Volume-12 , Issue-3 , Mar - 2024 Go Back

In recent years, advances in ML (Machine Learning) and AI (Artificial Intelligence) have changed the face of scientific study. Out of all of these, chatbot technology has come a long way in the last few years, especially since ChatGPT became a well-known AI language model. This in-depth study determines ChatGPT`s history, uses, main obstacles, and potential future developments. We start by looking at its history, advancement, and underlying technology before looking at its many uses in a range of industries, such as health care, education, and customer service. The study emphasizes the need to find a balance between human expertise and AI-assisted creativity while also examining potential downsides and ethical quandaries associated with employing ChatGPT in research. The essay addresses several moral issues with the state of computers today and how ChatGPT can cause people to oppose this notion. Furthermore, this work contains several biases and restrictions related to ChatGPT. It is remarkable that despite many difficulties and moral dilemmas, ChatGPT has attracted a lot of attention from researchers, businesses, and academics in a comparatively short amount of time. ChatGPT is a novel technology that can generate natural language answers to input or prompts through the use of state-of-the-art AI algorithms. Applications for it may be found in a range of domains, like content creation, NLP (Natural Language Processing), and customer service. The present analysis and research look into ChatGPT`s functioning, history, and impact on several academic fields. It examines ChatGPT`s advantages and disadvantages as well as its capabilities and limitations. Together with its potential applications for academics and researchers, it also discusses ChatGPT`s implications on information technology, employment, customer service, software development, cyber security, and education.

ChatGPT, AI, ChatBot, NLP, Language Model, GPT- 3.5, Generative AI

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Jatin Kumar Panjavani, "Transformative Potential: Chatbot GPT-3 and Its Influence Across Industries," International Journal of Computer Sciences and Engineering, Vol.12, Issue.3, pp.1-10, 2024.

Smart City Surveillance systems play a key role in city development, helping to improve public safety and the efficiency of urban planning. But, there are some challenges regarding the security and integrity of the data transmitted from street cameras, such as privacy protection, legal compliance and protection from data breaches. Traditional methods such as access control lists offers coarse-grained data access and considering this as loophole our suggestion is Attribute-Based Encryption (ABE), which ensures fine-grained access control. We use Enhanced Elliptic Curve Cryptography (EECC) encryption on a sensor-cloud architecture, allowing data owners to define role-based access rules. This approach guarantees confidentiality and integrity and reduces computational overhead for faster encryption, decryption and key generation compared to traditional CP-ABE. This helps improve street camera security and is a step towards more resilient smart cities.

Sensor cloud, Data Security, Enhanced Elliptic Curve Cryptography (EECC), Ciphertext Policy-Attribute Based Encryption (CP-ABE)

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Rajan Kumar Yadav, Munish Saran, Upendra Nath Tripathi, "A Secure Framework based on Sensor Cloud Architecture for Efficient Street Monitoring in Smart Cities using Enhanced Elliptic Curve Encryption," International Journal of Computer Sciences and Engineering, Vol.12, Issue.3, pp.11-18, 2024.

This study presents a thorough technique for the implementation of a Telegram chatbot created especially for educational institutions to expedite the tracking of attendance and academic results. The chatbot is constructed using Python`s Telebot library and DevOps concepts. It is then carefully deployed on an AWS EC2 instance to ensure smooth interaction with a variety of educational systems and to meet the specific needs of different academic institutions. Jenkins makes continuous integration easier by automating the deployment and build processes, which increases scalability and efficiency. The chatbot`s customization possibilities enable customized integration with current educational infrastructures, guaranteeing flexibility and adaptability. Additionally, the project`s strong design allows for future growth and the addition of sophisticated features like learning management system integration. The chatbot seeks to maintain ongoing relevance and efficacy in promoting student participation and optimizing academic administration procedures by continuing to be flexible in response to new technological developments. This study demonstrates how chatbot technology may revolutionize educational services and administration, supporting the further development of academic institutions in the digital era. This study highlights the revolutionary potential of chatbot technology in transforming student services and academic administration by connecting technological innovation with educational demands.

