Search results

Usage of learning management systems (LMSs) has become widespread with the disruption of face-to-face educations after the COVID-19 pandemic. There are several software products, usually named as LMS to enable and support distance education. However, selection of a suitable LMS is a complex multiple criteria decision making (MCDM) problem that requires consideration of many criteria and inputs from different parties like students, academicians, education managers, etc. Usability evaluation of LMS is one of the critical steps in deciding which LMS system to be adapted. There are several studies related to usability evaluation of LMS in the literature, but utilization of MCDM methods and real life case studies are very rare. Based on this motivation, perceived usability evaluation of SAKAI-LMS that is in use at an academic department is performed by employing axiomatic design procedure (ADP). This paper aims to discuss the aforementioned issues.

ADP is considered as a suitable MCDM method for perceived usability evaluation as it allows an easy approach to data fusion and setting performance targets for decision makers. A questionnaire is developed to collect data from three types of system users about predetermined usability criteria and their importance. After detailed statistical analyses and weighting criteria via analytical hierarch process (AHP), ADP is carried out to evaluate usability of the LMS.

It is found that the proposed ADP based approach is easy to apply in practical circumstances and able to quantify perceived usability of the LMSs.

The proposed approach provides an easy and practical evaluation of perceived usability of the LMSs for decision makers who are responsible for the implementation of LMSs. The developed novel and practical MCDM-based perceived usability approach for LMS in this study has been verified through a real life case study at an academic department. Perceived usability results, therefore, reflects only the views of this focus group and are not generalizable.

First time in the literature, a comprehensive ADP based MCDM approach is proposed based on the analyses of the related literature and information gathered from the system users.

The purpose of the paper is to propose a semantic model for describing open source software (OSS) in a machine–human understandable format. The model is extracted to support source code reusing and revising as the two primary targets of OSS through a systematic review of related documents.

Conducting a systematic review, all the software reusing criteria are identified and introduced to the web of data by an ontology for OSS (O4OSS). The software semantic model introduced in this paper explores OSS through triple expressions in which the O4OSS properties are predicates.

This model improves the quality of web data by describing software in a structured machine–human readable profile, which is linked to the related data that was previously published on the web. Evaluating the OSS semantic model is accomplished through comparing it with previous approaches, comparing the software structured metadata with profile index of software in some well-known repositories, calculating the software retrieval rank and surveying domain experts.

Considering context-specific information and authority levels, the proposed software model would be applicable to any open and close software. Using this model to publish software provides an infrastructure of connected meaningful data and helps developers overcome some specific challenges. By navigating software data, many questions which can be answered only through reading multiple documents can be automatically responded on the web of data.

This paper aims to evaluate the level of compliance of fire safety with the legal requirements in Malaysia government hospital buildings by evaluating via fire risk management.

Five government hospitals were selected. These five hospitals were selected due to the location of Penang, which is one of the fast-growing states in Malaysia (Salleh, 2019; Ebekozien, 2019). This state is the second most densely populated state after Wilayah Persekutuan Kuala Lumpur, with an average distribution of 1,490 persons per square km. This higher population caused the higher demand on the health-care services by the public (DOSM, 2016). The observation and building audit processes are as described. Hundreds of photos were taken for qualitative analysis, and all fire safety elements were measured for the descriptive analysis for each hospital. The framework of audit elements is created based on the Life Safety Code: NFPA 101 (2018), UBBL 1984: Part VII (Fire Requirements) and Part VIIII (Fire Alarms, Fire Detection, Fire Extinguishment and Fire Fighting Access). The cross-sectional descriptive evaluation is conducted in the case studies building in accordance with Life Safety Code of NFPA, also known as NFPA 101. To conduct the study, the information needed to assess the fire safety status was extracted from the CFSES software based on the NFPA 101 standard and prepared and compiled by the researcher as a checklist. In the next stage, gathered information was analysed using Computerised Fire Safety Evaluation System (CFSES) software. This method was developed based on the NFPA 101 standard and evaluated the fire risk from four dimensions of containment, extinguishment, people movement and general safety. This software gives the risk assessment results in three areas of fire control, exits and general safety. To assess the fire risk of the commercial buildings after entering the background information (height, age, number of stories, etc.) in the software, the software first calculates the score that the building should obtain in the three aspects of fire control, exit routes and general safety (minimum score required).

