Educational institutions face inherent uncertainties in student performance, stakeholder priorities, and data analysis. This paper explores how cloud computing, with its data storage, analytics, and collaboration tool...
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The automated detection of pipeline cracks is vital for maintaining the safety and operational lifespan of this crucial infrastructure. Early crack detection prevents costly repairs and minimizes the risk of catastrop...
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Due to the increases pest impacts on agricultural productivity, effective pest prediction models are required for efficient pest control in precision farming. The framework outlined in this research uses a Graph Convo...
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Canine cardiomegaly, marked by an enlarged heart, poses serious health risks if undetected, requiring accurate diagnostic methods. Current detection models often rely on small, poorly annotated datasets and struggle t...
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Machine Learning (ML) algorithms have experienced a significant increase in popularity owing to the digitisation of analogue processes and other technological advancements, like the Internet of Things (IoT). Dependabl...
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Depression, a prevalent mental health concern, demands efficient and accurate classification methods to support timely interventions. This study utilizes two mental health datasets to classify depression levels into h...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Depression, a prevalent mental health concern, demands efficient and accurate classification methods to support timely interventions. This study utilizes two mental health datasets to classify depression levels into high, medium, and low categories. Data preprocessing and visualization techniques were applied to extract meaningful patterns, followed by the implementation of six widely used machine learning algorithms: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB). Among these, the RF model demonstrated the highest accuracy, achieving 0.98 without cross-validation and 0.99 with cross-validation. Class-wise evaluation using precision, recall, and F1 score further established RF’s superior performance. To enhance interpretability and transparency, Local Interpretable Model-agnostic Explanations (LIME) was employed, providing insights into the RF model’s decision-making process. This research underscores the efficacy of the RF model for depression classification and highlights the importance of integrating explainable AI techniques in mental health diagnostics.
The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contrac...
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With the development of Fiber Bragg Grating (FBG) sensors, applications such as Structural Health Monitoring (SHM) systems, aerospace systems, and medical diagnostics have become highly accurate and more demanding. Ho...
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In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these chal...
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ISBN:
(数字)9798331531195
ISBN:
(纸本)9798331531201
In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user’s QoS.
The principle of virtualization and cloud-based technology has facilitated various industries in establishing expansive cloud environments. The Cloud is vulnerable to emerging threats and breaches due to the nature of...
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The principle of virtualization and cloud-based technology has facilitated various industries in establishing expansive cloud environments. The Cloud is vulnerable to emerging threats and breaches due to the nature of its networks. Creating an intrusion system that addresses security concerns in cloud networks is crucial. This paper examines specific security weaknesses associated with significant threats, such as Distributed Denial-of-Service (DDoS) assaults, which jeopardize both data security and service availability in Cloud systems. To address these concerns, a Hypervisor Controller-based detection system has been developed for the identification of DDoS attacks. The Hypervisor Controller employs Fuzzy Time Series Analysis and Expectation Maximization methods in a dual-phase process. In the preliminary phase, resource requests are categorized according to their compliance with the Service Level Agreement (SLA). Requests that contravene the Service Level Agreement (SLA) are promptly identified as potential malware and denied from system access, thereby preserving the system’s security and stability. In the second step, non-violation requests undergo rigorous examination utilizing a Fuzzy Time Series model, simulated by the Expectation Maximization method. This method proficiently manages large datasets, significantly enhancing detection accuracy. The proposed Hypervisor Controller has demonstrated effectiveness, robustness, and immediacy in detecting DDoS attacks across federated systems and cloud environments, as evidenced by experimental and performance evaluations. This study aims to enhance the security of Cloud-integrated systems by formulating comprehensive and effective solutions to address emerging threats.
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