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Dr. Mehrbakhsh Nilashi
Universiti Teknologi Malaysia

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0 Tourism
0 AI
0 Data Mining and Deep Learning
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Article
Published: 24 June 2021 in International Journal of Fuzzy Systems
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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.

ACS Style

Shahla Asadi; Mehrbakhsh Nilashi; Rabab Ali Abumalloh; Sarminah Samad; Ali Ahani; Fahad Ghabban; Salma Yasmin Mohd Yusuf; Eko Supriyanto. Evaluation of Factors to Respond to the COVID-19 Pandemic Using DEMATEL and Fuzzy Rule-Based Techniques. International Journal of Fuzzy Systems 2021, 1 -17.

AMA Style

Shahla Asadi, Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Sarminah Samad, Ali Ahani, Fahad Ghabban, Salma Yasmin Mohd Yusuf, Eko Supriyanto. Evaluation of Factors to Respond to the COVID-19 Pandemic Using DEMATEL and Fuzzy Rule-Based Techniques. International Journal of Fuzzy Systems. 2021; ():1-17.

Chicago/Turabian Style

Shahla Asadi; Mehrbakhsh Nilashi; Rabab Ali Abumalloh; Sarminah Samad; Ali Ahani; Fahad Ghabban; Salma Yasmin Mohd Yusuf; Eko Supriyanto. 2021. "Evaluation of Factors to Respond to the COVID-19 Pandemic Using DEMATEL and Fuzzy Rule-Based Techniques." International Journal of Fuzzy Systems , no. : 1-17.

Journal article
Published: 09 June 2021 in Journal of Retailing and Consumer Services
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Customers increasingly use various social media to share their opinion about restaurants service quality. Big data collected from social media provides a data platform to improve the service quality of restaurants through customers' online reviews, where online reviews are a trustworthy and reliable source that helps consumers to evaluate food quality. Developing methods for effective evaluation of customer-generated reviews of restaurant services is important. This study develops a new method through effective learning techniques for customer segmentation and their preferences prediction in vegetarian friendly restaurants. The method is developed through text mining (Latent Dirichlet Allocation), cluster analysis (Self Organizing Map) and predictive learning technique (Classification and Regression Trees) to reveal the customer’ satisfaction levels from the service quality in vegetarian friendly restaurants. Based on the obtained results of our experiments on the data vegetarian friendly restaurants in Bangkok, the models constructed by Classification and Regression Trees were able to give an accurate prediction of customers' preferences on the basis of restaurants' quality factors. The results showed that customers’ online reviews analysis can be an effective way for customers segmentation to predict their preferences and help the restaurant managers to set priority instructions for service quality improvements.

ACS Style

Mehrbakhsh Nilashi; Hossein Ahmadi; Goli Arji; Khalaf Okab Alsalem; Sarminah Samad; Fahad Ghabban; Ahmed Omar Alzahrani; Ali Ahani; Ala Abdulsalam Alarood. Big social data and customer decision making in vegetarian restaurants: A combined machine learning method. Journal of Retailing and Consumer Services 2021, 62, 102630 .

AMA Style

Mehrbakhsh Nilashi, Hossein Ahmadi, Goli Arji, Khalaf Okab Alsalem, Sarminah Samad, Fahad Ghabban, Ahmed Omar Alzahrani, Ali Ahani, Ala Abdulsalam Alarood. Big social data and customer decision making in vegetarian restaurants: A combined machine learning method. Journal of Retailing and Consumer Services. 2021; 62 ():102630.

Chicago/Turabian Style

Mehrbakhsh Nilashi; Hossein Ahmadi; Goli Arji; Khalaf Okab Alsalem; Sarminah Samad; Fahad Ghabban; Ahmed Omar Alzahrani; Ali Ahani; Ala Abdulsalam Alarood. 2021. "Big social data and customer decision making in vegetarian restaurants: A combined machine learning method." Journal of Retailing and Consumer Services 62, no. : 102630.

Review
Published: 15 May 2021 in Journal of Trace Elements in Medicine and Biology
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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.

ACS Style

Sima Taheri; Shahla Asadi; Mehrbakhsh Nilashi; Rabab Ali Abumalloh; Nawaf M.A. Ghabban; Salma Yasmin Mohd Yusuf; Eko Supriyanto; Sarminah Samad. A literature review on beneficial role of vitamins and trace elements: Evidence from published clinical studies. Journal of Trace Elements in Medicine and Biology 2021, 67, 126789 -126789.

