American Academic & Scholarly Research Center: AASRC Journal Systems
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Application of Artificial Intelligence (AI) in Records Management and Systems
Get PDFThe study aimed to clarify the concept of artificial intelligence and identify its potential roles in improving records management and systems, highlight its most prominent techniques that contribute to the effectiveness and efficiency of records management processes, identify the challenges and concerns facing the application of artificial intelligence techniques in records management and systems, and seek to find solutions and proposals. appropriate to address these fears and challenges. The study used the inductive approach by reviewing relevant scientific sources. The study reached several results, the most important of which is that artificial intelligence techniques such as machine learning, natural language processing, and image recognition can contribute significantly to improving the efficiency and effectiveness of records management in various fields. These technologies enable records to be classified more accurately, speed up search and retrieval processes, and analyze data in greater depth. And access to the most prominent challenges and concerns that must be addressed when applying artificial intelligence in records management. The most prominent of these are issues of privacy, security, and legal liability. The study presented many recommendations, the most important of which are: the need to provide an appropriate infrastructure to integrate artificial intelligence technology with records management, with the need to adopt innovative systems based on artificial intelligence to improve records management, and records management specialists must keep pace with developments in the field of artificial intelligence, with the recommendation to conduct more In-depth scientific studies on the application of artificial intelligence in records management.https://doi.org/10.24897/acn.64.68.20251224001
Impact of administrative practices on Job performance in Alhada Military Hospital
Get PDFAim: The current study's purpose was to find out the impact of administrative practices on job performance in Alhada Military Hospital. Methodology: A quantitative descriptive design was used in the study. 369 participants employed in Alhada Military Hospital were recruited based on convenience sampling. A survey was distributed to participants to obtain demographic data and data on administrative practices (human resources planning, communication, and decision-making). The collected responses were subsequently entered into SPSS software for descriptive statistics, ANOVA tests, and regression analysis to establish the association between research variables. Results: Overall, 369 participants participated in the survey, the demographic results showed males constituting a majority at 61.5% (227) and females accounting for 38.5% (142), and the majority of participants are between the ages of 30 and 40, representing a significant 57.2%. with more participants having a Bachelor's education. Results from the ANOVA test and regression analysis revealed a shows Human resources planning demonstrates a notably strong positive relationship with job performance, as indicated by a beta coefficient of 0.963 and a statistically significant T-value of 16.733 at p=0.01. In contrast, communication, with a Beta of 0.0650, shows a relatively weaker positive association with job performance, but it's still statistically significant with a T-value of 1.972 at p=0.0490. On the other hand, decision-making, bearing a beta of 0.0240 and a T-value of 0.423, does not exhibit a statistically significant relationship with job performance at p=0.672. Conclusion: Overall, the study found a strong association between these administrative practices and job performance, particularly for human resources planning. However, decision-making seemed to have a less pronounced impact. This study calls on healthcare policymakers to periodically review and update policies related to administrative practice
Enhancing the Ethics of Information Use in the Digital Age a Case Study at King Abdul-Aziz University
Get PDFIn the era of digital technology, the ethics of information usage has become a crucial issue, as information can be used positively or negatively. At King Abdul-Aziz University, researchers have found that protecting digital content and intellectual property rights can be achieved through the use of digital technologies, such as data encryption and access control techniques. The awareness of university staff regarding the culture of promoting information ethics was measured, and deficiencies in this area were identified. Based on these findings, recommendations have been formulated to enhance information ethics at the university and raise awareness among its staff about the importance of information ethics and how to apply them in their daily practices. The university's information ethics and intellectual property rights policies are clearly and comprehensively disseminated. Encouragement is given to university staff to participate in activities and events that contribute to the promotion of information ethics. These recommendations aim to protect the university's data and information content from falsification or intellectual property theft, fulfil the university's role in the field of information ethics at the local and international levels, and effectively generate income from digital content.https://doi.org/10.24897/acn.64.68.20251224001
Perception of Students in Faculty of Nursing Science about Clinical Training in Simulation Laboratories and Hospitals, Karari University, Omdurman-Sudan 2022
