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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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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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Implementing Robust Security Protocols for Virtual Images in Cloud Computing Platform

Implementing Robust Security Protocols for Virtual Images in...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Tiwari, Shiv Kumar Gupta, Manish Neogi, Subhrendu G. Amity School of Engineering and Technology Amity University Department of Computer Science Engineering Gwalior India The Neotia University Department of Computer Science Engineering West Bengal India

ISBN: (纸本)9798331517953

The new technology, with the aid of newly emerging knowledge known as cloud computing, can provide resources remotely and on demand. With the use of cloud computing, users can operate in settings where they are not dependent on physical infrastructure. Numerous protocols facilitate the utilization of cloud computing resources. Cloud computing is Internet-based technology that is made available to consumers of its resources and services and is billed depending on demand and usage. All cloud servers cannot see the precise place where the user is to be configured. User-generated secure keys are employed in the proposed work for both virtual machine (VM) switching and authentication. Every time a valid user is authenticated, a secure pass key is utilized to guarantee security and authentication between the user and the system. © 2024 IEEE.

关键词： Cloud platforms

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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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3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

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computer Modeling in engineering & sciences 2024年第2期138卷 1365-1384页

作者： Dun Cao Jia Ru Jian Qin Amr Tolba Jin Wang Min Zhu College of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time *** modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is *** paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

关键词： Internet of vehicles road networks 3D road model structure recognition GIS

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Computational Approaches for Identifying Potato Plant Pathogens 5

Computational Approaches for Identifying Potato Plant Pathog...

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

作者： Julian, Anitha Vignesh, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Potato cultivation is a vital component of global agriculture, providing a significant source of nutrition and income. However, the threat of diseases affecting potato crops remains a persistent challenge for farmers. In our study, we present a novel approach to automatically classify diseases in potato plants using advanced deep learning methods. The proposed approach makes use of Convolutional Neural Networks (CNNs), that perform efficiently for image processing applications. The dataset utilized in our research comprises high-resolution Images depicting diverse disease symptoms in potato plants, including late blight, early blight, and the oomycete pathogen Phytophthora infestans, a destructive ailment that impacts potato crops. To automatically discern between healthy and diseased plants, we implement a multi-layered CNN architecture that learns discriminative characteristics of images. This research significantly contributes to precision agriculture by providing an automated and efficient solution for identifying potato diseases. The proposed method in disease classification not only facilitates prompt disease management but also enables precise treatments, thereby reducing reliance on chemical interventions. The practical implications of these findings extend to farmers, agronomists, and researchers involved in sustainable and resilient potato cultivation. © 2024 IEEE.

关键词： disease identification early blight late blight potato disease

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A Secure Authentication Mechanism for IOT Devices Using Hyperledger Fabric 5th

A Secure Authentication Mechanism for IOT Devices Using Hype...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Infant Vinoth, P. Nagendra Kumar, D. Guhan, M.P.S. Archana, M.R. Santhana Hari, S. Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9789819718405

There is currently widespread use of IoT devices across various fields like health care, agriculture, manufacturing, transportation, and smart cities. IoT involves connecting objects and devices to the internet, which allows them to communicate, collect, and exchange data among the devices. These data that it gets require a need to properly provide data privacy so that these data are not misused by a malicious user. Since data privacy is important in this environment, which involves a lot of data transfer among the various IoT devices. So, in this paper, a blockchain-based platform called Hyperledger fabric is used for data exchange between IoT devices, and a new authentication mechanism has been proposed for the Hyperledger fabric platform so that we can ensure data privacy. Being a consortium blockchain, we in this paper provide a zero knowledge proof (ZKP) based authentication mechanism for each and every IoT device, so that we tend to eliminate the possibility of a malicious device on the Hyperledger fabric platform. The suggested system enhances the privacy of data transfer between IoT devices, and it is assessed based on a variety of performance indicators in order to demonstrate the potential advantages of the proposed mechanism. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Authentication

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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...

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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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Machine Learning (ML) based Electricity Price Prediction with Visualization 3

Machine Learning (ML) based Electricity Price Prediction wit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Abisha, D. Kumar, Prasanna D. Christy, Ida J. Evanjalin, Navedha R. Rajesh, P. Haris, Mohamed K. Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9798331532420

Electricity price prediction is crucial for businesses reliant on heavy machinery or computing services, as it aids in cost management and energy optimization. Accurate predictions allow these businesses to forecast annual electricity expenses, enhancing cost efficiency. This project aims to forecast electricity prices using machine learning techniques, specifically the Random Forest regression algorithm. The project utilizes historical data on electricity consumption and other influencing factors, including date and time, holiday indicators, day of the week, week of the year, forecasted and actual wind production, national load, temperature, wind speed, and CO2 intensity. The methodology involves converting string values to numerical values, handling missing data, and normalizing the dataset, identifying key features that significantly impact electricity prices, Splitting the data into training and testing sets, and training the model using the Random Forest regression algorithm and assessing the model's performance with appropriate metrics to ensure accuracy and reliability. The results demonstrate that the Random Forest model effectively predicts electricity prices with high accuracy. Visualizing these predictions in Power BI provides intuitive insights into the factors influencing electricity prices. These findings enable businesses to better anticipate electricity expenses, optimize energy usage, and improve financial planning. The comprehensive approach outlined in this project offers a valuable tool for understanding and forecasting electricity prices, thereby enhancing resource management and cost efficiency. © 2024 IEEE.

关键词： Data Preprocessing Electricity Pricing Energy Cost Optimization Machine Learning Random Forest Regression

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

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

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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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