A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques

Improving the efficiency of methods has been a big challenge in recommender systems. It has been also important to consider the trade-off between the accuracy and the computation time in recommending the items by the recommender systems as they need to produce the recommendations accurately and meanwhile in real-time. In this regard, this research develops a new hybrid recommendation method based on Collaborative Filtering (CF) approaches. Accordingly, in this research we solve two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques. Then, we use ontology to improve the accuracy of recommendations in CF part. In the CF part, we also use a dimensionality reduction technique, Singular Value Decomposition (SVD), to find the most similar items and users in each cluster of items and users which can significantly improve the scalability of the recommendation method. We evaluate the method on two real-world datasets to show its effectiveness and compare the results with the results of methods in the literature. The results showed that our method is effective in improving the sparsity and scalability problems in CF

A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique

Background: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning. Methods: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies. Results: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine. Conclusions: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare. © 2018 The Author

Social media addiction: applying the DEMATEL approach

While there is a growing number of studies investigating the determinants of social media addiction, there is a lack of research on examining the importance of such predictors and their inter-correlations and inter-dependences from psychotherapists’ and researchers’ point of views. Hence, utilizing the “Decision Making Trial and Evaluation Laboratory (DEMATEL)” technique, the current study investigated the clinical importance of social media addiction from the perspective of researchers and psychotherapists. Accordingly, by reviewing the literature 15 distal predictors of social media addiction were extracted and further classified into three groups of personality factors, comorbid symptoms, and psychosocial factors. From the data collected from 35 respondents, the results highlighted the group of personality factors as the most important dimension increasing the risk for developing social media addiction from the respondents’ perspective. Moreover, the DEMATEL results revealed the predictors of openness to experience (personality dimension), loneliness (psychosocial), and depression (comorbid) as the most important predictors of social media addiction within each group. The results and implications of the study are discussed

Factors influencing beliefs formation towards the adoption of social commerce in SME travel agencies

The purpose of this study is to investigate the factors that influence beliefs formation towards the adoption of social commerce in SME travel agencies. Accordingly, a distal-proximal model is developed to study CEOs’ beliefs towards the usefulness of social commerce. Data were collected through a questionnaire survey of travel agencies’ CEOs in Isfahan, Iran. With 180 collected data from respondents, the Partial Least Squares-Structural Equation Modeling approach was taken to assess both measurement and structural models of the study. The results revealed that CEOs’ innovativeness and attitude towards IT as individual factors, and organizational resources as institutional factor were significantly explained beliefs formation of respondents towards the usefulness of social commerce. However, it was found that the influences of CEOs’ IT knowledge, subjective norms (professional peers, employees) and firm size on perceived usefulness were found insignificant. Implications of the study are further discussed

Measuring country sustainability performance using ensembles of neuro-fuzzy technique

Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this technique has been rarely explored when developing sustainability assessment methods. In addition, most of the methods developed in the literature solely rely on fuzzy logic. The main shortcoming of solely using the fuzzy logic rule-based technique is that it cannot automatically learn from the data. This problem of fuzzy logic has been solved by the use of neural networks in many real-world problems. The combination of these two techniques will take the advantages of both to precisely predict the output of a system. In addition, combining the outputs of several predictors can result in an improved accuracy in complex systems. This study accordingly aims to propose an accurate method for measuring countries' sustainability performance using a set of real-world data of the sustainability indicators. The adaptive neuro-fuzzy inference system (ANFIS) technique was used for discovering the fuzzy rules from data from 128 countries, and ensemble learning was used for measuring the countries' sustainability performance. The proposed method aims to provide the country rankings in term of sustainability. The results of this research show that the method has potential to be effectively implemented as a decision-making tool for measuring countries' sustainability performance

An evaluation model for the implementation of hospital information system in public hospitals using multi-criteria-decision-making (MCDM) approaches

Background: Hospital Information System (HIS) is implemented to provide high-quality patient care. The aim of this study is to identify significant dimensional factors that influence the hospital decision in adopting the HIS. Methods: This study designs the initial integrated model by taking the three main dimensions in adopting HIS technology. Accordingly, DEMATEL was utilized to test the strength of interdependencies among the dimensions and variables. Then ANP approach is adapted to determining how the factors are weighted and prioritized by professionals and main users working in the Iranian public hospitals, in-volved with the HIS system. Results: The results indicated that "Perceived Technical Competence" is a key factor in the Human dimension. The respondents also believed that "Relative Advantage," "Compatibility" and "Security Concern" of Technology dimension should be further assessed in relation to other factors. With respect to Organization dimension, "Top Management Support" and "Vendor Support" are considered more important than others. Conclusion: Applying the TOE and HOT-fit models as the pillar of our developed model with significant findings add to the growing literature on the factors associated with the adoption of HIS and also shed some light for managers of public hospitals in Iran to success-fully adopt the HIS. © 2018 Ali Aliakbar Esfahani et al