Get PDFAbstract: Background: Basic nursing education is a practice-based professional career where clinical training is a crucial part in nursing programs. The study was aiming to assess the perception of nursing students about clinical training in simulation laboratories compared to training in hospitals at Karari University in Sudan. Methods: The research design was a descriptive, cross-sectional institutional based study. The study setting was the faculty of nursing sciences in Karari University Sudan. The sample size was 167 students in grades five, six and eight. Five points` Likert subscales of perception were used for; General training, Training environment, Supervision, Self-confidence and Training outcomes. Multivariate analysis was carried out for the mean scores of perception subscales and grade levels. The ethical concern and confidentiality were maintained. Results: Ninety-two students were in the age group 19 -24 and almost 71 % were females. The nursing students in grade eight perceived clinical training at hospital significantly different from grade five, the mean differences and confidence intervals were as follows: General training (0.38; 95 % CI: 0.11, 0.64), Training environment (0.53; 95 % CI: 0.17, 0.89), Self-confidence (0.37; 95 % CI: 0.11, 0.63) and Training outcomes (0.49; 95 % CI: 0.19, 0.79). The perception of Training environment at both simulation laboratories and hospitals was significantly high among students in grade six compared to grade five, (0.32; 95 % CI: 0.03, 0.61) and (0.34; 95 % CI: 0.01, 0.68) respectively. The perception of supervision about training at the two settings was statistically insignificant among the three grades. Conclusion: Perception scores about clinical training at hospitals was significantly high among nursing students in high grade level. More researches needed for perception about clinical training at simulation laboratories  
The key performance indicators (KPIs) of the Information Technology (IT) department at King Abdulaziz University are aligned with the (ITIL) framework to ensure the quality of software applications
Get PDFThis scholarly paper offers an in-depth examination of Key Performance Indicators (KPIs) within the field of Information Technology, particularly emphasizing alignment with ITIL standards for optimizing software application quality. The central theme revolves around the critical role of performance indicators in evaluating and enhancing organizational performance. The document begins by exploring the concept of KPIs, emphasizing their pivotal role in strategic planning and operational efficiency. It provides a comprehensive overview of different types of KPIs, notably focusing on quantitative indicators. These KPIs are further categorized into five distinct groups, each serving unique functions in performance measurement and management. Further, the paper delves into the methodologies for measuring task durations and conducting workload analyses. These aspects are crucial for organizations to understand resource allocation, operational bottlenecks, and areas requiring optimization. The analysis of task duration offers insights into the efficiency and effectiveness of various operational processes. The concluding section of the paper highlights the application of Power BI, a powerful business analytics tool, for accessing, analyzing, and sharing performance-related data and reports. This discussion underscores the importance of advanced data analysis tools in comprehensively understanding and improving organizational performance. The integration of such tools facilitates a more data-driven approach to performance management, enabling organizations to make informed decisions and strategize effectively.  
Knowledge Spiral: A Leadership-Level Assessment of Knowledge Practices in Makkah Health Cluster
Get PDFWhile Knowledge Management (KM) is critical for High-Reliability Organizations (HROs) like modern healthcare clusters, evaluative, leadership-focused research in the Saudi context remains scarce. This study provides a leadership-level assessment of KM effectiveness within Makkah Health Cluster (MHC), a key component of Saudi Arabia's Vision 2030 health sector transformation, to identify barriers hindering its contribution to organizational performance. A qualitative study was conducted with 13 senior leaders at MHC. Data was collected through semi-structured interviews and subsequently analyzed using a quantitative content analysis approach. In this two-stage process, qualitative themes were first identified, then systematically coded and quantified to measure their prevalence and intensity across the leadership cohort. The Nonaka and Takeuchi SECI model served as the guiding theoretical framework. The findings reveal a significant paradox: leaders universally acknowledge KM's strategic potential but perceive its current implementation as fragmented. A fragmented and non-integrated technological infrastructure of KM was the most significant barrier, identified in 92.3% of interviews. This was followed by a lack of incentives for knowledge sharing and insufficient organizational awareness (both identified in 76.9% of interviews). These foundational barriers have hindered the knowledge creation spiral, particularly impeding the Externalization (articulating tacit knowledge) and Combination (systematizing explicit knowledge) processes. KM at MHC is in a state of unrealized potential. The study concludes that without a deliberate, top-down strategic intervention to build a cohesive governance framework, a unified technological platform, and a supportive organizational culture, KM will remain a collection of ad-hoc activities rather than a core driver of organizational performance and high reliability.https://doi.org/10.24897/acn.64.68.20251224001
The role of Artificial Intelligence applications in supporting Digital Marketing strategy via Social Networks
