This study compares the performance of two popular end-to-end text-to-speech (TTS) systems, the Tacotron and its successor, the Tacotron 2, each used with rival vocoders, the WaveNet and the WaveGlow, respectively. We conducted experiments on Nawar Halabi’s dataset, which contains approximately three hours and forty-two minutes of Arabic speech and qualitatively evaluated the models using the mean opinion score (MOS). The original Tacotron with WaveNet achieved 4.2 on a scale of 5 of a score for mean opinion, thus outperforming Tacotron 2 with WaveGlow in terms of naturalness of speech. We found that crafted text analysis is a crucial step in improving end-to-end TTS for complex languages, such as Arabic. We recommend investing more in text preprocessing as a prior step to accounting for language-specific features such as diacritic, as well as enhancing the voice quality produced through prosody modeling.

The optimization of heat and mass distribution within triangular cavities has received substantial attention in engineering research. In this study, heat and mass transfer involving Casson fluid in a closed, partially heated, porous triangular chamber has been extensively analyzed. A heated triangular impediment has been placed inside the cavity to assist us in comprehending the circumstances. The governing partial differential equations are converted into a non-dimensional form by employing appropriate similarity variables. The well-known finite element method (FEM) has been used to solve these equations and investigating the effects of various physical parameters on the flow patterns, concentration isotherms, and local Nusselt numbers, including the partial heating length, buoyancy-driven force, Casson fluid characteristics, Soret and Dufour effects, Lewis number, Rayleigh number, heat generation/absorption, and porous medium features. It has been observed that the dispersion and movement of materials inside a fluid can be indirectly impacted by changes in the Casson parameter. The length of the heated wall significantly affects how evenly the temperature is supplied. When the wall length is shorter, the temperature drops noticeably and intensely. Although the Dufour parameter is small, the thermal diffusion effect is not as significant as mass diffusion. As the Soret parameter is increased, an inverse relationship to the Dufour effect is noticed. Convective heat transfer gradually prevails in the flow as the Darcy number rises, indicating increased permeability compared to other characteristics.

Maple syrup urine disease (MSUD) is a metabolic disorder characterized by a difficulty to digest and process proteins necessary for growth. To monitor and maintain the ideal growth of children with MSUD, caregivers need to carefully control the consumption of harmful branched-chain amino acids (BCAAs). The dietary limits of amino acids for MSUD patients are recommended and controlled by pediatricians and metabolic dietitians according to age, height, weight, and the prevailing percentage of amino acids in the body. This study introduces an intelligent dietary tool called MSUD Baby Buddy for caregivers of MSUD patients that tracks the amino acids intake out of baby formulas for babies 0–6 months old. This tool aims to provide accurate recommendations of the appropriate daily intake of protein and BCAAs based on the patients’ data, plasma BCAAs, and formula preferences. We use a knowledge-based system, including knowledge acquisition and verification, as well as knowledge management tool validation, and the ripple-down rules are employed for building the system. MSUD Baby Buddy can support the maintenance of adequate amino acid levels and increase awareness about the control of BCAAs. The average usability of MSUD Baby Buddy is 84.25, indicating that the tool is intuitive and may help caregivers to easily determine the recommended doses of formula based on patients’ biometric data and preferred formula. On the other hand, interviews with metabolic dietitians revealed some drawbacks, which were addressed to further improve the tool. MSUD Baby Buddy is expected to help caregivers of MSUD patients to independently track nutrient intake and reduce the number of visits to the pediatrician and metabolic dietitian.

