Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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IoT-Based Smart Lockers: A Validation for the Saudi Arabian Market
No full textThe adoption of IoT technology has revolutionized logistics and e-commerce worldwide. IoT smart lockers enable efficient last-mile delivery through secure, automated storing and retrieval. In this article, the viability of smart lockers in Saudi Arabia, in terms of Vision 2030 aspirations, is examined through a qualitative case study, with lessons drawn from international implementations such as Amazon Hub, Hive Box, and InPost Lockers. Economic savings, efficiency in logistics, and ease of use for consumers rank high, but face-to-face preference and compliance with laws and legislation hinder them. There is a critical necessity for localized adaptations for driving adoption, and collaboration with logistics providers and governments is paramount. Integration with Saudi smart city development is a critical opportunity for rollout. Consumer willingness and pilot studies in key urban locations must be considered in future studies. Actionable information for stakeholders for the rollout of IoT-powered smart lockers in Saudi Arabia’s modernization of its logistics infrastructure is presented through this paper
The Impact of Influencer Attributes on Purchase Intention: Evidence from Influencers’ Virtual Boutiques in Qatar
No full textThe rise of social media influencers has significantly reshaped the digital marketing landscape, introducing innovative strategies for promoting goods and services. This research explores the emerging phenomenon of Influencers\u27 Virtual Boutiques, a novel form of social commerce recently gaining traction in the Gulf Cooperation Council (GCC) region, particularly in Qatar. These virtual boutiques operate as curated digital storefronts where influencers endorse and sell products directly to their followers. This study examines four key attributes—trustworthiness, expertise, attractiveness, and authenticity—and their influence on the purchase intentions of followers from the influencer’s virtual boutiques. The findings demonstrated that authenticity is the strongest predictor of purchase intention, while trustworthiness, expertise, and attractiveness did not show significance. These results suggest a shift in consumer preferences, where relatability and genuine influencer engagement play a more crucial role than traditional credibility markers. This study contributes to the growing body of influencer marketing and social commerce research by offering empirical evidence on the emerging influencer virtual boutique model in the Qatari market
The Fight Against Drug Menace: Experiences Of Philippine Drug Enforcement Agency (PDEA) Agents
No full textThis study delved into the experiences of Agents in the Philippine Drug Enforcement Agency. Thus, the Philippine government has waged a decades-long battle against illegal drugs. As the frontline in fighting againts drug menace this study sought answers to questions about the experiences and addressed the problems encountered and aspirations of the informants in the performance of their duties. The research employed a transcendental phenomenological research design. Furthermore, it involved ten (10) agents from Philippine Drug Enforcement Agency Regional Office VIII, located at Government Center Palo, Leyte : six were subjected to a focused group discussion, and four were interviewed in depth. Interviews were conducted and recorded using a smartphone application, ensuring detailed and accurate transcriptions. They were selected through the purposive sampling method and utilized a validated interview guide. The gathered data were treated through the Thematic Analysis approach. The study identified eight themes : A Fruitful Encounter, Cleaning Up the Neighborhood, In the Line of Fire, Tough Row to Hoe, Turning Challenges into Stepping Stones, Joining Forces for a Common Cause, Building an Unbreakable Fortress and Laying the Foundation for Lasting Change. These themes highlighted the importance of the that PDEA agents face numerous challenges in their roles, yet they also encounter rewarding experiences. The key themes identified in the study include the significance of their work, the dangers they face, and their efforts to overcome obstacles and collaborate with other agencies and the community
Comparative Analysis Of Rental Values Of Residential Properties On Aker Road, Rumuolumeni
No full textOver the past two decades, the Rumuolumeni community in Obio/Akpor Local Government Area of Rivers State has experienced significant urbanization, driven by the establishment of industries, a tertiary institution, and business hubs. This rapid development has increased the demand for residential accommodation of various sizes and typologies to meet the housing needs of the growing population. Consequently, this study examines the trends of 1bedroom and 2 bedroom flats on Aker Road Rumuolumeni from 2019 to 2023 providing insights to guide investors in making informed decisions. Data for this study were collected from estate surveying and valuation firms, landlords, and tenants using well-structured questionnaires. The collected data were analyzed using trend analysis, one-way analysis of variance (ANOVA), and relative importance index (RII). These analytical techniques were employed to examine rental value trends, assess the statistical