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Performance Evaluation of Two-Tier Hybrid Cryptographic Models for Secure Data Transactions in Cloud Computing 2

Performance Evaluation of Two-Tier Hybrid Cryptographic Mode...

引用

2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Debnath, Pankoj Kar, Tonoy Chandra Ashraf, Din Md Arif, Redwanul Islam Lysuzzaman, Md Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350383652

Cloud computing is a trending and enormous technology. With the improvements in the field of Cloud computing, the main challenge arises in the transaction of data securely from one platform to another. Various cryptographic algorithms are used to provide a secure transaction of data. However, conventional cryptographic algorithms might not be sufficient enough for modern threats. To face these potential vulnerabilities, hybrid cryptographic algorithms can be the solution. Hybrid cryptography integrates multiple cryptographic algorithms to enhance performance and overall safety. RSA(Rivest-Shamir-Adleman) is the most common public key algorithm for encryption and decryption. This paper analyzes a modified form of RSA known as Modified-RSA that enhances security in hybrid cryptographic algorithms by incorporating three prime numbers instead of two. The Modified-RSA(MRSA) hybrid models are compared with the original RSA hybrid models with some theoretical and practical aspects. In this research, a comparative study of two-tier hybrid cryptographic models applied to secure data on the cloud is presented. Practical implementations were conducted with hybrid models of RSA and Modified-RSA using Python simulation. Encryption time, decryption time, average throughput, and efficiency were found using relatively more extensive data. The main motive was to find out the performance of every cryptographic hybrid and see which hybrid model is more efficient. © 2024 IEEE.

关键词： Cloud platforms

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Using Markov Chains for Time Series Analysis of Wireless Sensor Networks 2

Using Markov Chains for Time Series Analysis of Wireless Sen...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Saraswat, Birendra Kumar Vikram, Devendra Kumar, Mukesh Dixit, Krishna Kant Sharma, Ram Kumar Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Abes Institute of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department of Electrical Engineering UP Mathura India

ISBN: (纸本)9798350371055

Wireless Sensor Networks (WSNs) have enabled new and revolutionary technological packages by using imparting self-reliant sensing, control, and verbal exchange capabilities. Time collection analysis is an important approach used to become aware of significant styles and correlations within WSNs. especially, Markov chains were used to investigate time series responses of WSNs. A Markov chain is a stochastic version which captures deterministic and stochastic behavior by way of representing the possibility of a destiny kingdom primarily based upon the contemporary state. Inside the context of WSNs, a Markov chain is used to version the reputation of a sensor node given the statuses of its neighboring nodes. Via the usage of Markov chain analysis, important information may be determined which may be used to optimize the performance of a WSN, together with knowledge energy intake quotes and figuring out correlations among nodes. As a result, making use of Markov chains in WSNs can provide higher predictive and analytical capabilities for improving a WSN's operations. © 2024 IEEE.

关键词： chains contemporary especially operations wireless

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Predicting ChatGPT's Ability to Solve Complex Programming Challenges

Predicting ChatGPT's Ability to Solve Complex Programming Ch...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Ho, Nguyen May, James Ngo, Bao Formato, Jack Ngo, Linh Ho, Van Long Bui, Hoang Loyola University Maryland Department of Computer Science United States West Chester University Department of Computer Science United States Oberlin College Department of Computer Science United States International University School of Computer Science and Engineering Viet Nam

ISBN: (纸本)9798350362480

The recent emergence of Large Language Model (LLM)-based tools such as OpenAI's ChatGPT and Google's Gemini has sparked excitement across the software development industry, and offered promises to transform the software development process. Despite the enthusiasm, it remains uncertain whether these tools are already good enough at coding to replace the role of software developers. Currently, no studies have provided insights into the performance of LLMs, such as understanding which characteristics of a programming task might affect an LLM's performance, or predicting how an LLM will handle new programming challenges. In this work, we address these challenges by first creating a data collection framework to gather 3,323 programming tasks from Kattis, a widely-used programming challenge platform. We then use OpenAI's ChatGPT to solve these programming tasks. The solutions obtained from ChatGPT are submitted back to Kattis to evaluate their correctness and effectiveness. Next, we use the collected data, including both problem and solution information, to analyze the task characteristics that significantly influence ChatGPT's performance. Building on the analysis, we develop predictive models that can forecast the efficacy of ChatGPT on new programming problems. Our analysis indicates that factors such as the difficulty level of a programming challenge, or the readability complexity of a problem description can significantly affect the efficacy of ChatGPT. Finally, the experimental results show that our predictive model can correctly predict ChatGPT performance with an accuracy of up to 90% for easy problems, and up to 79% for difficult problems. © 2024 IEEE.

