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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Kulkarni, Radhika V. Kamble, Ruchita Joshi, Soham Jaiswal, Priten Jumbad, Varad Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350389609

One of the most serious natural disasters endangering forest resources and areas is forest fires. Decreased casualties and enhanced firefighting depend on precise and timely forest fire detection. Due to their reliance on ground inspection and field-of-view limitations, conventional firefighting techniques are inadequate for monitoring vast areas. Unmanned aerial vehicles (UAVs) are very difficult to identify forest fires, isolate fire pixels from the backdrop using Red Green Blue (RGB), grayscale color models, and extract luminance and chrominance from the original image. Over the past several years, machine-learning methods have been used to analyze forest images for detecting fires like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Random Forests, and many more. The goal is to determine the model for this crucial task by examining performance metrics across various algorithms. This study compares models like CNN, U-Net, and conventional machine learning models based on their accuracy, precision, and F1 score. Through thorough testing and analysis, this study aims to fully assess each model's strengths and flaws, ultimately aiding to development of more dependable and efficient methods for detecting forest fires. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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FoodSavior: Distributing Surplus Food to NGOs or Manure Producers Using Internet of Things and Machine Learning 5

FoodSavior: Distributing Surplus Food to NGOs or Manure Prod...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Uke, Shailaja Jadhav, Prithviraj Kakarwal, Urmila Kolawale, Sanika Nikam, Vivek Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331530013

By evaluating the quality of leftover food and enabling its best possible redistribution, this research presents a novel approach to addressing food waste in the restaurant sector. The system uses a Flutter an Dart-based application to control the food distribution process and hardware sensors like MQ4 to identify gasses that indicate spoiling. While spoiled food is sent to manure producers for use in agriculture, non-spoiled food is sent to NGOs to feed people in need. In order to minimize waste, the system analyzes sensor data and uses machine learning algorithms, such as Decision Trees, to forecast spoiling patterns and suggest preparation techniques. Restaurants can quickly enter food data into the app, which NGOs and manure producers may use to collaborate in real time. This data is processed by the Django-developed backend server, and Arduino C++ is used to control hardware activities. This solution promotes sustainability, social responsibility, and community welfare by facilitating a smooth collaboration between farmers, restaurants, and non-governmental organizations. The method helps create a more sustainable and just food environment by decreasing food waste and improving resource efficiency. This version emphasizes the objectives, innovation, and societal impact of the work while maintaining technical details. © 2024 IEEE.

关键词： C++ (programming language)

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Dynamic 3D Facial Expression Reconstruction from Images 5

Dynamic 3D Facial Expression Reconstruction from Images

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Bharadwaj, Rakhi Borse, Rushikesh Ahire, Purvesh Aldhar, Prathamesh Hoshing, Atharva Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350367171

In today's digital landscape, the demand for highly realistic 3D representations of human faces has surged, driven by the rapid expansion of virtual reality, augmented reality, and face recognition technologies. Despite significant strides in 3D facial geometry reconstruction from 2D images, achieving accurate and detailed translations of facial features and expressions remains a formidable challenge, critical for creating truly immersive digital experiences. In response, our research presents a groundbreaking approach to detailed reconstruction, specifically designed to convert single 2D images into intricately detailed and lifelike 3D models. Our methodology combines state-of-the-art techniques, such as UV displacement mapping and detail decoding, to meticulously capture even the subtlest nuances within 2D images, enhancing aspects like facial textures, sharp edges, and nuanced lighting effects. Furthermore, we employ advanced detail rendering techniques to further enrich the fidelity of the resulting 3D models, effectively narrowing the gap between digital representations and their real-world counterparts. Through the utilization of specialized loss functions, we meticulously optimize the reconstruction process, ensuring the faithful preservation of fine details while maintaining overall coherence and fidelity. This meticulous refinement promises to deliver immersive digital experiences that not only capture users' attention but also deeply engage them, offering a significant advancement in the realm of 3D facial reconstruction. © 2024 IEEE.

