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Unwinding the Significance of Federated Learning for Smart Agriculture

Unwinding the Significance of Federated Learning for Smart A...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Bhat, Asif Nazir Sahil Kumar, Gautam National Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350379716

Smart agriculture leverages cutting-edge information and communication technologies to optimize agricultural practices, and machine learning plays a significant role in achieving this goal. However, due to the conventional nature of centralized learning, machine learning-oriented solutions in agriculture face significant challenges, primarily the network overburden, time-delayed response and limited computational capabilities of edge nodes. This leads to the requirement of a more efficient and decentralized approach for learning, which can distribute the computational load and enable real-time data processing closer to the data sources. The presented paper un-winds the significance of Federated Learning in addressing these challenges associated with Centralized Learning, particularly in agriculture. The paper explores Federated Learning's ability to leverage the collective intelligence of distributed data sources while maintaining computational efficiency and scalability. It presents it as an ideal solution for the agricultural sector, where data is often fragmented and collected in diverse environments. In this work, a methodology is adopted to compare and analyze the performance of Federated Learning with Centralized Learning using classification-based statistical parameters viz. Accuracy, Precision, Recall and F1-Score and hosting nodes' parameters viz. Memory usage, CPU utilization, Model Converging Time and Node Energy Consumption. The experimental results clearly depict the edge of Federated Learning in analyzing the crop recommendation, as compared to the Centralized Learning. © 2024 IEEE.

关键词： Smart agriculture

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Placement of Swarm UAV for Data Collection: A Deep Reinforcement Learning Approach

Placement of Swarm UAV for Data Collection: A Deep Reinforce...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Ghosh, Aswini Sharma, Nelson Misra, Rajiv Indian Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350362480

Rapid technical advancements in recent times have made it possible to produce a class of affordable unmanned aerial vehicles (UAVs), which can serve a wide range of public and private users for different industrial or non-industrial requirements. It is very complex to set up a communication network among the UAVs, which is very essential for completing any task like collecting data in extreme weather conditions. For UAV placement, we have suggested a distributed approach using PPO-based reinforcement learning;here, UAVs are utilized as aerial access points to create a mesh network that connects to ground nodes positioned in a designated area and can collect data from ground nodes to predict weather conditions. Our objective is to fulfill each ground node's data rate requirements while deploying the fewest number of UAVs possible to cover it. The convex hull formation of swarm uavs satisfy our objective, which arranges the n uavs on a convex hull's vertex. In this study, we present a deep reinforcement learning-based method for building swarm uavs with convex hull patterns in the euclidean plane. The existing research on convex hull pattern generation uses heuristic and combinatorial optimization techniques and evaluates performance in terms of time and space consumed. Convex hull patterns increase the mutual visibility of swarm UAVs, which helps in the cooperation of UAVs. In our research for the first time, convex hull pattern formation around a given target point is accomplished using PPO-based Deep Reinforcement Learning (DRL). © 2024 IEEE.

关键词： Combinatorial optimization

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Intelligent Conversational AI for Microsoft Teams with Actionable Insights 8

Intelligent Conversational AI for Microsoft Teams with Actio...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Deena, D. Prasad, Chandrika Geetha, J. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

The proposed work presents the development process of a personalized AI assistant, leveraging advanced technologies like Azure OpenAI Services. The project entails significant development effort, adaptable from large to XL scales based on data intricacies and project scope. It offers versatile integration options, deploying seamlessly within or outside the Microsoft tenant ecosystem, catering to diverse environments and workflows. Utilizing natural language command interpretation, the AI assistant provides intuitive interfaces across various domains. Emphasis is placed on ethical considerations, including user privacy, algorithmic transparency, and bias mitigation. Additionally, the project incorporates prompt engineering in AI to optimize prompt selection for more effective generative AI outcomes. This holistic approach aims to revolutionize user experiences and operational efficiency across industries, marking a significant advancement in AI-driven technologies. Incorporating prompt engineering into AI involves designing and optimizing prompts to guide the model towards generating desired outputs effectively. This approach aims to enhance user experiences and operational efficiency by tailoring AI responses to specific objectives and contexts. By fine-tuning prompts, developers can improve the relevance and quality of generated outputs, leading to significant advancements in AI-driven technologies across industries. © 2024 IEEE.