significance of differences in rental values between one-bedroom and two- bedroom flats in the study area, and rank the factors influencing rental values based on their relative importance. The findings revealed that both flat types experienced slight growth between 2019 and 2020, and the growth spiked up the following year 2021. It was observed that the one bedroom flat recorded the highest growth at 61.03% while 2 bedroom flat recorded highest growth at 59.32%. Analyzing the rental values of the two property types to establish if there is a statistically significant difference in the mean rent paid on 1 bedroom and 2 bedroom flats within the study area from 2019 to 2023, it was observed that there was a statistically significant difference between the mean rents of the two residential property types. The finding further revealed the top 3 factors that impact rental value in the study area as cost of building material (RII 0.86), siting of Ignatius Ajuru University of Education (RII 0.82), and good roads (RII 0.80), while the least 2 factors impacting on rental values from the list of 16 identified factors were: green building practice (RII 0.45), income level of tenants (RII 0.49). It was recommended that investors consider the development of more 1 bedroom flats than 2 bedroom flats because the former attracts higher rental growth annually
AI-Driven forecasting in BRICS infrastructure investment: impacts on resource allocation and project delivery
No full textThis study explores the role of artificial intelligence (AI)-driven forecasting in improving resource allocation, cost prediction, and project delivery in infrastructure investment within the BRICS nations (Brazil, Russia, India, China, South Africa). Through an analysis of 100 infrastructure projects, the study evaluates the effectiveness of AI tools in addressing common challenges such as cost overruns, project delays, and inefficient resource utilization. Using machine learning models, optimization algorithms, and predictive analytics, the study demonstrates that AI can significantly enhance cost prediction accuracy, reduce project completion time deviations, and optimize resource allocation, resulting in overall cost savings. The results show an average prediction error of 5.00% for cost forecasts and a 5.42% deviation in project timelines. AI-driven optimization led to an average cost saving of 5.45%. Additionally, AI tools identified 25% more risks compared to traditional methods, contributing to more proactive risk management. However, the study also highlights the challenges of implementing AI in countries with varying levels of technological readiness, data quality, and organizational resistance. The findings suggest that AI can play a critical role in transforming infrastructure development in BRICS nations, provided that barriers to adoption are addressed
AI-Powered Fault Prediction and Optimization in New Energy Vehicles (NEVs) for the US Market
No full textThe automotive industry in the USA is going through a significant transformation as global efforts to mitigate climate change and diminish greenhouse gas emissions intensify. Focal to this Paradigm shift is the advancement of New Energy Vehicles (NEVs), which comprise electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and hydrogen fuel cell vehicles (FCEVs). This research project aimed to examine the deployment of AI in forecasting and optimizing fault management in NEVs. This study intended to leverage machine learning algorithms with data analytics to provide high reliability and operational efficiency within the US automotive industry with NEVs. The dataset for the present study was accessed from accredited automotive manufacturing companies. The dataset was designed to predict the faults and optimize maintenance at NEVs. It covered simulated real vehicle data, such as sensor readings, environmental factors, driving patterns, and maintenance logs needed to understand performance, diagnose faults, and optimize a vehicle\u27s maintenance schedule. Different algorithms were selected, such as Random Forest Classifier, Gradient Boosting Classifier, and Logistic Regression with other advantages, depending on the dataset\u27s characteristics and the problem\u27s complexity. Performance evaluation of the model was done with several metrics, most notably precision, recall, and F1-score. The results demonstrated that the Random Forest model attained the highest accuracy, followed closely by Gradient Boosting. AI-driven fault prediction models brought into play would greatly raise the level of impact that can be caused to the automotive industry in the US concerning the enhancement of NEV reliability and efficiency. Interpretation of the model\u27s predictions is important in fault management strategies because it converts raw predictive outputs to actionable insights
The Use of AI Applications by Students to Improve the Quality of Scientific Research: A Field Study on a Sample of Students from Al Kufra University - Faculty of Science"