关键词： 3D reconstruction

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Dynamic Emotion-Adaptive Attention Mechanism (DEAAM) 5th

Dynamic Emotion-Adaptive Attention Mechanism (DEAAM)

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Garugu, Suresh Kumar Madhuri, P. Shyamala Atchana, Venu Gopal Penmetsa, Gopala Krishnam Raju Bandaru, Venkata Naga Rani Sastry Eemani, V.R.J. Department of Computer Science and Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031770807

The Dynamic Emotion-Adaptive Attention Mechanism (DEAAM) offers a groundbreaking framework for analyzing emotions in real-time video streams, leveraging a state-of-the-art convolution neural network (CNN) for rapid and accurate emotion detection. By integrating an adaptive attention mechanism, DEAAM dynamically adjusts video sampling rates and focuses on areas of emotional intensity, optimizing computational resources for the most expressive moments. This system intricately combines facial landmark detection with saliency mapping to pinpoint critical expressive features, enhancing the depth and precision of the analysis. Furthermore, DEAAM incorporates temporal emotional coherence tracking, utilizing advanced recurrent neural networks to capture the evolution of emotional states, adding a rich contextual layer to the emotion recognition process. This comprehensive approach not only increases the accuracy of emotion analysis but also ensures efficiency by focusing on key emotional expressions. Ideal for applications in virtual communication, mental health assessment, and any domain requiring nuanced emotion recognition, DEAAM sets a new standard in empathetic technology, offering a sophisticated, contextually aware system for extracting real-time emotional insights from video feeds. © ICST institute for computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Recurrent neural networks

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Real-Time Network Monitoring and Reporting Using Network Intrusion Detection System 9

Real-Time Network Monitoring and Reporting Using Network Int...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Lonare, M.B. Joshi, Bhuvan Chandra Tripathy, Satyam Kumar Kumar, Shivam Tiwari, Shivanshu Army Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350394474

The increased participation in digital networks for communication, commerce, and critical infrastructure, calls for a robust Network Intrusion Detection System. This paper systematically examines the current landscape of NIDS, by analyzing the methodologies, algorithms, and technologies used in finding different forms of network threats. The paper begins by presenting the fundamental principle of the Intrusion Detection System and the evolving threat scenario and the benefits of using ML and Deep Learning when employed in this use case. We also dive into the categorization of NIDS into Signature-Based, anomaly-based, and Hybrid approaches. Evaluating each category's strengths and weaknesses. The paper aims to provide a comprehensive discussion and comparison of the various techniques proposed like MCF-MVO-ANN, an Intrusion detection system based on scalable K-means and Random Forest, etc. By synthesizing current knowledge this paper aims to serve as a valuable resource for researchers, practitioners, and decision-makers in the field of cybersecurity. © 2024 IEEE.

关键词： Network security

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Indian sign language recognition using TensorFlow and MediaPipe 1st

Indian sign language recognition using TensorFlow and MediaP...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Kanyal, Hoshiyar Singh Gupta, Shubham Singh, Smriti Kumari, Sarita Department of Computer Science and Engineering ABES Institute of Technology Ghaziabad India

ISBN: (纸本)9781032874036

With this work, a technical method for identifying Indian Sign Language is attempted to be developed. This work attempted to create a system for recognizing the hand movements from Indian Sign Language using machine learning techniques. An amalgamation of two- and three-dimensional illustrations of Indian Sign Language motions has been employed. The dataset was manually assembled. To identify landmarks in the pictures, the Media Pipe framework has been used. The findings show that the approach used in this study is successful in identifying the other Sign Language alphabets and gestures. The signs and gestures recognition for several more Indian regional languages could also be investigated and explored using this method. © 2025 the Author(s).

关键词： Gesture recognition

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Movie Recommendation Using Web Crawling 2nd

Movie Recommendation Using Web Crawling

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Raj, Pronit Kumar, Chandrashekhar Shekhar, Harshit Kumar, Amit Paul, Kritibas Jana, Debasish Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9783031845420

In today’s digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real-time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing the relevance and freshness of suggestions. By combining content-based filtering, collaborative filtering, and a hybrid model, we create a system that utilizes both historical and real-time data for more personalized suggestions. Our methodology shows that incorporating dynamic data not only boosts user satisfaction but also aligns recommendations with current viewing trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data reduction

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Self-supervised few-shot medical image segmentation with spatial transformations

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Neural Computing and Applications 2024年第30期36卷 18675-18691页

