Search

Parkinson's disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Fuzzy logic has gained substantial attention in PD diagnosis research. PD detection using fuzzy logic has presented more precise outcomes as compared with common machine learning approaches. In this research, a hybrid method combining supervised learning, unsupervised learning and feature selection techniques is developed. In a type-1 fuzzy system, the membership functions used for the fuzzification of the crisp inputs are mapped to single numbers. However, in a type-2 fuzzy system, these numbers are represented as intervals, adding an extra dimension to the definition of the membership function. The first step of the proposed method involves clustering the data using the Expectation–Maximization (EM) technique. The performance of EM clustering is performed using the Davies–Bouldin index. Subsequently, feature selection is performed using the backward stepwise regression. To predict the UPDRS, Type-2 Sugeno fuzzy inference system (T2SFIS) is implemented on the clusters generated from the previous steps. The Parkinson's telemonitoring dataset is used in this study for method evaluation. Using the EM algorithm, the PD dataset was clustered into 13 segments, and the most important features for accurate UPDRS prediction were chosen in each segment using backward stepwise regression. The hybrid method was evaluated using R-squared (R2) and RMSE. The evaluation results showed that the combination of EM, backward stepwise regression, and type-2 Sugeno FIS obtained the best accuracy in predicting Motor-UPDRS and Total-UPDRS. [ABSTRACT FROM AUTHOR]

Since social and environmental conditions have changed dramatically in recent years, the spectrum of diseases caused by infections is also evolving rapidly. The outspread of COVID-19 has resulted in an emergency situation across the globe with significant effects on the population's lives, families, and societies, leading to concerns the World Health Organization. Accordingly, the virus has substantially threatened the Malaysians' public health and contributed considerably to increased healthcare expenses. Since the novel coronavirus was found in China, Malaysia's government has started its actions according to the World Health Organization procedures and concentrated on addressing and preventing the spread of the infection. The present paper aims to find and evaluate the factors to respond to the COVID-19 outbreak in Malaysia, limiting the outspread of the disease in this country. This study used the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy Rule-Based techniques to evaluate the factors through a set of questionnaires completed by the health care professionals. According to the data analysis results, movement control order, international travel restrictions, and the mass gathering cancellations were of most importance in the prevention of COVID-19 infections transmission. [ABSTRACT FROM AUTHOR]

The trade-off between computation time and predictive accuracy is important in the design and implementation of clinical decision support systems. Machine learning techniques with incremental updates have proven its usefulness in analyzing large collection of medical datasets for diseases diagnosis. This research aims to develop a predictive method for heart disease diagnosis using machine learning techniques. To this end, the proposed method is developed by unsupervised and supervised learning techniques. In particular, this research relies on Principal Component Analysis (PCA), Self-Organizing Map, Fuzzy Support Vector Machine (Fuzzy SVM), and two imputation techniques for missing value imputation. Furthermore, we apply the incremental PCA and FSVM for incremental learning of the data to reduce the computation time of disease prediction. Our data analysis on two real-world datasets, Cleveland and Statlog, showed that the use of incremental Fuzzy SVM can significantly improve the accuracy of heart disease classification. The experimental results further revealed that the method is effective in reducing the computation time of disease diagnosis in relation to the non-incremental learning technique. [ABSTRACT FROM AUTHOR]

For the past decade, human capital has been recognized as one of the crucial assets of any firm's overall performance. Previous studies widely advocated a linear link between human capital and innovative firm performance, arguing that there are a variety of factors to examine if the relationship between human capital and innovative firm performance is to be properly understood. The focus of this study was to examine the effect of social capital on the relationship between human capital and innovative firm performance. Specifically, it examined the relationship between human capital and social capital and between human capital and innovative firm performance. It also examined the relationship between social capital and innovative firm performance. A total of 294 questionnaires were obtained from managerial staff in automotive companies in Malaysia and the data was analysed using the Partial Least Squares (PLS) test. The results indicated a direct effect between human capital and innovative performance. It was found that human capital is significantly related to social capital and that there is a significant relationship between social capital and innovative firm performance, indicating the ability of social capital to improve innovative firm performance. Finally, it revealed that innovative firm performance could be achieved by human capital through the role of valuable social capital and that good innovative firm performance leads to more prudent and sustainable organisations. The results provide pertinent implications for academia, policymakers and market players while also contributing to the research fields of strategic management, human capital, social capital and performance. [ABSTRACT FROM AUTHOR]