BACKGROUND:As of 2022, people are getting better at learning how to coexist with the Covid-19 global pandemic. In Saudi Arabia, many attempts have been made to raise public health awareness. However, most health awareness campaigns are generic and might not influence the desired behavior among individuals.OBJECTIVES:This study aims to apply geospatial intelligence and user modeling to profile the districts of the city of Jeddah. This customized map can provide a baseline for a customized health awareness campaign that targets the locals of each district individually based on the virus spread level.METHODOLOGY:It is ongoing research, which has resulted in the creation of a health messages library in the first phase [1]. This paper focuses on a second phase of the research study, which aims to provide a customized baseline for this campaign by applying the geospatial artificial intelligence technique known as space-time cube (STC). STC was applied to create a local map of the Saudi city of Jeddah, representing three different profiles for the city's districts. The model is built using valid COVID-19 clinical data obtained from one of Jeddah's general hospitals.RESULTS AND IMPLICATIONS:When applied, STC displays three profiles for the districts of Jeddah city: high infection, moderate infection, and low infection. To assess the geo-intelligent map, a new instrument was created and validated. The usability and practicality of this map were quantitatively evaluated in a cross-sectional survey using the goal-question-metric measurement framework, and a total of 43 participants filled out the questionnaire. The results indicate that the geo-intelligent map is suitable for everyday use, as evidenced by the participants' responses. We argue that the developed instrument can also be used to assess any geo-intelligence map. This research provides a legitimate approach to customizing health awareness messages during pandemics.

For years, several countries have been concerned about how to dispose of unused pharmaceuticals that can endanger human health and the environment. Moreover, some people are in desperate need of medical attention and medications, but they lack the financial resources to obtain them. In Saudi Arabia, there are no take-back medicine programs, and there is no published research on how medications properly are disposed. The aim of this research is to use the power of artificial intelligence to assist in the proper management and disposal of expired and unused medications and to develop a prototype device for collecting medication by automatically classifying medications for proper disposal and donation. In this research, artificial intelligence technologies such as web-based expert systems, image recognition and classification algorithms, chatbots, and the internet of things are used to assist in a take-back medications program. In conclusion, the prototype design of a web-based expert system and the device reduced improper disposal risks by providing significant advice on the safe disposal of unwanted pharmaceuticals. By using an organized method of collecting expired medications, the benefits were made possible.

This study aims to utilize the machine learning technique to build a model to recommend the suitable wind turbine type based on some variables, such as air speed and air density, as well as visualize the location of the recommended wind turbine selection on a 3D map. Particularly, we applied the K-nearest neighbor model (KNN) to determine the amount of energy produced by a single wind turbine. We applied it on 10 separate wind farms in Saudi Arabia. The results indicate that the model performs very well in predicting the best wind turbine type with the mean accuracy of 88%, where ten wind stations resulted from the optimized model with the suggested turbine type in each station. Adding more wind attributes and other factors may assist in increasing the model mean accuracy. The project’s findings will assist decision-makers in Saudi Arabia to make informed decisions as to what kind of wind turbine is suitable for a specific location. In the long run, this will help to make wind energy-a sustainable source of energy-one of the main goals of the 2030 vision, specifically under National Industrial Development and Logistics Program.

Mobile health (mHealth) has been widely invested in managing chronic diseases. The usage of mHealth could assist in improving patients’ understanding of their chronic diseases. This research contributes to this domain of knowledge by investigating the development and use of a mHealth that we call, Ana Alsukkary. This tool targets Saudi children with diabetes and their caregivers to help them better manage their diabetes. The tool allows children to share their blood sugar levels and communicate with each other while watching educational and motivational videos. Additionally, this app would allow parents/caregivers to communicate with each other and provide the location of stores that offer diabetes-friendly products. The Ana Alsukary app would additionally provide some information about the nutrition value amount in food and its effect on the blood sugar level. The tool has been built using Android-studio and technically evaluated using unit-testing and integration testing. The usability and ease of use has been evaluated qualitatively through a focus group where nine members participated. The results showed that the developed tool is convenient and easy to use. Caregivers have indicated that Ana Alsukary can assist several children to understand their diabetes condition and make adjustments with their lifestyle accordingly.