AMA Style

Sima Taheri, Shahla Asadi, Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Nawaf M.A. Ghabban, Salma Yasmin Mohd Yusuf, Eko Supriyanto, Sarminah Samad. A literature review on beneficial role of vitamins and trace elements: Evidence from published clinical studies. Journal of Trace Elements in Medicine and Biology. 2021; 67 ():126789-126789.

Chicago/Turabian Style

Sima Taheri; Shahla Asadi; Mehrbakhsh Nilashi; Rabab Ali Abumalloh; Nawaf M.A. Ghabban; Salma Yasmin Mohd Yusuf; Eko Supriyanto; Sarminah Samad. 2021. "A literature review on beneficial role of vitamins and trace elements: Evidence from published clinical studies." Journal of Trace Elements in Medicine and Biology 67, no. : 126789-126789.

Journal article
Published: 27 April 2021 in Arabian Journal for Science and Engineering
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Digital social media has played a key role in tourism and hospitality industry. The use of machine and deep learning has been effective in market segmentation and customers' preference prediction through social big data analysis. This paper develops a new method to analyze large set of open data in social networking sites for travellers segmentation and predict tourists' choice preferences using dimensionality reduction and deep learning techniques. Deep belief network was used for predicting the travellers’ choice preferences from their past ratings and online reviews. Self-organizing map was also used for clustering the travellers’ online ratings and reviews. The feature extraction is performed using latent Dirichlet allocation as an unsupervised learning technique. To improve the effectiveness of learning, a dimensionality reduction technique, higher-order singular value decomposition, is performed on the clusters for the prediction of missing values and traveller–traveller similarity calculation. The proposed method was evaluated on travellers’ online reviews and ratings which were crawled from TripAdvisor. The results showed the robustness of the proposed method in analysing the large text-based reviews and numerical datasets in tourism context.

ACS Style

Mehrbakhsh Nilashi; Sarminah Samad; Behrouz Minaei-Bidgoli; Fahad Ghabban; Eko Supriyanto‬. Online Reviews Analysis for Customer Segmentation through Dimensionality Reduction and Deep Learning Techniques. Arabian Journal for Science and Engineering 2021, 1 -13.

AMA Style

Mehrbakhsh Nilashi, Sarminah Samad, Behrouz Minaei-Bidgoli, Fahad Ghabban, Eko Supriyanto‬. Online Reviews Analysis for Customer Segmentation through Dimensionality Reduction and Deep Learning Techniques. Arabian Journal for Science and Engineering. 2021; ():1-13.

Chicago/Turabian Style

Mehrbakhsh Nilashi; Sarminah Samad; Behrouz Minaei-Bidgoli; Fahad Ghabban; Eko Supriyanto‬. 2021. "Online Reviews Analysis for Customer Segmentation through Dimensionality Reduction and Deep Learning Techniques." Arabian Journal for Science and Engineering , no. : 1-13.

Journal article
Published: 20 April 2021 in Computers & Industrial Engineering
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Tourism has been one of the biggest competitive industries in the world. Nowadays, medical and wellness tourism are quickly developing as a part of tourism for health and wellness care. Social networking sites have played an important role in developing these types of tourism. Online reviews on the tourism products in social networking sites are considered rich sources for tourists’ decision making. Machine learning techniques have proved to be effective in analysing the tourists’ online reviews. For big datasets of tourist online reviews, these techniques must be enough robust to accurately discover the hidden relationships of tourists’ preferences in the online reviews. In addition, scalable machine learning techniques are needed for examining big datasets analysis in tourism platforms to timely provide the required information regarding the tourists’ preferences on the products. This paper investigates the effectiveness of a hybrid method using clustering, Higher-Order Singular Value Decomposition (HOSVD) and Classification and Regression Trees (CART) in analysing tourists’ online reviews in TripAdvisor. We use HOSVD to find the similarities among the travellers in the datasets with huge sets of hotels ratings. Then, we use CART to predict travellers’ preferences on the quality dimensions of spa hotels in TripAdvisor. To evaluate the method, the data is collected from the travellers’ online reviews on Malaysian spa hotels in TripAdvisor. The results showed that our method outperforms the methods which solely rely on prediction machine learning techniques. We demonstrate that the use of clustering and prediction machine learning techniques combined with the HOSVD is robust in analysing the tourists’ online reviews for discovering the tourists’ preferences in social networking sites.

