Supporting pilgrims in navigating densely crowded religious sites

Every year, a large number of pilgrims visit Mecca in Saudi Arabia. During their stay, they perform a number of rites in and around the city. Due to large crowds forming on particular days, incidents frequently occur, where people are injured, sometimes fatally. In this article, we investigate the question whether the use of dynamic public signage can help people to navigate in this setting. We present an analysis of the situation in Mecca based on a literature review and on interviews with pilgrims, and then introduce a prototypical dynamic sign system aimed at supporting pilgrims in navigating one particular area. To evaluate the system, we conducted a user study in a realistic setting, and the results suggest that dynamic signage may be a feasible option in this setting. The design and evaluation of our prototype also led to a number of insights regarding the design of such systems. We discuss difficulties encountered during the design process and the evaluation and reflect on implications for the design and evaluation of systems supporting navigation for large crowds.     

Right of way

Pedestrian models typically represent interactions between agents in a symmetric fashion. In general, these symmetric relationships are valid for a large number of crowd simulation scenarios. However, there are many cases in which symmetric responses between agents are inappropriate, leading to unrealistic behavior or undesirable simulation artifacts. We present a novel formulation, called right of way, which provides a well-disciplined mechanism for modeling asymmetric relationships between pedestrians. Right of way is a general principle, which can be applied to different types of pedestrian models. We illustrate this by applying right of way to three different pedestrian models (two based on social forces and one based on velocity obstacles) and show its impact in multiple scenarios. Particularly, we show how it enables simulation of the complex relationships exhibited by pilgrims performing the Islamic religious ritual, the Tawaf.     

Desertification Detection in Makkah Region based on Aerial Images Classification

Desertification has become a global threat and caused a crisis, especially in Middle Eastern countries, such as Saudi Arabia. Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification. The vegetation area in Makkah has been damaged because of desertification through wind, floods, overgrazing, and global climate change. The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area. In this paper, we propose an automatic desertification detection system based on Deep Learning techniques. Aerial images are classified using Convolutional Neural Networks (CNN) to detect land state variation in real-time. CNNs have been widely used for computer vision applications, such as image classification, image segmentation, and quality enhancement. The proposed CNN model was trained and evaluated on the Arial Image Dataset (AID). Compared to state-of-the-art methods, the proposed model has better performance while being suitable for embedded implementation. It has achieved high efficiency with 96.47% accuracy. In light of the current research, we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.     

The influence of attitudes on personal computer utilization among knowledge workers: the case of Saudi Arabia

Since the introduction of personal computers (PCs) in the early 1980s, Saudi Arabia has made major investments in PCs to match its rapidly growing economy. As a result, the PC business has become one of the fastest growing sectors in the Kingdom of Saudi Arabia.Our paper reports on the results of a study which investigates the relationships between end-users' attitudes and PC utilization among knowledge workers in the context of Saudi Arabia. To gain a better understanding of the factors that influence the use of PCs, we adopted Triandis' theory which suggests that behavior is determined by attitudes, social norms, habits and expected consequences of behavior. Our study is based on previous efforts to test the theory's validity in Saudi Arabia.Our results suggest that PC utilization is determined by individual attitudes, personal characteristics, such as PC experience, facilitating conditions, such as PC access and social factors. We also observed that respondents to our questionnaire differ in the level of importance they attribute to the factors hypothesized as influencing PC utilization compared to Canadian respondents in a previous study.     

Prediction of COVID-19 Pandemic Spread in Kingdom of Saudi Arabia

A significant increase in the number of coronavirus cases can easily be noticed in most of the countries around the world. Inspite of the consistent preventive initiatives being taken to contain the spread of this virus, the unabated increase in the cases is both alarming and intriguing. The role of mathematical models in predicting and estimating the spread of the virus, and identifying various preventive factors dependencies has been found important and effective in most of the previous pandemics like Severe Acute Respiratory Syndrome (SARS) 2003. In this research work, authors have proposed the Susceptible-Infectected-Removed (SIR) model variation in order to forecast the pattern of coronavirus disease (COVID-19) spread for the upcoming eight weeks in perspective of Saudi Arabia. The study has been performed by using SIR model with a proposed simplification using average progression for further estimation of β and γ values for better curve fittings ratios. The predictive results of this study clearly show that under the current public health interventions, there will be an increase in the COVID-19 cases in Saudi Arabia in the next four weeks. Hence, a set of strong health primitives and precautionary measures are recommended in order to avoid and prevent the further spread of COVID-19 in Saudi Arabia.     

