500 research outputs found
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Mobile Health and Telemedicine: Awareness, Adoption and Importance of Health Study
No full textIn 2016, the U.S. Government health expenditures reached 10,345. Health is seen as impacting both one's quality of life and finances. The Affordable Care Act (ACA) (2008 - 2016) brought the issue of cost to the forefront for all people especially those in the health disparate communities. Advances in health informatics coupled with new approaches to healthcare delivery may hold promise for this large industry in the USA that critically needs to be cost effective in order to sustain itself. This paper reports a study that investigated importance of health, mobile health (m-Health) and telemedicine awareness along with its adoption in a health disparate community that has one of the Historical Black Colleges & Universities (HBCUs) in the country. The findings were that, all participants owned a mobile (cell) phone with smart features. Although a large number them indicated that their health was very important to them, there was lack of awareness and adoption of m-Health and telemedicine
A Deep Learning Framework for Malware Classification
No full textIn this article, the authors propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses serious security threats to financial institutions, businesses, and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples. Nowadays, machine learning approaches are becoming popular for malware classification. However, most of these approaches are based on shallow learning algorithms (e.g. SVM). Recently, convolutional neural networks (CNNs), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Inspired by this, the authors propose a CNN-based architecture to classify malware samples. They convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, namely Malimg and Microsoft, demonstrate that their method outperforms competing state-of-the-art algorithms
User's Segmentation on Continued Knowledge Management System Use in the Public Sector
No full textKnowledge management systems (KMS) can help an organization support knowledge management activities and thereby increase organizational performance. This study extends the expectation-confirmation model for predicting mandatory continued KMS use in the public sector. The models are assessed using data from a sample of 627 employees of the Kaohsiung City government in Taiwan and analyzed using the finite mixture partial least squares (FIMIX-PLS) method. The results of this study indicate that (1) data heterogeneity (i.e., educational level) segments two specific groups that show different perceptions toward continued KMS use; (2) the results of aggregate-based data analysis are different from the results of group-specific data analysis; (3) compatibility, relative to confirmation, has larger impact on perceived usefulness regardless of groups; (4) the effect of user satisfaction on continued usage behavior is significant different between the two groups; (5) cognition-driven continued use and emotion-driven continued use are identified in the two groups
Research on Collective Human Mobility in Shanghai Based on Cell Phone Data
No full textThe high-frequency mobility of a massive population has caused an enormous influence on the urban internal structure, which is unable to be described by traditional data sources. While recent advances in location-based technologies provides new opportunities for researchers to understand daily human movements and the structure as a whole. The article aims to explore human spatial movements and their aggregate distribution in Shanghai using large-scale cell phone data. The trajectory of each individual is extracted from cell phone data after data cleansing. Then, an indicator system which includes mobility intensity, mobility stability, influential range, and temporal variation is developed to describe collective human mobility features in census tracts scale. Finally, spatial elements are extracted using the indicator system and the structure of human mobility in Shanghai is discussed
Understanding Country Level Adoption of E-Commerce: A Theoretical Model Including Technological, Institutional, and Cultural Factors
No full textThis paper provides a theoretically grounded model of e-commerce adoption to explain differences in adoption rates among countries. The model extends the existing culture-policy-technology (CPT) framework to examine causal relationships between the technological, institutional, and cultural factors in order to examine country-level e-commerce adoption. Thus, interesting relationships among macro-level factors are hypothesized. The paper highlights the important of risk mitigating mechanisms or institutions to facilitate adoption of e-commerce in countries with high uncertainty avoidance. A call for empirical examination into country level adoption is answered by analyzing macro level data from 69 countries. The hypotheses are confirmed using PLS analytical procedures. The study is timely as e-commerce technology has now taken hold in several countries but its revenues in proportion to the overall total revenues remain low. The study is motivated by significant different in e-commerce adoption rates among countries. The paper makes significant contributions to literature and practice
Trust, Risk and Alternative Website Quality in B-Buyer Acceptance of Cross-Border E-Commerce
