In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.

The rapid growth of Information and Communication Technologies (ICT)—specifically, the Internet—has given emergence to e-learning. Resultantly, web-based e-learning systems are being increasingly developed to enhance the learning process. However, the utilization of such systems is low, mainly owing to poor quality content and overall design problems. To improve usage, it is imperative to identify the factors with the most significant impact on the quality of these systems so that the e-learning industry keeps these factors in consideration while developing e-learning systems. This study focused on the identification and prioritization of factors related to the design quality of e-learning systems through a hierarchical quality model. Thus, firstly, an extensive literature review was conducted to identify the factors that most affect the quality of web-based e-learning systems. Secondly, among the identified factors, only those with the most significant effect were considered. To identify the most important quality criteria, a survey was conducted. An instrument was deployed among 157 subjects, including e-learning designers, developers, students, teachers, and educational administrators. Finally, a second instrument was distributed among 51 participants to make a pairwise comparison among the criteria and rank them according to their relative importance. The identified and prioritized factors were classified into four main categories. Among these four factors, content was identified as the most important factor, whereas design was found to be the least important factor.

Agile software development does not include elicitation and management of non-functional requirements (NFRs). Several techniques have been proposed by researchers to elicit, manage and prioritize NFRs. The major issue with such techniques is that they involve customer or users to elicit and prioritize NFRs, which becomes problematic because they do not have exposure of NFRs. They may only be familiar with very few common NFRs such as performance, efficiency and security etc. There could be several other NFRs such as confidentiality, scalability, usability, maintainability, portability, reliability and many others which may not be known to the stakeholders. Consequently, some relevant NFRs may be missed and some wrong NFRS may be implemented in the final product which can degrade its overall quality. Secondly, it is very time consuming to elicit and prioritize NFRs from the stakeholders who have very low exposure. It is especially critical in the case of agile software development where product is developed and delivered at a very rapid pace. To overcome the challenge, this study proposed a model to prioritize NFRs for agile software development to produce quality software products. Thus, firstly, an extensive literature review was conducted to identify the NFRs which were further ranked through a survey carried out in agile software companies. Moreover, the NFRs were analyzed and prioritized using multi criteria decision making analysis technique called analytical hierarchical technique (AHP). The evaluation results showed that the NFRs prioritization model reduce the overhead of meetings and discussion required to gather and identify the NFRs common to agile products.

Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions. The second stage incorporates a web tool to increase the visualization of the model by promising patient health diagnoses. The proposed model was thoroughly trained, validated, and tested utilizing a database related to the HAM 10,000 dataset. The model accomplished an accuracy of 96.25% in classifying skin lesions, exhibiting significant areas of strength. The results achieved with the proposed model validated by evaluation methods and user feedback indicate substantial improvement over the current state-of-the-art methods for skin lesion classification (malignant/benign). In comparison to other models, sequential CNN surpasses CNN transfer learning (87.9%), VGG 19 (86%), ResNet-50 + VGG-16 (94.14%), Inception v3 (90%), Vision Transformers (RGB images) (92.14%), and the Entropy-NDOELM method (95.7%). The findings demonstrate the potential of deep learning, convolutional neural networks, and sequential CNN in disease detection and classification, eventually revolutionizing melanoma detection and, thus, upgrading patient consideration.

In the higher education sector, there is a growing trend to offer academic information to users through websites. Contemporarily, the users (i.e., students/teachers, parents, and administrative staff) greatly rely on these websites to perform various academic tasks, including admission, access to learning management systems (LMS), and links to other relevant resources. These users vary from each other in terms of their technological competence, objectives, and frequency of use. Therefore, academic websites should be designed considering different dimensions, so that everybody can be accommodated. Knowing the different dimensions with respect to the usability of academic websites is a multi-criteria decision-making (MCDM) problem. The fuzzy analytic hierarchy process (FAHP) approach has been considered to be a significant method to deal with the uncertainty that is involved in subjective judgment. Although a wide range of usability factors for academic websites have already been identified, most of them are based on the judgment of experts who have never used these websites. This study identified important factors through a detailed literature review, classified them, and prioritized the most critical among them through the FAHP methodology, involving relevant users to propose a usability evaluation framework for academic websites. To validate the proposed framework, five websites of renowned higher educational institutes (HEIs) were evaluated and ranked according to the usability criteria. As the proposed framework was created methodically, the authors believe that it would be helpful for detecting real usability issues that currently exist in academic websites.

At present global software development (GSD) is gaining considerable attention in software industry. The management of global software projects presents substantial complexity owing to several inherent challenges of GSD. The project management practices used to execute in-house development projects are inadequate to address the unique challenges posed by global software projects, making their management a formidable task. Software organizations rely on traditional project management practices with the aim of managing global software projects, often resulting in impairments or failures. This paper explores the critical success factors (CSFs) in project management for global projects by developing a framework for effective project management within the context of GSD. The study focuses on identifying and prioritizing CSFs in software project management within a GSD setting utilizing Multi Criteria Decision Making (MCDM) analysis methods. Therefore, present research provides an extensive literature review of CSFs in software project management within GSD. Additionally, the research applies the combinatorial approach to assess the various dimensions and CSFs of software project management in GSD. The proposed approach aids in measuring and comparing the effect of several dimensions and CSFs of software project management in GSD. Five dimensions and twenty factors have been determined through literature review and further evaluated for prioritization using the combinatorial approach. The identified dimensions and factors will be valuable in devising strategies to effectively manage global software projects.