Search

Recognizing the power of consumer-led dialogue over traditional company-led marketing, travel and tourism organizations increasingly appreciate the role of consumer advocacy. This study explored consumer advocacy as an outcome of corporate social responsibility (CSR) through brand admiration and attitude and evaluated the conditional indirect influence of altruistic values. Our findings revealed that CSR directly impacts travel consumer advocacy, significantly mediated by brand admiration and attitude. The findings also demonstrated the significant conditional indirect influence of altruistic values. Our research suggests that an effective CSR strategy in a travel and tourims organization offers a competitive edge by cultivating travel and tourism consumers into brand advocates. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recently, the tendency towards employing a combined system for generating electricity has significantly increased. In this respect, the combination of a photovoltaic (PV) system and an energy storage system has captured the attention. In this respect, this paper develops a control system for a combined energy system, comprised of a PV and a battery in the grid-connected mode. There are several research works being carried out to exploit the highest power from PV systems, due to their high investment costs. An interesting and effective approach is utilizing a maximum power point tracking so-called "MPPT" algorithm for the PV panel to raise efficiency. This paper proposes the fuzzy logic controller (FLC) and the modified shuffled frog leaping algorithm (MSFLA) to optimally tune the parameters of the controller and follow the maximum power point (MPP). In this respect, an efficient theoretical framework has been developed for the MPPT algorithm to assess its performance under various conditions. Besides, a comprehensive comparison has been made to evaluate the model in different weather conditions, the method developed in this paper is associated with 99% efficiency while other methods result in 95%–97% efficiency with low speed at reaching the MPP that highlight the superior performance of the presented technique and other methods. Accordingly, the techniques studied are ranked as follows based on their performance: MSFLA-FLC > FLC > IC > P&O while their response times are 0.13s, 0.18s, 0.26s and 0.32s,respectively. Image 1 • Combination of a photovoltaic system and an energy storage system. • Proposing the fuzzy logic controller (FLC) and the modified shuffled frog leaping algorithm (MSFLA) for MPPT. • Desirable results compared to other approaches in terms of the transient and steady-state performance. • Effective power flow plan between the grid and the combined energy system. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis. • The contribution of deep learning is investigated for Parkinson's Disease diagnosis. • A bibliometric analysis and literature review is performed. • The available research papers retrieved and analyzed in the Scopus database. • The literature review presented a research gap in deep learning approaches related to incremental learning. [ABSTRACT FROM AUTHOR]

• A hybrid method is proposed for PD disease diagnosis. • SOM and EM are used for the clustering the data. • ANFIS ensemble is used for Total and Motor-UPDRS. • SVD is used for dimensionality reduction for computation time reduction. • ANFIS ensemble is used for predicting Total-UPDRS and Motor-UPDRS. The use of machine learning techniques for early diseases diagnosis has attracted the attention of scholars worldwide. Parkinson's Disease (PD) is one of the most common neurological and complicated diseases affecting the central nervous system. Unified Parkinson's Disease Rating Scale (UPDRS) is widely used for tracking PD symptom progression. Motor- and Total-UPDRS are two important clinical scales of PD. The aim of this study is to predict UPDRS scores through analyzing the speech signal properties which is important in PD diagnosis. We take the advantages of ensemble learning and dimensionality reduction techniques and develop a new hybrid method to predict Total- and Motor-UPDRS. We accordingly improve the time complexity and accuracy of the PD diagnosis systems, respectively, by using Singular Value Decomposition (SVD) and ensembles of Adaptive Neuro-Fuzzy Inference System (ANFIS). We evaluate our method on a large PD dataset and present the results. The results showed that the proposed method is effective in predicting PD progression by improving the accuracy and computation time of the disease diagnosis. The method can be implemented as a medical decision support system for real-time PD diagnosis when big data from the patients is available in the medical datasets. [ABSTRACT FROM AUTHOR]