Green IT has attracted policy makers and IT managers within organizations to use IT resources in cost-effective and energy-efficient ways. Investigating the factors that influence decision-makers' intention towards the adoption of Green IT is important in the development of strategies that promote the organizations to use Green IT. Therefore, the objective of this study stands to understand potential factors that drive decisions makers in Malaysian manufacturing sector to adopt Green IT. This research accordingly developed a model by integrating two theoretical models, Theory of Planned Behavior and Norm Activation Theory, to explore individual factors that influence decision' makers in manufacturing sector in Malaysia to adopt Green IT via the mediation of personal norms. Accordingly, to determine predictive factors that influence managerial intention toward Green IT adoption, the researchers conducted a comprehensive literature review. The data was collected from 183 decision-makers from Malaysian manufacturing sector and analyzed by Structural Equation Modelling. This research provides important preliminary insights in understanding the most significant factors that determined managerial intention towards Green IT adoption. The model of Green IT adoption explained factors which encourages individual decision-makers in the Malaysian organizations to adopt Green IT initiatives for environment sustainability. [ABSTRACT FROM AUTHOR]

Social media and digital technology have had significant contributions and impacts on the hospitality and accommodation businesses. Online traveller reviews have been rich sources of information for the traveller's decision-making process in social media websites. TripAdvisor, a popular travel review site and social media platform, is mainly developed as a free business consultation service to help the travellers to make right decisions in their trips. The aim of this research is to use the multi-criteria ratings provided by the travellers in social media networking sites for developing a new recommender system for hotel recommendations in e-tourism platforms. We extend the crisp-based multi-criteria algorithms to fuzzy-based multi-criteria algorithms for finding the similarities between the travellers based on their provided ratings. To develop the recommendation method, we use clustering and prediction machine learning techniques. We evaluate the recommendation system on TripAdvisor data. Our experiments confirm that the use of clustering and prediction machine learning with the aid of fuzzy-based recommendation algorithms can significantly improve the quality of recommendations in tourism domain. [ABSTRACT FROM AUTHOR]

In this research, five machine learning methods are employed to create the formula and make the model for communication between the melting and solidification processes to find the latent heat and communication between the thermal conductivity and the thermal diffusivity. Paraffin wax was the PCM, while SiO 2 , ZnO, Fe 2 O 3 , and Al 2 O 3 nanoparticles were utilized as additives to enhance the thermal conductivity by considering four concentrations between 2 and 8 wt.%. The K-Nearest Neighbours (KNN), the decision tree, the stochastic gradient descent (SGD) regressor, the Support Vector Machines (SVM), and the Huber regression are the algorithms that are used in this study. Using the Huber regression method, the formula for the latent heat of the solidification-process is created by the melting-process (by latent heat and wt.) with the polynomial form by degree 2, and the R-Squared value is 0.998, and MSE is 0.971172, and by the KNN (K = 3) is made the model with the linear form. The R2 value is 0.999, and MSE is 0.41809, and for the latent heat of the melting-process by the solidification-process (by latent heat and wt.) with the same methods reached to best the R2 for creating the formula and making the model, respectively the R2 values are 0.996 and 0.999, and the MSEs are 1.482458 and 0.608658. Using the Huber regression method, reach the best result for creating the formula in the polynomial form by degree 2 for the thermal conductivity by the thermal diffusivity and wt. and create the formula with the R2 value of 0.998 and MSE is 0.000033, and make the model by the SVM with the R2 value of 0.999 in the linear form, and MSE is 0.000016. The same methods are used for thermal diffusivity by the thermal conductivity and wt.; the R2 values for the formula and the model, respectively, are 0.997 in polynomial form (degree = 2) and 0.999 in linear form. MSEs, respectively, are 0.000020 and 0.000006. [ABSTRACT FROM AUTHOR]