User oriented techniques to support interaction and decision making with large educational databases

Information Technology is developing rapidly and providing policy/decision makers with large amounts of information that require processing and analysis. Decision support systems (DSS) aim to provide tools that not only help such analyses, but enable the decision maker to experiment and simulate the effects of different policies and selection strategies. The specific context of this research, set in Saudi Arabia, is administrative decision making using large educational databases.     

Exploring e-mentoring: co-designing & un-platforming

ABSTRACT

The underrepresentation of women in the STEM workforce is a global issue. In Saudi Arabia, women constitute 48% of undergraduates in STEM, but their participation in STEM careers remains particularly low. We explored the current state of e-mentoring, and the potential for co-designing to enhance e-mentoring experiences for youth in the Saudi context. We report on how we sought to understand the opportunities, barriers and requirements of e-mentoring for young women in Saudi Arabia through (1) the application of a traditional programme of STEM e-mentoring, (2) workshops on the redesign of e-mentoring, and (3) a co-design activity as the first phase of an alternative e-mentoring process. The study demonstrated that the traits of the participants’ generation had a major effect on the findings; more so than cultural norms. This means that the findings have wider implications than just within the Saudi context. We identify a number of recommendations for designing e-mentoring programmes for young women and teenagers, including un-platforming traditional approaches to e-mentoring and the inclusion of co-design activities as a first step in the e-mentoring process.     

Factors affecting the adoption of information and communication technology in small and medium enterprises: a perspective from rural Saudi Arabia

ABSTRACT

Small and medium enterprises (SMEs) can play an important role in the national economy of developing countries. The adoption of information and communication technology (ICT) has enabled local SMEs to participate in the international market. However, little research has addressed issues related to SMEs adopting ICTs, especially in rural areas of Middle Eastern developing countries such as Saudi Arabia. Using the extended technology-organization-environment framework with personal innovativeness, this study examined the factors that influence the adoption of ICTs among SMEs in rural areas of Saudi Arabia. The study found that relative advantages, top management support, culture, regulatory environment, owner/manager innovativeness and ICT knowledge had a significant relationship with ICT adoption among SMEs in Saudi Arabia, whereas compatibility, complexity and a competitive environment had no significant relationship with ICT adoption. The findings of this study will potentially help SME managers/owners and the Saudi government in the successful adoption and diffusion of ICT in SMEs located in rural areas in Saudi Arabia.     

The accessibility of Saudi Arabia government Web sites: an exploratory study

This study is intended to provide an exploratory evaluation of Saudi Arabia government Web sites based on the Web Accessibility Guidelines 2.0 provided by the W3C. The results indicate that the Saudi government Web sites have made many of the accessibility mistakes as predicted. In the light of the study findings, this paper will present some recommendations for improving Saudi government Web sites, as well as discuss future implications.     

Computer aided design of post-tensioned concrete reservoirs

A computer method based on the classical shell and plate theories is presented for the elastic analysis of cylindrical water tanks subjected to axisymmetrical loading and post-tensioning loads. A spherical dome or circular plate roof, cylindrical container, top and bottom ring beams together with a circular plate foundation are considered as possible components of a water tank in the flexibility formulation. Classical shell, plate, and ring beam theories are used to obtain flexibility coefficients. A computer program has been developed based on this formulation and successfully used in the design of post-tensioned concrete water tanks with diameters of 80 m built in Saudi Arabia.     

