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A situation based predictive approach for cybersecurity intrusion detection and prevention using machine learning and deep learning algorithms in wireless sensor networks of industry 4.0
No full text"‘Hear My Voice’: Children and Young People in Schools and Research
No full textPupil ‘voice’ is heralded as a key tenet of education policy, practice and research; however, ensuring that voice is authentically heard in the spaces that children occupy remains a challenge. Models of participation – relating to the degree of power either shared or transferred – range from tokenistic approaches to true pupil–teacher partnerships in which pupils initiate ideas and share decision-making with adults. Shier’s (2001) five-step Pathway to Participation model is considered and practitioners are invited to reflect on their own position and identify necessary steps to increase pupil involvement in their local setting. For guidance, a philosophy club intervention for early years and primary-age children is presented as a real-world example of facilitating voice on both group and individual levels. This chapter argues the case for schools to genuinely embrace and utilise pupil voice as part of a whole-school approach to enhance mental health and wellbeing within learning communitie
IoT Centric Data Protection Using Deep Learning Technique For Preserving Security And Privacy In Cloud
Get PDFThe Internet of Things (IoT) describes a system where interconnected physical objects are connected online. As the collection and sharing of vast amounts of personal data grow, so do concerns over user privacy within IoT environments. While IoT devices offer significant advantages in terms of productivity, accuracy, and financial benefits by minimizing human intervention and providing exceptional flexibility and convenience, they also face challenges related to communication overhead, security, and privacy. To address these issues, a novel Internet of Things-based Cloud Information Security Preservation (IoT-CISP) has been proposed. This approach enhances the model’s effectiveness and ensures security by first separating sensitive data from non-sensitive data using an SVM classifier, and then employing this data for partial decryption and analysis. Sensitive data is protected through Okamoto-Uchiyama encryption, ensuring that data storage, analysis, and sharing are conducted securely to maintain the system’s safety and privacy. The effectiveness of this novel method was assessed against existing methodologies using parameters like precision, accuracy, F1 score, and recall, revealing its superior security and efficiency compared to other schemes. Results demonstrate that the IoT-CISP approach offers encryption times that are 31.24%, 23.12%, and 33.03% shorter than those of the CP-ABE, GDBR, and HP-CPABE algorithms, respectively
Load balancing in cloud computing via intelligent PSO-based feedback controller
No full textLoad balancing effectively distributes network load and balances the load during the scheduling and allocationprocess. Hence various load balancing techniques in task scheduling and resource allocation along with VMmigration has been presented previously but they have a heavy load on some VM and violate cloud service levelagreement with a single point of failure. Therefore, a novel Intelligent PSO-based Feedback Controller has beenproposed with regulated Scheduling, Allocation, and VM migration to perform optimal load balancing. In thisproposed technique, a novel Intelligent Weighted filtering based PSO Approach is used to reduce computationtime during task scheduling and resource allocation. This approach uses a multi-objective PSO algorithm withPareto dominance to achieve high quality of service, throughput, scalability, low response time, and optimalbilateral transposed conv filtering. Moreover, during VM migration existing techniques result in service levelagreement violations owing to inefficient VM placement among PMs. To overcome these issues, a Double Deep Qproximal model with a feedback controller has been proposed. The double weight set in the offline and onlineupdating process in the decision model maintains a smooth service level agreement with the cloud. Also,centralized and decentralized controller algorithm fails with a single point of failure and coordination issue incomplicated situations with instruction mixing of processes. Finally, the conditional GAN feedback controller hasbeen used to eliminate a single point of failure with high fault tolerance, low energy consumption and migrationtime
Ensemble Classification with Lazy Predict on Three Diabetes Datasets: A Comparative Study with Resampling Techniques
No full textMillions of people throughout the world suffer from the chronic illness diabetes mellitus. Effective diabetes care and complication avoidance depend on early diabetes prediction and diagnosis. Using the three distinct datasets—the PIMA India dataset, the NHANES dataset, and Mendeley’s diabetes dataset—we give a thorough analysis of diabetic prediction in this study. Lazy Predict enables us to efficiently evaluate a wide range of classifiers on each dataset, providing valuable insights into model performance. The top-performing model on each dataset is selected as the best individual model. Furthermore, ensembles are created by combining the predictions of the top ten models without any resampling and with resampling techniques. Random forest achieved the highest accuracy of 79% on the PIMA dataset, XGB achieved the highest accuracy of 99% on Mendeley’s dataset, and the dummy classifier attained the highest accuracy of 88%. for the NHANES dataset. However, the ensembles without oversampling consistently outperformed their counterparts with resampling. Surprisingly, the ensemble without oversampling exhibited the highest accuracy overall, followed by the ensemble with oversampling, challenging the common notion that resampling always leads to improved performance
Curiosity
No full textAn article on curiosity by Miles Berry, Professor of Computing Education at the University of Roehampton, discusses the advent of AI chatbots that provide an interactive style of learning close to personal tutoring. While not all students are curious enough to make use of these things themselves, Berry suggests that this is where schools can empower and motivate pupils to learn things for themselves, rather than just listening attentively to what is taught.He moves on to provide three practical suggestions on how this could be done, including a shift in focus from theory and problems to projects, offering students music and gaming applications, and allowing them to tinker with code. Berry also promotes the use of questions in lessons to encourage curiosity in the classroom