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

Current research explores the adverse impact of energy consumption and economic development in the ASEAN region. The choice of the region stems from the fact that the human development index of the region ranges widely from 0.93 of Singapore to that of Myanmar, which is 0.58. besides that, the region's Ecological Footprint is increasing with time. Another aspect of the region stems from the fact that, on average, the region has had an ecological deficit since the 1970s. This research examines the origins of environmental degradation in South Asia by analyzing the lopsided impact of urbanization and economic expansion on economic growth and energy use. This study applies the non-linear autoregressive distributive lag (NARDL) method to examine the asymmetry that results from both the positive and negative disruptions of financial development and energy consumption, using an annual dataset spanning 1980 to 2021. According to the NARDL model's findings, both positive and negative shocks in energy consumption significantly contribute to increasing environmental degradation over the long run and decreasing the density of CO 2 emissions in the short run. While several factors can cause short-term changes in CO 2 emissions, only an adverse shift in financial development can have a lasting and major effect on global emissions. In addition, the ARDL model's findings suggest that less environmental deterioration occurs due to economic progress, but CO 2 emissions increase due to rising energy consumption. This article argues that governments should try to achieve high economic success through energy consumption and economic growth that is less taxing on the environment. [Display omitted] • Energy consumption and economic expansion in ASEAN countries cause environmental degradation. • ASEAN countries have depleted their biodiversity and natural resources due to economic growth. • The Human Development Index (HDI) measures life expectancy, education, and income. • Financial development and Energy consumption affect CO2 emissions from 1980 till 2021. • Environmental management is necessary for Economic Expansion. [ABSTRACT FROM AUTHOR]

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

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. • A novel adoption model of green IT in manufacturing industries in Malaysia is proposed. • Structural equation modeling and neural network as an effective approach to for green IT adoption. • Managerial interpretation is the most significant factor for green IT adoption. [ABSTRACT FROM AUTHOR]

Electric vehicles have significantly contributed to the sustainability of world economic growth. It is of critical importance to understand and examine the factors impacting the consumers' attitude toward the adoption of electric vehicles. However, limited study investigated the effect of altruism on electric vehicles adoption from a pro-environmental behaviour perspective. Therefore, the present work aimed at identifying the influencing factors on consumers' intention to use electric vehicles. To this end, a model has been developed based on two theoretical models called the Norm Activation Model and the Theory of Planned Behaviour. The potential consumers in Malaysia were selected to answer questionnaires. Accordingly, 177 valid questionnaires were collected and the influencing factors on the electric vehicles purchase intention were empirically analyzed using a structural equation model. According to the results, perceived value, attitude, the ascription of responsibility, subjective norms, personal norms, perceived consumer effectiveness, and awareness of consequences affected the consumers' electric vehicles' purchase intention significantly and positively. Consumers' behaviour regarding the adoption of electric vehicles can be understood better through the findings of this study, while electric vehicle development can be promoted as well. [ABSTRACT FROM AUTHOR]

The present study aims to determine the impact of green innovation (GI) on the overall performance of an organization while keeping the variable of environmental management (EM) as a moderator. We used a dataset consisting of four data years, from 2014 to 2017, of A-share companies listed on the Shanghai Stock Exchange (SSE). The concept of green innovation refers to the use of advancements in technology that enable savings in energy, along with the recycling of waste material. When advanced technology is utilized in the production process, the products are referred to as green products and the whole process of adopting such technologies and product design is referred to as "Corporate Environmental Management". Such innovations improve the overall financial performance of companies as it enables them to improve their social image by reducing their carbon footprint and ensures their long-term sustainability. The main issue is the limited focus and attention given to the topic, from the perspective of companies. This research focuses on the impact of green innovation and the importance of environmental management for the sustainability of companies. Our findings suggest that the relationship between green innovation and the performance of the company is positive and verifies the existence of moderating effects of environmental management on the relationship between green innovation and firm performance. Implications are given to academia and practitioners. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• A new method is proposed for iris recognition. • The method is developed by neural network and genetic algorithm. • The method is evaluated on CASIA-Iris V3 and UCI machine datasets. In the new millennium, with chaotic situation that exist in the world, people are threaten with multifarious terrorist attacks. There have been several intelligent ways in order to recognize and diminish these assaults wisely. Biometric traits have proven to be one of the useful ways for tackling these problems. Among all these traits, the iris recognition systems are appropriate tools for the human identification not only the iris pattern is well-known features, but also it has numerous features such as compactness representation, uniqueness texture, and stability. In spite of the fact that there have been published many approaches in these areas, there are still abundant problems in these approaches like time consuming, and computational complexity. In order to solve these obstacles, we propose the extravagant iris recognition methods that are based on combination of two dimensional Gabor kernel (2-DGK), step filtering (SF) and polynomial filtering (PF) for feature extraction and hybrid radial basis function neural network (RBFNN) with genetic algorithm (GA) for matching task. To assess the performance of the proposed method, we use two benchmarks in our algorithm and implemented it on CASIA-Iris V3, UBIRIS. V1 and UCI machine learning repository datasets. The experimental results of the proposed method reveal that the method is efficient in the iris recognition. [ABSTRACT FROM AUTHOR]

