Browsing by Author "Ali, Gohar"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    ItemMetadata only
    Improving M-Learners’ Performance Through Deep Learning Techniques by Leveraging Features Weights
    (IEEE, 2020-07-07) Adnan, Muhammad; Habib, Asad; Ashraf, Jawad; Shah, Babar; Ali, Gohar
    Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners' interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive M-learning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse M-learners thus helping M-learners in enhancin...
  • No Thumbnail Available
    ItemMetadata only
    Load Balancing in Partner-Based Scheduling Algorithm for Grid Workflow
    (The Science and Information Organization, 2016) Roman, Muhammad; Ashraf, Jawad; Habib, Asad; Ali, Gohar
    Automated advance reservation has the potential to ensure a good scheduling solution in computational Grids. To improve global throughput of Grid system and enhance resource utilization, workload has to be distributed among the resources of the Grid evenly. This paper discusses the problem of load distribution and resource utilization in heterogeneous Grids in advance reservation environment. We have proposed an extension of Partner Based Dynamic Critical Path for Grids algorithm named Balanced Partner Based Dynamic Critical Path for Grids (B-PDCPG) that incorporates a hybrid and threshold based mechanism to achieve load balancing to an allowed value of variation in workload among the resources in Partner Based Dynamic Critical Path for Grids algorithm. The proposed load balancing technique uses Utilization Profiles to store the reservation details and check the loads from these profiles on each of the resources and links. The load is distributed among resources based on the processing element capacity and number of processing units on resources. The simulation results, using Gridsim simulation engine, show that the proposed technique has balanced the workload very effectively and has provided better utilization of resources while decreasing the workflow makespan.