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Built-Up and Land Use Land Cover Mapping for Sustainable Planning in Himalayan Region by Using Machine Learning and Sentinel-2 Satellite Imagery 1

Built-Up and Land Use Land Cover Mapping for Sustainable Pla...

引用

1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Singh, Suraj Saini, Rashmi Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350364590

The Himalaya region, known for its ecological sensitivity, also faces recurring natural hazards such as earthquakes, landslides, sinking, glacier bursts, and flash floods, which necessitate the implementation of effective planning and management strategies to ensure sustainable development. This study focuses on Joshimath, a subdivision of Chamoli district in Uttarakhand, India, situated in the Greater Himalaya region, covering an area of 4563.52 km2, Our objective is to map built-up areas using Machine learning techniques and Sentinel-2 satellite multispectral imagery to support sustainable planning initiatives. Two machine learning models, Random Forest (RF) and Support Vector Machine (SVM), were employed in this study. RF achieved an overall accuracy of 92.62% with a kappa value of 0.911, while SVM achieved an accuracy of 90.557 with a kappa value of 0.886. Precision and recall metrics for the built-up class were calculated, with RF achieving 94% precision and 83.22% recall, and SVM achieving 93.48% precision and 82.24% recall. Additionally, the total area classified as built-up by RF was 236.96 km2, whereas SVM classified 198.67 km2. The results demonstrate the effectiveness of machine learning techniques, particularly RF and SVM, in accurately mapping built-up areas in the Himalaya region. This information is crucial for sustainable planning and management practices, enabling stakeholders to make informed decisions regarding land use and development while preserving the ecological integrity of the region. © 2024 IEEE.

关键词： Mapping

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A Novel Hybrid Deep Neural Network for Early Detection and Classification of Chicken Diseases 5

A Novel Hybrid Deep Neural Network for Early Detection and C...

引用

5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Md Shihab Islam, Md Fakrul Rahman, Sharukh Foysal, Nasir Uddin Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka1216 Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350394290

Detection and classification of chicken diseases has been a longstanding concern for farmers. Relying solely on expert opinions could be a costly option. Moreover, there are certain chicken diseases that spread quickly, and necessary procedures are immediately needed to carryon. On the other hand, the lack of research activity or low availability of data on this topic leads to many misunderstandings and challenges so as to correctly detect a certain type of chicken disease. The existing techniques show low accuracy on disease classification, which leads to a complex engineering problem and critical analysis is required for sustainable development. In this paper, we develop a novel hybrid deep neural network, namely ChickenNet21, which combines a Convolutional Neural Network (CNN) and a fine-tuned Visual Geometry Group (VGG) model to address this challenge. Using a large dataset of 6,812 farm-labeled fecal images, we assess the effectiveness of ChickenNet21 and it achieves 98.83% accuracy in identifying chicken diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Review of Deep Learning Methods for Hyperspectral Image Compression 3

A Comprehensive Review of Deep Learning Methods for Hyperspe...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Afrin, Afsana Mamun, Md. Al Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

Rapid advancement in the development of hyperspectral image (HSI) sensors has significantly enhanced the capabilities of capturing detailed spectral information across a wide range of wavelengths, enabling more precise analysis and interpretation of the observed scenes. Managing the storage of these extensive images poses a crucial challenge, requiring the application of compression techniques. Deep Learning (DL) algorithms have seen a massive rise in popularity over the past few years for HSI compression as well as HSI analysis. This review paper provides a comprehensive summary of the current literature on deep learning methods for HSI compression. Additionally, it explores the challenges, opportunities, and potential directions for future research in HSI compression. © 2024 IEEE.

关键词： Image enhancement

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An Extended Reality Platform Powered by Large Language Models: A Case Study on Teaching Dance Costumes 7

An Extended Reality Platform Powered by Large Language Model...