关键词： Deep learning

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Tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features 3rd

Tracking of Ship and Plane in Satellite Videos Using a Convo...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Sharma, Devendra Srivastava, Rajeev Computer Science and Engineering Department Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Object tracking is one of the latest research topic in various applications of computer vison. Satellite videos are provided by the commercial satellite makes it possible to extend into earth observation. Moving objects are occupy the small region of pixel and they are confused with neighboring regions also with objects. Due to the resolution constraints, the nearest similar objects can rarely be distinguished by appearance features. Conventional tracking techniques in satellite videos with hand-crafted features do not give better results. Deep learning-based techniques have proven to be superior in visual tracking benchmarks, but this is not fully explored for the object tracking in the satellite videos. Regression network is used to integrate the regression model with convolutional layers. Both motion feature and appearance feature are taken from pre-trained deep neural networks VGG16, VGG19, and ResNet50. When tracker faces obscure appearance information, motion features of object are used for discrimination and complementary information to improve the performances of model. Experimental analysis on ship and plane represents the ResNet50 feature extractor gives better tracking result. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Productivity Analysis of Employees in Corporate Sector Using Gesture Recognition

Productivity Analysis of Employees in Corporate Sector Using...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Siddiqui, Aveesah Gadge, Siddhi Shelar, Shruti Shaikh, Shainila Don Bosco Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350382693

In today's dynamic work environments, understanding and optimizing employee productivity are essential for organizational success. This paper introduces an approach for productivity analysis using gesture recognition techniques. By harnessing the capabilities of MediaPipe for body joint detection and LSTM (Long Short-Term Memory) models for action classification, the proposed system enables real-time detection of employee actions. These actions are categorized into four key behaviors: nodding, showing interest, yawning, and resting. Additionally, a mathematical model is introduced to compute overall productivity based on these categorized actions. Moreover, the distribution of actions over time is visualized using graphs, providing valuable insights into employee behavior patterns and facilitating informed decision-making for productivity optimization. © 2024 IEEE.

关键词： Gesture recognition

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MalwareGuard: Trust Score Prediction System

MalwareGuard: Trust Score Prediction System

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Kaul, Priya Mane, Abhiraj Mendonca, Crezel Maurya, Vivek Department of Computer Engineering Don Bosco Institute of Technology Mumbai India

ISBN: (纸本)9798331505134

In the rapidly evolving landscape of cybersecurity, the menace of phishing attacks looms large, necessitating innovative solutions to safeguard users and organizations alike. MalwareGuard is a cutting-edge Python-based cybersecurity tool meticulously crafted to counter cyber threats and fortify users' online experiences. This project aims at empowering users by providing real-time insights into the authenticity of domains, thereby significantly reducing the risk of falling victim to fraudulent schemes. Through a meticulous three-phase approach, this project delves into the development of a TrustScore system aimed at quantitatively assessing website trustworthiness. From data acquisition and parameterization in Phase 1 to leveraging machine learning models for TrustScore accuracy prediction in Phase 3, this project represents a significant stride towards enhancing online security. PhishTank database is integrated into the project to examine whether the domain is listed in the database of known phishing sites. © 2024 IEEE.

关键词： Phishing

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Blockchain Approaches for Healthcare: A Review and Outlook 5

Blockchain Approaches for Healthcare: A Review and Outlook

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Shaikh, Natasha Kasar, Smita Maharashtra Institute of Technology Department of Computer Science & Engineering Aurangabad India

ISBN: (纸本)9798350379945

Nowadays, the application of information technology worldwide is accelerating the digital transformation of the healthcare sector. Incorporating blockchain technology into healthcare provides better and more innovative solutions for healthcare providers to enhance the patient's care. By establishing a shared ledger that is cooperatively maintained and impenetrable to tampering, blockchain (BC) technology offers a novel approach to record historical data and ensure the reliability and security of data stored in distributed networks. Nevertheless, new issues relating to privacy and security arise with the integration of the blockchain into the healthcare industry. Hence, this research review concentrates on offering a thorough literature assessment of the chosen papers to emphasize the research subjects that are currently being investigated in blockchain-based healthcare systems and highlight their limitations and potential directions for strengthening the digital security of healthcare data. By analyzing current trends and implications, this research aims to provide a concise overview of existing approaches, and how BC is being utilized to enhance data security, interpretability, and transparency in the healthcare system. The research analyzes 25 literature papers on BC technology for the healthcare domain. Further, the evaluation based on the metrics utilized and year-wise analysis of the selected publications is carried out in this research. Finally, the research also provides an overview of blockchain-related healthcare mechanisms that provide solutions for various applications. © 2024 IEEE.