关键词： Software design

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A Real Time Prediction Model for Round Winners of Counter Strike:Global Offensive 3

A Real Time Prediction Model for Round Winners of Counter St...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Paul, Rosebell Sajeer, Rihan Vaishnav, M.V. Abraham, Savio Vinu Sahajan, Shreya SCMS School of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350396157

This research study aims to create a full time winner predictor program for Counter-Strike Global Offensive tournaments that can predict the round winner of the game being spectated. The algorithm creates accurate predictions of the round winner. The data required for the prediction has to be extracted from the game servers. The data is collected from the game servers using an API. The prediction can be viewed live by the user with the help of a GUI. The project can provide CS:GO enthusiasts and bettors with real-time insights and predictions for round winners in competitive matches. System aims to enhance the user's understanding of match dynamics and support informed decision making. © 2024 IEEE.

关键词： Forecasting

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Integrated Colorization and Styling Approach for Enhancing Image Clarity 3

Integrated Colorization and Styling Approach for Enhancing I...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Shalini, Gollapalli Madhu Anand, Rakam Sankeerth, Thalloju Reddy, H. Venkateswara Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350375190

Image exploration adopts several dimensionalities with web-based tasks, integrating Streamlit and OpenCV to create an interactive platform for advanced image enhancement. Alongside the transformative tool enabling conversion of black and white images into vibrant compositions, an innovative styling suite offers diverse artistic filters. Notably, the addition of a camera option allows users to capture photos directly within the interface. Through Streamlit's intuitive interface and OpenCV's robust image processing, users experience real-time previews for seamless colorization and filtering. The proposed methodology involves designing a responsive web interface, optimizing Streamlit widgets for file uploads and style selection, and leveraging OpenCV for image processing. Results demonstrate the application's effectiveness, providing immediate insights into style impact. Challenges include processing speed optimization, with future research directions exploring advanced Machine Learning (ML) models for dynamic style transfer. In summary, this research advances interactive image processing, offering a refined platform for creative visual content enhancement. The results obtained is promising leading to effective image colorization and styling, thereby improving the quality of the image. © 2024 IEEE.

关键词： Image enhancement

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Revolutionizing Rural Healthcare in India: AI-Powered Chatbots for Affordable Symptom Analysis and Medical Guidance 7

Revolutionizing Rural Healthcare in India: AI-Powered Chatbo...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Abisha, D. Mahalakshmi, M. Pritiga, T. Thanusiya, M. Punitha Sahaya Sherin, A. Navedha Evanjalin, R. National Engineering College Department of Computer Science And Engineering Kovilpatti India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

The healthcare sector is currently a major focal point globally, given the rising prevalence of health issues. Managing rural healthcare poses a significant challenge in India, where treating the diseases with early detection can have a crucial impact. In rural areas, obtaining physical consultations is both expensive and time-consuming. Symptom checker chatbot adoption holds the key to the solution. Artificial intelligence(AI) and chatbot technology have come to light as viable ways to enhance healthcare services. AI chatbots can simulate human-like interactions and help with patient triage and medical advice. The concept involves developing an AI-powered medical chatbot capable of diagnosing illnesses and offering fundamental information about the ailment prior to seeking advice from a physician. This initiative aims to lower healthcare expenses and enhance access to medical information through the utilization of medical chatbots. These chatbots are software programs employing natural language for user interactions. They store data in a database to recognize sentence keywords, enabling them to make informed query decisions and provide answers to questions. This shows a medical chatbot with basic symptom analysis and conversational approach may diagnose patients with some degree of accuracy;this implies that a successful bot might be feasible. Instead of trying to replace the essential human components of healthcare, the ultimate goal should be to leverage technology, such as AI chatbots, to improve patient outcomes and care. © 2024 IEEE.

关键词： Rural areas

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CFFS: An Improved Cluster-Driven Firefly Strategy for FANET Security 2

CFFS: An Improved Cluster-Driven Firefly Strategy for FANET ...