ACS Style

Mehrbakhsh Nilashi; Sarminah Samad; Ali Ahani; Hossein Ahmadi; Eesa Alsolami; Marwan Mahmoud; Hamsa D. Majeed; Ala Abdulsalam Alarood. Travellers decision making through preferences learning: A case on Malaysian spa hotels in TripAdvisor. Computers & Industrial Engineering 2021, 158, 107348 .

AMA Style

Mehrbakhsh Nilashi, Sarminah Samad, Ali Ahani, Hossein Ahmadi, Eesa Alsolami, Marwan Mahmoud, Hamsa D. Majeed, Ala Abdulsalam Alarood. Travellers decision making through preferences learning: A case on Malaysian spa hotels in TripAdvisor. Computers & Industrial Engineering. 2021; 158 ():107348.

Chicago/Turabian Style

Mehrbakhsh Nilashi; Sarminah Samad; Ali Ahani; Hossein Ahmadi; Eesa Alsolami; Marwan Mahmoud; Hamsa D. Majeed; Ala Abdulsalam Alarood. 2021. "Travellers decision making through preferences learning: A case on Malaysian spa hotels in TripAdvisor." Computers & Industrial Engineering 158, no. : 107348.

Journal article
Published: 31 March 2021 in Sustainability
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This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

ACS Style

Mehrbakhsh Nilashi; Shahla Asadi; Rabab Abumalloh; Sarminah Samad; Fahad Ghabban; Eko Supriyanto; Reem Osman. Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART). Sustainability 2021, 13, 3870 .

AMA Style

Mehrbakhsh Nilashi, Shahla Asadi, Rabab Abumalloh, Sarminah Samad, Fahad Ghabban, Eko Supriyanto, Reem Osman. Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART). Sustainability. 2021; 13 (7):3870.

Chicago/Turabian Style

Mehrbakhsh Nilashi; Shahla Asadi; Rabab Abumalloh; Sarminah Samad; Fahad Ghabban; Eko Supriyanto; Reem Osman. 2021. "Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)." Sustainability 13, no. 7: 3870.

Journal article
Published: 12 March 2021 in Journal of Cleaner Production
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Green information technology (IT) adoption has helped enhance the overall organization’s environmental sustainability. Developing the strategies for effective adoption of Green IT is one of the essential goals of decision-makers. This study purposes to investigate the factors that influence decision-makers’ intention to use Green IT and the proposed green IT adoption model in Malaysian manufacturing firms. The 183 valid data were obtained using survey questionnaires from Malaysia’s manufacturing industries’ industrial managers and examine collect data through two analytical techniques. Two-staged structural equation modeling and artificial neural network applied for hypotheses evaluation and finding the significance level of every factor in the model. The outcomes of hypotheses evaluation through structural equation modeling revealed that managerial interpretation and ascription of responsibility have a significant role in predicting the adoption of green information technology in manufacturing companies. Besides, the Artificial Neural Network (ANN) results showed that the managerial interpretation and ascription of responsibility are considered as the most significant factors of green information technology adoption. This study will help the decision-makers and policymakers develop policies and programs for the effective employment of green information technology in manufacturing industries.

ACS Style

Shahla Asadi; Mehrbakhsh Nilashi; Sarminah Samad; Parveen Fatemeh Rupani; Hesam Kamyab; Rusli Abdullah. A proposed adoption model for green IT in manufacturing industries. Journal of Cleaner Production 2021, 297, 126629 .

AMA Style

Shahla Asadi, Mehrbakhsh Nilashi, Sarminah Samad, Parveen Fatemeh Rupani, Hesam Kamyab, Rusli Abdullah. A proposed adoption model for green IT in manufacturing industries. Journal of Cleaner Production. 2021; 297 ():126629.

Chicago/Turabian Style

Shahla Asadi; Mehrbakhsh Nilashi; Sarminah Samad; Parveen Fatemeh Rupani; Hesam Kamyab; Rusli Abdullah. 2021. "A proposed adoption model for green IT in manufacturing industries." Journal of Cleaner Production 297, no. : 126629.

Journal article
Published: 09 March 2021 in Telematics and Informatics
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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.

ACS Style

Mehrbakhsh Nilashi; Shahla Asadi; Behrouz Minaei-Bidgoli‬; Rabab Ali Abumalloh; Sarminah Samad; Fahad Ghabban; Ali Ahani. Recommendation agents and information sharing through social media for coronavirus outbreak. Telematics and Informatics 2021, 61, 101597 -101597.