No full textCross-border e-commerce (CBEC) has become an imperative mode for global trade. Research on cross-border e-commerce historically focuses mainly on the customer's behavior intention to purchase on a CBEC platform. However, B-buyers are more important compared with C-buyers for CBEC platforms. This is because B-buyers can contribute more gross merchandise volume (GMV) in a CBEC platform, and thus more margin for the firm. The authors apply trust transfer theory, perceived risk, and alternative website quality to study repurchase intention, focusing on B-buyers. The results show that perceived risk, trust in provider, and trust in the website affect repurchase intention significantly, where trust in website is found to be the most important factor. In addition, the authors found that the dimensions of perceived risk in CBEC context can be classified as the following: customer duties risk, confiscation risk, delivery risk, financial risk, and privacy risk. The contributions of the study are addressed lastly
Multi-Image Hiding Blind Robust RGB Steganography in Transform Domain
No full textSteganography is a widely-used technique for digital data hiding. Image steganography is the most popular among all other kinds of steganography. In this article, a novel key-based blind method for RGB image steganography where multiple images can be hidden simultaneously is described. The proposed method is based on Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) which provides enhanced security as well as improve the quality of the stego. Here, the cover image has been taken as RGB although the method can be implemented on grayscale images as well. The fundamental concept of visual cryptography has been utilized here in order to increase the capacity to a great extent. To make the method more robust and imperceptible, pseudo-random number sequence and a correlation coefficient have been used for embedding and the extraction of the secrets, respectively. The robustness of the method is tested against steganalysis attacks such as crop, rotate, resize, noise addition, and histogram equalization. The method has been applied on multiple sets of images and the quality of the resultant images have been analyzed through various matrices namely ‘Peak Signal to Noise Ratio,' ‘Structural Similarity index,' ‘Structural Content,' and ‘Maximum Difference.' The results obtained are very promising and have been compared with existing methods to prove its efficiency
Harmonization and Categorization of Metrics and Criteria for Evaluation of Recommender Systems in Healthcare From Dual Perspectives
No full textResearchers' choice of metrics and criteria in evaluating recommender systems depends on what the researcher feels is popular among other researchers, or sometimes based on the objective of the research. There is no harmonized set of criteria and metrics that can be referenced when evaluating recommender systems in healthcare. In this article, a set of metrics and criteria are harmonized and categorized as a guide for evaluating recommender systems. By means of an online survey, the opinions of forty-four experienced researchers and other stakeholders from eight countries and four continents were sought on the relevance of identified metrics and criteria. Analysis of the results show speed and timeliness are at the top. Topping the list of criteria is the provision of information that will guide users to useful decisions. The result is presented from two logical perspectives. Four categories are then identified as a useful guide for evaluating recommender systems
Human Computer Interaction During Clinical Decision Support With Electronic Health Records Improvement
No full textThis study investigated the most common challenges of human-computer interaction (HCI) while using electronic health records (EHR) based on the experience of a large Russian medical research center. The article presents the results of testing DSS implemented in the mode of an additional interface with the EHR. The percentage of erroneous data for two groups of users (with and without notifications) is presented for the entire period of the experiment and the weekly dynamics of changes. The implementation of CDSS in the supplemented interface mode of the main medical information system (MIS) has had a positive effect in reducing user errors in the data. The results of users' survey are presented, showing a satisfactory evaluation of the implemented system. This study is part of a larger project to develop complex CDSS on cardiovascular disorders for medical research centers
Hotel Guests' Perceptions of Green Technology Applications, and Practices in the Hotel Industry
No full textAlthough sustainability and technology are two major concerns in the lodging industry, sustainability applications and practices are quite new and in need of more research. This study was conducted to examine and understand hotel guests' perceptions of green technology applications and practices. Data were collected in 2018 from 210 respondents via an online survey. The Technology Acceptance Model was applied to examine how ease of use and usefulness of green technology applications and practices can influence guests' booking decisions. The results showed significant positive correlations between behavioral belief and usefulness and between usefulness and intention to use green technology applications and practices. However, behavioral belief and ease of use were not correlated, nor were ease of use and intention to adopt green technology. These results demonstrate that guests do believe in the important role of green technology in sustainability and they intend to book hotels that adopt this technology