more sustainable and resilient agricultural futur. © 2024 IEEE.

关键词： Crops

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OpenVAS vs the Rest: Unveiling the Competitive Edge in Vulnerability Scanners 3

OpenVAS vs the Rest: Unveiling the Competitive Edge in Vulne...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Sharma, Mayank Desai, Divyanshu Arun, Aditya Ranganathan Priya, L. Rajagopalan, Narendran National Institute of Technology Dept. of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350381931

In an increasingly interconnected and digital world, robust vulnerability management is paramount to safeguarding critical information assets and maintaining the integrity of network infrastructures. This paper explores into the heart of vulnerability assessment by presenting a comprehensive analysis of OpenVAS, an open-source vulnerability scanning tool, and its distinct advantages over other competing solutions. OpenVAS has emerged as a standout contender in the realm of vulnerability scanners, owing to its unique features, exceptional performance, and a commitment to open-source principles. Our study begins by providing an overview of the vulnerability management landscape, highlighting the importance of timely and accurate vulnerability assessment. Key advantages examined in this paper include OpenVAS's extensive coverage of vulnerability databases, ease of customization, scalability, and its robust community support. Moreover, its open-source nature ensures transparency and flexibility, making it an attractive choice for both small businesses and large enterprises. This paper aims to serve as a valuable resource for cybersecurity practitioners, IT professionals, and decision-makers seeking a thorough understanding of vulnerability assessment tools. By shedding light on OpenVAS's unparalleled strengths, it provides insights that empower organizations to make informed choices for their cybersecurity posture. In an era where cyber threats evolve rapidly, OpenVAS emerges as the beacon of proactive vulnerability management, offering distinct advantages that redefine the standards for the industry. As cybersecurity assumes an increasingly critical role in the digital age, this paper underscores the significance of choosing a tool that not only detects vulnerabilities but also empowers organizations to address them effectively. © 2024 IEEE.

关键词： Cybersecurity

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Discover Image Restoration: Analysis and Novel Architecture Proposal 7

Discover Image Restoration: Analysis and Novel Architecture ...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Pawar, Poonam Ainapure, Bharati Vishwakarma University Faculty of Science And Technology Dept of Computer Engineering Pune India

ISBN: (纸本)9798350359299

Digital image quality is essential for a variety of applications in visual information processing, including medical diagnostics, defence, and many other applications. However, there are several challenges that must be overcome before an image can be used, and these often result in noise, distortion, and degradation. The field of image restoration is growing in significance as it focuses on repairing and restoring damaged images. With the increasing number of imaging equipment and the wide range of applications, image restoration techniques are becoming more and more important. Because of the challenges posed by low light levels, sensor noise, and other environmental limitations, more analysis on restoration strategies is needed. This study provides researchers, practitioners, and students with a clear understanding of image restoration techniques by providing an extensive overview of the developments in different fields. This research study offers a comprehensive analysis by analysing the development of these techniques, from traditional methodologies to the most recent deep learning approaches. Here we have proposed a novel architecture for image restoration, designed to facilitate seamless implementation and achieve high-quality results. © 2024 IEEE.

关键词： Image reconstruction

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Smart Agriculture Inventory Management System Using Machine Learning Algorithms 1

Smart Agriculture Inventory Management System Using Machine ...

引用

1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Kanyal, Hoshiyar Singh Jain, Harshit Tyagi, Harsh Yadav, Satyarth Prakash ABES Institute of Technology Ghaziabad Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350353648

In the agricultural sector, smallholder farmers in India face significant challenges with post-harvest losses, fluctuating market prices, and unsustainable practices. This research proposes a mobile-based application designed specifically for farmers in region of India, focusing on specific crops. To reduce agricultural waste, the app features an optimal storage location based on crop type. To improve farmer income, the app includes a crop recommendation module that integrates historical yield data, weather forecasts, soil characteristics, and real-time market prices. Machine learning models, validated with field data, suggest high-yield, profitable crops and optimal sales timing. A market information hub provides real-time mandi prices and demand forecasts. With a user-friendly interface and visual explanations, the app addresses the technological constraints of smallholder farmers. This research aims to reduce agricultural waste, improve farmer income, and promote sustainable agriculture practices. © 2024 IEEE.

关键词： Agricultural economics

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Bone Tumour Detection and Classification Using Deep Learning Based on Computed Tomography 2

Bone Tumour Detection and Classification Using Deep Learning...

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Rajath, S. Archana, R.A. Shivakumar, Kunaal Mathew, Kevin S Krishna, Mohit Bms Institute of Technology and Management Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

Bone Tumours may develop in any part of the bone and they usually occur in young adults or adolescents. Diagnosis requires at least a radiologist, surgeon, pathologist, and oncologist. For any suspected bone tumour lesion, radiography, CT, and magnetic resonance imaging are required. A groundbreaking initiative is underway in the realm of medical imaging, geared towards achieving early and accurate detection of bone tumours. By combining deep learning techniques to create an accessible diagnostic instrument for health care providers and patients. Three distinct deep learning models, including a basic Convolutional Neural Network (CNN), VGG-16, and DenseNet, will be implemented and trained for bone tumour classification. These models will leverage transfer learning to optimize their performance. The ultimate deliverable will be a user-friendly web platform built using Flask, offering a seamless interface for users to access and benefit from the trained models. This project is poised to make a significant impact in the realm of medical diagnostics, potentially revolutionizing bone tumour detection by facilitating early intervention and improving patient care. © 2024 IEEE.