引用

7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Garzarella, Silvia Vallasciani, Giacomo Cascarano, Pasquale Hajahmadi, Shirin Cervellati, Elena Marfia, Gustavo University of Bologna Dept. of the Arts Bologna Italy University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331521578

In this paper, we present an Extended Reality (XR) platform powered by Large Language Models (LLMs), designed to support education in dance history and cultural heritage. The platform here applied to a case study on a dance costume, allows users to interact with 3D models, annotate them, and explore their historical and cultural contexts collaboratively. Using features like image-to-LLM queries, users can gain insights into costume details and performance history, and the annotation tools enable uploading multimedia resources, thereby simulating an archival research environment. By integrating LLMs, the platform provides tailored information and context on demand, enriching the user experience with detailed explanations about objects, such as costume construction, usage, and cultural significance. © 2025 IEEE.

关键词： Hosiery manufacture

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'Precision Agriculture Farming' Enhancing Farming Efficiency through Technology Integration 1

'Precision Agriculture Farming' Enhancing Farming Efficiency...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Prashant Kumar, Pratyaksh Singh, Praveen Kumar Rana, Arun Kumar Galgotias College of Engineering And Technology Dept. Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350356816

Precision agriculture, or precision farming, or smart farming, refers to a contemporary approach that combines technology and data analytics with agricultural practices. This research paper examines the concept of precision agriculture, its principles, advantages, disadvantages, and potential to transform farming practices. Through the use of various technologies including GPS, sensors, drones, and data analytics, precision agriculture seeks to maximize resource efficiency, enhance crop yields, reduce the environmental footprint, and boost overall profitability for the farm. The paper also addresses case studies and real-life implementations of precision agriculture and illustrates its effectiveness in various sectors of agriculture. It further identifies the challenges and constraints of adopting precision agriculture and proposes possible solutions to overcome these challenges. This paper generally offers insight into the revolutionary potential of precision agriculture in reshaping the future of farming. © 2024 IEEE.

关键词： Smart agriculture

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Prediction and Modelling of Suicidal Mental Tendencies 8

Prediction and Modelling of Suicidal Mental Tendencies

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Guruprasad, Sunitha Rao, Arnav G. Shreeraksha Shivani, K. Varsha, S.N. Yashoda, K. St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

Suicide remains a pressing public health concern worldwide, necessitating effective predictive and preventive strategies. Predicting suicidal tendencies is a complex endeavor, as it involves multifaceted interactions between biological, psychological, and environmental factors. Advances in computational modeling and machine learning have provided promising avenues for understanding and forecasting these tendencies. Such models often integrate clinical data, including psychiatric history, symptom severity, and demographic information, with biological markers and psychosocial variables. The objective of our work is to identify patterns and risk factors that may indicate heightened suicidal risk. Ongoing research seeks to refine predictive models, incorporate real-time monitoring, and develop targeted interventions, aiming to reduce the incidence of suicide and provide timely support to those at risk. The application of suicide text analysis extends to user-friendly interfaces, allowing individuals to input text, receive predictions, and access relevant support. The analysis involves preprocessing techniques such as text normalization, stop-word removal, and stemming to enhance the quality of input data. Various machine learning algorithms, ranging from Naive Bayes to sophisticated ensemble methods, are explored to determine their effectiveness in predicting suicidal ideation. © 2024 IEEE.

关键词： Prediction models

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Enhancing Security in Communication Networks through Quantum Key Distribution 2

Enhancing Security in Communication Networks through Quantum...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Purushottam, Vaishnavi V Meena, S. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bengaluru India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Quantum Key Distribution or QKD revolutionizes secure communication using quantum mechanics. Unlike traditional cryptographic methods vulnerable to advances in computing power, QKD guarantees unconditional security by detecting any eavesdropping attempt. Quantum Key Distribution (QKD) constitutes a through development in the realm of secure communications by utilizing the ideas of quantum mechanics to provide potentially unbreakable encryption. This study examines the most current advancements in QKD, including the recently announced Measurement-Device-Independent QKD (MDI-QKD) and protocols like BB84 and E91. We analyze the real-world implementation, including satellite-based QKD and quantum repeaters. Furthermore, we discuss the challenges and opportunities of integrating QKD into present communication infrastructure. The significance of QKD in attaining secure communication networks for the next generation is underscored by our findings, which also have ramifications for forthcoming research and applications. Ultimately, this research aims to contribute to the development of robust and impenetrable communication networks in an increasingly interconnected world. © 2024 IEEE.

