Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using video learning analytics and data mining techniques. Data from the student information system, learning management system and mobile applications were analyzed using eight different classification algorithms. Furthermore, data transformation and preprocessing techniques were carried out to reduce the features. Moreover, genetic search and principle component analysis were carried out to further reduce the features. Additionally, the CN2 Rule Inducer and multivariate projection can be used to assist faculty in interpreting the rules to gain insights into student interactions. The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio.
Automated fruit classification is a stimulating problem in the fruit growing and retail industrial chain as it assists fruit growers and supermarket owners to recognize variety of fruits and the status of the container or stock to increase business profit and production efficacy. As a result, intelligent systems using machine learning and computer vision approaches were explored for ripeness grading, fruit defect categorization, and identification over the last few years. Recently, deep learning (DL) methods for classifying fruits led to promising performance that effectively extracts the feature and carries out an end-to-end image classification. This paper introduces an Automated Fruit Classification using Hyperparameter Optimized Deep Transfer Learning (AFC-HPODTL) model. The presented AFC-HPODTL model employs contrast enhancement as a pre-processing step which helps to enhance the quality of images. For feature extraction, the Adam optimizer with deep transfer learning-based DenseNet169 model is used in which the Adam optimizer fine-tunes the initial values of the DenseNet169 model. Moreover, a recurrent neural network (RNN) model is utilized for the identification and classification of fruits. At last, the Aquila optimization algorithm (AOA) is exploited for optimal hyperparameter tuning of the RNN model in such a way that the classification performance gets improved. The design of Adam optimizer and AOA-based hyperparameter optimizers for DenseNet and RNN models show the novelty of the work. The performance validation of the presented AFC-HPODTL model is carried out utilizing a benchmark dataset and the outcomes report the promising performance over its recent state-of-the-art approaches.
Higher educational institutes (HEI) are adopting ubiquitous and smart equipment such as mobile devices or digital gadgets to deliver educational content in a more effective manner than the traditional approaches. In present works, a lot of smart classroom approaches have been developed, however, the student learning experience is not yet fully explored. Moreover, module historical data over time is not considered which could provide insight into the possible outcomes in the future, leading new improvements and working as an early detection method for the future results within the module. This paper proposes a framework by taking into account module historical data in order to predict module performance, particularly the module result before the commencement of classes with the goal of improving module pass percentage. Furthermore, a video streaming server along with blended learning are sequentially integrated with the designed framework to ensure correctness of teaching and learning pedagogy. Simulation results demonstrate that by considering module historical data using time series forecasting helps in improving module performance in terms of module delivery and result outcome in terms of pass percentage. Furthermore, the proposed framework provides a mechanism for faculties to adjust their teaching style according to student performance level to minimize the student failure rate.
The huge development in the number of Vehicle factories have resulted in many people having lost their life due to accident, which has made vehicular Ad-hoc networks (VANETs) hot topic to enable improved communication between vehicles aimed at reducing the loss of life. The main challenge in this area is vehicle mobility, which has direct effect on network stability. Thus, most previous studies that discussed clustering focused on cluster formation, cluster-head selection and the stability of cluster to reduce the impact of mobility in the network, with little attention given to the clusters when passing from base-station to neighbor base-station. Therefore, this study focused on handover problem that occurs after cluster formation and cluster-head election during cluster passing from base station to base station, known as overlapping area. As the cluster in an overlapping area receives two signals from different base stations, the signal arriving at the cluster becomes weak due to interference between two frequencies resulting in loss of cluster information in the overlapping area. In this study, proposed a novel method named Intelligent Cluster-Head (ICH), which is a controller on two clusters that are used to change uplink between clusters to solve the handover problem in the overlapping area. The proposed method was evaluated with VMaSC-1hop method. The proposed method achieved percentage of packet loss up to 0.8%, percentage of packet delivery ratio (PDR) 99%, percentage of number of disconnected links 0.12% and percentage of network efficiency 99% in the cells edge.
Abstract:In this paper, we scrutinized the energy storage options used in mitigation of the intermittent nature of renewable energy resources for desalination process. In off-grid islands and remote areas, renewable energy is often combined with appropriate energy storage technologies (ESTs) to provide a consistent and reliable electric power source. We demonstrated that in developing a renewable energy scheme for desalination purposes, product (water) storage is a more reliable and techno-economic solution. For a King Island (Southeast Australia) case-study, electric power production from renewable energy sources was sized under transient conditions to meet the dynamic demand of freshwater throughout the year. Among four proposed scenarios, we found the most economic option by sizing a 13 MW solar photovoltaic (PV) field to instantly run a proportional RO desalination plant and generate immediate freshwater in diurnal times without the need for energy storage. The excess generated water was stored in 4 × 50 ML (mega liter) storage tanks to meet the load in those solar deficit times. It was also demonstrated that integrating well-sized solar PV with wind power production shows more consistent energy/water profiles that harmonize the transient nature of energy sources with the water consumption dynamics, but that would have trivial economic penalties caused by larger desalination and water storage capacities.
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