AMA Style

Mehrbakhsh Nilashi, Shahla Asadi, Behrouz Minaei-Bidgoli‬, Rabab Ali Abumalloh, Sarminah Samad, Fahad Ghabban, Ali Ahani. Recommendation agents and information sharing through social media for coronavirus outbreak. Telematics and Informatics. 2021; 61 ():101597-101597.

Chicago/Turabian Style

Mehrbakhsh Nilashi; Shahla Asadi; Behrouz Minaei-Bidgoli‬; Rabab Ali Abumalloh; Sarminah Samad; Fahad Ghabban; Ali Ahani. 2021. "Recommendation agents and information sharing through social media for coronavirus outbreak." Telematics and Informatics 61, no. : 101597-101597.

Journal article
Published: 09 February 2021 in Technology in Society
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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.

ACS Style

Elaheh Yadegaridehkordi; Mehrbakhsh Nilashi; Mohd Hairul Nizam Bin Md Nasir; Saeedeh Momtazi; Sarminah Samad; Eko Supriyanto; Fahad Ghabban. Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques. Technology in Society 2021, 65, 101528 .

AMA Style

Elaheh Yadegaridehkordi, Mehrbakhsh Nilashi, Mohd Hairul Nizam Bin Md Nasir, Saeedeh Momtazi, Sarminah Samad, Eko Supriyanto, Fahad Ghabban. Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques. Technology in Society. 2021; 65 ():101528.

Chicago/Turabian Style

Elaheh Yadegaridehkordi; Mehrbakhsh Nilashi; Mohd Hairul Nizam Bin Md Nasir; Saeedeh Momtazi; Sarminah Samad; Eko Supriyanto; Fahad Ghabban. 2021. "Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques." Technology in Society 65, no. : 101528.

Journal article
Published: 01 October 2020 in Journal of Cleaner Production
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ACS Style

Shahla Asadi; Mehrbakhsh Nilashi; Sarminah Samad; Rusli Abdullah; Marwan Mahmoud; Monagi H. Alkinani; Elaheh Yadegaridehkordi. Factors impacting consumers’ intention toward adoption of electric vehicles in Malaysia. Journal of Cleaner Production 2020, 282, 1 .

AMA Style

Shahla Asadi, Mehrbakhsh Nilashi, Sarminah Samad, Rusli Abdullah, Marwan Mahmoud, Monagi H. Alkinani, Elaheh Yadegaridehkordi. Factors impacting consumers’ intention toward adoption of electric vehicles in Malaysia. Journal of Cleaner Production. 2020; 282 ():1.

Chicago/Turabian Style

Shahla Asadi; Mehrbakhsh Nilashi; Sarminah Samad; Rusli Abdullah; Marwan Mahmoud; Monagi H. Alkinani; Elaheh Yadegaridehkordi. 2020. "Factors impacting consumers’ intention toward adoption of electric vehicles in Malaysia." Journal of Cleaner Production 282, no. : 1.

Review
Published: 01 September 2020 in International Journal of Environmental Science and Technology
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Coronavirus Disease 2019 (COVID-19) is the official name of a respiratory infectious disease caused by a new coronavirus that started first in Wuhan, China, and outspread worldwide with an unexpectedly fast speed. Flights have been canceled worldwide and transportation has been closed nationwide and across international borders. As a consequence, the economic activity has been stopped and stock markets have been dropped. The COVID-19 lockdown has several social and economic effects. Additionally, COVID-19 has caused several impacts on global migration. On the other hand, such lockdown, along with minimal human mobility, has impacted the natural environment somewhat positively. Overall carbon emissions have dropped, and the COVID-19 lockdown has led to an improvement in air quality and a reduction in water pollution in many cities around the globe. A summary of the existing reports of the environmental impacts of COVID-19 pandemic are discussed and the important findings are presented focusing on several aspects: air pollution, waste management, air quality improvements, waste fires, wildlife, global migration, and sustainability.

ACS Style

P. F. Rupani; M. Nilashi; R. A. Abumalloh; S. Asadi; S. Samad; S. Wang. Coronavirus pandemic (COVID-19) and its natural environmental impacts. International Journal of Environmental Science and Technology 2020, 17, 4655 -4666.

AMA Style

P. F. Rupani, M. Nilashi, R. A. Abumalloh, S. Asadi, S. Samad, S. Wang. Coronavirus pandemic (COVID-19) and its natural environmental impacts. International Journal of Environmental Science and Technology. 2020; 17 (11):4655-4666.

