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E-Education is an important aspect that facilitates the online and virtual education. The advancements in the education institutes are increasing tremendously which requires high-end servers, software’s, and applications that leads to the huge investment cost. The optimized solution is adoption of cloud services. Hosting the applications and docume...
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... Moreover, while adaptive learning models like VARK cater to students' learning preferences [10,11], they often overlook non-academic factors in performance prediction [12][13][14][15][16]. Furthermore, security is another major concern, as current e-learning platforms are vulnerable to data breaches [17][18][19][20][21]. The proposed model seeks to address these issues by integrating AI with Federated Learning, creating a secure, real-time, personalised learning ecosystem that takes both academic and non-academic factors into account. ...
Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) have revolutionised e-learning by creating personalised, adaptive, and secure environments. However, challenges such as privacy, bias, and data limitations persist. E-FedCloud aims to address these issues by providing more agile, personalised, and secure e-learning experiences. This study introduces E-FedCloud, an AI-assisted, adaptive e-learning system that automates personalised recommendations and tracking, thereby enhancing student performance. It employs federated learning-based authentication to ensure secure and private access for both course instructors and students. Intelligent Software Agents (ISAs) evaluate weekly student engagement using the Shannon Entropy method, classifying students into either engaged or not-engaged clusters. E-FedCloud utilises weekly engagement status, demographic information, and an innovative DRL-based early warning system, specifically ID2QN, to predict the performance of not-engaged students. Based on these predictions, the system categorises students into three groups: risk of dropping out, risk of scoring lower in the final exam, and risk of failing the end exam. It employs a multi-disciplinary ontology graph and an attention-based capsule network for automated, personalised recommendations. The system also integrates performance tracking to enhance student engagement. Data are securely stored on a blockchain using the LWEA encryption method.
... Moreover, while adaptive learning models like VARK cater to students' learning preferences [10,11], they often overlook non-academic factors in performance prediction [12][13][14][15][16]. Furthermore, security is another major concern, as current e-learning platforms are vulnerable to data breaches [17][18][19][20][21]. The proposed model seeks to address these issues by integrating AI with Federated Learning, creating a secure, real-time, personalised learning ecosystem that takes both academic and nonacademic factors into account. ...
Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) have revolutionised e-learning by creating personalised, adaptive, and secure environments. However, challenges such as privacy, bias, and data limitations persist. E-FedCloud aims to address these issues by providing more agile, personalised, and secure e-learning experiences. This study introduces E-FedCloud, an AI-assisted adaptive e-learning system that automates personalised recommendations and tracking, thereby enhancing student performance. It employs federated learning-based authentication to ensure secure and private access for both course instructors and students. Intelligent Software Agents (ISAs) evaluate weekly student engagement using the Shannon Entropy method, classifying students into either engaged or not-engaged clusters. E-FedCloud utilises weekly engagement status, demographic information, and an innovative DRL-based early warning system, specifically ID2QN, to predict the performance of not-engaged students. Based on these predictions, the system categorises students into three groups: risk of dropping out, risk of scoring lower in the final exam, and risk of failing the end exam. It employs a multi-disciplinary ontology graph and an attention-based capsule network for automated, personalised recommendations. The system also integrates performance tracking to enhance student engagement. Data is securely stored on a blockchain using the LWEA encryption method.
... In addition, the authors have introduced a framwork that provides training and testing data privacy in [332], this framwork is a secure cloud-intelligent network that is supported by privacy-preserving machine learning similar to [333] that authors have introduced a framwork called SPDDL (Secure and Privacy-preserving distributed deep learning) for secure and privacy-preserving Distributed Deep Learning (DDL) that makes better security, efficiency, and functionality and preserves users identities from external adversaries. Likewise, a framwork that is secure and provides authentication for E-Education (online and virtual education) in cloud systems has been presented in [334] to overcome cost, time processing, and security challenges. As opposed to [335] that authors have indicated how to make use of several software-as-a-service (SaaS) on a platform-as-a-service (PaaS) by analyzing data security also the integration between machine learning algorithms and secure data protection algorithms has been studied in [335] similar to [336] that the authors produced and introduced a Phishing Detection (PD) framework that has a three-step feature and developed in machine learning and a Secure Storage Distribution (SSD) in order to provide security and privacy Cloud-IoT data. ...
Privacy-Preserving Cloud Computing is an emerging technology with many applications in various fields. Cloud computing is important because it allows for scalability, adaptability, and improved security. Likewise, privacy in cloud computing is important because it ensures that the integrity of data stored on the cloud maintains intact. This survey paper on privacy-preserving cloud computing can help pave the way for future research in related areas. This paper helps to identify existing trends by establishing a layered architecture along with a life cycle and an ecosystem for privacy-preserving cloud systems in addition to identifying the existing trends in research on this area.
A Web Vulnerability Scanner (WVS) is a software tool that assesses the security of web applications by conducting automated penetration tests. It speeds up the process, reduces costs, and eliminates the need for specialized testing engineers. This study evaluates the vulnerability detection capabilities of six WVSs, three commercial scanners, and three open-source scanners. The goal is to identify and mitigate potential security risks before they are exploited by malicious users. The study employed two well-known vulnerable web applications and four relevant metrics, such as detection rate of accuracy, recall, precision, and the ability to detect different vulnerabilities using the Open Web Application Security Project (OWASP) as a reference.
Outcome-Based Education (OBE) is a student-centric approach to education that focuses on the outcomes of student learning. This paper discusses the pedagogy followed for the skill-oriented course Web Application Development using Full Stack Module-1. It emphasizes the front-end design of web applications using HyperText Markup Language (HTML), Cascading Style Sheets (CSS), JavaScript, and jQuery. The criteria for analysis and evaluation were based on the rubrics defined in the course structure according to OBE. The results show that there is an improvement in the students skills from the perspective of industry expectations in terms of communication, presentation, and report writing skills. The findings of this paper it is clear that OBE can be an effective approach to teaching and learning skills-oriented courses. The results also suggest that OBE can help students develop the skills that are necessary to succeed in the workforce.