Get PDFThe study aimed to identify the role of artificial intelligence applications in supporting the digital marketing strategy via social networking platforms and to clarify its uses and benefits for marketing, by relying on both documentary research to create a cognitive framework and the content analysis method through reviewing and analyzing the literature and analyzing a group of models of marketing strategies via Social networks and artificial intelligence applications in order to develop a proposal for effective artificial intelligence applications and marketing strategies through social networks. The proposal begins with a study of the current situation and ends with measurement and evaluation. One of the most important findings of this study is that there are several types of marketing strategies via social media networks, including the social commerce strategy, the social content strategy, and others. Among the findings of the study is the availability of many artificial intelligence applications that can be used to support digital marketing strategy. It has also been shown that social media platforms increase awareness of brands and the products or services provided by them and it also helps maintain long-term communication and interaction between customers and businesses. The study recommends activating the role of artificial intelligence in the field of digital marketing through the use of various artificial intelligence applications and developing a detailed marketing strategy that takes advantage of all social media platforms in addition to employing appropriate tools and techniques in this field through the implementation of this strategy to ensure its success and achieve the goals
CNN for Detecting Objects in Autonomous Vehicle Environments using 2D Images: Current Solutions, Challenges and Future Trends
Get PDFObject recognition within the surrounding environment is pivotal for the advancement and safety of autonomous vehicles (AV). It is imperative for these vehicles to precisely identify and differentiate between key objects, such as pedestrians, traffic lights, lanes, and other vital elements. Any inaccuracy in this domain can have severe repercussions, potentially resulting in catastrophic accidents. Recent methodologies have capitalized on the capabilities of Convolutional Neural Networks (CNN) to enhance object detection within the AV domain. In the context of AV, data acquisition predominantly stems from two primary sources: (a) Light Detection and Ranging (LiDAR) systems, which produce a 3D Point Cloud representation of objects, and (b) cameras, offering a 2D visual perspective of the environment. Notably, there has been a predominant emphasis on leveraging 3D data in contemporary research. This paper seeks to address this research gap by concentrating on 2D object detection techniques in the AV environment. We will discuss the latest advancements in object detection for AVs harnessing CNNs, with a focus on Lane Detection, Pedestrian Recognition, and the detection of Traffic Signs and Lights. Furthermore, we will present an overview of emerging datasets created explicitly for AV research, detailing their unique specifications. The paper will conclude by shedding light on current challenges and forecasting prospective trends in AV object detection
Artificial Intelligence Risks to Information Privacy and Security on the Future of Organizations: A Modern Vision
Get PDFThis study aims to analyze and understand the risks of artificial intelligence on the privacy and security of information in the advanced digital environment, and what measures and strategies can be taken to maintain security and privacy in the face of these risks and challenges in the future. To achieve this goal, the researcher relied on the descriptive and analytical approach, it includes all organizations in the Kingdom of Saudi Arabia, regardless of their locations. The size of the study population was (80) employees, and it was a random sample of the study population, and the questionnaire was the tool for collecting information. The current study reached a set of results, most notably: - Providing specialized educational and training programs in the fields of artificial intelligence and related technology. - Organizing and monitoring the economic and social impact. - Providing an encouraging business climate for technology companies and startups working in the field of artificial intelligence. - Cloud-based network security solutions. - Behavior analysis and advanced threat classification. In the light of the findings of the current study, it recommends the following: • Identify how the increasing use of artificial intelligence impacts the privacy and security of information in these areas. • Analyzing security threats related to hacking, data theft, and exploiting vulnerabilities in artificial intelligence systems. • The need to develop new laws or amend existing laws. • Study the techniques and tools available to protect data and information from security threats associated with artificial intelligence, such as encryption and cyber security. • Liability for potential damages from the impact of artificial intelligence must be limited
Combination Prediction Model of Traffic Accident Based on Rough Set
Get PDFVehicles accidents have become the first public nuisance in the world. A dramatic rise of traffic accidents results from sharp increase of vehicles with the rapid development of economy. Accident forecasting is designed to help decision-making and planning before casualty and loss occur. Calculating weight coefficient is a key for combination forecast. The result of the forecast will be straightly influenced if the selection of the weighting coefficient is illogicality. A new method of combination forecasting applied in traffic accident is showed in this paper. It is based on the rough sets theory, and the weighting coefficient of all the forecast methods is distributed, so that the calculation of the weighting coefficient will be more impersonal and simpler, and the result of the forecast will be more exactly. In this paper, two samples were used to check the accuracy of this method. The Percent of errors were approximately about 0.5%and 2.7%. Compared with another method for combination forecasting- artificial neutral network, the Percent of errors were 1.1% and 3.05%. Respectively