Introduction: The traditional approach may not be suitable for crop disease prediction because there may be many privacy and security concerns. We have introduced the Federated learning approach, which does not allow ...
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Introduction: The traditional approach may not be suitable for crop disease prediction because there may be many privacy and security concerns. We have introduced the Federated learning approach, which does not allow the sharing of complete data across multiple devices. This research approach is the combination of federated learning (FL) with deep neural networks to increase crop disease prediction as well as data privacy. Objective: The main objective of our article is to develop a robust and privacy-preserving crop disease prediction model that is combined with federated learning and deep neural networks within a federated learning environment. This strategy improves prediction accuracy while maintaining data sovereignty by storing data locally and securely. Material/Method: Initially we deployed the IoT sensors (in different parts of the land, and then we collected the data continuously to monitor the environmental conditions. By taking the image data and the collected sensor data, the model trained and understood the factors for crop health. The image data will be preprocessed through resizing, normalization augmentation, etc. We have used deep neural network classifiers like CNN, RNN, LSTM, and GRU. The federated learning technique is implemented. A global model is created to update the aggregate of the locally trained model. Result: Unlike traditional centralized approaches, our method maintains data sovereignty by keeping data local while gaining global insights from a federated paradigm. This is especially important in places where data privacy regulations prevent data exchange. Furthermore, our approach combines IoT sensor data with deep learning frameworks to provide a more comprehensive understanding of crop health. We have estimated accuracy, precision, recall, and F1-score AUC-ROC and PR-AUC to evaluate the model’s performance on the real-time dataset. Our experimental results reveal that CNN achieved 99% training accuracy and 94% testing accuracy as co
In this research paper, we're looking at how This paper can make computers better at spotting different diseases in medical images. This paper got our idea from how our brains focus on important stuff when This pa...
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Clinical photo segmentation is a crucial challenge in many fields, including radiology. It helps diagnose and treat diverse illnesses by precisely segmenting modern-day organs or lesions. These days, deep ultra-modern...
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The prompt expansion of the Metaverse presents challenges for secure and ethical user interactions. Existing security frameworks often lack integration of ethical considerations such as user consent and regulatory com...
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The Artificial Intelligence (AI)-assisted academic writing platform is a digital tool that leverages the use of artificial intelligence (AI) technology to help the platform users, i.e., students, teachers, and pr...
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ISBN:
(纸本)9789819644063
The Artificial Intelligence (AI)-assisted academic writing platform is a digital tool that leverages the use of artificial intelligence (AI) technology to help the platform users, i.e., students, teachers, and professionals in various aspects of academic writing. This work reviews existing AI-assisted academic writing platforms, discusses core system design features that are important considerations in designing such frameworks, and provides a critical discussion of the strengths and weaknesses of existing platforms. This work also proposes a robust and secure framework for an AI-assisted academic writing platform based on Azure cloud services. The platform aims to provide comprehensive supervisory support, free from time and location constraints, to help its users e.g., teachers in terms of helping with their workload in assessing academic reports, and to students by providing support to enhance their academic writing. The proposed framework is based on Microsoft (MS) Azure cloud services because it provides reliability, scalability, and a wide range of services such as Web Server and Content Management System (CMS), Azure Database for MySQL and OpenAI Azure Service i.e., ChatGPT in a secure environment. In addition, the proposed framework integrates with a Private Cloud to further enhance the platform’s functionality and secure environment. The main objective of the platform is to deliver an interactive system that provides reliable feedback on English writing in a range of academic fields. The proposed platform is then evaluated by piloting on the PolyU SPEED academic course led by 16 teachers and tested on academic reports of 273 students. The survey-based evaluation results show that the proposed AI-assisted academic writing platform is easy to use and showcases the ability to identify issues related to specific genre-based content and coherence, provide assistance in grammar and vocabulary, and highlight issues in reference style with reasonably high accuracy.
The rapid proliferation of 5G networks and Internet of Things (IoT) technology has created new opportunities and challenges in the quest for enhanced latency performance. This research paper explores the implementatio...
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Intrusion detection system is highly effective and easy to understand in the situation of ubiquitous cyber threats. The trustworthiness and interpretability of traditional intrusion detection systems are balanced due ...
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This review paper investigates the application of Big Data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil managem...
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ISBN:
(数字)9798331515911
ISBN:
(纸本)9798331515928
This review paper investigates the application of Big Data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big Data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big Data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big Data and conventional soil science practices..
Multi-Agent Large Language Models (LLMs) are gaining significant attention for their ability to harness collective intelligence in complex problem-solving, decision-making, and planning tasks. This aligns with the con...
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Multi-Agent Large Language Models (LLMs) are gaining significant attention for their ability to harness collective intelligence in complex problem-solving, decision-making, and planning tasks. This aligns with the concept of the wisdom of crowds, where diverse agents contribute collectively to generating effective solutions, making it particularly suitable for educational settings. Senior design projects, also known as capstone or final year projects, are pivotal in engineering education as they integrate theoretical knowledge with practical application, fostering critical thinking, teamwork, and real-world problem-solving skills. In this paper, we explore the use of Multi-Agent LLMs in supporting these senior design projects undertaken by engineering students, which often involve multidisciplinary considerations and conflicting objectives, such as optimizing technical performance while addressing ethical, social, and environmental *** propose a framework where distinct LLM agents represent different expert perspectives, such as problem formulation agents, system complexity agents, societal and ethical agents, or project managers, thus facilitating a holistic problem-solving approach. This implementation leverages standard multi-agent system (MAS) concepts such as coordination, cooperation, and negotiation, incorporating prompt engineering to develop diverse personas for each agent. These agents engage in rich, collaborative dialogues to simulate human engineering teams, guided by principles from swarm AI to efficiently balance individual contributions towards a unified solution. We adapt these techniques to create a collaboration structure for LLM agents, encouraging interdisciplinary reasoning and negotiation similar to real-world senior design projects. To assess the efficacy of this framework, we collected six proposals of engineering and computerscience of typical senior capstone projects and evaluated the performance of Multi-Agent and single-agent LL
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