Planetscope Nanosatellites Image Classification Using Machine Learning

To adopt sustainable crop practices in changing climate, understanding the climatic parameters and water requirements with vegetation is crucial on a spatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps are one of the highest resolution data that can transform agricultural practices and management on a large scale. High-resolution PS nanosatellite data was utilized in the current study to monitor agriculture’s spatiotemporal assessment for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVI was utilized to assess the vegetation pattern change in the study area. The current study area has sparse vegetation, and exposed soil exhibits brightness due to low soil moisture, constraining NDVI. Therefore, a machine learning (ML) based Random Forest (RF) classification model was used to compare the vegetation extent and computational cost of NDVI. The RF model has been compared with NDVI in the current investigation. It is one of the most precise classification methods because it can model the complexity of input variables, handle outliers, treat noise effectively, and avoid overfitting. Multinomial Logistic Regression (MLR) was implemented to compare the performance of both NDVI and RF-based classification. RF model provided good accuracy (98%) for all vegetation classes based on user accuracy, producer accuracy, and kappa coefficient.     

Analyzing the Implications of COVID-19 Pandemic through an Intelligent-Computing Technique

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 virus or COVID-19) disease was declared pandemic by the World Health Organization (WHO) on March 11, 2020. COVID-19 has already affected more than 211 nations. In such a bleak scenario, it becomes imperative to analyze and identify those regions in Saudi Arabia that are at high risk. A preemptive study done in the context of predicting the possible COVID-19 hotspots would facilitate in the implementation of prompt and targeted countermeasures against SARS-CoV-2, thus saving many lives. Working towards this intent, the present study adopts a decision making based methodology of simulation named Analytical Hierarchy Process (AHP), a multi criteria decision making approach, for assessing the risk of COVID-19 in different regions of Saudi Arabia. AHP gives the ability to measure the risks numerically. Moreover, numerical assessments are always effective and easy to understand. Hence, this research endeavour employs Fuzzy based computational method of decision making for its empirical analysis. Findings in the proposed paper suggest that Riyadh and Makkah are the most susceptible regions, implying that if sustained and focused preventive measures are not introduced at the right juncture, the two cities could be the worst afflicted with the infection. The results obtained through Fuzzy based computational method of decision making are highly corroborative and would be very useful for categorizing and assessing the current COVID-19 situation in the Kingdom of Saudi Arabia. More specifically, identifying the cities that are likely to be COVID-19 hotspots would help the country’s health and medical fraternity to reinforce intensive containment strategies to counter the ills of the pandemic in such regions.     

SWGMM: a semi-wrapped Gaussian mixture model for clustering of circular–linear data

Finite mixture models are widely used to perform model-based clustering of multivariate data sets. Most of the existing mixture models work with linear data; whereas, real-life applications may involve multivariate data having both circular and linear characteristics. No existing mixture models can accommodate such correlated circular–linear data. In this paper, we consider designing a mixture model for multivariate data having one circular variable. In order to construct a circular–linear joint distribution with proper inclusion of correlation terms, we use the semi-wrapped Gaussian distribution. Further, we construct a mixture model (termed SWGMM) of such joint distributions. This mixture model is capable of approximating the distribution of multi-modal circular–linear data. An unsupervised learning of the mixture parameters is proposed based on expectation maximization method. Clustering is performed using maximum a posteriori criterion. To evaluate the performance of SWGMM, we choose the task of color image segmentation in LCH space. We present comprehensive results and compare SWGMM with existing methods. Our study reveals that the proposed mixture model outperforms the other methods in most cases.     

MODIS satellite data evaluation for sand and dust storm monitoring in Saudi Arabia

ABSTRACT

The impacts of wind-blown desert sand and dust are a major concern of environmental and climate study due to their global extent. This article investigates the sand and dust storms detection in Saudi Arabia using Moderate Resolution Imaging Spectroradiometer (MODIS) data, both from Terra and Aqua satellite systems for the years 2002–2011. Normalized Difference Dust Index (NDDI) is applied for the detection of sand and dust storms whilst MODIS band 31 is applied to discriminate atmospheric sand and dust from that present on the ground. In addition, the data from Meteosat satellite, AERONET station, and meteorological stations are used to validate NDDI-based sand and dust storm events. The results of the study show that NDDI can successfully identify and differentiate sand and dust storms from clouds whilst MODIS band 31 can discriminate aerial and surface sand and dust over Saudi Arabia. The results also show that the multi-source data, that is MODIS, Meteosat, AERONET, and meteorological stations, can be very valuable for tracking sand and dust storm events. As no such attempt in the past has been made in Saudi Arabia, it is envisaged that the results of this study will be helpful in planning remote-sensing data for the climate change study in the region.     

