scholarly journals Satellite imagery to select a sample of rooftops for a PV installation project in Jeddah, Saudi Arabia

2021 ◽  
Vol 2042 (1) ◽  
pp. 012014
Author(s):  
Luke S. Blunden ◽  
Mostafa Y.M. Mahdy ◽  
Abdulsalam S. Alghamdi ◽  
AbuBakr S Bahaj
Keyword(s):  
Neural Network ◽  
Saudi Arabia ◽  
Satellite Imagery ◽  
Small Sample ◽  
Small Scale ◽  
Solar Photovoltaic ◽  
Segmentation Approach ◽  
Stratified Sample ◽  
Roof Area ◽  
The City

Abstract A region-based convolutional neural network image segmentation approach (Mask R-CNN) was applied to identification of flat rooftops from satellite imagery in the city of Jeddah in Saudi Arabia. The model was trained on a small sample of rooftops (202) digitized from a 0.5 m resolution image (covering 0.21 km2) and then was applied to an independent area 4.5 km away. The precision and recall of the model were 0.98 and 0.96 respectively in terms of identifying rooftops in the independent area. A spatially stratified sample of rooftops was drawn from those identified by the model and the median roof area of the sample was not significantly different from the area as a whole. The results, although at a small scale, demonstrate the effectiveness of this approach for selecting buildings with appropriate rooftops for solar photovoltaic (PV) installation, in the context of closely spaced flat-roofed buildings, without requiring cadastral mapping or LIDAR datasets.

scholarly journals EFFECT OF SALES PROMOTION PROGRAMS ON PURCHASING BEHAVIOR AT HYPERMARKETS IN RIYADH, KSA

2020 ◽  
pp. 16-31
Author(s):  
Dr. Iyad A. Al-Nsour
Keyword(s):  
Saudi Arabia ◽  
Key Words ◽  
Sales Promotion ◽  
Marketing Communication ◽  
Purchasing Behavior ◽  
Public And Private ◽  
Price Discounts ◽  
Stratified Sample ◽  
Research Population ◽  
The City

The study aims at determining the effect of sales promotion programs using main four programs - price discounts, free samples, buying vouchers and celebrities - on purchasing behavior of consumers in Saudi Arabia, as well as diagnosing the statistical differences in using the sales promotion programs according to the demographical variables of the consumer. The research population consists of all Saudi and non-Saudi buyers residing in the city of Riyadh reaches 3.874 million people in 2018. The unit sample represents the total number of Saudi and non-Saudi employees working in public and private sectors in the city of Riyadh. The proportional stratified sample is used and the calculated sample size is 387 employees. The study concludes that the sales promotion programs have a positive significant effect on the purchasing behavior of the consumer, and the price discount program is the engine program of purchasing behavior. The study finds that there are statistical differences in the perception of sales promotion programs according to age, education and marital status. Finally, the study recommends a set of implications that enhance the marketing communication uses and some recommendations are presented. KEY WORDS: Sales Promotion, Purchasing Behavior , Hypermarkets , Riyadh , KSA.

scholarly journals Factors Influencing Social Perception of Residential Solar Photovoltaic Systems in Saudi Arabia

2019 ◽  
Vol 11 (19) ◽  
pp. 5259 ◽  
Author(s):  
Khalid Alrashoud ◽  
Koji Tokimatsu
Keyword(s):  
Saudi Arabia ◽  
Solar Energy ◽  
Social Acceptance ◽  
Statistical Significance ◽  
Energy Transition ◽  
Relative Advantage ◽  
Small Scale ◽  
Photovoltaic Systems ◽  
Solar Photovoltaic ◽  
Cross Sectional

Saudi Arabia has taken major steps to shift from an oil-centered to more environmentally-focused economy. One approach made recently is to enable households to possess and generate electricity by using small-scale residential solar photovoltaic systems (RSPSs). However, the number of applications to install this technology in residences is significantly low. Social acceptance of solar energy is essential for a successful energy transition. Hence, the present study aims to examine factors that may potentially motivate or impede individuals from purchasing RSPSs based on the diffusion of innovations theory. A cross-sectional, web-based survey is conducted including 1498 participants from the five main regions of Saudi Arabia. Results revealed a good cognition level in relation to solar energy, where the majority (64–83%) of respondents are aware of the benefits. An overwhelming proportion of the respondents (97%) associate RSPSs with a significantly positive image, with no significant variation in the acceptance or rejection rates among the five areas covered by the survey (p = 0.1). The results also show high statistical significance for the differences between RSPS acceptors and rejecters in all innovation attributes (p < 0.001). However, the perception of relative advantage has a higher correlation with acceptance RSPSs. These perceived advantages were of rather long-term nonfinancial benefits, such as environmental protection against global warming and provision of unlimited power, rather than the revenue related to direct costs benefits. The study also revealed that the installation cost was the most significant barrier to adopting the RSPS, which can be a focus for RSPS dissemination policies.