Oral diseases have been described by the World Health Organization (WHO) as the most prevalent diseases globally, affecting some 3.5 billion people. This leads to significant health and economic burdens and can impact the quality of life of affected individuals. Therefore, dentists have a great responsibility to efficiently diagnose and determine the best treatment option. However, some do not have the experience and knowledge to make the right clinical decisions. For this reason, artificial intelligence (AI) techniques, mainly rule-based systems, have been used in dentistry to aid physicians in making faster and more reliable decisions. This scoping review aims to explore and summarize the application of rule-based systems widely employed in dentistry and to evaluate their performance and practical significance. We conducted a scoping review following the methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) on five databases: Web of Science, Scopus, Google Scholar, Saudi Digital Library, and the IEEE Xplore. We searched for literature published in English up to October 2021. Two reviewers evaluated each potentially relevant study for inclusion/exclusion criteria, and any discrepancies were resolved by a third researcher. Of 303 studies, 19 fulfilled this review’s inclusion criteria. We identified two domains based on the methodology used in the included studies: (i) uncertainty management approaches employed in the rule-based system (n = 16) and (ii) integrating machine learning techniques with the rule-based system (n = 5). The vast majority of included publications used fuzzy logic to manage uncertainty (n = 11). A hybrid fuzzy rule-based system and neural network achieved the highest accuracy of 96%. From a medical perspective, the articles were aimed at diagnosis (n = 11), treatment (n = 3), and both diagnosis and treatment (n = 4), while less attention was paid to detection and classification (n = 1). The review also found that periodontology was the most commonly addressed specialty. In an analysis of the current literature, rule-based systems were found reliable to assist dental practitioners in decision-making. Clinical decision-making involves a high level of uncertainty, which explains the tendency to use fuzzy logic in rule-based systems. These systems can also be used as educational tools primarily for both dental interns and less experienced general dentists to aid in making reliable decisions.

Background and Objectives: In this study, we assessed the potential impact of employee empowerment on health care workers' performance during the novel coronavirus SARS-CoV-2 (COVID-19) pandemic. In particular, we aimed to determine the empowerment practices that would have the greatest positive effect on employee performance. Understanding the relationship between performance and empowerment can help health care providers better manage worker stress during any global crisis. This understanding is crucial in guiding policies and interventions aimed at maintaining health care workers' psychological well-being and their overall performance. Methods: This cross-sectional study evaluated the relationship between employee empowerment and performance, determining the best empowerment practices for health care leaders to utilize. Frontline health care workers (n = 100) selected using convenience and snowball sampling completed the survey between March 15 and 31, 2020. This is the period when the pandemic just started to accelerate in Saudi Arabia. We conducted Pearson's correlation analysis to assess whether there was a relationship between performance and health care workers' empowerment practice, and stepwise linear regression analysis to investigate the impact each of these empowerment practices on health care workers' performance. Results: Our results indicate that health care workers' performance can be expected to increase the most through 2 empowerment practices: giving employees the discretion to change work processes and offering performance-based rewards ( R 2 = 0.301, P < .05). Conclusion: Our findings suggest that health care leaders must invest in these 2 practices to better equip frontline health care workers. During a global crisis, additional discretion granted to employees helps reduce their anxiety and burnout and hence empowers them with the flexibility to adapt to unforeseen circumstances and improve the quality of their interactions with health service recipients.

BACKGROUND:Designing a health promotion campaign is never an easy task, especially during a pandemic of a highly infectious disease such as COVID-19. In Saudi Arabia, many attempts have been made to raise public awareness about COVID-19 infection and precautionary health measures. However, most of the health information delivered through the national dashboard and the COVID-19 awareness campaigns are generic and do not necessarily make the impact needed to be seen on individuals' behavior. Health messages need to be applicable and reverent to the individual in the audience. OBJECTIVE:In light of Fogg-Behavior model, this research aims to build and validate a behavior-change-based messaging campaign to promote precautionary health behavior in individuals during the COVID-19 pandemic. Intervention messages can then be targeted appropriately during the pandemic. METHODS:An initial library of 32 text-based and video-based messages were developed and validated based on Fogg behavior model for behavior change. Based on this model, three groups of messages were created to reflect the model's three theoretical concepts of motivation, ability and triggers. Each group of messages is designed to target different segment of the audience. The content of the messages was developed based on resources from the World Health Organization and the Ministry of Health in Saudi Arabia. The validity of this content was evaluated by domain experts through the content validity index. RESULTS:Fogg-Behavior Model was used to segment the audience into three different groups based on their perceived ability and motivation. The three groups of messages designed for those groups were found relevant to Fogg theoretical concepts. Thirteen professional health care workers (n = 13) evaluated the content of the message libraries in Arabic and English. Thirty-two messages were found to have acceptable content validity (I-CVI = 0.87). CONCLUSIONS:This research introduced Fogg Behavior Model as a behavior change model to develop targeted messages for three groups of the audience based on their motivation and ability level toward maintaining precautionary behavior during the pandemic. This targeted awareness messaging campaign can be utilized by health authorities to raise individuals' awareness about the precautionary measures that should be taken, maintain these measures and hence help in reducing the number of positive cases in the city of Jeddah.