One of the most important parts of any system is authentication. Appreciated as the first and the last line of defense in the great majority of cases, authentication systems can usually prevent the kleptomaniac from unauthorized accessing to users’ data. However, the traditional text-based password is still used in many websites and applications which are vulnerable to different kinds of attacks. Accordingly, there exist some other alternative ways to boost this traditional method. In this study, we classified and identified different types of authentication systems in a variety of platforms. Their usage, similarity, usability, performance and drawbacks were discussed. The goal of this study is to provide useful, classified information with the aim of understanding of how different authentication systems work and of what their usability and drawbacks are to the readers. [ABSTRACT FROM AUTHOR]

• A SWOT analysis of COVID-19 and Sustainable Development Goals (SDGs) is performed. • A bibliometric analysis of previous works related to SDGs and the covid-19 crisis is performed. • COVID-19 crisis has caused an unprecedented "income shock" that is assumed to prompt food insecurity. The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments' aims to meet UN Sustainable Development Goals (SDGs); leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies. [ABSTRACT FROM AUTHOR]

• The impact of multi-criteria ratings on recommendation agents performance is investigated. • LDA for feature extraction and EM and SOM for data clustering are used. • Online customers' reviews from TripAdviosr are analysed. • Sparsity issue was alleviated by the clustering techniques. • The accuracy of CF recommender systems was improved by multi-criteria ratings. Recommender Systems (RSs) have played an important role in online retailing portals and customers' decision-making processes. Recommender systems that are based on the conventional Collaborative Filtering (CF) approach rely on single customers' ratings on retailing websites. Multi-criteria CF (MCCF) approaches that rely on multi-aspects of the products have provided more reliable and effective recommendations on retailing websites. However, these approaches should be improved in terms of accuracy by solving sparsity issues and incorporating criteria ratings. In addition, most of the recommendation agents that are based on MCCF cannot learn automatically from the features of the products to model customers' preferences and generate accurate recommendations on retailing websites. Besides, although previous studies have utilized single and multi-criteria ratings in recommendation agents of tourism websites, still, if there is a lack of ratings of items, most of these systems will fail to generate accurate recommendations to users. In this research, we develop a new recommendation agent based on a MCCF approach to effectively improve the performance of previous recommendation systems for tourism websites. The results demonstrated that the method can predict the most relevant products to users, particularly when the dataset is sparse. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoTs) market for consumers includes a huge variety of smart home devices. This research is conducted to investigate what factors influence customer purchase of smart home security systems. A new model is developed and the data collection is performed from the customers' reviews on the smart home security systems on Amazon.com. Totally, 12,678 reviews were collected to investigate customers' preferences in purchasing smart home security systems. To analyze the collected data, we develop a new method using text mining, unsupervised clustering, and a neuro-fuzzy system. The factors are discovered from textual reviews using the Latent Dirichlet Allocation (LDA) technique. To segment the customers' preferences the Expectation-Maximization (EM) technique was used. ANFIS technique was then used to find the relationship between the factors and customers' purchase intention. It was demonstrated that the use of text mining can be effective in the analysis of customers' preferences from online customer reviews. The results of the data analysis are provided and discussed. • Factors Impacting Customer Purchase Intention of Smart Home Security Systems. • A Method for Big Social Data Analysis Using Machine Learning Techniques. • New Factors for Smart Home Security Systems Online Customers' Reviews. • Fuzzy Logic Approach for Modeling of Smart Home Security Systems. [ABSTRACT FROM AUTHOR]

Online reviews have been used effectively to understand customers' satisfaction and preferences. COVID-19 crisis has significantly impacted customers' satisfaction in several sectors such as tourism and hospitality. Although several research studies have been carried out to analyze consumers' satisfaction using survey-based methodologies, consumers' satisfaction has not been well explored in the event of the COVID-19 crisis, especially using available data in social network sites. In this research, we aim to explore consumers' satisfaction and preferences of restaurants' services during the COVID-19 crisis. Furthermore, we investigate the moderating impact of COVID-19 safety precautions on restaurants' quality dimensions and satisfaction. We applied a new approach to achieve the objectives of this research. We first developed a hybrid approach using clustering, supervised learning, and text mining techniques. Learning Vector Quantization (LVQ) was used to cluster customers' preferences. To predict travelers' preferences, decision trees were applied to each segment of LVQ. We used a text mining technique; Latent Dirichlet Allocation (LDA), for textual data analysis to discover the satisfaction criteria from online customers' reviews. After analyzing the data using machine learning techniques, a theoretical model was developed to inspect the relationships between the restaurants' quality factors and customers' satisfaction. In this stage, Partial Least Squares (PLS) technique was employed. We evaluated the proposed approach using a dataset collected from the TripAdvisor platform. The outcomes of the two-stage methodology were discussed and future research directions were suggested according to the limitations of this study. • Customer Satisfaction and Restaurants Service Quality during COVID-19 Outbreak. • The Use of Social Data for Analyzing Customer Satisfaction. • Moderation Effect of COVID-19 Safety Precautions on Restaurants' Quality Dimensions and Satisfaction. • A New Two-Stage Methodology Using PLS-SEM and Machine Learning. [ABSTRACT FROM AUTHOR]