关键词： computerized tomography

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Multi-Objective Task Scheduling in Cloud Environments using Dynamic Velocity-Adaptive Particle Swarm Optimization

Multi-Objective Task Scheduling in Cloud Environments using ...

引用

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kumar, Ravi Vardhan, Manu Natinal Institute of Technology Dept. of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350373783

In the realm of cloud computing, tackling the multi-objective task scheduling problem means finding the best way to assign tasks to virtual machines, taking into account conflicting goals like minimizing execution time, load balance and cost. This intricate task demands smart algorithms to strike a balance between these competing objectives, ensuring the most efficient use of resources and meeting user expectations in the cloud. Different optimization techniques, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have been explored to handle this challenge and optimize task scheduling in the cloud environment. In classical PSO, particles can move at varying speeds in each iteration. In this study, we apply Velocity Stagnation Particle Swarm Optimization (VSPSO) variants where velocity stagnation applied during particles movement. This approach provides particles with a third movement option, enabling them to maintain the same velocity as in the previous iteration. VSPSO has a greater capacity to balance the exploration and exploitation. VSPSO adjusts the initial term of the PSO velocity equation to prevent premature convergence. Furthermore, the population of VSPSO is split into two swarms to ensure *** the inherent capabilities of PSO, VSPSO algorithm dynamically updates particle velocities to attain 5-6% total execution time 6-10% reduced cost and optimal balance load. Through meticulous comparisons with other algorithms, including Genetic Algorithm and Ant Colony Optimization, this study scrutinizes the effectiveness of our enhanced PSO approach. The overarching goal is to push the boundaries of multi-objective task scheduling in cloud computing by attaining minimal execution time, reduce cost and optimal balance load on each virtual machines. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A Transformer Based Indian Signed Language Recognition 1

A Transformer Based Indian Signed Language Recognition

引用

1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Saproo, Varun Aggarwal, R.K. National Institute of Technology Kurukshetra Dept. of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350365016

Effective communication is essential to human interaction. The people with hearing and speaking disability primarily rely on Signed Language for communication. Each Sign Language has its own grammar and vocabulary. Individuals who can speak and hear proficiently often struggles to understand and respond to Sign Language gestures, creating a barrier to meaningful communication. This paper attempts to bridge the communication gap by using sophisticated deep learning models like transformers. INCLUDE is a dataset tailored for the Indian population, focusing specifically on Indian Sign Language. Our work involves breaking down videos into frames and employing image processing techniques. Furthermore, we utilize pre-trained models, such as VGG16, EfficientNet, and MobileNet, to extract latent embedding, which are then processed through Transformer network for classification. Hyperparameter tuning is used to optimize model performance. This allowed us to reach an accuracy of 92 % which is better than existing approaches. The proposed method was also tested on LSA64 dataset giving an impressive accuracy of 99.34%. © 2024 IEEE.

关键词： Deep learning

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Evolve Aggregation Management of Data for Heterogeneity 3

Evolve Aggregation Management of Data for Heterogeneity

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kolekar, Vikas Patil, Medini Shete, Shreyash Vishwakarma Institute of Information Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

Cloud computing is the most emerging technology. It has the capability to provide various services like infrastructure, security, data management, databases, and network over the internet. Several cloud service providers in the market provide computing resources. Cloud brokering in cloud computing is a crucial part as it acts as a middleware between cloud users and cloud services providers. In this paper we aim to provide an overview on adaptive data management middleware for cloud brokers. To manage the heterogeneous data and other factors, policy-based task scheduling can be integrated with the cloud broker, which supports dynamic data placement and takes decisions and actions at the runtime considering the SLA (service level agreements) and QoS (quality of service) requirements without any manual involvement. By aggregation of data, the next task of scheduling and assigning priority will be taken carefully. At the end service requests should be fulfilled by utilizing minimum resources. © 2024 IEEE.

关键词： Cloud platforms

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SIGN LANGUAGE RECOGNITION USING CNN 2

SIGN LANGUAGE RECOGNITION USING CNN

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Allam, Sangeetha Balaram, Allam Rao, Yedavalli Venkata Raghava Kiran, Ajmeera Valpadasu, Hema Navya, Soleti Mlr Institute of Technology Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Cse Aiml Avniet Jntuh Aiml Dept India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad Dundigal India Computer Science and Engineering Mlr Institute of Technology Telangana Hyderabad India Mlr Institute of Technology Department of Computer Science and Information Technology India

ISBN: (纸本)9798350370188

Conversation between both the deaf and the general population is becoming exceedingly challenging, and there is no reputable translator accessible in society to aid. This program enables real-time voice and signal language translation, in distinctive: 1. recognizing male or female signals 2. Establishing a system learning model for interpreting image-to-text data. 3. Seeking innovative terms Four. Constructing Sentences 5. After creating all of the content, 6. Is to obtain the audio output. The goal of this research is to develop a framework for a potential sign language interpreter who could also translate conversations between sign languages into written and spoken English. If there had a translator like this, it would be much easier for many deaf and partially deaf people to interact with others in everyday situations © 2024 IEEE.

关键词： Translation (languages)

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