作者： Titoriya, Ankit Kumar Singh, Maheshwari Prasad Singh, Amit Kumar Department of Computer Science & amp Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Deep learning-based segmentation models often struggle to achieve optimal performance when encountering new, unseen semantic classes. Their effectiveness hinges on vast amounts of annotated data and high computational resources for training. However, a promising solution to mitigate these challenges is the adoption of few-shot segmentation (FSS) networks, which can train models with reduced annotated data. The inherent complexity of medical images limits the applicability of FSS in medical imaging, despite its potential. Recent advancements in self-supervised label-efficient FSS models have demonstrated remarkable efficacy in medical image segmentation tasks. This paper presents a novel FSS architecture that enhances segmentation accuracy by utilising fewer features than existing methodologies. Additionally, this paper proposes a novel self-supervised learning approach that utilises supervoxel and augmented superpixel images to further enhance segmentation accuracy. This paper assesses the efficacy of the proposed model on two different datasets: abdominal magnetic resonance imaging (MRI) and cardiac MRI. The proposed model achieves a mean dice score and mean intersection over union of 81.62% and 70.38% for abdominal images, and 79.38% and 65.23% for cardiac images. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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A Comprehensive Visual Debugger For Error Resolution in C++ 15

A Comprehensive Visual Debugger For Error Resolution in C++

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Uke, Shailaja Patankar, Abheerav Patil, Aaditya Patwardhan, Srujan Phand, Swaraj Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper introduces a sophisticated visual debugger that seamlessly integrates C++ code analysis with C # WPF visualisation for an enhanced debugging experience. The tool operates by extracting symbol information from target C++ source files during debugging sessions, utilizing PDB files, specifically tailored for the Visual Studio environment. The core functionality of the tool involves the extraction and storage of symbol information within a meticulously designed stack frame and stack trace classes. Leveraging C++ capabilities, the project captures not only call stack information but also variables and data structures associated with each stack frame, providing a comprehensive view of the program's execution state. To facilitate interoperability and visualization, the project employs a marshalling mechanism to seamlessly transfer the extracted data from the C++ backend to a C# WPF frontend. The Windows Presentation Foundation application serves as an intuitive and interactive interface, presenting the call stack alongside associated variables and data structures in a visually appealing manner. This tool is positioned to significantly enhance the debugging process for developers working in C++ within the Visual Studio environment. By seamlessly integrating symbol extraction, data structuring, marshalling, and visualization, it provides a holistic and efficient solution for diagnosing complex code scenarios. The resulting visualization aids developers in gaining a deeper understanding of their code's execution flow, enabling quicker identification and resolution of issues during the debugging process. © 2024 IEEE.

关键词： Interoperability

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Advancing Software-Defined Vehicles: An End-to-End Framework with Digital Twin Based Attestation for OTA Updates 17

Advancing Software-Defined Vehicles: An End-to-End Framework...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Agrawal, Krish Rohan, Jha Luthra, Nishkarsh Sekhar, Pilla Venkata Sharma, Hrishesh Banda, Gourinath Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

Software-defined vehicles (SDVs) are an emerging paradigm in the automotive industry where vehicles' functionality, performance, and safety can be enhanced and updated through software, even after production. Unlike traditional vehicles, which rely primarily on physical components, SDVs leverage advanced connectivity, real-time data analytics, and cloud integration to adapt to changing regulations, driver preferences, and environmental conditions. This shift enables vehicles to evolve continuously, responding to new technological advances and customer needs. In this paper, we propose an end-to-end framework to demonstrate and realize the full benefits of SDVs. We incorporate the concept of digital twin (of the vehicle) driven software update authorization to download or update an application on the vehicle. In our framework, attestation refers to the verification of each software update's compatibility and functionality with the vehicle's current ECUs before deployment. Any impending/requested update is first verified for its compatibility with the vehicle's architecture (as per the twin) on the cloud. The idea is via the SDVs' digital twin - since each and every ECU is virtualized - the simulation and testing of the actual hardware setup with the new application software can be done without direct impact on the physical vehicle. Once the application is installed across all relevant virtualized ECUs, the framework confirms compatibility, ensuring smooth deployment and functionality in the real-world vehicle. Post this successful attestation, the installation will be done on the real vehicle corresponding to that digital twin. Through this proposed framework, we intend to ensure the safety of the updation, while the new updates contribute to the functionality and performance improvements. © 2025 IEEE.

关键词： Automotive industry

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