Chicago/Turabian Style

P. F. Rupani; M. Nilashi; R. A. Abumalloh; S. Asadi; S. Samad; S. Wang. 2020. "Coronavirus pandemic (COVID-19) and its natural environmental impacts." International Journal of Environmental Science and Technology 17, no. 11: 4655-4666.

Journal article
Published: 17 July 2020 in Journal of Cleaner Production
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The main purpose of this paper is to develop an efficient multi-stage methodology to predict carbon dioxide emissions based on two important variables including the energy consumption and economic growth using the clustering, prediction machine learning techniques, and dimensionality reduction. To do so, we use the self-organizing map clustering algorithm to cluster the data and the adaptive neuro-fuzzy inference system and artificial neural network to construct the prediction models in each cluster of the self-organizing map to predict carbon dioxide emissions considering a set of input parameters including economic growth and energy consumption in Group 20 nations. Furthermore, we use the singular value decomposition for dimensionality reduction and missing values’ prediction in the dataset. The results of the analysis of a real-world dataset found that the developed multi-stage approach was capable of predicting the carbon dioxide emissions on two indicators. To validate the proposed method, the results are compared with other existing methods. The outcomes demonstrate that the adaptive neuro-fuzzy inference system and artificial neural network techniques combined with the self-organizing map and singular value decomposition technique provide 0.065 accuracy in terms of the mean average error. In addition, when comparing singular value decomposition-self-organizing map-adaptive neuro-fuzzy inference system method with the singular value decomposition-self-organizing map-adaptive-artificial neural network method, the singular value decomposition-self-organizing map-adaptive neuro-fuzzy inference provides with 0.104 accuracy in predicting CO2 emissions. Moreover, the multiple linear regression provides the worst accuracy (0.522) results compared with the artificial neural network and adaptive neuro-fuzzy inference system techniques. The analysis regarding the relationship between economic development, carbon dioxide emissions, and the energy consumption is extremely vital from the energy and economic policy-making aspects in Group 20 countries given that the primary focus of this group has been the governance of the global economy.

ACS Style

Abbas Mardani; Huchang Liao; Mehrbakhsh Nilashi; Melfi Alrasheedi; Fausto Cavallaro. A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques. Journal of Cleaner Production 2020, 275, 122942 .

AMA Style

Abbas Mardani, Huchang Liao, Mehrbakhsh Nilashi, Melfi Alrasheedi, Fausto Cavallaro. A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques. Journal of Cleaner Production. 2020; 275 ():122942.

Chicago/Turabian Style

Abbas Mardani; Huchang Liao; Mehrbakhsh Nilashi; Melfi Alrasheedi; Fausto Cavallaro. 2020. "A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques." Journal of Cleaner Production 275, no. : 122942.

Journal article
Published: 15 July 2020 in Journal of Cleaner Production
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Over the past decades, building manufacturing has caused serious environmental impacts, despite its role in the national economic growth. Thus, in developing strategic plans for economic growth, many governments consider the application of green manufacturing building and technologies as key factors towards a greener economy and lower carbon emission. However, so far, there have been limited efforts relating to the application of eco-efficiency ideas in building manufacturing. In fact, environmental sustainability in building project and delivery is still at a nascent stage. Thus, this study aims to identify and rank the sustainability indicators for assessing green building manufacturing in Malaysia by considering Green Building Index (GBI), which is the most applied sustainability rating tool in the country. Data is collected from a panel of experts and fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is performed to reveal the importance level and relationships among sustainability indicators in green building manufacturing. Results show that “Energy Efficiency” and “Indoor Environmental Quality” are the most important, while “Water Efficiency” and “Innovation” are the least important criteria in assessing green building manufacturing in Malaysia. This study can serve as a guideline to select and promote the optimum practices in green building manufacturing.

ACS Style

Elaheh Yadegaridehkordi; Mehdi Hourmand; Mehrbakhsh Nilashi; Eesa Alsolami; Sarminah Samad; Marwan Mahmoud; Ala Abdulsalam Alarood; Azida Zainol; Hamsa D. Majeed; Liyana Shuib. Assessment of sustainability indicators for green building manufacturing using fuzzy multi-criteria decision making approach. Journal of Cleaner Production 2020, 277, 122905 .

