RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids.

Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain.

A comprehensive secure system enabling healthcare 5.0 using federated learning, intrusion detection and blockchain.

Recently, the use of the Internet of Medical Things (IoMT) has gained popularity across various sections of the health sector. The historical security risks of IoMT devices themselves and the data flowing from them are major concerns. Deploying many devices, sensors, services, and networks that connect the IoMT systems is gaining popularity. This study focuses on identifying the use of blockchain in innovative healthcare units empowered by federated learning. A collective use of blockchain with intrusion detection management (IDM) is beneficial to detect and prevent malicious activity across the storage nodes. Data accumulated at a centralized storage node is analyzed with the help of machine learning algorithms to diagnose disease and allow appropriate medication to be prescribed by a medical healthcare professional. The model proposed in this study focuses on the effective use of such models for healthcare monitoring. The amalgamation of federated learning and the proposed model makes it possible to reach 93.89 percent accuracy for disease analysis and addiction. Further, intrusion detection ensures a success rate of 97.13 percent in this study.

Evaluating the effectiveness of the Ministry of Health restriction policy on seasonal antibiotic consumption trends in Saudi Arabia, 2016-2020.

Background: Understanding antibiotic consumption patterns over time is essential to optimize prescribing practices and minimizing antimicrobial resistance. This study aimed to determine whether the antibiotics restriction policy launched by the Saudi Ministry of Health in April 2018 has impacted antibiotic use by assessing changes and seasonal variations following policy enforcement. Methods: Quarterly sales data of J01 antibacterial for systemic use in standard units were obtained from the IQVIA-MIDAS database, spanning from the first quarter of 2016 to the last quarter of 2020. Antibiotics consumption was measured in defined daily doses per 1,000 inhabitant per day- in a quarter (DDDdq). A comparative analysis of antibiotic consumption pre- and post-policy periods introduction was conducted by computing the average consumption values for each period. Statistical comparison of the mean differences between the two periods were then made using independent samples t-test, Mann-Whitney U Test where needed. Time series analysis was employed to estimate the projected antibiotic consumption in the post-policy period if the restriction policy had not been implemented, which was then compared to actual consumption values to evaluate the effectiveness of the restriction policy. Results: During the pre-policy, there were seasonal trends of the total and oral antibiotic consumption through quarters, with higher consumption observed in the first and fourth quarters. In contrast, parenteral antibiotic consumption did not appear to follow a clear seasonal pattern. Following the restriction policy, there was a significant reduction in total and oral antibiotic use, with mean reductions of -96.9 DDDdq (p-value = 0.002) and -98 DDDdq (p-value = 0.002), respectively. Conversely, a significant increase in parenteral antibiotic consumption was observed with a mean increase of +1.4 DDDdq (p-value < 0.0001). The comparison between the forecasted and actual models showed that the actual antibiotics consumption for total, oral, and parenteral were lower than the corresponding forecasted values by 30%, 31%, and 34%, respectively. Conclusion: Overall, our analysis of antibiotics consumption from 2016 to 2020 displays great success for the policy implemented by the Saudi Ministry of Health in significantly reducing the total and oral use of antibiotics. However, future studies are needed to explore the increased consumption of the parenteral antibiotics as well as the persistent high consumption patterns during the fall and winter months even after the implementation of the restriction policy.

Knowledge, perceptions, and readiness of telepharmacy among community pharmacists.

Telepharmacy is a practical part of telemedicine that refers to providing pharmaceutical services within the scope of the pharmacist's obligations while maintaining a temporal and spatial distance between patients, users of health services, and healthcare professionals. The present study was a cross-sectional study conducted among community pharmacists in Saudi Arabia between March and May 2022 to assess their knowledge, perceptions, and readiness for telepharmacy. The survey was filled out by 404 respondents. The majority of respondents were male (59.90%) and the age of more than half of them was between 30 and 39 years old (54.46%). Most participants worked in urban areas (83.66%), and 42.57% had less than five years of experience in a pharmacy. Most participants agreed that telepharmacy is available in Saudi Arabia (82.67%). Approximately 70% of pharmacists felt that telepharmacy promotes patient medication adherence, and 77.72% agreed that telepharmacy increases patient access to pharmaceuticals in rural areas. More than 72% of pharmacists said they would work on telepharmacy initiatives in rural areas for free, and 74.26% said they would work outside of usual working hours if necessary. In the future, this research could aid in adopting full-fledged telepharmacy pharmaceutical care services in Saudi Arabia. It could also help academic initiatives by allowing telepharmacy practice models to be included as a topic course in the curriculum to prepare future pharmacists to deliver telepharmacy services.