• This study investigates the impact of eWOM on travel decision-making during the COVID-19 outbreak. • A two-stage methodology is developed using structural equation modeling and cluster analysis. • There is a significant relationship between trust and perceived risk. • There is a significant relationship between perceived risk and the decision to travel. • It is found that the perceived risk has a higher impact on the decision to travel in the female sample. Social media users share a variety of information and experiences and create Electronic Word of Mouth (eWOM) in the form of positive or negative opinions to communicate with others. In the context of the COVID-19 outbreak, eWOM has been an effective tool for knowledge sharing and decision making. This research aims to reveal what factors of eWOM can influence travelers' trust in their decision-making to travel during the COVID-19 outbreak. In addition, we aim to find the relationships between trust in eWOM and perceived risk, and perceived risk and the decision to travel. These relationships are investigated based on online customers' reviews in TripAdvisor's COVID-19 forums. We use a two-stage data analysis which includes cluster analysis and structural equation modeling. In the first stage, a questionnaire survey was designed and the data was collected from 1546 respondents by referring to the COVID-19 forums on TripAdvisor. Specifically, we use k -means to segment the users' data into different groups. In the second stage, Structural Equation Modeling (SEM) was performed to inspect the relations between the variables in the hypothesized research model using a subsample of 679 respondents. The results of the first stage of the analysis showed that three segments could be discovered from the collected data for trust based on eWOM source and eWOM message attributes. These segments clearly showed that there are significant relationships between trust and perceived risk, and between perceived risk and the decision to travel. The results in all segments showed that users with a low level of trust have a high level of perceived risk and a low level of intention to travel during the COVID-19 outbreak. In addition, it was found that users with a high level of e-trust have a low level of perceived risk and a high level of intention to travel. These results were confirmed in all segments and these relationships were confirmed by SEM. The results of SEM revealed that visual and external information moderated the relationship between eWOM length and trust, and experience moderated the relationship between trust and perceived risk. For the moderating role of gender, it was found that the perceived risk has a higher impact on the decision to travel in the female sample. [ABSTRACT FROM AUTHOR]

Today's societies consider the transportation sector as a key component of the economy that contributes significantly to sustainability and global economic development. However, the challenges resulting from transportation are expected to increase more seriously in the years to come in light of socioeconomic growth and increasing living standards. One of the most unfavorable outcomes of the transportation industries is climate change, which necessitates sustainable strategies to reduce emissions of greenhouse gasses. Although the use of electric vehicles (EVs) has obvious social advantages, including improvements to air quality along with beneficial effects of reducing CO 2 emissions, EVs are being adopted quite slowly as a clean and green alternative form of transportation in Malaysia. Therefore, the present study has focused on the factors which affect consumers' intention to use EVs in the Malaysian context. An extensive review of previous studies was carried out to identify the determinants of adopting EVs in the prior literature. The survey questionnaire was designed with reference to the decision-making trials alongside the Decision-Making Trials and Evaluation Laboratory (DEMATEL) method, after which questionnaires were distributed to Malaysian consumers of EVs. According to the results of the data analysis, environmental concern, trust in EVs, personal norms, price value, attitudes regarding EVs, and subjective norms are the most important factors influencing the adoption of EVs in the Malaysian context. The findings of the study provide directions for policymakers and automotive manufacturers. [ABSTRACT FROM AUTHOR]

