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

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

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

The Problem of Data Extraction in Social Media: A Theoretical Framework

In today's rapidly evolving digital landscape, the pervasive growth of social media platforms has resulted in an era of unprecedented data generation. These platforms are responsible for generating vast volumes of data on a daily basis, forming intricate webs of patterns and connections that harbor invaluable insights crucial for informed decision-making. Recognizing the significance of exploring social media data, researchers have increasingly turned their attention towards leveraging this data to address a wide array of social research issues. Unlike conventional data collection methods such as questionnaires, interviews, or focus groups, social media data presents unique challenges and opportunities, demanding specialized techniques for its extraction and analysis. However, the absence of a standardized and systematic approach to collect and preprocess social media data remains a gap in the field. This gap not only compromises the quality and credibility of subsequent data analysis but also hinders the realization of the full potential inherent in social media data. This paper aims to bridge this gap by presenting a comprehensive framework designed for the systematic extraction and processing of social media data. The proposed framework offers a clear, step-by-step methodology for the extraction and processing of social media data for analysis. In an era where social media data serves as a pivotal resource for understanding human behavior, sentiment, and societal dynamics, this framework offers a foundational toolset for researchers and practitioners seeking to harness the wealth of insights concealed within the vast expanse of social media data

Research trends on CAPTCHA: A systematic literature

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain

Identifying Critical Success Factors: the case of ERP Systems in Higher Education

This paper reports on a study that uses a combination of techniques to formally characterize and determine the critical success factors influencing the effective usage of enterprise resource planning (ERP) systems, with special reference to higher education institutions. The thirty-seven ERP success factors identified from the literature are classified into: Critical, Active, Reactive and Inert categories. The classification of decision factors can generally support organizations to explore their current challenges and to adequately prepare decisions in a more participatory way for future endeavors. This study suggests a possible alternative that decision makers should take when a factor or a set of factors dominates during the implementation of ERP systems