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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Sharma, Dishit Nijhara, Prajjwal Banerjee, Dip Sankar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

Katz centrality measures a node’s influence within a network, considering the total number of walks between pairs of nodes rather than solely the shortest paths. This paper presents an algorithm for dynamically updating Katz centrality scores as the underlying graph evolves. Our method employs a frontier-based breadth-first search (BFS) to efficiently derive updated Katz scores in dynamic graphs. Notably, our algorithm accommodates node insertions and deletions, addressing a significant limitation of current state-of-the-art methods. © 2024 IEEE

关键词： Graph algorithms

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Automated Video Transformation for PIB Press Release: A Literature Survey 1

Automated Video Transformation for PIB Press Release: A Lite...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Bharadwaj, Rakhi Kachare, Mitali Kadam, Vaibhav Jagtap, Rahul Ghuge, Pranjal Dhore, Manikrao Otari, Samarth Mahajan, Parth Meshram, Pratik Mhaske, Krishna Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350377002

This paper investigates new trends in press release distribution, with a special emphasis on the shift from text-to-video formats. Modern audiences' short attention spans demand a more captivating method of disseminating information. We suggest a software program that does this job automatically by turning press releases into interesting videos. This program creates visually appealing material by using a collection of photos and videos that are not protected by copyright. Furthermore, our software integrates cutting-edge technologies like Natural Language Processing for keyword extraction along with algorithms like Long Short-Term Memory for language translation. By improving accessibility and relevance, these features enable a wider range of users to consume the information. This study demonstrates how technology has the power to change conventional communication techniques, making them more interesting and available to modern audiences. Notably, it's important to clarify that this paper exclusively focuses on conducting a thorough literature survey of existing research papers in this domain. © 2024 IEEE.

关键词： Presses (machine tools)

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Campus Compass: A Flutter-based Interactive Map for Campus Navigation 15

Campus Compass: A Flutter-based Interactive Map for Campus N...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Tadas, Srilakshmi R. Bipin Krishna, G.A. Manohar, B.N. Srinivasa, K. Kavitha, M. Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

ISBN: (纸本)9798331300579

This work developed a Flutter-based interactive mobile application for effortless campus navigation called "Campus Compass." The app utilizes the Google Maps API to provide an interactive map display and routing functionality. The primary goal of the app is to assist students in navigating campus, finding their way to classes, and discovering new places on campus. The app includes features such as an interactive map display, search functionality, routing, real-time information about events and classes, and emergency contact information. The user interface of the app is designed to be user-friendly and intuitive, making it easy for students to use. The search feature allows users to search for specific buildings, services, or landmarks, and the routing feature provides turn-by-turn navigation to the user's destination. Real-time information about events and classes happening on campus is fetched from the university's website through web scraping. The Campus Compass app has been thoroughly tested on various devices and operating systems to ensure optimal performance. This app is expected to improve the overall campus experience for students, making it easier for them to navigate and find their way around the campus. In conclusion, the Campus Compass app is a valuable tool for students to explore their campus and find the resources they need. It is an efficient and user-friendly mobile application that makes campus navigation more convenient and less stressful. The app is expected to be well-received by students and make a positive impact on their campus experience. © Grenze Scientific Society, 2024.

关键词： Compasses (magnetic)

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Climate Geoscience–Based Disaster Management in Healthcare Analysis by Markov Q-Transfer Adversarial with Cloud Computing