Littering contributes significantly to environmental pollution. Previous studies have noted that children are more likely to litter than adults. This target age group can be easily reached through mobile applications and games. Therefore, this study aims to investigate the effect of a gamified application in raising awareness on the effect of littering in the environment. We developed a gamified app, called DoItRight to promote an environment friendly behavior and improve the littering behavior of children. The DoItRight app is in Arabic language and targets children between 5 and 13 years old. It is a gamified application that enables kids to learn the importance of picking up litters and dropping it in trash cans. The app was evaluated using the System Usability Scale (SUS) standardized instrument which was administered on the target audience. The results of the evaluation showed that the DoItRight app has an SUS score of 93.25 which represents an A+ grade and a percentile range of 96 to 100. This indicates that the DoItRight app is technically usable and can potentially serve the purpose of increasing kids' awareness about the downsides of littering on the environment.

The visualization of objects of an abstract nature has always been a challenge for chemistry learners. Thus, augmented reality (AR) and virtual reality (VR) have been heavily invested in as immersive learning methods for these concepts. This study targets the segment of the chemistry curriculum involving the chemical elements of the periodic table. For this purpose, we developed the AR educational tool called MicroWorld. This Arabic educational AR app was developed in unity with Vuforia SDK. Using MicroWorld, students can visualize chemical elements microstructures in 3D, see 3D models of the elements in their substantial forms, and combine two chemical elements to see how certain chemical compounds can be formed. In this work, MicroWorld's usability was evaluated by junior high school students and chemistry teachers using the Arabic system usability scale (A-SUS). The A-SUS average score was 71.5 for junior high school students, while the scale for teachers reached 76. This research aims to design, develop, and evaluate the AR app, MicroWorld. This app was built and evaluated through the lens of the design science research paradigm.

Designing a health promotion campaign is never an easy task, especially during a pandemic of a highly infectious disease, such as Covid-19. In Saudi Arabia, many attempts have been made toward raising the public awareness about Covid-19 infection-level and its precautionary health measures that have to be taken. Although this is useful, most of the health information delivered through the national dashboard and the awareness campaign are very generic and not necessarily make the impact we like to see on individuals’ behavior. The objective of this study is to build and validate a customized awareness campaign to promote precautionary health behavior during the COVID-19 pandemic. The customization is realized by utilizing a geospatial artificial intelligence technique called Space-Time Cube (STC) technique. This research has been conducted in two sequential phases. In the first phase, an initial library of thirty-two messages was developed and validated to promote precautionary messages during the COVID-19 pandemic. This phase was guided by the Fogg Behavior Model (FBM) for behavior change. In phase 2, we applied STC as a Geospatial Artificial Intelligence technique to create a local map for one city representing three different profiles for the city districts. The model was built using COVID-19 clinical data. Thirty-two messages were developed based on resources from the World Health Organization and the Ministry of Health in Saudi Arabia. The enumerated content validity of the messages was established through the utilization of Content Validity Index (CVI). Thirty-two messages were found to have acceptable content validity (I-CVI=.87). The geospatial intelligence technique that we used showed three profiles for the districts of Jeddah city: one for high infection, another for moderate infection, and the third for low infection. Combining the results from the first and second phases, a customized awareness campaign was created. This awareness campaign would be used to educate the public regarding the precautionary health behaviors that should be taken, and hence help in reducing the number of positive cases in the city of Jeddah. This research delineates the two main phases to developing a health awareness messaging campaign. The messaging campaign, grounded in FBM, was customized by utilizing Geospatial Artificial Intelligence to create a local map with three district profiles: high-infection, moderate-infection, and low-infection. Locals of each district will be targeted by the campaign based on the level of infection in their district as well as other shared characteristics. Customizing health messages is very prominent in health communication research. This research provides a legitimate approach to customize health messages during the pandemic of COVID-19.