Extensions to emergency vehicle location models

This paper is concerned with extending models for the maximal covering location problem in two ways. First, the usual 0–1 coverage definition is replaced by the probability of covering a demand within the target time. Second, once the locations are determined, the minimum number of vehicles at each location that satisfies the required performance levels is determined. Thus, the problem of identifying the optimal locations of a pre-specified number of emergency medical service stations is addressed by goal programming. The first goal is to locate these stations so that the maximum expected demand can be reached within a pre-specified target time. Then, the second goal is to ensure that any demand arising located within the service area of the station will find at least one vehicle, such as an ambulance, available. Erlang's loss formula is used to identify the arrival rates when it is necessary to add an ambulance in order to maintain the performance level for the availability of ambulances. The model developed has been used to evaluate locations for the Saudi Arabian Red Crescent Society, Riyadh City, Saudi Arabia.     

Online socializing: How does it affect the information seeking behavior and the educational preferences in Saudi Arabia?

There are three major issues, among others, associated with the rapid adoption of information technology in Saudi Arabia. These are the changes in online socializing (through Social Media), the information seeking behavior, and the eLearning developments in the local academic institutions. In this causal exploratory research study the main idea was to find the effect of the changes of the former on the latter two. Saudi Arabia is the geographic scope of the study as the most important and influential country in the region. A pre-tested and moderated questionnaire, administered both on- and offline, was used to gather the relevant data. Findings indicate a shift from the conventional to the online information seeking behaviors and a preference of a blended educational system, both traditional (classroom) and eLearning or similar, despite the deep and dramatic penetration of social media in the country that could lead to the false assumption that the local population, especially young people, would turn their back on the conventional education processes.     

Remote sensing-based geological mapping of the area west of Al Madinah,Saudi Arabia

The paper describes the methodology and results obtained during the geological mapping of the area north-west of the Holy city of Al Madinah Al Munawwarah in Saudi Arabia on the basis of Landsat Thematic Mapper data at a scale of 1:100000. The application of very sophisticated image processing algorithms yielded an image basis. The spectral units displayed were interpreted and then correlated to the lithological units along significant profiles during a field campaign. A thin section study of the rock samples supported the lithological classification.

Generally, the region can be divided into two main geological units, namely the Precambrian basement rocks and the basaltic exIrusive rocks of Harrat AIRahat plateau which is covering the basemenI rocks in the east and the southeast.The basement rocks consist of a wide range of intrusive sedimentary and extrusive series, all metamorphorised with varying intensities.

The lack of vegetation cover and the effective methods used in the remote sensing data interpretation allowed a practically clear distinction between the different lithological units, as verified during the field work. This result testifies to the high value of the application of remote sensing data in Saudi Arabia.     

A 0-1 goal programming model for nurse scheduling

In this study, a computerized nurse-scheduling model is developed. The model is approached through a 0-1 linear goal program. It is adapted to Riyadh Al-Kharj hospital Program (in Saudi Arabia) to improve the current manual-made schedules. The developed model accounts both for hospital objectives and nurses’ preferences, in addition to considering some recommended policies that are displayed in the literature. Hospital objectives include ensuring a continuous service with appropriate nursing skills and staffing size, while avoiding additional costs for unnecessary overtime. Nurses preferences, which are deduced from a survey conducted on-purpose for the sake of this study, include mainly fairness considerations, in terms of ratio of night shifts and weekends off, in addition to avoiding isolated days on and off. The model is implemented in an experimental phase of six-month period using LINGO and is considered to perform reasonably well, based both on some quality criteria displayed in the literature and on the feedback obtained from a second survey, that has been developed to assess the scheduling system performance.     

Human action recognition using an ensemble of body‐part detectors

This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body‐part detectors, followed by grouping of part detections, to perform human identification. Three example‐based body‐part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self‐occlusions through the use of ten sub‐classifiers that detect body parts over a specific range of viewpoints. Each sub‐classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint‐invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state‐of‐the‐art methods.