Application of Structural Equation Modeling (SEM) to solve environmental sustainability problems: a comprehensive review and meta-analysis

Most methodological areas assume common serious reflections to certify difficult study and publication practices, and, therefore, approval in their area. Interestingly, relatively little attention has been paid to reviewing the application of Structural Equation Modeling (SEM) in environmental sustainability problems despite the growing number of publications in the past two decades. Therefore, the main objective of this study is to fill this gap by conducting a wide search in two main databases includingWeb of Science and Scopus to identify the studies which used SEM techniques in the period from 2005 to 2016. A critical analysis of these articles addresses some important key issues. On the basis of our results, we present comprehensive guidelines to help researchers avoid general pitfalls in using SEM. The results of this review are important and will help researchers to better develop research models based on SEM in the area of environmental sustainability

An evaluation model for the implementation of hospital information system in public hospitals using multi-criteria-decision-making (MCDM) approaches

Background: Hospital Information System (HIS) is implemented to provide high-quality patient care. The aim of this study is to identify significant dimensional factors that influence the hospital decision in adopting the HIS. Methods: This study designs the initial integrated model by taking the three main dimensions in adopting HIS technology. Accordingly, DEMATEL was utilized to test the strength of interdependencies among the dimensions and variables. Then ANP approach is adapted to determining how the factors are weighted and prioritized by professionals and main users working in the Iranian public hospitals, in-volved with the HIS system. Results: The results indicated that "Perceived Technical Competence" is a key factor in the Human dimension. The respondents also believed that "Relative Advantage," "Compatibility" and "Security Concern" of Technology dimension should be further assessed in relation to other factors. With respect to Organization dimension, "Top Management Support" and "Vendor Support" are considered more important than others. Conclusion: Applying the TOE and HOT-fit models as the pillar of our developed model with significant findings add to the growing literature on the factors associated with the adoption of HIS and also shed some light for managers of public hospitals in Iran to success-fully adopt the HIS. © 2018 Ali Aliakbar Esfahani et al

An evaluation model for the implementation of hospital information system in public hospitals using multi-criteria-decision-making (MCDM) approaches

Background: Hospital Information System (HIS) is implemented to provide high-quality patient care. The aim of this study is to identify significant dimensional factors that influence the hospital decision in adopting the HIS. Methods: This study designs the initial integrated model by taking the three main dimensions in adopting HIS technology. Accordingly, DEMATEL was utilized to test the strength of interdependencies among the dimensions and variables. Then ANP approach is adapted to determining how the factors are weighted and prioritized by professionals and main users working in the Iranian public hospitals, in-volved with the HIS system. Results: The results indicated that "Perceived Technical Competence" is a key factor in the Human dimension. The respondents also believed that "Relative Advantage," "Compatibility" and "Security Concern" of Technology dimension should be further assessed in relation to other factors. With respect to Organization dimension, "Top Management Support" and "Vendor Support" are considered more important than others. Conclusion: Applying the TOE and HOT-fit models as the pillar of our developed model with significant findings add to the growing literature on the factors associated with the adoption of HIS and also shed some light for managers of public hospitals in Iran to success-fully adopt the HIS

A literature review on beneficial role of vitamins and trace elements: evidence from published clinical studies

COVID-19 is a kind of SARS-CoV-2 viral infectious pneumonia. This research aims to perform a bibliometric analysis of the published studies of vitamins and trace elements in the Scopus database with a special focus on COVID-19 disease. To achieve the goal of the study, network and density visualizations were used to introduce an overall picture of the published literature. Following the bibliometric analysis, we discuss the potential benefits of vitamins and trace elements on immune system function and COVID-19, supporting the discussion with evidence from published clinical studies. The previous studies show that D and A vitamins demonstrated a higher potential benefit, while Selenium, Copper, and Zinc were found to have favorable effects on immune modulation in viral respiratory infections among trace elements. The principles of nutrition from the findings of this research could be useful in preventing and treating COVID-19