Background Presently, dietary management approaches are mostly oriented toward using calorie-counting and diet-tracking tools that draw our attention away from the nutritional value of our food. To improve individuals’ dietary behavior, primarily that of people with type 2 diabetes, a simple technique is needed to increase their understanding of the nutritional content of their food. Objective This study aimed to design, develop, and evaluate a customized nutrient-profiling tool called EasyNutrition. EasyNutrition was built to introduce the new concept of nutrient profiling by applying the Intelligent Nutrition Engine, an algorithm that we developed for ranking different food recipes based on their nutritional value. This study also aimed to investigate the efficacy of EasyNutrition in lowering glycated hemoglobin (HbA1c) levels and improving dietary habits among people with type 2 diabetes. Methods We evaluated the utility of EasyNutrition using design science research in three sequential stages. This paper has elaborated on the third stage to investigate the efficacy of EasyNutrition in managing type 2 diabetes. A quasi-experimental study was conducted in a diabetes treatment center (n=28). The intervention group utilized EasyNutrition over 3 months, whereas participants in the control group utilized the standard of care provided by the center. Dietary habits and HbA1c levels were measured to capture any change before and after experimenting with EasyNutrition. Results The intervention group (n=9) exhibited a statistically significant change between the pre- and postexposure results of their HbA1c (t9=2.427; P=.04). Their HbA1c dropped from 8.13 to 6.72. This provided preliminary evidence of the efficacy of using a customized nutrient-profiling app in reducing HbA1c for people with type 2 diabetes. Conclusions This study adds to the evidence base that a nutrient-profiling strategy may be a modern adjunct to diabetes dietary management. In conjunction with reliable dietary education provided by a registered dietician, EasyNutrition may have some beneficial effects to improve the dietary habits of people with type 2 diabetes.

One of the main concerns for online shopping websites is to provide efficient and customized recommendations to a very large number of users based on their preferences. Collaborative filtering (CF) is the most famous type of recommender system method to provide personalized recommendations to users. CF generates recommendations by identifying clusters of similar users or items from the user-item rating matrix. This cluster of similar users or items is generally identified by using some similarity measurement method. Among numerous proposed similarity measure methods by researchers, the Pearson correlation coefficient (PCC) is a commonly used similarity measure method for CF-based recommender systems. The standard PCC suffers some inherent limitations and ignores user rating preference behavior (RPB). Typically, users have different RPB, where some users may give the same rating to various items without liking the items and some users may tend to give average rating albeit liking the items. Traditional similarity measure methods (including PCC) do not consider this rating pattern of users. In this article, we present a novel similarity measure method to consider user RPB while calculating similarity among users. The proposed similarity measure method state user RPB as a function of user average rating value, and variance or standard deviation. The user RPB is then combined with an improved model of standard PCC to form an improved similarity measure method for CF-based recommender systems. The proposed similarity measure is named as improved PCC weighted with RPB (IPWR). The qualitative and quantitative analysis of the IPWR similarity measure method is performed using five state-of-the-art datasets (i.e. Epinions, MovieLens-100K, MovieLens-1M, CiaoDVD, and MovieTweetings). The IPWR similarity measure method performs better than state-of-the-art similarity measure methods in terms of mean absolute error (MAE), root mean square error (RMSE), precision, recall, and F-measure.

Mitochondria are highly dynamic cellular organelles with the ability to change size, shape, and position over the course of a few seconds. Mitochondrial organelle movement refers to the problem of finding fission and fusion and generates energy for the cell. In this paper, we proposed a deep learning method [mitochondrial organelle movement classification (MOMC)] for mitochondrial movement classification using a convolutional neural network. We present a three-step feature description strategy, such as local descriptions, which is first extracted via the GoogLeNet, followed by the production of mid-level features by ResNet-50 and global descriptor features by Inception-V3 model and final classification of the position of mitochondrial organelle movement. Our method consists of a deep classification network, MOMC for gathering the organelle position, and a verification network for classification accuracy by removing false positives. Using machine learning methods, logistic regression (LR), support vector machine (SVM), and convolutional neural networks (CNNs), we found that the CNN better classified the shape of mitochondrial organelles (fission and fusion). Employing 24 types (position) of images, a convolutional neural network was trained to identify mitochondrial organelle movement with 96.32% accuracy. This enabled the discovery of position, further advancing the clinical utility of human mitochondrial organelles.

Assistive technology (AT) involvement in therapeutic treatment has provided simple and efficient healthcare solutions to people. Within a short span of time, mobile health (mHealth) has grown rapidly for assisting people living with a chronic disorder. This research paper presents the comprehensive study to identify and review existing mHealth dementia applications (apps), and also synthesize the evidence of using these applications in assisting people with dementia including Alzheimer's disease (AD) and their caregivers. Six electronic databases searched with the purpose of finding literature-based evidence. The search yielded 2818 research articles, with 29 meeting quantified inclusion and exclusion criteria. Six groups and their associated sub-groups emerged from the literature. The main groups are (1) activities of daily living (ADL) based cognitive training, (2) monitoring, (3) dementia screening, (4) reminiscence and socialization, (5) tracking, and (6) caregiver support. Moreover, two commercial mobile application stores i.e., Apple App Store (iOS) and Google Play Store (Android) explored with the intention of identifying the advantages and disadvantages of existing commercially available dementia and AD healthcare apps. From 678 apps, a total of 38 mobile apps qualified as per defined exclusion and inclusion criteria. The shortlisted commercial apps generally targeted different aspects of dementia as identified in research articles. This comprehensive study determined the feasibility of using mobile Health based applications for dementia including AD individuals and their caregivers regardless of limited available research, and these apps have capability to incorporate a variety of strategies and resources to dementia community care.

Healthy Eating is a two-part system that should strike a balance between food quality and food quantity. In this study, we have designed, developed, and evaluated a nutrition app called, Easy Nutrition to highlight the nutritional value/quality of the food we eat. We introduced the novel concept of Nuval rather than old concepts such as calorie counting. In this context, Easy Nutrition presents the food nutrition in a simple, easy to understand manner. Easy Nutrition also tackles the cultural differences by suggesting recipes tailored to users’ food preferences. This paper delineates the build and evaluate phase of Easy Nutrition. Easy Nutrition has been evaluated from a sociotechnical perspective in for its of utility and quality. We conducted a cross-sectional study on Amazon Mechanical Turk platform to evaluate Easy Nutrition on a wide population. The results show that Easy Nutrition demonstrates a fairly high level of usability (SUS = 69.1), attractiveness (mean = 1.59), and hedonic and pragmatic quality.

Diet-related chronic diseases are on the rise. Current dietary management approaches are mostly calorie-counter tools that draw our attention away from the nutritional quality of our food choices. To improve consumers' dietary behavior, we need a simple technique to educate them about nutrition and increase their understanding of the nutritional quality of their food. This study aims to design a dietary tool to promote a nutrient-dense diet. To this end, we applied the concept of Nutrient Profiling to classify food recipes based on their nutritional quality, by developing the Intelligent Nutrition Engine. This engine undergirds our mobile-based application, Easy Nutrition, which was designed to enable users to find food recipes and understand their nutritional quality. To evaluate the usability and understandability of our approach, we piloted the prototype of Easy Nutrition on 24 consumers. The results indicate that our approach provides a sustainable avenue to help consumers manage their diets.

Wearable Technologies continue to dramatically change healthcare system in various ways. The proliferation of these wearable technologies used in healthcare has made the emerging discipline confusing to understand. To better understand the rapid, fast-moving change, we propose a taxonomy to classify wearable technologies in terms of three major dimensions: application, form, and functionality. This taxonomy is evaluated by conducting both literate and market mapping. By doing so, we were able to classify a number of existing wearable technologies in light of the taxonomy dimensions. This DSR project concludes with some practical implications as design principles.