scholarly journals Res-WGAN: Image Classification for Plant Small-scale Datasets

2020 ◽  
Author(s):  
Mi Jiaqi ◽  
Hao xia ◽  
Yang Si ◽  
Yang Xiande ◽  
Gao Wanlin ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
User Study ◽  
Data Augmentation ◽  
Small Sample Size ◽  
Rare Plants ◽  
Small Sample ◽  
Primary Data ◽  
Small Scale ◽  
Rare Plant

Abstract Background: Artificial identification of rare plants is an important yet challenging problem in plant taxonomy. Although deep learning-based method can accurately predict rare plant category from training samples, accuracy requirements of only few experts are satisfied due to the small sample size. Thus, effective data augmentation is vital to improve the expressive power and robustness of deep learning models, especially for plant small-sample classification tasks. Different from traditional methods, a generative adversarial network (GAN) can mimic the distribution of primary data and produce similar but nonidentical samples. Data augmentation for automated classification of rare plant samples with GAN has not been studied for a long time. Result: In this study, we present a novel GAN model, referred to as residual Wasserstein GAN (Res-WGAN), for extending rare plant dataset efficiently. With a premise of minimized size of training parameters, residual block serves as elementary building block to increase the model’s expression ability. A loss function based on Wasserstein distance with gradient penalty is used to ensure diversity of output. Moreover, we draw on the super-resolution GAN (SRGAN) and consider perceptual loss into the function. Perceptual loss guarantees similarity between generated samples and original samples in high-dimensional features. Benefiting from these improvements, the expanded dataset improves the classification accuracy on the residual neural network (ResNet), a classic convolutional neural network, and restrains the overfitting phenomenon effectively by using transfer learning. More similar experiments are presented in this paper to prove the practicality of the model. Conclusions: Our proposed method produces better diversity and sharpness of generated samples and more accurate classification results than other data augmentation methods. In addition, a user study confirms that it is an ideal alternative strategy for small-scale rare plant identification. Developing a robust and effective small-scale plant classification method to replace expert testimony is highly relevant for agricultural automation development.

Small Scale and Low Latency Oriented Neural Network Physical Unclonable Function and its Evaluation

2020 ◽  
Vol 140 (12) ◽  
pp. 1297-1306
Author(s):  
Shu Takemoto ◽  
Kazuya Shibagaki ◽  
Yusuke Nozaki ◽  
Masaya Yoshikawa
Keyword(s):  
Neural Network ◽  
Small Scale ◽  
Low Latency ◽  
Physical Unclonable Function

The Worrying Trend of Diabetes Mellitus in Saudi Arabia: An Urgent Call to Action

2020 ◽  
Vol 16 (3) ◽  
pp. 204-210 ◽  
Author(s):  
Asirvatham A. Robert ◽  
Mohamed A. Al Dawish
Keyword(s):  
Diabetes Mellitus ◽  
Saudi Arabia ◽  
Present Report ◽  
Adult Population ◽  
Current Trend ◽  
Fold Increase ◽  
Developed Countries ◽  
Evidence Base ◽  
Small Sample ◽  
Cross Sectional

From last few years, the pervasiveness of diabetes mellitus (DM), in Saudi Arabia, is growing at a frightening rate. Overall, one-fourth of the adult population is affected by DM, which is further predicted to rise to more than double by the year 2030. The most alarming is possibly the escalation propensity of diabetes, in recent years, where a nearly ten-fold increase has been witnessed over the past thirty years in Saudi Arabia. However, the number of research arbitrations on the prevalence and incidence of DM is woefully inadequate, as compared to developed countries. Apart from this, most of the existing research data carried out in Saudi Arabia is cross-sectional, with small sample sizes, which most often involve only certain parts of the country. Consequently, the present scenario demands more multidimensional and multisectoral research to strengthen the evidence base and to accumulate greater knowledge as a basis for measures and programmes to confront diabetes and its complications. Thus, the present report makes an attempt to depict the current trend of diabetes as well as intends to put forward essential measures for controlling diabetes in Saudi Arabia.

Towards a Customized¬ Awareness Campaign to Educate the Public about COVID-19: Development and Validation of a Messaging Campaign to Promote Precautionary Behavior Using Geospatial Intelligence in Saudi Arabia (Preprint)

2020 ◽  
Author(s):  
Mayda Alrige ◽  
Hind Bitar Bitar ◽  
Maram Meccawi ◽  
Balakrishnan Mullachery
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Content Validity ◽  
Awareness Campaign ◽  
Artificial Intelligence Technique ◽  
The Public ◽  
High Infection ◽  
Local Map ◽  
The City ◽  
Intelligence Technique

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 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. OBJECTIVE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.

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