AMA Style

Elaheh Yadegaridehkordi, Mehdi Hourmand, Mehrbakhsh Nilashi, Eesa Alsolami, Sarminah Samad, Marwan Mahmoud, Ala Abdulsalam Alarood, Azida Zainol, Hamsa D. Majeed, Liyana Shuib. Assessment of sustainability indicators for green building manufacturing using fuzzy multi-criteria decision making approach. Journal of Cleaner Production. 2020; 277 ():122905.

Chicago/Turabian Style

Elaheh Yadegaridehkordi; Mehdi Hourmand; Mehrbakhsh Nilashi; Eesa Alsolami; Sarminah Samad; Marwan Mahmoud; Ala Abdulsalam Alarood; Azida Zainol; Hamsa D. Majeed; Liyana Shuib. 2020. "Assessment of sustainability indicators for green building manufacturing using fuzzy multi-criteria decision making approach." Journal of Cleaner Production 277, no. : 122905.

Correspondence
Published: 20 May 2020 in Journal of Infection and Public Health
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ACS Style

Mehrbakhsh Nilashi; Sarminah Samad; Salma Yasmin Mohd Yusuf; Elnaz Akbari. Can complementary and alternative medicines be beneficial in the treatment of COVID-19 through improving immune system function? Journal of Infection and Public Health 2020, 13, 893 -896.

AMA Style

Mehrbakhsh Nilashi, Sarminah Samad, Salma Yasmin Mohd Yusuf, Elnaz Akbari. Can complementary and alternative medicines be beneficial in the treatment of COVID-19 through improving immune system function? Journal of Infection and Public Health. 2020; 13 (6):893-896.

Chicago/Turabian Style

Mehrbakhsh Nilashi; Sarminah Samad; Salma Yasmin Mohd Yusuf; Elnaz Akbari. 2020. "Can complementary and alternative medicines be beneficial in the treatment of COVID-19 through improving immune system function?" Journal of Infection and Public Health 13, no. 6: 893-896.

Journal article
Published: 15 May 2020 in Expert Systems with Applications
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Parkinson’s Disease (PD) is one of the most prevalent neurological disorders characterized by impairment of motor function. Early diagnosis of PD is important for initial treatment. This paper presents a newly developed method for application in remote tracking of PD progression. The method is based on deep learning and clustering approaches. Specifically, we use the Deep Belief Network (DBN) and Support Vector Regression (SVR) to predict Unified Parkinson's Disease Rating Scale (UPDRS). The DBN prediction models were developed by different epoch numbers. We use a clustering approach, namely, Self-Organizing Map (SOM), to improve the accuracy and scalability of prediction. We evaluate our method on a real-world PD dataset. In all, nine clusters were detected from the data with the best SOM map quality for clustering, and for each cluster, a DBN was developed with a specific number of epochs. The results of the DBN prediction models were integrated by the SVR technique. Further, we compare our work with other supervised learning techniques, SVR and Neuro-Fuzzy techniques. The results revealed that the hybrid of clustering and DBN with the aid of SVR for an ensemble of the DBN outputs can make relatively better predictions of Total-UPDRS and Motor-UPDRS than other learning techniques.

ACS Style

Mehrbakhsh Nilashi; Hossein Ahmadi; Abbas Sheikhtaheri; Roya Naemi; Reem Alotaibi; Ala Abdulsalam Alarood; Asmaa Munshi; Tarik Rashid; Jing Zhao. Remote tracking of Parkinson's Disease progression using ensembles of Deep Belief Network and Self-Organizing Map. Expert Systems with Applications 2020, 159, 113562 .

AMA Style

Mehrbakhsh Nilashi, Hossein Ahmadi, Abbas Sheikhtaheri, Roya Naemi, Reem Alotaibi, Ala Abdulsalam Alarood, Asmaa Munshi, Tarik Rashid, Jing Zhao. Remote tracking of Parkinson's Disease progression using ensembles of Deep Belief Network and Self-Organizing Map. Expert Systems with Applications. 2020; 159 ():113562.

Chicago/Turabian Style

Mehrbakhsh Nilashi; Hossein Ahmadi; Abbas Sheikhtaheri; Roya Naemi; Reem Alotaibi; Ala Abdulsalam Alarood; Asmaa Munshi; Tarik Rashid; Jing Zhao. 2020. "Remote tracking of Parkinson's Disease progression using ensembles of Deep Belief Network and Self-Organizing Map." Expert Systems with Applications 159, no. : 113562.

Journal article
Published: 09 April 2020 in Technology in Society
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E-commerce is becoming a major contributor to the worldwide economic system, owing to its adaptability and ease of use for both customers and service providers. Recommender systems are embedded in most modern e-commerce websites, as efficient tools for guiding users to view additional items provided by e-commerce portals. These items are matched with customers' interests depending on their current activities, or on preferences stated in their profiles. As service providers are more concerned with the long-term behavior of customers, and specifically customer loyalty (which bears directly on the long-term success of e-commerce websites), most recommender systems have been developed to consider that aspect. This study investigates the major factors in the loyalty formation of female online shoppers through an e-commerce recommender agent. A new model is introduced, developed, and analyzed for helping to improve e-commerce customer loyalty via the recommender systems. Based on the implications of the results, we can understand research constructs and highlight research outcomes to help in managing recommender systems more effectively.

ACS Style

Rabab Ali Abumalloh; Othman Ibrahim; Mehrbakhsh Nilashi. Loyalty of young female Arabic customers towards recommendation agents: A new model for B2C E-commerce. Technology in Society 2020, 61, 101253 .

AMA Style

Rabab Ali Abumalloh, Othman Ibrahim, Mehrbakhsh Nilashi. Loyalty of young female Arabic customers towards recommendation agents: A new model for B2C E-commerce. Technology in Society. 2020; 61 ():101253.

Chicago/Turabian Style

Rabab Ali Abumalloh; Othman Ibrahim; Mehrbakhsh Nilashi. 2020. "Loyalty of young female Arabic customers towards recommendation agents: A new model for B2C E-commerce." Technology in Society 61, no. : 101253.

Article
Published: 24 March 2020 in International Journal of Fuzzy Systems
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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.

ACS Style

Mehrbakhsh Nilashi; Hossein Ahmadi; Azizah Abdul Manaf; Tarik Rashid; Sarminah Samad; Leila Shahmoradi; Nahla Aljojo; Elnaz Akbari. Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates. International Journal of Fuzzy Systems 2020, 22, 1376 -1388.

AMA Style

Mehrbakhsh Nilashi, Hossein Ahmadi, Azizah Abdul Manaf, Tarik Rashid, Sarminah Samad, Leila Shahmoradi, Nahla Aljojo, Elnaz Akbari. Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates. International Journal of Fuzzy Systems. 2020; 22 (4):1376-1388.

Chicago/Turabian Style

Mehrbakhsh Nilashi; Hossein Ahmadi; Azizah Abdul Manaf; Tarik Rashid; Sarminah Samad; Leila Shahmoradi; Nahla Aljojo; Elnaz Akbari. 2020. "Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates." International Journal of Fuzzy Systems 22, no. 4: 1376-1388.

Article
Published: 11 March 2020 in Journal of Materials Science: Materials in Electronics
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The purpose of this analytical research is the capability of sensing different K+ concentrations by the ion-sensitive field effect transistor (ISFET) based on graphene. By gate voltage variation values, K+ ion concentration in the electrolyte can be measured, since there are interactions between the K+ and the gate ions. We have applied the adaptive neuro-fuzzy inference system (ANFIS) model to predict the current–voltage (I–V) characteristic which has resulted in satisfactory performance.

ACS Style

Elnaz Akbari; Narjes Nabipour; Seyed Morteza Hadavi; Mehrbakhsh Nilashi. Analytical investigation of ion-sensitive field effect transistor based on graphene. Journal of Materials Science: Materials in Electronics 2020, 31, 6461 -6466.

AMA Style

Elnaz Akbari, Narjes Nabipour, Seyed Morteza Hadavi, Mehrbakhsh Nilashi. Analytical investigation of ion-sensitive field effect transistor based on graphene. Journal of Materials Science: Materials in Electronics. 2020; 31 (8):6461-6466.

Chicago/Turabian Style

Elnaz Akbari; Narjes Nabipour; Seyed Morteza Hadavi; Mehrbakhsh Nilashi. 2020. "Analytical investigation of ion-sensitive field effect transistor based on graphene." Journal of Materials Science: Materials in Electronics 31, no. 8: 6461-6466.