Few articles have focused on the green aspect of intellectual capital namely green intellectual capital. Nowadays, due to increasing concerns regarding environmental issues, it is essential to investigate the effects of green intellectual capital on organisational sustainability. Hence, this study examines the linkage between the dimensions of green intellectual capital (green human capital, green structural capital, and green relational capital) and business sustainability. Data were gathered from 168 manufacturing small medium enterprises (SMEs) in Malaysia, and analysed using the Partial Least Squares approach. The results show that green structural capital and green relational capital have positive relationship with business sustainability, while green human capital did not. Finally, the implications and limitations of this study were discussed and the recommendations for future studies presented. Image 10907 • The linkage between the green intellectual capital dimensions and business sustainability was explored. • The Natural Resource-Based View and the Intellectual Capital-Based View were used as underpinning theories for the study. • Green structural capital and green relational capital significantly influence business sustainability. • Green intellectual capital was found to push the manufacturing SMEs towards cleaner production. [ABSTRACT FROM AUTHOR]

Abstract The crucial role of customers' positive experience and their subsequent word-of-mouth have been highlighted by both scholars and practitioners for all industry sectors. In response to an increasing concern of environmental sustainability and sensitivity of consumers for deteriorating environment, eco-friendly (green) products and services gained tremendous attention. TripAdvisor is increasingly known as one of the most popular e-tourism platforms. Understanding and predicting the traveler' preferences by advanced big data analytics technology is an important task that the recommendation engine of this platform does. In this paper, we aim to develop a new soft computing method with the aid of machine learning techniques in order to find the best matching eco-friendly hotels based on the several quality factors in TripAdvisor. We develop the method using dimensionality reduction and prediction machine learning techniques to improve the scalability of prediction from the large number of users' ratings. The proposed soft computing method is evaluated on a large dataset discovered from the TripAdvisor platform. The results show that the combination of dimensionality reduction and prediction machine learning techniques is robust in processing the large number of the ratings provided by users on the features of eco-friendly hotels and predicting travelers' choice preferences of eco-friendly hotels in TripAdvisor. [ABSTRACT FROM AUTHOR]