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 474-483页

作者： Srinivas, Jhade Rebecca, B. Bhargavi, Kovvuri N Swetha, Velma Reddy Spandana, S. Degala, Divya Priya Department of Computer Science and Engineering-Data Science KG Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE Aditya University AP Surampalem India Department of Computer Science and Engineering (AI-ML) Malla Reddy Engineering College Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Analysis and reaction to natural disasters have made extensive use of deep learning methods using semantic segmentation networks. These implementations’ foundation is based on convolutional neural networks (CNNs), which are capable of precisely identifying and locating the relevant areas of interest within satellite imagery or other types of remote sensing data. This helps with rescue planning, restoration efforts, and disaster evaluation. Difficulties in the provision of healthcare services, due to natural disasters, are made worse by the existing problems prevalent in most healthcare systems. The aim of this research is to propose a novel technique in climate change–based disaster management with their public healthcare analysis using cloud computing with deep learning techniques. In this research, the cloud network has been used in LiDAR image collection for climate change–based disaster-struck regions and their public healthcare analysis. This collected image has been classified and optimized using a deep Markov Q-transfer adversarial neural network with binary grey whale swarm optimization. Experimental analysis has been carried out for various disaster management–based public healthcare datasets in terms of classification accuracy, specificity, NSE, sensitivity, and F-1 score. The proposed technique attained a classification accuracy of 97%, F-1 score of 94%, specificity of 96%, sensitivity of 95%, and NSE of 57%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial neural network Cloud computing Disaster management LiDAR image Whale swarm optimization

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Audio Spoof Detection Using Inverse Gammatone Cepstral Coefficients and Multi- Layer Perceptron Classifier 5

Audio Spoof Detection Using Inverse Gammatone Cepstral Coeff...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Nandal, Abhishek Dua, Mohit National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798331528539

The rise of synthetic audio and voice cloning technologies has necessitated the development of robust methods for detecting audio spoofing. Accurate and reliable spoof detection is critical for maintaining the integrity of voice-based authentication systems and preventing malicious misuse of synthetic speech. This paper presents a approach to audio spoof detection utilizing Inverse Gammatone Cepstral Coefficients (IGTCC) for feature extraction and a neural network for classification. The IGTCC method captures essential audio characteristics, enhancing the model's ability to differentiate between genuine and spoofed audio. Our system is evaluated using the Fake or Real (FoR) dataset, which comprises a balanced collection of both authentic and spoofed audio samples. The dataset includes various spoofing techniques, providing a comprehensive benchmark for testing our model's effectiveness. The proposed neural network architecture is trained and validated on this dataset, demonstrating its efficacy in accurately identifying spoofed audio samples. The system achieves a remarkable Equal Error Rate (EER) of 1.6%, underscoring its potential for deployment in real-world applications. This significant improvement over existing methods highlights the effectiveness of our approach in addressing the challenges of audio spoof detection. © 2024 IEEE.

关键词： Multilayer neural networks

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Performance Analysis of Machine Learning Techniques in Plant Leaf Disease Detection

Performance Analysis of Machine Learning Techniques in Plant...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Gupta, Sankalap Kumar, Piyush Pandit, Mohammad Khalid National Institute of Technology Hamirpur Department of Computer Science & Engineering India

ISBN: (纸本)9798350376876

Agriculture is one of the most important sectors in nutrition and economy worldwide. One of the most effective ways to combat the contraction of agricultural activities is the management of crop diseases, which impact food security and the earnings of many people. This study deals with applying a variety of ML techniques that are used in the disease diagnosis of plant leaves based on the dataset PlantVillage, especially corn, apples, and potatoes. Feature extraction was performed by Histogram of Oriented Gradients(HOG), and traditional models: Support Vector Machine, Random Forest, Logistic Regression, and Multilayer Perceptron were used for training. The ensemble techniques of bagging, XGBoost, stacking, and voting classifiers brought more improvement in the model performance. Separation of diseases with very close features is still a challenging task. The results seem quite promising for identifying leaf diseases through machine learning techniques, but because the conditions are indistinguishable, further research becomes obligatory. © 2024 IEEE.

关键词： Adversarial machine learning

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TruChit: A Blockchain-Based Trusted Chit Fund System with Creditworthiness Evaluation 2

TruChit: A Blockchain-Based Trusted Chit Fund System with Cr...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sharma, Akhilesh Chandrakar, Preeti Department of Computer Science & Engineering National Institute of Technology Raipur India