Journal article
Published: 04 March 2020 in Journal of Cleaner Production
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Since consumers, governments, and society in general are increasingly concerned about the loss of natural resources, along with pollution of the environment, there is currently a significant tendency to recognize the value of green innovation toward the achievement of sustainable development. Hotels are considered responsible for a considerable proportion of the environmental pollution caused by the tourism industry. Yet, few studies have considered the effects that green innovation may have on sustainable performance in the hotel industry. Consequently, the present study aimed to investigate the factors influencing the adoption of green innovation, and its potential effects on the performance of the hotel industry. Data collection was performed through inspection of 183 hotels in Malaysia. Data analysis was carried out employing the partial least squares method. The two factors of environmental and economic performance were determined to have the strongest influence, affecting the green innovation procedures positively and significantly. The results of the present study have major implications for hospitality research, since they demonstrate the importance and potential of green innovation in promoting sustainable performance in the hotel industry. The proposed model and the identified influencing factors of green innovation can assist policy makers and hotel managers in understanding the drivers leading to the adoption of these practices in the hotel industry.

ACS Style

Shahla Asadi; Seyedeh OmSalameh Pourhashemi; Mehrbakhsh Nilashi; Rusli Abdullah; Sarminah Samad; Elaheh Yadegaridehkordi; Nahla Aljojo; Nor Shahidayah Razali. Investigating influence of green innovation on sustainability performance: A case on Malaysian hotel industry. Journal of Cleaner Production 2020, 258, 120860 .

AMA Style

Shahla Asadi, Seyedeh OmSalameh Pourhashemi, Mehrbakhsh Nilashi, Rusli Abdullah, Sarminah Samad, Elaheh Yadegaridehkordi, Nahla Aljojo, Nor Shahidayah Razali. Investigating influence of green innovation on sustainability performance: A case on Malaysian hotel industry. Journal of Cleaner Production. 2020; 258 ():120860.

Chicago/Turabian Style

Shahla Asadi; Seyedeh OmSalameh Pourhashemi; Mehrbakhsh Nilashi; Rusli Abdullah; Sarminah Samad; Elaheh Yadegaridehkordi; Nahla Aljojo; Nor Shahidayah Razali. 2020. "Investigating influence of green innovation on sustainability performance: A case on Malaysian hotel industry." Journal of Cleaner Production 258, no. : 120860.

Journal article
Published: 21 February 2020 in Symmetry
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Sustainable products and their marketing have played a crucial role in developing more sustainable consumption patterns and solutions for socio-ecological problems. They have been demonstrated to significantly decrease social consumption problems. Neuromarketing has recently gained considerable popularity and helped companies generate deeper insights into consumer behavior. It has provided new ways of conceptualizing consumer behavior and decision making. Thus, this research aims to investigate the factors influencing managers’ decisions to adopt neuromarketing techniques in sustainable product marketing using the fuzzy analytic hierarchy process (AHP) approach. Symmetric triangular fuzzy numbers were used to indicate the relative strength of the elements in the hierarchy. Data were collected from the marketing managers of several companies who have experience with sustainable product marketing through online shopping platforms. The results revealed that the accuracy and bias of neuromarketing techniques have been the main critical factors for managers to select neuromarketing in their business for advertising and branding purposes. This research provides important results on the use of neuromarketing techniques for sustainable product marketing, as well as their limitations and implications, and it also presents useful information on the factors impacting business managers’ decision making in adopting neuroscience techniques for sustainable product development and marketing.

ACS Style

Mehrbakhsh Nilashi; Elaheh Yadegaridehkordi; Sarminah Samad; Abbas Mardani; Ali Ahani; Nahla Aljojo; Nor Shahidayah Razali; Taniza Tajuddin. Decision to Adopt Neuromarketing Techniques for Sustainable Product Marketing: A Fuzzy Decision-Making Approach. Symmetry 2020, 12, 305 .

AMA Style

Mehrbakhsh Nilashi, Elaheh Yadegaridehkordi, Sarminah Samad, Abbas Mardani, Ali Ahani, Nahla Aljojo, Nor Shahidayah Razali, Taniza Tajuddin. Decision to Adopt Neuromarketing Techniques for Sustainable Product Marketing: A Fuzzy Decision-Making Approach. Symmetry. 2020; 12 (2):305.

Chicago/Turabian Style

Mehrbakhsh Nilashi; Elaheh Yadegaridehkordi; Sarminah Samad; Abbas Mardani; Ali Ahani; Nahla Aljojo; Nor Shahidayah Razali; Taniza Tajuddin. 2020. "Decision to Adopt Neuromarketing Techniques for Sustainable Product Marketing: A Fuzzy Decision-Making Approach." Symmetry 12, no. 2: 305.