The conventional education system in developing countries has been enhanced recently by implementing the latest technology of distributed ledger. Disruptive technology is a fundamental requirement for greater accountability and visibility. We explored the key factors affecting the intentions of educational institutions to use blockchain technology for e-learning. This study proposed an expanded model of Technology Acceptance Model by integrating the diffusion of innovation theory. Based on an online survey, the conceptual model was tested and validated using structural equation modeling. The results showed that compatibility had a significant impact on blockchain use in smart learning environments. Other significant effects were also found on adoption of blockchain technology. This study offers an expanded Technology Acceptance Model for implementing blockchain that could assist decision makers in building a smart learning environment for the educational institutes for the emerging economies.

Using Google cluster traces, the research presents a task offloading algorithm and a hybrid forecasting model that unites Bidirectional Long Short-Term Memory (BiLSTM) with Gated Recurrent Unit (GRU) layers along an attention mechanism. This model predicts resource usage for flexible task scheduling in Internet of Things (IoT) applications based on edge computing. The suggested algorithm improves task distribution to boost performance and reduce energy consumption. The system’s design includes collecting data, fusing and preparing it for use, training models, and performing simulations with EdgeSimPy. Experimental outcomes show that the method we suggest is better than those used in best-fit, first-fit, and worst-fit basic algorithms. It maintains power stability usage among edge servers while surpassing old-fashioned heuristic techniques. Moreover, we also propose the Deep Deterministic Policy Gradient (D4PG) based on a Federated Learning algorithm for adjusting the participation of dynamic user equipment (UE) according to resource availability and data distribution. This algorithm is compared to DQN, DDQN, Dueling DQN, and Dueling DDQN models using Non-IID EMNIST, IID EMNIST datasets, and with the Crop Prediction dataset. Results indicate that the proposed D4PG method achieves superior performance, with an accuracy of 92.86% on the Crop Prediction dataset, outperforming alternative models. On the Non-IID EMNIST dataset, the proposed approach achieves an F1-score of 0.9192, demonstrating better efficiency and fairness in model updates while preserving privacy. Similarly, on the IID EMNIST dataset, the proposed D4PG model attains an F1-score of 0.82 and an accuracy of 82%, surpassing other Reinforcement Learning-based approaches. Additionally, for edge server power consumption, the hybrid offloading algorithm reduces fluctuations compared to existing methods, ensuring more stable energy usage across edge nodes. This corroborates that the proposed method can preserve privacy by handling issues related to fairness in model updates and improving efficiency better than state-of-the-art alternatives.

In smart cities, unmanned aerial vehicles (UAVS) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings forth a pressing concern: security and privacy vulnerabilities. This study introduces the SP-IoUAV (Secure and Privacy Preserving Intrusion Detection and Prevention for UAVS) model, tailored specifically for the Internet of UAVs ecosystem. The challenge lies in safeguarding UAV operations and ensuring data confidentiality. Our model employs cutting-edge techniques, including federated learning, differential privacy, and secure multi-party computation. These fortify data confidentiality and enhance intrusion detection accuracy. Central to our approach is the integration of deep neural networks (DNNs) like the convolutional neural network-long short-term memory (CNN-LSTM) network, enabling real-time anomaly detection and precise threat identification. This empowers UAVs to make immediate decisions in dynamic environments. To proactively counteract security breaches, we have implemented a real-time decision mechanism triggering alerts and initiating automatic blacklisting. Furthermore, multi-factor authentication (MFA) strengthens access security for the intrusion detection system (IDS) database. The SP-IoUAV model not only establishes a comprehensive machine framework for safeguarding UAV operations but also advocates for secure and privacy-preserving machine learning in UAVS. Our model’s effectiveness is validated using the CIC-IDS2017 dataset, and the comparative analysis showcases its superiority over previous approaches like FCL-SBL, RF-RSCV, and RBFNNs, boasting exceptional levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and F-score (99.92%).

Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages technologies for enhanced crop production, with disease detection being a vital element. Prompt identification of diseases in paddy leaves is critical for curtailing their propagation and reducing crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited for widespread application. These ML/DL methods cannot ensure data privacy, as they involve sharing training data with a central server, overlooking competitive and regulatory considerations. As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified limitations of centralized training. This paper utilizes the FL approach for the classification of rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized client-server setup. Both IID (independent and identically distributed) and non-IID datasets were utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them, MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently, we trained these models using both IID and non-IID datasets in a federated learning environment. The framework’s performance was assessed through diverse scenarios, comparing it with traditional and federated learning models. The evaluation considered metrics like validation accuracy, loss as well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving 99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation, EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved 90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the F-TL framework’s superiority over traditional distributed deep-learning classifiers, demonstrating its effectiveness in both single and multiclient instances. Notably, the framework’s strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as a valuable alternative for rice-leaf disease classification compared to existing tools.

Optimization algorithms play a crucial role in solving complex challenges across various fields, including engineering, finance, and data science. This study introduces a novel hybrid optimization algorithm, the Hybrid Crayfish Optimization Algorithm with Differential Evolution (HCOADE), which addresses the limitations of premature convergence and inadequate exploitation in the traditional Crayfish Optimization Algorithm (COA). By integrating COA with Differential Evolution (DE) strategies, HCOADE leverages DE’s mutation and crossover mechanisms to enhance global optimization performance. The COA, inspired by the foraging and social behaviors of crayfish, provides a flexible framework for exploring the solution space, while DE’s robust strategies effectively exploit this space. To evaluate HCOADE’s performance, extensive experiments are conducted using 34 benchmark functions from CEC 2014 and CEC 2017, as well as six engineering design problems. The results are compared with ten leading optimization algorithms, including classical COA, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Moth-flame Optimization (MFO), Salp Swarm Algorithm (SSA), Reptile Search Algorithm (RSA), Sine Cosine Algorithm (SCA), Constriction Coefficient-Based Particle Swarm Optimization Gravitational Search Algorithm (CPSOGSA), and Biogeography-based Optimization (BBO). The average rankings and results from the Wilcoxon Rank Sum Test provide a comprehensive comparison of HCOADE’s performance, clearly demonstrating its superiority. Furthermore, HCOADE’s performance is assessed on the CEC 2020 and CEC 2022 test suites, further confirming its effectiveness. A comparative analysis against notable winners from the CEC competitions, including LSHADEcnEpSin, LSHADESPACMA, and CMA-ES, using the CEC-2017 test suite, revealed superior results for HCOADE. This study underscores the advantages of integrating DE strategies with COA and offers valuable insights for addressing complex global optimization problems.

The incorporation of digital technology in education represents a substantial transformation in the realm of academia. In the present day, with new technology being readily available to both educators and students, educational institutions encourage their students to embrace it, enabling students facing geographical constraints to easily continue their education through internet-based learning. The primary objective of this study is to investigate the viewpoints of undergraduate students in the field of mass communication concerning the integration of Information and Communication Technology (ICT) in the UAE. Based on a Technology Acceptance Model (TAM), the data were gathered using structured questionnaires. Results revealed that knowledge sharing and accessibility are significant factors affecting the perceived ease of use and perceived usefulness among the undergraduate students. Further, perceived ease of use significantly affects perceived usefulness. Finally, both perceived ease of use and perceived usefulness remained as significant factors regarding ICT acceptance among the Emirati students. It is concluded that educational institutions must invest significantly in robust IT infrastructure. This step ensures that e-learning experiences are seamless and accessible to all students. Further, to address some students’ connectivity challenges, offering alternative access options or providing offline resources can be beneficial. Finally, the practical implications and limitations are discussed.