No full textThis study aimed to investigate the use of AI applications by students in improving the quality of scientific research. The researchers employed a descriptive survey method using an electronic questionnaire as a data collection tool. The questionnaire was distributed to students of the Faculty of Science at Al Kufra University, with a total of 56 respondents. The study reached several important conclusions, the most notable of which are: AI applications positively contribute to enhancing the quality of scientific research and play an effective role in improving researchers\u27 performance in research processes. Additionally, students frequently use AI applications in scientific research, with 67.9% of them reporting frequent use, while 17.8% stated they always use these applications. On the other hand, 14.3% of the students reported that they rarely use these applications. The study recommended that supervisors and reviewers closely monitor the work submitted by graduating students, as many of these works have become standardized and do not reflect the personal contribution of the researcher. This can help ensure the quality and originality of the research
Advanced Machine Learning Techniques for Cybersecurity: Enhancing Threat Detection in US Firms
No full textUS corporations\u27 computing technologies are evolving towards new technologies to detect, respond, and prevent new threats using sophisticated machine learning (ML) methods for their cybersecurity systems. To be sure, machine learning is not a silver-bullet solution, but it does have speed, scalability, and pattern detection capacity which have no match. Robust cybersecurity is built on a multi-faceted strategy incorporating cutting-edge machine learning models with traditional countermeasures and human expertise. By collaborating, engineers, legislators lawyers can ensure safe and responsible execution in business, especially in the high-stakes world of US companies. This paper describes how machine learning (ML) can enhance threat detection systems, enabling enterprises to move from reactive to proactive defense strategies. But beyond the effectiveness of the technologies, we emphasize the need for accountability, transparency and ethical governance in deploying these technologies. Finding the right spot for the combination of machine learning\u27s computational capabilities without abandoning decisions because of any relationship remains part of ethical assessment and passive strategy. But, as attacks become more complex, we need our defenses to do the same. However, this study uses the power of machine learning to study more and implement it correctly so US companies can create a resilient and agile cybersecurity solution that will safeguard their digital assets in an increasingly interconnected world
Predictive Analytics for Intraoperative Complications: Enhancing Perioperative Safety with AI
No full textIntraoperative problems significantly impact patient safety and surgical outcomes, with early detection of such problems being important to improve perioperative care. This article explores algorithms of machine learning to predict intraoperative complications using preoperative and intraoperative data from a large retrospective cohort of 121,898 adult surgical procedures at a single academic medical center between 2012 and 2016. To model the prediction of outcomes such as acute kidney injury (AKI), delirium, deep vein thrombosis (DVT), pulmonary embolism (PE), and pneumonia, five models were trained: logistic regression, support vector machine, random forest, gradient boosting tree (GBT), and deep neural network (DNN). Combination datasets performed better than preoperative or intraoperative data alone. The highest AUROC was 0.91 (GBT; pneumonia), 0.85 (aKI; GBT), 0.88 (DVT; GBT), 0.76 (PE; DNN), and 0.999 (delirium; GBT) (Table 2). Including missing data variables yielded significant performance gain in all categories. SHapley Additive exPlanations (SHAP) discovered significant, patient-specific risk factors in a clinically relevant manner, thus enhancing interpretability. These findings demonstrate the potential for AI-driven predictive analytics to provide physicians with interpretable, real-time decision support, reduce complication rates and enhance perioperative safety overall
Utilizing Generative AI for Financial Literacy
No full textThis article examines the potential of generative artificial intelligence systems to address declining financial literacy rates in an increasingly complex economic landscape. By analyzing both theoretical foundations and practical applications, we explore how AI-powered solutions can provide personalized financial guidance, education, and behavioral nudging that adapts to individual circumstances and knowledge levels. The article investigates generative AI\u27s capabilities for creating customized budgeting frameworks, explaining investment concepts, monitoring financial health, and delivering tailored educational content—all at scale and with accessibility not possible through traditional approaches. While highlighting these promising applications, we also critically assess important limitations including accuracy concerns, dependency on user query skills, interpretation challenges, privacy considerations, and ethical implications of automated financial advice. Through a proposed empirical research framework and implementation strategy, we outline pathways for effective integration with existing financial services while considering diverse user needs. This examination ultimately suggests that generative AI, when thoughtfully implemented with appropriate guardrails, holds significant promise for democratizing access to high-quality financial guidance while potentially reducing financial distress and enhancing economic resilience across diverse populations