Constructing Frugal Sales System for Small Enterprises

In the current study, the authors report on the application of the design science methodology to construct, utilize, and evaluate a frugal information system that uses mobile devices and cloud computing resources for documenting daily sales transactions of very small enterprises (VSEs). Small enterprises play significant roles in the socioeconomic landscape of a community by providing employment opportunities and contributing to the gross domestic product. However, VSEs have very little access to innovative information technologies that could help them manage their challenges that are restricting their effective growth, sustainability, and participation in a knowledge economy. The results of a field-evaluation experiment, involving 22 VSE entrepreneurs using a newly constructed system, MobiSales, disclosed that user behavior, which demonstrates confidence, excitement, enthusiasm, energy, and trust varied when employing a mobile electronic device for social interactions, as compared to using it for business transactions

Recognition of Human Emotion using Radial Basis Function Neural Networks with Inverse Fisher Transformed Physiological Signals

Emotion is a complex state of human mind influenced by body physiological changes and interdependent external events thus making an automatic recognition of emotional state a challenging task. A number of recognition methods have been applied in recent years to recognize human emotion. The motivation for this study is therefore to discover a combination of emotion features and recognition method that will produce the best result in building an efficient emotion recognizer in an affective system. We introduced a shifted tanh normalization scheme to realize the inverse Fisher transformation applied to the DEAP physiological dataset and consequently performed series of experiments using the Radial Basis Function Artificial Neural Networks (RBFANN). In our experiments, we have compared the performances of digital image based feature extraction techniques such as the Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and the Histogram of Images (HIM). These feature extraction techniques were utilized to extract discriminatory features from the multimodal DEAP dataset of physiological signals. Experimental results obtained indicate that the best recognition accuracy was achieved with the EEG modality data using the HIM features extraction technique and classification done along the dominance emotion dimension. The result is very remarkable when compared with existing results in the literature including deep learning studies that have utilized the DEAP corpus and also applicable to diverse fields of engineering studies

Meta-Analysis of Factors Influencing Student Acceptance of Massive Open Online Courses for Open Distance Learning

This study aimed to apply the meta-analysis methodology to systematically synthesize results of primary studies to discover the main significant factors influencing student acceptance of massive open online courses (MOOCs) for open distance learning (ODL). An abundance of studies on MOOCs exists, but there is a lack of meta-analysis research on student acceptance of MOOCs, which is a novel contribution of the current study. The meta-analysis methodology was applied to investigate effect sizes, statistical heterogeneity, and publication bias across 36 primary studies involving 14233 participating students. The study findings show satisfaction to be the main significant factor influencing student acceptance of MOOCs. The findings can enlighten stakeholders in the decision-making process of implementing MOOCs for ODL and advance technology acceptance models. Moreover, this study has the potential to theoretically contribute to technology acceptance research by situating the widely known technology acceptance models in the context of education

Development of Research Administration and Management System for Higher Education Institutions in Developing Countries: Case Study of Durban University of Technology

Research information management has become an essential activity for higher education institutions (HEIs) worldwide as a mechanism to aggregate, curate, utilize and improve the transparency of information about research. It has led to the evolution of proprietary software systems for administering and managing research information in HEIs. However, the literature reveals that most proprietary software systems are usually inflexible, costly to maintain and do not adequately satisfy the dynamic requirements of HEIs in developing countries. Consequently, the demand for current information systems is to incorporate a high degree of formalism into software development processes to produce correct, flexible, usable and cost-effective systems. This paper reports on the development of a web-based research administration and management system (RAMS) that addresses pertinent issues associated with research information management in the context of HEIs in developing countries. The Zermelo-Fraenkel specification language has been utilized to formally specify the requirements of RAMS in close collaboration with the intended users who evaluated its usability. The overall results of the usability evaluation show that RAMS is effective, useful, easy to use, learnable and satisfactory

Binding site identification of COVID-19 main protease 3D structure by homology modeling

The influx of coronavirus in 2019 (COVID-19) has recorded millions of infection cases with several deaths worldwide. There is no effective treatment, but recent studies have shown that its enzymes maybe considered as potential drug target. The purpose of this work was to identify the binding site in-silico and present the 3D structure of COVID-19 main-protease (Mpro) by homology modeling through multiple alignment followed by optimization and validation. The modeling was done by Swiss-Model template library. The obtained homotrimer oligo-state model was verified for reliability using PROCHECK, Verify3D, MolProbity and QMEAN. HHBlits software was used to determine structures that matched the target sequence by evolution. Structure quality verification through Ramachandran plot showed an abundance of 99.3% of amino acid residues in allowed regions while 0.1% in disallowed region. The Verify3D rated the structure a 90.87% PASS of residues having an average 3D-1D score of at least 0.2, which validates a good environment profile for the Mpro model. The features of the secondary structure indicated that the structure contains 32.05% α-helix and 37.17% random coil with 25.92 extended strand. The result of this study suggests that blocking expression of this protein may constitute an efficient approach for infection transmission blockage

Rab-KAMS: A reproducible knowledge management system with visualization for preserving Rabbit Farming and Production Knowledge

The sudden rise in rural-to-urban migration has been a key challenge threatening food security and most especially the survival of Rabbit Farming and Production (RFP) in Sub-Saharan Africa. Currently, significant knowledge of RFP is going into extinction as evident by the drastic fall in commercial rabbit farming and production indices. Hence, the need for a system to proactively preserve RFP knowledge for future potential farmers cannot be overemphasized. To this end, knowledge archiving and management are key concepts of ensuring long-term digital storage of conceptual blueprints and specifications of systems, methods and frameworks with capacity for future updates while making such information readily accessible to relevant stakeholders on demand. Therefore, a reproducible Rabbit production' Knowledge Archiving and Management System (Rab-KAMS) is developed in this paper. A 3-staged approach was adopted to develop the Rab-KAMS. This include a knowledge gathering and conceptualization stage; a knowledge revision stage to validate the authenticity and relevance of the gathered knowledge for its intended purpose and a prototype design stage adopting the use of unified modelling language conceptual workflows, ontology graphs and frame system. For seamless accessibility and ubiquitous purposes, the design was implemented into a mobile application having interactive end-users' interfaces developed using XML and Java in Android 3.0.2 Studio development environment while adopting the V-shaped software development model. The qualitative evaluation results obtained for Rab-KAMS based on users' rating and reviews indicate a high level of acceptability and reliability by the users. It also indicates that relevant RFP knowledge were correctly captured and provided in a user-friendly manner. The developed Rab-KAMS could offer seamless acquisition, representation, organization and mining of new and existing verified knowledge about RFP and in turn contributing to food security

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms

Improving the Dependability of Destination Recommendations using Information on Social Aspects

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process.Content-based filtering; Recommender Systems; Ontology; Social Attributes, Destination recommendation