Chit fund is a peer-to-peer saving and borrowing scheme operated among trusted groups of people. It is a reliable source of funds in emergencies with no guarantor and low-interest rate. Despite their enduring benefits, chit funds face challenges related to trust, transparency, and security. At present, unscrupulous subscribers might join chit-funds, borrow money, and make payment defaults. This leads to disruptions in contribution cycle and affects overall functioning. Additionally, non-transparent record-keeping and transaction processes, hinder participants from verifying fund activities, creating a susceptible environment for fraud and malpractice. These challenges are an obstacle to the sustainability and trustworthiness of the chit-fund system. Therefore, to overcome aforementioned challenges, this paper proposes TruChit, a blockchain-based chit fund system with creditworthiness evaluation framework. It leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess the creditworthiness of subscribers, which ensures the credibility and reliability of individual participants. Further, proposes permissioned blockchain-assisted chit-fund framework with role-based access control to instill trust, security, and transparency within chit-fund operations. Moreover, the efficacy of TruChit is evaluated by analyzing credit score dataset and achieves 93.5% accuracy, which is better than other approaches. © 2024 Elsevier B.V.. All rights reserved.

关键词： Blockchain

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A Primitive Based Machine Learning Framework for the Network Data-plane 18

A Primitive Based Machine Learning Framework for the Network...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Singh, Sourabh Kanhaiya, Kunvar Magadum, Pralhad Kushwaha, Aniruddha Singh Mondal, Ayan Indian Institute of Technology Department of Computer Science & Engineering Indore India

ISBN: (纸本)9798350391725

The SDN-based network architecture currently lacks a framework for the efficient development and deployment of machine learning (ML) functions within the data plane. This paper addresses this gap by proposing a unified, modular ML framework designed to support data-plane deployment. The framework operates by identifying a minimal set of key primitives that can be effectively shared across various ML models. We demonstrate its functionality using defined primitives for Artificial Neural Network (ANN) models, incorporating specific approximations to enhance computational speed. We benchmark the framework through simulations to evaluate approximate resource usage and cycle computation. Our results indicate that these approximations result in only a minimal accuracy loss. © 2024 IEEE.

关键词： Adversarial machine learning

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DeepFish: A Comprehensive Exploration of Deep Learning Methods for Accurate Fish Species 8

DeepFish: A Comprehensive Exploration of Deep Learning Metho...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Bhand, Aarohi More, Priyanka Gawande, Shreyash Deshmane, Swaraj Bharsakale, Pranjal Bhand, Rahi Sakhare, Sachin Thombare, Vipin Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350391770

The DeepFish exploration aims to develop a web-based application that leverages deep learning algorithms to accurately identify and provide detailed information about various fish species based on user-uploaded images, specifically for the unique fish species in Maharashtra. The system utilizes state-of-the-art computer vision techniques to process the uploaded fish images and employs a trained model to predict the fish species. The paper presents a rigorous comparative analysis of deep learning algorithms, to accurately identify the most efficient model for our application with minimum response time and maximum accuracy for identification. The Fish Prediction website provides users with a brief description and relevant information about the predicted fish species including common habitat, diet, physical characteristics, and consumption status, enhancing their knowledge and understanding of the diverse aquatic life. Overall, it provides an accessible and user-friendly platform for fish identification and information retrieval, benefiting researchers, fishing enthusiasts, and conservationists © 2024 IEEE.

关键词： Invertebrates

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AVATAR-RETAIL: The Digital ShoppingWorld 15

AVATAR-RETAIL: The Digital ShoppingWorld

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Gahana, R. Ekeshwar, J. Nihal Jain, T.P. Shanthakumara, A.H. Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

ISBN: (纸本)9798331300579

With cutting-edge functionality, 3D Shopping (Avatar Retail) promises to revolutionize the online shopping experience by seamlessly integrating gamification, social media, and augmented reality features. By immersing users in a realistic virtual shopping environment, the application aims to bridge the gap between traditional in-store purchases and the convenience of online buying. Through dynamic 3D navigation, avatar customization, and gamification components, Avatar Retail enhances user engagement and ensures seamless performance scaling on various platforms. Augmented reality plays a central role, allowing users to interact with products in a fully immersive online environment while facilitating instant communication and collaboration. Extensively tested across multiple platforms and devices, Retail Realm sets new standards for immersive online shopping, combining technology and user-centric design to make a significant impact on the e-commerce sector. © Grenze Scientific Society, 2024.

关键词： Gamification

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