• This study investigates the impact of service quality on customers' satisfaction during COVID-19. • The use of text mining to discover satisfaction dimensions. • A new method for online customers' reviews analysis is proposed. • New findings are presented on the impact of service quality on customers' satisfaction during COVID-19. • Evaluation of method through the data in TripAdvisor. The COVID-19 pandemic has caused major global changes both in the areas of healthcare and economics. This pandemic has led, mainly due to conditions related to confinement, to major changes in consumer habits and behaviors. Although there have been several studies on the analysis of customers' satisfaction through survey-based and online customers' reviews, the impact of COVID-19 on customers' satisfaction has not been investigated so far. It is important to investigate dimensions of satisfaction from the online customers' reviews to reveal their preferences on the hotels' services during the COVID-19 outbreak. This study aims to reveal the travelers' satisfaction in Malaysian hotels during the COVID-19 outbreak through online customers' reviews. In addition, this study investigates whether service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. Accordingly, we develop a new method through machine learning approaches. The method is developed using text mining, clustering, and prediction learning techniques. We use Latent Dirichlet Allocation (LDA) for big data analysis to identify the voice-of-the-customer, Expectation-Maximization (EM) for clustering, and ANFIS for satisfaction level prediction. In addition, we use Higher-Order Singular Value Decomposition (HOSVD) for missing value imputation. The data was collected from TripAdvisor regarding the travelers' concerns in the form of online reviews on the COVID-19 outbreak and numerical ratings on hotel services from different perspectives. The results from the analysis of online customers' reviews revealed that service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. In addition, the results showed that although the customers are always seeking hotels with better performance, they are also concerned with the quality of related services in the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

The environmental effects of supply chain operations are under the significant influence of Green Supply Chain Management (GSCM), with a potential enhancement of organizational sustainability performance. Even though previous studies have pointed to the contribution of integration of various activities associated with GSCM, no investigations have been carried out for the possible interdependencies of these activities and their application effects. Consequently, this paper aims at investigating the associations of GSCM methods and performance of manufacturing firms using the Natural Resource-Based View (NRBV) as well as Institutional Theory. The collected data from manufacturing firms were analyzed using the partial least squares technique. Accordingly, mimetic, normative, and coercive pressures were found to affect GSCM positively and significantly. Moreover, the GSCM and the firms' environmental, operational and economic performances were found to be positively and significantly associated. In addition, according to the moderation analysis, collaborative capabilities had significant moderating effects on the association of green chain management and environmental as well as economic performance. However, collaborative capabilities could not moderate the association of GSCM and operational performance. Managers, supply chain specialists and also policy makers would benefit from the findings of the present study along with the inspirations resulting from the contribution of specific drivers in implementing GSCM methods, while application of the mentioned methods could be effective in achieving the desired performance levels. Furthermore, industrial sectors are provided with managerial and theoretical insights by the results obtained in the present paper to concentrate on environmental awareness through the adoption of GSCM methods. • Green supply chain management practices and impact on firm performance. • The use of Natural Resource-Based View (NRBV). • 270 manufacturing firms in Malaysia from a variety of sectors were studied. [ABSTRACT FROM AUTHOR]

