Customer Patronage Intentions and Moderating Effect of Customer Mood on Retailscape Elements and Customer Joy: A Study of Grocery Retail Stores in Riyadh

This research aimed to explore the retailscape components and identify the mediation impact of customer mood between retailscape elements and customer joy evidence from Retail Stores in Riyadh. The researcher identifies the research variables based on a critical review of previous literature. The research sample consists of 289 respondents from the population which includes customers of grocery retail stores in Riyadh. SEM by using smart PLS wad conducted as an analysis tool. The research concludes that retailscape elements can influence customer joy and customer patronage. As well as, the results indicate that mood of customer mediate the relationship between customer joy and retailscape. Finally, the researcher recommended that more future research may conduct and address more factors such as behavioral intentions and customer satisfaction. Keywords: Retailscape, customer joy, customer mood, Retail, customer patronage JEL Classifications: M40; M41 DOI: https://doi.org/10.32479/irmm.1118

Character-level word encoding deep learning model for combating cyber threats in phishing URL detection

A cyber threat is generally a malicious activity that damages or steals data, or something that disrupts digital life. Such threats include viruses, security breaches, DoS attacks, and data theft. Phishing is a type of cyber threat whereby the attackers mimic a genuine URL or a webpage and steal user data, 21% fall into the phishing category. The novel approach of using the character-level encoding of URLs is introduced. Unlike word-level encoding, the use of character-level encoding decreases the discrete workspace and can be effective even in an energy-constrained environment. The experimental results of comparisons to other state-of-the-art methods demonstrate that the proposed method achieved 98.12% of true positive instances. Moreover, Conclusions: An experimental evaluation was performed to demonstrate the efficiency, and it was observed that the accuracy reached an all-time high of 98.13%. the experiments prove that the proposed method can operate efficiently even in energy-saving modes of phishing detection systems

The Impact of Social Media Attributes on Purchase Intention in the Saudi Foodservice

This study aims to investigate the impact of social media attributes on customer purchase intention in the Saudi foodservice.  It is a quantitative study based on the use of a questionnaire designed to fulfill the key purpose of this study. A judgmental sampling strategy is used to select the participants due to the wide population size. 380 questionnaires were collected and 357 were analyzed. SPSS.26 was used to analyze the data and to test the study hypotheses. The findings of this study revealed that social media attributes (perceived usefulness, perceived ease of use, critical mass and perceived playfulness) have has a positive impact on purchase intention. Keywords: Purchase Intention, Foodservice, Social Media Attributes, Saudi Arabia JEL Classifications: M40; M41 DOI: https://doi.org/10.32479/irmm.971

The relationship between prosocial voice and the patient safety culture in the Saudi public hospitals

This study examines the direct linkage between pro social voice and patient safety among public hospitals in Saudi Arabia.The researcher employed the quantitative survey design for data collection, where 127 out of 251 healthcare firms were chosen in Saudi Arabia with the following division-70 organizations from the central region and 57 from the western region.The hospitals comprising the sample all operate under the oversight of the Saudi Ministry of health. The researcher distributed 30 questionnaires in each of the 127 Saudi hospitals to staff workers working in the nursing units in both regions. A total of 1793 questionnaires were returned and the rate of response was calculated by dividing the number of returned questionnaires with the total number of participants.The present study made use of regression analysis to examine the relationship between pro social voice and patient safety culture. The findings revealed a positive and significant relationship between the two. Some recommendations for future studies were provided at the end of the study

Radiologic Management of Vascular Malformations’ Interventional, Classification and Diagnosis

This study aimed at analyzing the diverse group of congenital vascular malformations, with respect to their place within the broader classification of vascular anomalies and their pathologic, clinical, and radiologic diagnosis and management. And the study discuss some of the techniques, agents, and approaches used in the interventional treatment of this difficult group of lesions. The researchers are aware and acknowledge that there are several different techniques and agents that can be used to treat these lesions. The techniques and agents described in this article have been used for years by the experts with good results. The aim of this study is to share experience in the management of vascular malformations with these techniques at Jordanian hospitals, and to assess the patient satisfaction levels by the evaluation of the follow-up of patients with vascular malformations treated in the Interventional Radiology Unit from January 2016 to December 2016. Patients were classified according to the hemodynamics of the lesions (high- vs. low-flow)

Horizontal Review on Video Surveillance for Smart Cities: Edge Devices, Applications, Datasets, and Future Trends

The automation strategy of today’s smart cities relies on large IoT (internet of Things) systems that collect big data analytics to gain insights. Although there have been recent reviews in this field, there is a remarkable gap that addresses four sides of the problem. Namely, the application of video surveillance in smart cities, algorithms, datasets, and embedded systems. In this paper, we discuss the latest datasets used, the algorithms used, and the recent advances in embedded systems to form edge vision computing are introduced. Moreover, future trends and challenges are addressed

A generalized laser simulator algorithm for mobile robot path planning with obstacle avoidance

This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment’s borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm

Liver Tumor Segmentation in CT Scans Using Modified SegNet

The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase