Predicting students' performance in English and Mathematics using data mining techniques

This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students' performance prediction, and the relationship between these two subjects. The study employed the archival data of students who were 16 years old in 2019 and sat for the Malaysian Certificate of Examination (MCE) in 2021. The learning of English and Mathematics is a concern in many countries. Three main factors, namely students' past academic performance, demographics, and psychological attributes were scrutinized to identify their impact on the prediction. This study utilized the Orange software for the DM process. It employed Decision Tree (DT) rules to determine the characteristics of students with low, moderate, and high performance in English and Mathematics subjects. DT and Naïve Bayes (NB) techniques show the best predictive performance for English and Mathematics subjects, respectively. Such characteristics and predictions may cue appropriate interventions to improve students' performance in these subjects. This study revealed students' past academic performance as the most critical predictor, as well as a few demographics and psychological attributes. By examining top predictors derived using four different classifier types, this study found that students' past Mathematics performance predicts their MCE English performance and students' past English performance predicts their MCE Mathematics performance. This finding shows students' performances in both subjects are interrelated.

Defining a BIM-Enabled Learning Environment—An Adaptive Structuration Theory Perspective

Digitalization of the AEC-FM industry has resulted in the reassessment of knowledge, knowledge management, teaching and learning, workflows and networks, roles, and relevance. Consequently, new approaches to teaching and learning to meet the demands of new jobs and abilities, new channels of communication, and a new awareness are required. Building Information Modelling (BIM) offers opportunities to address some of the current challenges through BIM-enabled education and training. This research defines the requisite characteristics of a BIM-enabled Learning Environment (BLE)—a web-based platform that facilitates BIM-enabled education and training—in order to develop a prototype version of the BLE. Using a mixed-methods research design and an Adaptive Structuration Theory (AST) perspective for interpreting the findings, 33 features and 5 distinct intentions behind those features were identified. These findings are valuable in taking forward the development of the BLE as they suggest a BLE requires the integration of functions from three existing types of information technology application (virtual learning environments, virtual collaboration platforms, and BIM applications). This study will inform the design of a web-based BLE for enhanced AEC-FM education and training, and it also provides a starting point for researchers to apply AST to evaluate the use of a BLE in different educational and training contexts.

Automatic Target Detection from Satellite Imagery Using Machine Learning

Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 × 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

BIMp-Chart—A Global Decision Support System for Measuring BIM Implementation Level in Construction Organizations

Building Information Modeling (BIM) is recognized as one of the most significant technological breakthroughs in the Architecture, Engineering, and Construction (AEC) industry. The pace of implementation of BIM in AEC has increased during the past decade with an enhanced focus on sustainable construction. However, BIM implementation lags its potential because of several factors such as readiness issues, lack of previous experience in BIM, and lack of market demand for BIM. To evaluate and solve these issues, understanding the current BIM implementation in construction organizations is required. Motivated by this need, the main objective of this study is to propose a tool for the measurement of BIM implementation levels within an organization. Various sets of indexes are developed based on their pertinent Critical Success Factors (CSFs). A detailed literature review followed by a questionnaire survey involving 99 respondents is conducted, and results are analyzed to formulate a BIMp-Chart to calculate and visualize the BIM implementation level of an organization. Subsequently, the applicability of the BIMp-Chart is assessed by comparing and analyzing datasets of four organizations from different regions, including Qatar, Portugal, and Egypt, and a multinational organization to develop a global measurement tool. Through measuring and comparing BIM implementation levels, the BIMp-Chart can help the practitioners identify the implementation areas in an organization for proper BIM implementation. This study helps understand the fundamental elements of BIM implementation and provides a decision support system for construction organizations to devise proper strategies for the effectual management of the BIM implementation process.

UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection

Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.