To avoid the spread of the COVID-19 crisis, many countries worldwide have temporarily shut down their academic organizations. National and international closures affect over 91% of the education community of the world. E-learning is the only effective manner for educational institutions to coordinate the learning process during the global lockdown and quarantine period. Many educational institutions have instructed their students through remote learning technologies to face the effect of local closures and promote the continuity of the education process. This study examines the expected benefits of e-learning during the COVID-19 pandemic by providing a new model to investigate this issue using a survey collected from the students at Imam Abdulrahman Bin Faisal University. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed on 179 useable responses. This study applied Push-Pull-Mooring theory and examined how push, pull, and mooring variables impact learners to switch to virtual and remote educational laboratories. The Protection Motivation theory was employed to explain how the potential health risk and environmental threat can influence the expected benefits from e-learning services. The findings revealed that the push factor (environmental threat) is significantly related to perceived benefits. The pull factors (e-learning motivation, perceived information sharing, and social distancing) significantly impact learners' benefits. The mooring factor, namely perceived security, significantly impacts learners' benefits. • Investigating the Impact of Coronavirus Pandemic (COVID-19) on Education. • Examining the Expected Benefits of the E-Learning during the COVID-19 Pandemic. • Adopting the Push-Pull-Mooring Model. • Push Factor (Environmental Threat) is Significantly Related to Perceived Benefits. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• The impact of social media on traveler' decision making during COVID-19 outbreak. • A recommendation agent for traveler' decision making through social media. • Use of text mining approach to develop recommendation agent. • Evaluation of method through the data in Netherland forums in TripAdvisor. The novel outbreak of coronavirus disease (COVID-19) was an unexpected event for tourism in the world as well as tourism in the Netherlands. In this situation, the travelers' decision-making for tourism destinations was heavily affected by this global event. Social media usage has played an essential role in travelers' decision-making and increased the awareness of travel-related risks from the COVID-19 outbreak. Online consumer media for the outbreak of COVID-19 has been a crucial source of information for travelers. In the current situation, tourists are using electronic word of mouth (eWOM) more and more for travel planning. Opinions provided by peer travelers for the outbreak of COVID-19 tend to reduce the possibility of poor decisions. Nevertheless, the increasing number of reviews per experience makes reading all feedback hard to make an informed decision. Accordingly, recommendation agents developed by machine learning techniques can be effective in the analysis of such social big data for the identification of useful patterns from the data, knowledge discovery, and real-time service recommendations. The current research aims to adopt a framework for the recommendation agents through topic modeling to uncover the most important dimensions of COVID-19 reviews in the Netherland forums in TripAdvisor. This study demonstrates how social networking websites and online reviews can be effective in unexpected events for travelers' decision making. We conclude with the implications of our study for future research and practice. [ABSTRACT FROM AUTHOR]

This study aims to investigate the travellers' choice behaviour towards green hotels through existing online travel reviews on TripAdvisor. Accordingly, a method combining segmentation and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques was developed to segment travellers based on their provided reviews and to prioritize green hotel attributes based on their level of importance in each segment. The data were taken from travellers' online reviews of Malaysian eco-friendly hotels on TripAdvisor. The results showed that the sleep quality was one of the most imporant factors for eco-hotel selection in the majority of segments. The developed method in this study was able to analyse travellers' reviews and ratings on eco-friendly hotels to identify the future choice behaviour and aid travellers in their decision-making process. The study provides new insights for hotel managers and green policy makers on developing environmental-friendly practices. • The use of online reviews in green hotel choice and customer segmentation. • The use of TOPSIS in ranking the features of green hotels. • The use of CART in predicting travellers' preferences. • The use of TripAdvior data for method evaluation. [ABSTRACT FROM AUTHOR]

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of Electric Vehicles • Presenting Modified Harmony Search Algorithm for optimizing Micro-grid • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management Modern power systems are seeking for alternative energy sources will less emission to conventional fossil-fuel power plant to mitigate the global concerns on environmental issues. On one hand, renewable energies have turned to the first choice of system operators. On the other hand, the system should provide the required infrastructure to appropriately accommodate such energy sources. In this respect, microgrids (MG) would provide the needed conditions for integrating renewable energy sources (RESs). Thus, the optimal energy management of such systems in the presence of highly uncertain renewable power generation is of great importance. Accordingly, this paper provides a stochastic programming framework for the optimal scheduling of an MG equipped with RESs and plug-in electric vehicles (PEVs). The power sources considered include a wind energy system in the form of wind turbine (WT), a solar photovoltaic (PV) system, a fuel cell (FC), a microturbine (MT), besides a battery storage system (BSS). The mentioned problem is formulated as a single-objective optimization problem aimed at minimizing the total operating cost over the scheduling period. The MG is considered in the grid-connected mode where it can transact power with the upstream system. The uncertainty of the problem is due to the intermittent power output of the wind energy system and the PV unit, as well as uncertain behavior of the EV owners in charging/discharging their vehicles. The proposed stochastic optimization problem is the solved using an effective and efficient optimization algorithm named "modified harmony search (MHS) algorithm". Finally, the simulation results are discussed and the superior performance of the suggested algorithm is verified through making a comprehensive comparison with some well-known methods. [ABSTRACT FROM AUTHOR]