COVID-19 advising application development for Apple devices (iOS).

One of humanity's most devastating health crises was COVID-19. Billions of people suffered during this pandemic. In comparison with previous global pandemics that have been faced by the world before, societies were more accurate with the technical support system during this natural disaster. The intersection of data from healthcare units and the analysis of this data into various sophisticated systems were critical factors. Different healthcare units have taken special consideration to advance technical inputs to fight against such situations. The field of natural language processing (NLP) has dramatically supported this. Despite the primitive methods for monitoring the bio-metric factors of a person, the use of cognitive science has emerged as one of the most critical features during this pandemic era. One of the essential features is the potential to understand the data based on various texts and user inputs. The deployment of various NLP systems is one of the most challenging factors in handling the bulk amount of data flowing from multiple sources. This study focused on developing a powerful application to advise patients suffering from ailments related to COVID-19. The use of NLP refers to facilitating a user to identify the present critical situation and make necessary decisions while getting infected. This article also summarises the challenges associated with NLP and its usage for future NLP-based applications focusing on healthcare units. There are a couple of applications that reside for android-based systems as well as web-based chat-bot systems. In terms of security and safety, application development for iOS is more advanced. This study also explains the block meant of an application for advising COVID-19 infection. A natural language processing powered application for an iOS operating system is indeed one of its kind, which will help people who need to advise proper guidance. The article also portrays NLP-based application development for healthcare problems associated with personal reporting systems.

Knowledge, Perceptions, and Readiness of Telepharmacy among Hospital Pharmacists in Saudi Arabia.

Telepharmacy is a technology-based service that provides promoted services such as counseling, medication administration and compounding, drug therapy monitoring, and prescription review. It is unclear whether hospital pharmacists possess the necessary knowledge, attitudes, and willingness to practice telepharmacy. The current study sought to investigate Saudi Arabian hospital pharmacists' understanding, attitudes, and level of preparedness for telepharmacy services. A total of 411 pharmacists responded to the survey. Only 43.33% of the respondents agreed that telepharmacy is available in Saudi Arabia and 36.67% of the respondents agreed that patients in rural areas can have more medication access and information via telepharmacy. Only 29.33% of pharmacists agreed that telepharmacy improves patient medication adherence, and about 34.00% of the pharmacists agreed that telepharmacy saves patients money and time by eliminating the need for them to travel to healthcare facilities. This research found that hospital pharmacists were unsure of their level of knowledge, their attitude toward telepharmacy, and their willingness to incorporate it into their future pharmacy practices. To ensure that tomorrow's pharmacists have the skills they need to provide telepharmacy services, telepharmacy practice models must be incorporated into the educational programs that prepare them.

Developing a blockchain-based digitally secured model for the educational sector in Saudi Arabia toward digital transformation.

New universities and educational organizations are increasing in Saudi Arabia with the increase in the need for high-quality education. This increased the need for a fast transformation to digitise the educational system in Saudi Arabia, which is one of the important pillars of the Saudi Vision 2030. The students who study in these organizations suffer the verification of academic records and other educational documents. Students who want to study at universities abroad also face the challenge of academic records and certificates verification. A secure, fast, and transparent model is required in the education sector in order to verify academic certificates issued by various educational organizations. Blockchain technology can be used with high data security to empower the educational sector of Saudi Arabia in the digital transformation and to help the educational organizations in verifying academic documents. In order to avoid any document fraud and forgery, along with the ease of verification of academic records and educational documents for the students. This research focuses on developing a model which will be helpful in achieving digital transformation in academic document verification by blockchain technology.

Do crashes happen more frequently at sunset in Ramadan than the rest of the year?