• A new method for big social data analysis to reveal traveller' behavior is presented. • EM clustering approach is used for customers' segmentation. • Neural network combined with fuzzy logic is used for preference prediction. • The method uses HOSVD for data dimensionality reduction. • The method is evaluated on eco-friendly hotels data. Sustainable tourism is an emerging trend around the world. Eco-friendly (green) hotels are environmentally friendly properties that are becoming more popular among green travellers. Electronic Word-of-Mouth (e-WOM) is a method of communicating with customers to share their experiences and is a powerful marketing tool for hotel marketing. This paper investigates the role of online reviews of eco-friendly hotels for preference learning using multi-criteria decision-making and machine learning techniques. We develop a new method using multi-criteria decision making, supervised and unsupervised learning techniques. The Expectation-Maximization (EM) algorithm is used as an unsupervised learning technique to cluster travellers' online reviews. We use the Higher-Order Singular-Value Decomposition technique along with a similarity measure to find the most similar customers based on their preference. To predict travellers' preference for eco-friendly hotels, we employ a neuro-fuzzy system, the Adaptive Neuro-Fuzzy Inference System, as a supervised learning technique. To select the most important criteria, we use the entropy-weight approach in each segment. Several experiments were performed on the collected data from the Czech Republic's eco-friendly hotels on the TripAdvisor platform. The results demonstrated that the hybrid approach is effective for customers' segmentation, and preference learning and prediction in eco-friendly hotels. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the current close-to-smart power systems, renewable energies are the most significant elements that need to be carefully addressed in power system studies. In this regard, microgrids (MGs) have been introduced recently to activate the large penetration of renewables. However, integration of such generation technologies that are associated with severe uncertainty in their power output would significantly impact the scheduling of energy resources in MGs. Thus, efficient energy management systems are required to be employed in MGs. Therefore, this paper presents a day-ahead scheduling framework for an MG equipped with a solar photovoltaic (PV) unit. In this respect, different climate conditions and their impacts on the power output of the PV unit and the optimal scheduling of the MG have been investigated in this paper. To this end, four different days from the four seasons have been used to extract the data of solar irradiance. The scheduling problem has been formulated in a single-objective optimization framework, where the objective function is defined as minimizing the total operating cost over the scheduling period. An effective optimization algorithm named "modified krill herd (MKH)" algorithm is proposed to solve the mentioned day-ahead scheduling problem, while there are renewable and non-renewable generating units, besides an energy storage system. Furthermore, a comprehensive comparison has been made between the MKH algorithm and some well-known optimization algorithms to verify the superior performance of the suggested method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Adoption of green information technology (Green IT) as an initiative of pro-environmental behavior is quite sparse among Malaysian students. Due to the need to integrate personality traits in environmental behavioral studies, the current study deriving from the planned behavior theory attempts to investigate the influence of attitudinal factors on students' pro-ecological behavioral intention to practice Green IT. Extremely few studies explored the moderating role of personality traits in the context of Green IT adoption and thus this study attempts to fill the current research gap. The personality traits of openness, agreeableness and conscientiousness were included in the research model as moderating variables. A number of 262 pieces of data were collected from students. Based on the partial least squares approach and bootstrapping method, the results revealed that except social norms, other variables greatly influenced the intention to practice Green IT. Moreover, the moderating effects analysis showed that the personality trait of conscientiousness was the only trait that significantly moderated the relationships in the proposed model. • Students' pro-ecological behavior of Green IT practice was investigated. • The behavior was investigated through the lens of theory of planned behavior. • Moderating impact of Agreeableness, Openness, and Conscientiousness were explored. • Personal norm, attitude and PCB were found significant factors. • Conscientiousness was the only personality trait moderating the relationships. [ABSTRACT FROM AUTHOR]

The paper develops a control scheme for a hybrid energy system, including a photovoltaic (PV) panel and a battery energy storage (BES) system. The capital cost of PV systems is considerably high today and it has been continuously trying to mitigate the manufacturing cost and also, several research works have been devoted to developing effective methods to maximize the power output of these generation systems. In this respect, the most efficient technique is named maximum power point tracking (MPPT) for solar PV systems. To this end, this paper utilizes the fuzzy controller and BAT algorithm (BA) to find the optimal values of the parameters of the controller for the MPPT. The presented framework is known as BAT-Fuzzy. Using the mentioned method, the membership functions of the input and output, which are triangular, are autonomously set. The results obtained from simulating the proposed model on a test system shows the superiority of the BAT-Fuzzy technique in terms of the preciseness and convergence rate, while taking into consideration altering values of solar irradiance and temperature. It also performs better, considering the efficiency of the system in practical cases. Besides, the impact of temperature variations and also, different states of the partial shading conditions (PSC) have discussed. The PV system alone does not produce power over some hours of the day and it should be operated jointly with a storage system to generate the required power over all hours of the day. The presented method is simulated using a test system and the results are compared to some well-known methods, verifying that the developed framework is associated with a fast response during transients with enhanced stability. [ABSTRACT FROM AUTHOR]