Purpose: Through harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type. Design/methodology/approach: The DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases. Findings: An experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes. Originality/value: The DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.

Detecting intrusions from the supervisory control and data acquisition (SCADA) systems is one of the most essential and challenging processes in recent times. Most of the conventional works aim to develop an efficient intrusion detection system (IDS) framework for increasing the security of SCADA against networking attacks. Nonetheless, it faces the problems of complexity in classification, requiring more time for training and testing, as well as increased misprediction results and error outputs. Hence, this research work intends to develop a novel IDS framework by implementing a combination of methodologies, such as clustering, optimization, and classification. The most popular and extensively utilized SCADA attacking datasets are taken for this system’s proposed IDS framework implementation and validation. The main contribution of this work is to accurately detect the intrusions from the given SCADA datasets with minimized computational operations and increased accuracy of classification. Additionally the proposed work aims to develop a simple and efficient classification technique for improving the security of SCADA systems. Initially, the dataset preprocessing and clustering processes were performed using the multifacet data clustering model (MDCM) in order to simplify the classification process. Then, the hybrid gradient descent spider monkey optimization (GDSMO) mechanism is implemented for selecting the optimal parameters from the clustered datasets, based on the global best solution. The main purpose of using the optimization methodology is to train the classifier with the optimized features to increase accuracy and reduce processing time. Moreover, the deep sequential long short term memory (DS-LSTM) is employed to identify the intrusions from the clustered datasets with efficient data model training. Finally, the proposed optimization-based classification methodology’s performance and results are validated and compared using various evaluation metrics.

Remote sensing (RS) data can be attained from different sources, such as drones, satellites, aerial platforms, or street-level cameras. Each source has its own characteristics, including the spectral bands, spatial resolution, and temporal coverage, which may affect the performance of the vehicle detection algorithm. Vehicle detection for urban applications using remote sensing imagery (RSI) is a difficult but significant task with many real-time applications. Due to its potential in different sectors, including traffic management, urban planning, environmental monitoring, and defense, the detection of vehicles from RS data, such as aerial or satellite imagery, has received greater emphasis. Machine learning (ML), especially deep learning (DL), has proven to be effective in vehicle detection tasks. A convolutional neural network (CNN) is widely utilized to detect vehicles and automatically learn features from the input images. This study develops the Improved Deep Learning-Based Vehicle Detection for Urban Applications using Remote Sensing Imagery (IDLVD-UARSI) technique. The major aim of the IDLVD-UARSI method emphasizes the recognition and classification of vehicle targets on RSI using a hyperparameter-tuned DL model. To achieve this, the IDLVD-UARSI algorithm utilizes an improved RefineDet model for the vehicle detection and classification process. Once the vehicles are detected, the classification process takes place using the convolutional autoencoder (CAE) model. Finally, a Quantum-Based Dwarf Mongoose Optimization (QDMO) algorithm is applied to ensure an optimal hyperparameter tuning process, demonstrating the novelty of the work. The simulation results of the IDLVD-UARSI technique are obtained on a benchmark vehicle database. The simulation values indicate that the IDLVD-UARSI technique outperforms the other recent DL models, with maximum accuracy of 97.89% and 98.69% on the VEDAI and ISPRS Potsdam databases, respectively.