Journal of Hunan University Natural Sciences

With the continuous efforts to implement several smart cities, several challenges face these initiatives at the global level, the most prominent of which is data privacy. There is a lack of research on the factors that affect the perception of individuals' privacy, such as the risk of privacy, data sensitivity, privacy Awareness. In addition, it is not clear what those factors are, and they could swing people's intention to adopt smart services. Concerns about data privacy are categorized based on data activities to unauthorized retrieval, unauthorized use, unauthorized access, unauthorized sharing, insecure storage, and insecure transmission. Each of these issues might lead to a personal data breach and expose the data to be compromised, especially in the case of healthcare data. Therefore, this study aims to identify factors that affect the adoption of smart city healthcare services and subsequently propose a generic adoption model to focus on data and information privacy in this model, especially in health care. This model is developed based on two ways to extract the factors. First: the theories used are the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Privacy calculus theory (PCT). Secondly: extract some factors from the literature review and studies related to this research. The model is expected will help to obtain the acceptance, adoption of smart city services and the extent of their impact on data and information privacy from the perspective of individuals and fill gaps. It also can be used in countries similar to Oman, such as in the Arabian Gulf countries.

Keywords: smart city, healthcare services, privacy, privacy concern, technology acceptance model.

In higher education, digital technologies completely change teaching and learning, with the rapid technological development compounding the problem. Because of the extensive usage of technology among today's college and university students, higher education institutions worldwide have recognized the need to use it in teaching and learning for certain reasons. Over the last two decades, studies have speculated on the beneficial and negative effects of students' constant interaction with technology in distance education. This study intended to investigate the effect of digital technology and educational outcomes through the mediating effect of student engagement. The convenience sampling approach was used to obtain 378 students and staff data. In addition, this study analyzed the data using the Partial Least Square Structural Equation Model (PLS-SEM). The study's findings indicated that digital technology and student engagement positively and significantly affect educational outcomes in distance education. In addition, the results revealed that digital technology indirectly influences educational outcomes through the mediation effect of student engagement. This study concluded that to comprehend the changes occurring in higher education. More attention should be devoted to establishing policies and strategies to increase student engagement in digital technologies in distance education of higher education contexts. From a distance education viewpoint, the current review investigated the determinants of educational outcomes through the mediating effect of student engagement. These findings have significant implications as distance education becomes a basic delivery mode of study in the coming decades. The methodological approach used in this study and the digital technology in distance education greatly improve current literature in the educational sector.

Keywords: distance education, digital technology, educational outcomes, higher education, Partial Least Square Structural Equation Model.

Over the last two decades, medical imaging examinations, and technologies together have been exponentially increased. With the increased demand for medical examinations, the demand for medical imaging experts is also increased. Manual identification and annotation of biomedical concepts tend to be rigorous and error-prone due to the varied knowledge of imaging experts. There is a critical need for automated Medical Concept Detection methods. Finding the relevant biomedical concepts present in a medical image holds the key to solve many automated clinical diagnosis problems, a machine learning pipeline for medical information retrieval, and other related issues, like creating and managing legacy or cloud-based descriptive digital repository. Appropriate mapping from biomedical image concepts into precise textual summary highly depends on the efficiency of Medical Concept Detection techniques. A novel clustering technique is presented as a complementary data preconditioning step to reach high concept detection results. The authors grouped 8767 Concept unique Identifiers (CUIs) into 970 clusters (label size decreased by 26% approximately using 97.7% images from the dataset). The main objective of this research is to examine the state-of-the-art convolution-based deep learning pre-trained and full-scale training models for the task of multi-label classification of medical concepts using medical image input. The research work evaluates the performance of transfer learning networks: InceptionV3, Xception, Dense Convolution Network (DenseNet) 121, VGG-16, and MobileNet. This work also presents one full-scale learning CNN architecture for the identification of relevant biomedical concepts that exist in medical images. Transfer learning technique using Xception model has achieved the highest F1 score of 36.29. The shallow VGG-like full-scale training architecture also has shown a promising result with an F1 score of 20.018. The obtained results reflect the significant improvement from previous experiments, offering state-of-the-art performance, with new data preconditioning precedence for highly variable and complex datasets.

Keywords: concept detection, concept annotation, deep learning, medical image processing, neural networks, machine learning.

The development of type-2 diabetes mellitus (T2DM) has been linked to environmental, genetic, and epigenetic risk factors. Long non-coding RNAs (lncRNAs) play a role in the pathophysiology of complicated disorders and can also be used as diagnostic indicators. The literature strongly supports the implication of these lncRNAs in certain metabolic disorders. In this regard, one of the lncRNAs Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1) expressions is also under investigation concerning diabetes and its related complications. There are few researches on the relationship between T2DM progression and MALAT1 dysregulation, and this study was undertaken to investigate this link further and gain a better knowledge of its clinical significance in the diagnosis and prognosis of T2DM. Therefore, the purpose of this study was to assess the gene expression of lncRNA MALAT1 in healthy controls, type 2 diabetics, and T2DM patients with the presence of coronary artery disease complications. This study was the case-control one performed on blood samples followed by centrifugation to separate the buffy coat. Then total RNA was extracted from buffy coat using triazole method followed by quantifying its expression levels by quantitative polymerase chain reaction (qPCR). The results of our study showed the significant up-regulation of lncRNAs-MALAT1 expression in patients with T2DM 1.22 (3.7) and T2DM with related coronary artery disease (CAD) complications 1.95 (2.10) (p-value <0.001). These findings suggest that modulating the expression of lncRNA MALAT1 may be a future strategy for diagnosing and treating diabetes-related complications like CAD.

Keywords: long non-coding RNAs, metastasis associated lung adenocarcinoma transcript 1, quantitative polymerase chain reaction. 

Human fingerprint features extraction is important for building a fingerprint classification system. The extracted features must be unique and stable by remaining unchanged for the same fingerprint with various rotation degrees. This paper will analyze the most famous methods used to extract fingerprint features. K-means, LBP, WPT, and minutiae methods will be investigated; the obtained experimental results will be analyzed and compared to raise suitable recommendations for using these methods. The process of extracting the fingerprint's characteristics is one of the important things for use in discrimination systems, which requires saving memory and accelerating the classification process to take the appropriate decision and as quickly as possible. The stability of the properties of the fingerprint image, regardless of the different rotations, will lead to the use of a single features vector for the fingerprint with its different conditions and thus treat all the rotated fingerprints as one fingerprint image. This factor will be studied in this research to recommend the experts interested in building fingerprint recognition systems.

Keywords: fingerprint, features, local binary pattern, K-means, wavelet packet tree, bifurcation, ridge, Euclidean distance.