引用

2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Gupta, Shikha Sharma, Neetu Engineering College Ajmer Department Of Computer Science And Engineering Ajmer India

ISBN: (纸本)9798350381665

Flying Ad hoc Networks are becoming highly appealing due to their versatile applications in domains like surveillance, search and rescue, environmental monitoring, and communication relays, offering significant societal benefits. However, the use of these networks in dynamic environments leaves them open to a range of security risks, including denial-of-service, jamming, and routing attacks, in addition to manipulation and information dropping. To address these challenges, we introduce CFFS, a security approach employs the capabilities of the nature-inspired firefly algorithm. Our framework detects anomalous behavior by dynamically computing weighted parameters for firefly nodes using the Gradient Descent Method. Furthermore, we enhance the efficiency of CFFS by employing a cluster-oriented network system. Additionally, we employ a supervised support vector machine learning method to achieve our objective of threat identification. Through comprehensive experiments, we observe a sufficient enhancement in metrics, including 23.2% reduction in total delay, along with average improvements of 31.6% and 29.3% in threat identification and throughput, respectively over state-of-the-art approaches. © 2024 IEEE.

关键词： Support vector machines

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Gesture-Driven Virtual Mouse: Empowering Accessibility through Hand Movements 5

Gesture-Driven Virtual Mouse: Empowering Accessibility throu...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Suresh, Danush Ramyadevi, R. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

The mouse has been a vital tool for human-computer interaction, with wired, wireless, and Bluetooth variations requiring power to connect a dongle to a PC. The proposed work uses the latest technology in computer vision and machine learning to recognize hand movements without needing extra equipment. Compatible with CNN models through mediapipe implementation, it enables various actions via various hand movements, all on a single computer. Utilizing a camera as the input device, the system relies on Python and OpenCV. The camera's output is displayed on the system's screen, allowing users to fine-tune their interactions. © 2024 IEEE.

关键词： computer vision gesture recognition hand gestures interaction Virtual mouse

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A Comparative Analysis on Various Machine Learning Methods for GAN Based Video Anomaly Detection 7th

A Comparative Analysis on Various Machine Learning Methods f...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Chiranjeevi, Rahul Shyam, C. Hirthik Kaushiik, K. Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9783031699818

In recent years, surveillance has undergone tremendous change and developed into an essential instrument for maintaining security and keeping an eye on sensitive areas. This essay investigates the idea of what defines surveillance. It explores the crucial topic of anomaly detection, which is a vital component of contemporary surveillance systems. The expanding importance of the use of deep learning methods is highlighted in this paper’s discussion of current developments in surveillance technology. Through the processing of massive volumes of data, deep learning has transformed surveillance by allowing more precise and effective anomaly detection. Examining several kinds of deep learning techniques, their special qualities and uses are highlighted. This study concludes with an in-depth analysis of the monitoring, highlighting the role of deep learning in improving anomaly detection. It is an invaluable tool for researchers, professionals and decision makers interested in the development of surveillance technology and its use in different contexts. © IFIP International Federation for Information Processing 2024.

关键词： Contrastive Learning

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Transformer based Hybrid System for Speech Transcription and Disorder Detection 4

Transformer based Hybrid System for Speech Transcription and...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bhargavi, N.M. Bharadwaj, M. Shashank Koushik, M. Alva, Khushi U. Vedavathi, N. Vidyavardhaka College of Engineering Department of Computer Science & Engineering Mysuru India

ISBN: (纸本)9798331529635

Stuttering is a speech disorder accompanied by disruptions in the fluency of speaking and is characterized by involuntary repetitions, prolongations, or blocks in speech. The early detection and recommendation for personalized therapies are considered to be very important in effective treatment. Deep learning and machine learning-based techniques like ANN's and CNN's have held much promise as solutions in the implementation of an automatic stuttering detection and therapy suggestion system. The paper is interested in a number of methods followed for detection of stuttering and focuses on the integration of wav2vec for learning speech representation and BERT for classification based on text for stuttering events. We review existing models and techniques;compare the performances, and an experimental evaluation on the approach presented, using wav2vec and BERT. Our results show that deep learning models, especially language and speech pre-trained models, significantly improve the stuttering detection accuracy. © 2024 IEEE.

关键词： Contrastive Learning

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