The Internet of Things (IoT) refers to the network of devices which contain electronics, sensors or software that enables them to connect at anytimeand anywhere through a cyber-physical system. Before the establishment of such a system, it should be considered to what extent the users are ready to adopt and use it in their daily routines. Therefore, this paper explores users' attitudestowardsusing IoT technologies to receive healthcare services. This is in contrast to most previous research, which has studied the technical requirements or devices of the IoT that are required in healthcare services, or ways in which connectivity and performance can be improved using the IoT. Based on known models of technology acceptance, an integrated framework was developed to investigate the impact of security and privacy concerns, and familiarity with the technology, on users' trust in the IoT, and then to measure the effect of that trust on Omani users' attitudes regarding use ofIoT technologies to receive healthcare services. This framework enabled the measurement of risk perception as a mediator between user trust and their attitudes towards using the IoT. Data were collected from 387 respondents and were analysed using SPSS 25 and AMOS 25 statistics software. Exploratory and confirmatory analysis and structural equation modelling were applied. The findings showed that levels of security, privacy and familiarity affected trustin the IoT. Furthermore, these levels of trust in the IoT were found to affect both users' perceptions of risk in, and their attitude towards, using the IoT. The users' risk perception partially mediated the relations between users' trustand their attitude regarding use of the IoT. The framework was supported and interpreted by 40 per cent of the variance in the attitude towards usingthe IoT in healthcare, while the mediator showed 47 per cent of the variance in the attitude towards using the IoT inhealthcare.

In medicine, it is well known that healthy individuals have different physical and mental characteristics. Ancient Indian medicine, Ayurveda and the Persian-Arabic traditional Unani medicine has two distinct approaches for the classification of human subjects according to their temperaments. The individual temperament is an important foundation for personalized medicine, which can help in the prevention and treatment of many diseases including COVID-19. This paper attempts to explore the relationship of the utmost important concepts of these systems called individual temperament named as Prakruti in Ayurveda and Mizaj in Unani practice using mathematical modelling. The results of mathematical modelling can be adopted expediently for the development of algorithms that can be applied in medical informatics. For this, a significant literature review has been carried out. Based on the previous researchers' reviews the essential parameters have been identified for making the relationship and hypothesis were framed. The mathematical modelling was adopted to propose the existence of the relationship between the parameters of such an ancient and rich medicine systems. The hypotheses are validated through the mathematic driven model. Doi: 10.28991/esj-2021-01258 Full Text: PDF

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Travel recommendation agents have been a helpful tool for travelers in their decision-making for destination choices. It has been shown that sparsity can significantly impact on the accuracy of recommendation agents. The COVID-19 outbreak has affected the tourism and hospitality industry of almost all countries in the world. Tourists who have planned to travel are canceling or postponing trips due to this pandemic. Accordingly, this will impact the rate of travelers’ online reviews on tourism products. Hence, the lack of data, in terms of ratings and textual reviews on hotels, will be a major issue for travel recommendation agents during the COVID-19 outbreak in the context of tourism and hospitality. This will be a new challenge for the researchers in the development of travel recommendation agents. Machine learning has been found to be effective in dealing with the data sparsity in recommendation agents. Therefore, developing new algorithms would be helpful to overcome the sparsity issue in travel recommendation agents. This research provides a new method through neurofuzzy, dimensionality reduction, and clustering techniques and evaluates it on the TripAdvisor dataset to see its effectiveness in solving the sparsity issue. The results showed that the method which used the fuzzy logic technique with the aid of clustering, dimensionality reduction, and fuzzy logic is more efficient in addressing the sparsity problem and presenting more accurate results. The results of the method evaluation are presented and discussed, and several suggestions are provided for future studies.

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Measuring brain activity through Electroencephalogram (EEG) analysis for eye state prediction has attracted attention from machine learning researchers. There have been many methods for EEG analysis using supervised and unsupervised machine learning techniques. The tradeoff between the accuracy and computation time of these methods in performing the analysis is an important issue that is rarely investigated in the previous research. This paper accordingly proposes a new method for EEG signal analysis through Self-Organizing Map (SOM) clustering and Deep Belief Network (DBN) approaches to efficiently improve the computation and accuracy of the previous methods. The method is developed using SOM clustering and DBN, which is a deep layer neural network with multiple layers of Restricted Boltzmann Machines (RBMs). The results on a dataset with 14980 instances and 15 attributes representing the values of the electrodes demonstrated that the method is efficient for EEG analysis. In addition, compared with the other supervised methods, the proposed method was able to significantly improve the accuracy of the EEG prediction.

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This paper endeavors to investigate whether the Islamic financial system can tackle the issue of financial exclusion in India or not. The present study has made an earnest attempt to explore the discriminating factors behind choosing of the institutes (conventional or Islamic), in decreasing order of their importance. Data for the study are collected from 635 respondents, who are customers of Islamic and traditional financial institutes. The area selected for the survey is the state of Kerala, which is considered as the Islamic finance hub in India. The data collected are analyzed by employing the discriminant analysis along with drawing inferences from descriptive statistics. The study finds various factors in descending order of their importance. The factors are type of employment, religion (Muslim/Non-Muslim), income and gender. These are discriminating factors for choosing particular institutes (conventional or Islamic). The study shows that Islamic finance system was chosen by those, particularly Muslims, who did not have good employment and sufficient income. Hence, it is recommended that extensive formal beginning of Islamic finance in India, will lead to higher financial inclusion, since generally the financially excluded individuals belong to the said segments of the society, furthermore, Islamic finance is highly fascinated by the mentioned groups, the planners should think accordingly. The study is novel in its’ approach as it evidently illustrates that Islamic financial system is chosen by those, who do not have good employment, Muslims and those who earn less. Thus, there should be extensive formal commencement of Islamic finance in India to kick off higher financial inclusion.