关键词： Quantum communication

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The Role of Medical Expert Systems in Diagnosis of Chronic Kidney Diseases 2

The Role of Medical Expert Systems in Diagnosis of Chronic K...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Kumar, Kuchipudi Prasanth Bharati, K.F. Jawaharlal Nehru Technological University Dept. of Computer Science and Engineering Andhra Pradesh Ananthapuramu515002 India Jntua College of Engineering Dept. of Computer Science and Engineering Andhra Pradesh Ananthapuramu515002 India

ISBN: (纸本)9798350370188

The ability to forecast kidney diseases in real-time is critical for preserving patients' lives. Early detection of these diseases not only extends the patient's longevity but also reduces treatment costs. It is very advised to develop a reliable expertise system for detecting these problems before they worsen. These smart solutions have the potential to save many lives. This review study assesses the Dice score efficacy characteristics obtained from various methodologies utilized in previous studies for detecting renal abnormalities or chronic kidney diseases. It gives a complete overview of medical expert systems in proposed methodology and emphasizes the most effective methods for identifying chronic kidney disease (CKD). © 2024 IEEE.

关键词： Diagnosis

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EMRChain: Electronic Medical Records Management System using Blockchain 3

EMRChain: Electronic Medical Records Management System using...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rajasekharan, Arjun Koshy, Rani College of Engineering Trivandrum Dept. of Computer Science and Engineering Kerala Thiruvananthapuram India College of Engineering Trivandrum Department of Computer Science and Engineering Kerala Thiruvananthapuram India

ISBN: (纸本)9798350354348

Electronic Medical Records (EMRs) are traditionally managed by central authorities, posing significant security risks such as data breaches, limited interoperability, and restricted patient control. This system leverages blockchain technology to address these issues, ensuring data remains immutable, transparent, and highly secure. Utilizing the Ethereum network and IPFS technology, our system provides a decentralized framework for storing and sharing medical records. Healthcare professionals, patients, and authorized parties can seamlessly exchange and access records through Ethereum's decentralized structure and smart contracts, enabling automated and auditable interactions. To enhance confidentiality, all data uploaded to IPFS undergoes AES 256-bit encryption, with the encrypted Content Identifier (CID) stored on the blockchain. This ensures that intercepted CIDs cannot be accessed without the decryption key. IPFS technology facilitates decentralized storage and retrieval, eliminating the need for a centralized server and improving scalability and resilience. Patients retain full control over their health information, granting or revoking access as necessary. This system ensures secure patient identification, precise access management, and data confidentiality while allowing authorized parties to track and trace record access and modifications. Our blockchain-based EMR management system addresses the inherent risks of traditional centralized systems by providing a secure, transparent, and decentralized solution. By leveraging Ethereum and IPFS, it enhances data security, patient control, and interoperability, thus paving the way for more robust and patient-centric healthcare information management. This approach ensures that healthcare data remains secure, patients maintain control over their information, and authorized parties can effectively manage and access the records. © 2024 IEEE.

关键词： Ethereum

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Advanced Forecasting and Analysis Techniques for Optimizing Website Performance: A Comparative Study of Time Series Models 2

Advanced Forecasting and Analysis Techniques for Optimizing ...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Raikar, Gayathri Harshith, D. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bangalore India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Analyzing website performance is crucial for optimizing a site's functionality, enhancing user engagement, and aligning with business goals. This evaluation focuses on essential metrics such as average engagement time per session, engagement rate, and events per session, which significantly impact user experience and overall business success. Through effective performance analysis, organizations can improve user satisfaction, boost conversion rates, and enhance their profitability and reputation. This paper reviews different approaches for analyzing these key metrics, explores their effects on user behavior and business performance, and offers best practices for optimizing website performance. By employing detailed performance analysis, businesses can make informed decisions and realize their strategic goals in the digital environment. © 2024 IEEE.

关键词： Channel Performance Data Analysis Time Series Forecasting User Engagement Website Performance

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