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Objective of the study: The objective is to identify factors that influence student's acceptance of Learning Management System (LMS) using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This study investigates how UTAUT factors affect students' intention and attitudes to use Blackboard as a LMS at King Abdulaziz University in Saudi Arabia. Methodology/approach: This study proposes a research model based on UTAUT factors 'Effort Expectancy (EE), Performance Expectancy (PE), Perceived Functionality (PF), Facilitating Condition (FC), Social Influence (SI)', Behavioural Intention to use (BI) and Usage Behaviour. The survey research methodology was adopted using questionnaire to identifying factors that influencing the students' intention and attitudes towards LMS. Originality/relevance: The study relates corporate LMS to support educational institutions to enhance the students' acceptance and attitudes to use in Saudi Arabia. Main result: The results indicate that PE, PF, FC and SI factors were significant and directly influence on students' BI Blackboard. Both PE and FC are second factors that affect students' intention and EE factor does not impact the student's BI. Theoretical/methodological contributions: The study contributes to the body of knowledge in developing the research model using UTAUT model by showing the factors affecting the students' intention in using LMS and usage behavior in Saudi Arabia. Social/management contributions: This study contributes in understanding the critical factors affecting the Saudi Arabia students' intentions in using Blackboard. Another study should be extended to other universities by using different methods to test the model and incorporate different moderating factors to accept and use the Blackboard. [ABSTRACT FROM AUTHOR]

Dyslexic individuals have serious difficulties in learning to read, and several software programs have been developed to overcome them. Previous research studies found that in most cases, providing correct spelling and word recognition with greater accuracy constitutes the key function. However, more research has to be conducted on the software programs for dyslexia. Another issue is visual-spatial attention; the aspect of directing attention to a location of what should be learnt while reading has been ignored in most studies. This research analysis of the previous studies outlines some research gaps as follows: recording children's eye movements during reading were uncertain. Another research gap is understanding the frequencies of Arabic word recognition for dyslexic children the meaning of the Arabic word during reading for dyslexic were not adequately presented in the previous research. Therefore, this paper presents an experimental study on identifying Arabic words and their meaning by dyslexic individuals focusing on the sequence of interactions. A decision-making analysis of the key interactive components of the Arabic alphabet puzzle app for dyslexics is performed, and the finding of the study reveals that the speed and accuracy of visual word recognition and meaning description increases and significantly influences visual short-term memory The analysis of the most important factors that influence all the other factors of the user experience of the Arabic alphabet puzzle app for dyslexics shows that 'desirability' constitutes the key attribute in enhancing visual-spatial attention and accelerating word recognition and meaning description for Arabic-speaking dyslexics. [ABSTRACT FROM AUTHOR]

Individuals who are deaf or those having hearing problems can communicate through alternative modes of communication, such as sign language, both within and outside their community. As a result, the sign serves as a tool that mediated communication goals. With the aid of recent advances in computer vision, there has been promising progress in the fields of motion and gesture detection using deep learning. In this work, convolutional neural networks are utilised to develop a model for recognising the Arabic language's alphabet signs in order to aid communication goal. The research used the Arabic Alphabets Sign Language Dataset (ArASL2018), which contains sets of images indicating each specific sign for each letter of the alphabet. Experimental analysis was carried out using the proposed model. The results of the analysis reveal a recognition accuracy of 94.46%. Furthermore, the comparative analysis with the previous studies indicates that the proposed model outperformed all of them in terms of recognition accuracy. [ABSTRACT FROM AUTHOR]

As a futuristic emblem, smart cities represent a goal to provide all of the services required for a highquality existence while conserving natural resources to the maximum extent possible. It turns out that the "Line City in Saudi Arabia" is the most famous and modern of these smart cities in terms of preserving 100 percent natural life, owing to the fact that they rely totally on natural energy and do not use any automobiles. The issues and challenges that have been highlighted will assist in the improvement of municipal management. One of the most generally implemented projects in the field of smart cities is the technological and integrative developments of the use of sensors and data collecting in conjunction with rising difficulties aimed at enhancing human well-being and improving human livelihood. This is especially true in Saudi Arabia, where visitors can arrive quickly by just walking through the checkpoints, resulting in a journey of little more than a few minutes to reach any regular destination. It also splits the city into many different levels of the ground, enabling for the conservation of natural excess energy. [ABSTRACT FROM AUTHOR]

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TFIDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news. [ABSTRACT FROM AUTHOR]

Land use and census data are of a significant value to urban planners, decision-makers, and investors. Important decisions are made based upon historical data, i.e, inductive reasoning. Such data become increasingly valuable at a fine-grain level to decision-makers in businesses where audience segmentation is needed. In rapidly developing countries, land use and census data are widely available at a coarse-grain level. However, accurate fine-grain mapping of such data is a very challenging task. In this paper, we propose a hierarchical methodology that enriches Geographical Information Systems (GIS) by utilizing existing datasources, rule-based algorithms, and workers (mechanical turks) to identify and map socioeconomic data at a fine-grain level. The proposed methodology was tested and evaluated on multiple cities in Saudi Arabia, with an overall accuracy of 90%. The output of this work, a GIS enriched with socioeconomic data and population count, is of high importance to decision makers and can be utilized in different problems to help make a better informed decision. [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]

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]

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]

Nowadays, sustainability is recognized as one of the most important development paradigms and included in the international and national strategies of almost all organizations. Sustainability assessment methods have been important for monitoring sustainability performance. These methods are developed to help decision-makers in their attempts to make society more sustainable. Various methods have been proposed for assessing the sustainability performance, however, this research is the first attempt to employ fuzzy clustering and supervised machine learning techniques to country sustainability assessment. This research tries to extend the previous sustainability assessment systems by the use of these techniques to reveal the relationships between human sustainability, ecological sustainability and overall sustainability performance by discovering the decision rules. The decision rules discovered from the Sustainability Assessment by Fuzzy Evaluation data of 128 countries are used to predict the country sustainability performance. The method proposed in this paper is flexible to accept large number indicators of sustainability to be used in the assessment of countries sustainability. The results of our analysis on countries sustainability data showed that the proposed method has potential to be used as a decision-making tool for sustainability assessment through a large set of indicators. [ABSTRACT FROM AUTHOR]