Objectives: Because the epidemiology of road traffic injuries (RTIs) can differ in time due to differences in traffic dynamics or behaviors, this paper aims to examine whether RTIs are more likely to occur at sunset in Ramadan than in other months in KSA. Methods: A nationwide cross-sectional study of all RTIs recorded in the Saudi Red Crescent Authority database. Cases were those who sought emergency care following any RTI in 2021. Differences in counts of RTIs between Ramadan and other months were compared using Chi-2 tests. A logistic regression model was constructed to evaluate the association between Ramadan and the likelihood of sunset RTIs. Results: The total number of RTIs was 112,188, of which 9922 (8.8%) occurred in Ramadan. Higher percentages of RTIs during Ramadan as compared to other months were observed among males (82.2% vs. 79.6%; P < .01) and non-Saudis (42.7% vs. 38.9%; P < .01). Interaction effects between Ramadan and region were significant in the regression model (P < .01). RTIs in Ramadan were almost two times more likely to occur at sunset than in other months in the Northern Borders (OR = 2.14; 95% CI:1.44-3.17), while a negative association was found in Bahah region (OR = 0.67; 95% CI: 0.44-0.99). Conclusion: RTI burden is higher in Ramadan than in other months, and that varies by region. Further investment in prevention strategies, such as increased enforcement and awareness programs, is warranted in regions with a higher RTIs burden to improve traffic safety and population health.

An extended DeLone and McLean's model to determine the success factors of e-learning platform.

Due to the COVID-19 pandemic, all Saudi universities have adopted e-learning systems to ensure that educational activities continue. Shaqra University adopted a platform called the Shaqra University e-learning platform. This study aimed to identify the factors contributing to the success of that platform in Shaqra University, based on students' responses. This research has proposed an extension of well-known DeLone and McLean's Information Systems Success (D&M ISS) model to check and validate the success factors of the Shaqra University platform. The questionnaire was adopted in this study to collect data from students currently enrolled at Shaqra University. One thousand online links to the questionnaire were randomly distributed among current students enrolled in Shaqra University. The results revealed that the instrument adopted in this study was valid and reliable. Also, the results showed that the model was a good fit for the Saudi context. The proposed factors of instructor's quality, learner quality, and perceived usefulness positively impacted the e-learning platform. On the other hand, the factors information quality, system quality and service quality had no positive impact on the use of the e-learning platform.

Impact analysis of keyword extraction using contextual word embedding.

A document's keywords provide high-level descriptions of the content that summarize the document's central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document's content that can reflect the authors' main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors' provided keywords. The average similarity of our approach with author-assigned keywords is 51%.

Assistive technology acceptance for visually impaired individuals: a case study of students in Saudi Arabia.

Assistive technology (AT) helps students who suffer from visual impairments to achieve their study goals; however, AT's adoption in Saudi universities is not yet explored. This paper adopts and then extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to incorporate factors influencing the AT's acceptance based on a designed survey. The survey data was analyzed using Structural Equational Modelling (SEM) with the Partial Least Squares (PLS) technique. The results showed that the factors influencing technology acceptance in this context differed from those previously found to influence acceptance in other contexts. The differences were further studied using post-interview, which shows that the differences are related to limited awareness of visual disability and AT and psychological sensitivity of disabled users in Saudi culture. Moreover, this study provides a list of recommendations for overcoming barriers that limit the acceptance of assistive techniques by Saudi students with visual disabilities. This work's results provide recommendations for the Saudi government and administrators concerning access to assistive technology in universities and facilitate access to other technologies and other contexts.

Cultural acceptance of robotic telestroke medicine among patients and healthcare providers in Saudi Arabia. Results of a pilot study.

OBJECTIVE: To determine the degree of satisfaction and acceptance of stroke patients, their relatives, and healthcare providers toward using telestroke technology in Saudi Arabia. METHODS: A cross-sectional study was conducted between October and December 2012 at King Abdulaziz Medical City, Ministry of National Guard Affairs, Riyadh, Saudi Arabia. The Remote Presence Robot (RPR), the RP-7i (FDA- cleared) provided by InTouch Health was used in the study. Patients and their relatives were informed that the physician would appear through a screen on top of a robotic device, as part of their clinical care. Stroke patients admitted through the emergency department, and their relatives, as well as healthcare providers completed a self-administered satisfaction questionnaire following the telestroke consultation sessions. RESULTS: Fifty participants completed the questionnaire. Most subjects agreed that the remote consultant interview was useful and that the audiovisual component of the intervention was of high quality; 98% agreed that they did not feel shy or embarrassed during the remote interview, were able to understand the instruction of the consultant, and recommended its use in stroke management. Furthermore, 92% agreed or strongly agreed that the use of this technology can efficiently replace the physical presence of a neurologist. CONCLUSION: Results suggest that the use of telestroke medicine is culturally acceptable among stroke patients and their families in Saudi Arabia and favorably received by healthcare providers.