The utmost zones in the case studies (44.3%) occupied by limited mobility are located at low-rise buildings or at the first floor to third floor of the hospital buildings. Hospitals managements lacked in creating the maximum exit route and egress the occupants to disclosed the building during evacuation, it correlates to the patients' mobility positions strategy to assign their categories that fell on effortless mobilisation. Surveyed hospitals were built with the non-combustible materials, even though four of the case studies were built before 1984. Hospitals were equipped with hazard separations and vertical smoke pores, and in most of the zones, sprinkler system is installed only in the corridors, equipped with communication system and system of communication with fire and relief organisations and has a fire detection and alarm system throughout the building. Results of fire risk assessment on four groups of elements were tested via CFSES revealed from 122 zones of surveyed hospitals; 102 or 84% of zones give the highest failed rate to comply the NFPA 101 requirements in terms of people movement in the building. The high-occupied Penang General Hospital contributed as the highest case study for not complying with the minimum requirements in all dimensions: people movement elements (41 zones), fire containment (31 zones), fire extinguisher (31 zones) and general safety (20). Fire extinguishment (62 zones) recorded the highest numbers of zones that complied with NFPA 101 (2013). The overall results of the fire risk assessment suggested that in terms of the fire control, egress and general safety aspects, the fire risk assessment score was unacceptable (failed) in all hospital buildings studied, and in the three areas mentioned, the general safety, egress/exit routes and fire control were in a worse status in terms of the score obtained in the software. None of the surveyed hospital received the minimum safety score in the three areas mentioned. The involvement of Emergency Response Team is crucial to overcome this egress or fire exit requirement and parameters.

Several limitations exist in this research that cannot be controlled. Firstly, the occupancy rates only determined during peak hour. Accessibility into hospital compound permitted only during daytime. Secondly, the fire safety audits and fire safety risk management in this research are not being conducted by a professional architect or engineer and as a result must be relied on the direct inspection checklist to create valid results. Thirdly, this research has some limitations which need to be noted but does not affect the robustness of the study’s findings. This study focuses only on five selected public hospitals in one state of the northern region of Malaysia and excluded data gathering from all other parts of Malaysia. The perception of hospital operators regarding fire safety issues from different state hospitals may allow comparisons.

The findings of this paper should make a key practical contribution to the body of knowledge. In practice, the proposed framework should expand the knowledge of public hospital fire safety management plan concerning the level of fire safety compliance with the requirements in government hospital buildings and develop a fire safety management plan framework for government hospital buildings.

This paper develops an early framework component related to the occupants’ safety which gives the basis for future research in hospital fire safety settings as it imparts early investigation into the consequence of investigating the phenomenon from the operators’ perspective as an attempt to improve public health-care fire safety performance in hospitals.

This paper has created a few measurement tools that can be applied among public hospital buildings stakeholders to perform the fire safety audit and risk management and rate the performance of Fire Safety Management in public hospitals.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.

Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.

The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.

This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.

This study aims to identify user perception of different qualities of optical character recognition (OCR) in texts. The purpose of this paper is to study the effect of different quality OCR on users' subjective perception through an interactive information retrieval task with a collection of one digitized historical Finnish newspaper.

This study is based on the simulated work task model used in interactive information retrieval. Thirty-two users made searches to an article collection of Finnish newspaper Uusi Suometar 1869–1918 which consists of ca. 1.45 million autosegmented articles. The article search database had two versions of each article with different quality OCR. Each user performed six pre-formulated and six self-formulated short queries and evaluated subjectively the top 10 results using a graded relevance scale of 0–3. Users were not informed about the OCR quality differences of the otherwise identical articles.

The main result of the study is that improved OCR quality affects subjective user perception of historical newspaper articles positively: higher relevance scores are given to better-quality texts.

To the best of the authors’ knowledge, this simulated interactive work task experiment is the first one showing empirically that users' subjective relevance assessments are affected by a change in the quality of an optically read text.

This paper aims to prove the following hypothesis Problem Statement: HYPOTHESIS (1) User Experience collection of mobile applications can be done using the Crowdsourcing mechanism; (2) User Experience collection of mobile applications are influenced by the mindset of Crowdmembers, culture/ethnicity/social background, ease of interface use and rewards, among other factors.

The authors of this paper, did a literature review first to find if Crowdsourcing was applicable and a used method to solve problems in Software Engineering. This helped us to narrow down the application of Crowdsourcing to the Requirements Engineering-Usability (User Experience) collection. User experience collection of two Malayalam language-based mobile applications, AarogyaSetu and BevQ was done as the next step. Incorporating findings from Study I, another study using AarogyaSetu and Manglish was launched as Study II. The results from both cases were consolidated and analyzed. Significant concerns relating to expectations of Crowd members with User Experience collection were unraveled and the purpose of Study was accomplished.

(1) Crowdsourcing is and can be used in Software Engineering activities. (2) Crowd members have expectations (motivating factors) of User Interface and other elements that enable them to be an effective contributor. (3) An individual’s environment and mindset (character) are influential in him becoming a contributor in Crowdsourcing. (4) Culture and social practices of a region strongly affects the crowd-participating decision of an individual.

This is purely self-done work. The value of this research work is two-fold. Crowdsourcing is endorsed significant in Software Engineering tasks, especially in User Experience collection of mobile applications. Two, the Crowd service requesters can be careful about designing the questionnaire for Crowdsourcing. They have to be aware and prepared to meet the expectations of the Crowd. This can ensure the active participation of potential contributors. Future researchers can use the results of this work to base their research on similar purposes.

Testing business processes is crucial to assess the compliance of business process models with requirements. Automating this task optimizes testing efforts and reduces human error while also providing improvement insights for the business process modeling activity. The primary purposes of this paper are to conduct a literature review of Business Process Model and Notation (BPMN) testing and formal verification and to propose the Business Process Evaluation and Research Framework for Enhancement and Continuous Testing (bPERFECT) framework, which aims to guide business process testing (BPT) research and implementation. Secondary objectives include (1) eliciting the existing types of testing, (2) evaluating their impact on efficiency and (3) assessing the formal verification techniques that complement testing.

The methodology used is based on Kitchenham's (2004) original procedures for conducting systematic literature reviews.

Results of this study indicate that three distinct business process model testing types can be found in the literature: black/gray-box, regression and integration. Testing and verification approaches differ in aspects such as awareness of test data, coverage criteria and auxiliary representations used. However, most solutions pose notable hindrances, such as BPMN element limitations, that lead to limited practicality.

The databases selected in the review protocol may have excluded relevant studies on this topic. More databases and gray literature could also be considered for inclusion in this review.

Three main originality aspects are identified in this study as follows: (1) the classification of process model testing types, (2) the future trends foreseen for BPMN model testing and verification and (3) the bPERFECT framework for testing business processes.

To address the key problem of lack of use in the advancement of open government data (OGD) portals from the aspect of good usability, which is an essential prerequisite to the acceptance and usage of a portal, this paper aims to develop a usability framework including design principles and criteria for OGD portals and to discover problems in the present usability design.

This study builds the usability framework by extending usability principles for general websites to address the specific needs of OGD portals. Criteria for each principle are developed accordingly based on the literature. A comparative heuristic evaluation involving five expert evaluators and 13 Chinese province-level OGD portals has been carried out to test the capability of the usability framework.

A usability framework with 24 principles and 63 criteria has been built. The heuristic evaluation shows OGD portals performed better in meeting general principles than the OGD portals specific ones. Insufficient help functions weakened OGD portals' usability. Similarities and differences were found of Chinese OGD portals compared with similar studies in the United States.

This paper proposed a usability framework for OGD portals and proved its capability in recognizing usability problems and its causes by carrying out a comparative heuristic evaluation in China. By comparing the evaluation results with other studies in the United States, the findings and lessons learnt in this study can thus be shared across international borders.