Academic results, AWS Cloud, Chatbot, Continuous Integration, DevOps, Jenkins, Telegram

[1] Eleni Adamopoulou, Lefteris Moussiades “Machine Learning With Applications, Chatbots: History, Technology, And Applications”, Science Direct, Vol.2, 2020.
[2] Aishwarya Gupta, Divya Hathwar, Anupama Vijayakumar “Introduction To AI Chatbots”, International Journal of Engineering Research & Technology (IJERT), Vol.9, Issue.7, pp.255-258, 2020.
[3] Chinedu Wilfred Okonkwo, Abejide Ade-Ibijola “Computers and Education: Artificial Intelligence, Chatbots applications in education: A systematic review”, Science Direct, Vol.2, 2021.
[4] Aadil Hasan “A Review Paper on DevOps Methodology”, International Journal of Creative Research Thoughts (IJCRT), Vol.8, Issue.6, pp.2583-2589, 2020.
[5] Dhaya Sindhu Battin “Devops, A New Approach To Cloud Development & Testing”, Journal of Emerging Technologies and Innovative Research (JETIR), Vol.7, Issue.8, pp.982-985, 2020.
[6] Arpita S.K, Amrathesh, Dr. Govinda Raju M “A review on Continuous Integration, Delivery and Deployment using Jenkins”, Journal of University of Shanghai for Science and Technology, Vol.23, Issue.6, pp.919-922, 2021.
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[8] Rahul Saini, Rachna Behl “An Introduction to AWS – EC2 (Elastic Compute Cloud)”, International Conference on Research in Management & Technovation, Vol.24, pp.99-102, 2020.
[9] Eleni Rütz, Martin, Fachhochschule Wedel, Wedel “DEVOPS: A SYSTEMATIC LITERATURE REVIEW”, ResearchGate, 2019.
[10] Laiby Thomas, Subramanya Bhat “A Comprehensive Overview of Telegram Services - A Case Study”, International Journal of Case Studies in Business, IT, and Education (IJCSBE), Vol.6, No.1, pp.288-301, 2022.
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[12] Mahadevi S. Namose, Shobha D. Patil “Standard DevOps Pipeline”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.641-646, 2019.

N. Meghana, K. Sri Lakshmi, M. Naga Lakshmi Teja Sree, K. Srujana, N. Ashok, "Enhancing Efficiency and Scalability: DevOps Implementation for Educational Chat Systems," International Journal of Computer Sciences and Engineering, Vol.12, Issue.3, pp.19-24, 2024.

In today`s digital medicine era, many medical photographs are generated daily. As a result, there is a growing need for sophisticated tools to assist medical professionals across various specialties in their diagnostic efforts. Thanks to the evolution of artificial intelligence, convolutional neural network (CNN) techniques have made significant strides in this field. These algorithms are crucial in medical image categorization, object detection, and semantic segmentation. However, while medical imaging classification has garnered widespread attention, object recognition and semantic imaging segmentation have received less focus. In this review, we will explore the development of object detection and semantic segmentation in medical imaging studies, along with a discussion on how to accurately identify the location and boundaries of diseases.

Object detection, Image classification, Convolution neural network, Deep Learning, Machine Learning

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Abhishek Thoke, Sakshi Rai, "Application of Object Detection in Medical Image Diagnosis using Deep Learning," International Journal of Computer Sciences and Engineering, Vol.12, Issue.3, pp.25-29, 2024.

This comprehensive review delves into the dynamic realm of AI-driven fitness assistance and robotic navigation, exploring the evolving challenges and advancements in human pose estimation, fitness assessment, and user engagement during workout sessions. The surveyed studies employ diverse methodologies, spanning from real-time exercise pose identification using OpenCV and MediaPipe to innovative applications like sound localization and deep learning. The paper also explores the integration of robotics in fitness assistance, showcasing systems for social support and personalized workout recommendations. Furthermore, it investigates advancements in robotic navigation, employing both complex and simplified approaches to seamlessly integrate into workout scenarios. This integration aims to provide in-depth workout analysis and accurate guidance to users while autonomously navigating the environment. The convergence of computer vision, machine learning, image processing, and the Internet of Things emerges as a pivotal approach, offering a holistic solution for immersive fitness experiences in both home and gym settings.

Autonomous Robot Navigation, Human Pose Estimation, Mediapipe, Computer Vision, Deep Learning

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