关键词： Electronic health record

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Integrating Error Level Analysis and Sensor Pattern Noise for Image Forgery Detection

Integrating Error Level Analysis and Sensor Pattern Noise fo...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Surve, Divya Nimkar, Anant Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350379525

This research integrates Sensor Pattern Noise (SPN) with Error Level Analysis (ELA) using multi-modal deep learning techniques to enhance image forgery detection. ELA is effective at detecting compression artifacts but is sensitive to subtle edits and depends heavily on image quality and compression settings. SPN complements ELA by analyzing unique camera sensor noise patterns, which are less influenced by these variables. The study employs Convolutional Neural Networks (CNNs) and the Adam optimizer to improve forgery detection across datasets including CASIA 2.0, CASIA 1.0 and DSO-1, achieving significant accuracy improvements. CASIA 2.0 demonstrates a maximum accuracy of 95% with the ELA-SPN approach, highlighting robust detection capabilities enabled by feature combination. CASIA 1.0 achieves the highest performance of 86% using a voting scheme, while DSO-1 shows varied performance, reaching accuracies around 70%. This multi-modal approach leverages SPN with CNNbased deep learning to enhance detection rates and provide a reliable method for verifying digital image authenticity. By integrating SPN with CNNs, the study advances digital image forensics through effective feature fusion, overcoming limitations of traditional image-based forgery detection methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Evaluating Public Opinion Through Twitter Sentiment Analysis 2

Evaluating Public Opinion Through Twitter Sentiment Analysis

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Jadhav, Narendra More, Priyanka Dixit, Akhilesh Sharma, Akshay Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350367461

In the digital age, social media platforms like Twitter have become a central forum for public discourse, reflecting diverse opinions on various topics. This paper explores the application of sentiment analysis to Twitter data to extract and quantify these opinions. This work processes and analyzes a large dataset of tweets using machine-learning techniques to identify underlying attitudes and divide them into positive, negative, and neutral categories. The methodology comprises preprocessing such as text normalization and noise removal, sentiment categorization using a model based on Natural Language Processing (NLP) techniques, and data gathering via the Twitter API. The results reveal patterns and trends in public sentiment on key issues, providing insights that can benefit businesses, policymakers, and researchers. This paper discusses the potential real-world applications of this technology in marketing, political science, and public relations. © 2024 IEEE.

关键词： Tweets

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Bridging the Knowledge Gap: Improving BERT models for answering MCQs by using Ontology-generated synthetic MCQA Dataset 37

Bridging the Knowledge Gap: Improving BERT models for answer...

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37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Sahil, Sahil Kumar, P. Sreenivasa Department of Computer Science and Engineering Indian Institute of Technology Madras India

BERT-based models possess impressive language understanding capabilities but often lack domain-specific knowledge, limiting their performance on specialised tasks such as medical multiple-choice question answering (MCQA). In this paper, we study how biomedical ontologies, rich repositories of medical knowledge, can be harnessed to enhance BERT-based models for medical MCQA task. Our contributions include OntoMCQA-Gen, a system which leverages different biomedical ontologies to construct BioOntoMCQA, a large synthetic MCQA dataset. OntoMCQA-Gen exploits the subclass-class relationships, definitions of concepts, and also synonym relationships from the ontologies to create this dataset of MCQs automatically. We then use this synthetic dataset to fine-tune various BERT-based models to answer medical MCQs. We evaluated these fine-tuned BERT models on the challenging MedMCQA and MedQA datasets of questions from admission examinations for medical degrees in India and USA, respectively. Our evaluation study on these datasets shows that fine-tuning the BERT-based models on BioOntoMCQA results in significantly improved accuracy scores. BioBERT and PubMedBERT, pretrained on the large medical corpus, have also shown significant improvements with our technique of fine-tuning ontology-generated synthetic data. This finding highlights the effectiveness of incorporating biomedical ontologies to enhance the BERT-based model in the medical domain. Moreover, our results underscore the importance of using ontology-generated data along with model adaptation for specialised domains, contributing to a novel advancement in natural language processing. Copyright © 2024 by the authors.

关键词： Ontology

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