Fair East Publishers: E-Journals
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Sectoral distribution of commercial banks’ credit and economic growth in Nigeria
No full textThe persistent economic challenges have prompted investigation about the effectiveness of commercial banks’ credit in driving economic growth in Nigeria. The main objective of this paper is to determine the relationship between commercial banks’ credit distributed to the various sectors and economic growth in Nigeria from 2014q1 to 2023q4 using autoregressive distributed lag (ARDL) bounds test approach. The Central Bank of Nigeria (CBN) Statistical Bulletin is the secondary source from where the data for the study were collected. The theoretical framework of the study is finance-led growth hypothesis. The commercial banks’ credit distributed to production and general commerce sectors had a positive relationship with economic growth in Nigeria. The commercial banks’ credit distributed to government and services sectors had a negative relationship with economic growth in Nigeria. A higher percentage of commercial banks’ credit should be distributed to production and general commerce sectors than government and services sectors in order to achieve a sustainable economic growth in Nigeria.
Keywords: Institutions, Economic Growth, ARDL Model, Nigeria
The role of fiber-reinforced and self-healing concrete in enhancing U.S. infrastructure durability
No full textThe durability of U.S. infrastructure has become a critical concern, as aging systems and increasing environmental stresses continue to strain the nation’s roads, bridges, and public structures. Over 40% of U.S. roadways are in poor or mediocre condition, while more than 46,000 bridges are structurally deficient. Conventional concrete, though widely used, is prone to cracking, corrosion, and structural fatigue, contributing to mounting maintenance costs and safety risks. This review paper explores the role of fiber-reinforced concrete (FRC) and self-healing concrete (SHC) as innovative solutions for enhancing the durability and resilience of U.S. infrastructure. The scope of the review encompasses recent advancements in FRC and SHC technologies, their mechanical and durability characteristics, and real-world applications in various infrastructure sectors. FRC integrates synthetic or natural fibers such as steel, glass, or polypropylene into the concrete mix to improve tensile strength, crack resistance, and impact durability. Meanwhile, SHC leverages biological or chemical agents such as bacterial spores or encapsulated healing agents that autonomously repair microcracks when exposed to water or environmental stimuli. Key findings highlight that FRC significantly enhances structural performance under cyclic loading and extreme environmental conditions, thereby extending service life and reducing maintenance frequency. SHC, on the other hand, shows promise in prolonging infrastructure lifespan by restoring structural integrity autonomously without human intervention. Together, these technologies present a sustainable and cost-effective approach to addressing the infrastructure durability crisis in the U.S. This paper recommends broader adoption of fiber-reinforced and self-healing concrete in federal and state infrastructure projects, particularly in high-stress applications such as highways, tunnels, and marine structures. Future research should focus on improving cost-efficiency, scalability, and the long-term performance of SHC systems in diverse climates. Integrating smart sensing and AI-based monitoring tools with these advanced materials may further revolutionize infrastructure maintenance and durability strategies.
Keywords: Fiber-Reinforced Concrete, Self-Healing Concrete, Infrastructure Durability, Concrete Innovation, Structural Performance, Sustainable Construction Materials, Smart Infrastructure
Transforming cybersecurity in Moroccan banking: Implementing a smart system with machine learning and biometric recognition
No full textIn the current era of digital transformation, the banking sector is undergoing significant changes, particularly within the Moroccan banking system, where data security has emerged as a critical concern. This paper investigates the pressing challenges posed by cyber threats, particularly the vulnerabilities associated with cloud-based data storage and online transactions. As incidents of cybercrime continue to escalate, there is an urgent need for effective intruder detection mechanisms to protect sensitive customer information. To address these challenges, this study proposes a Smart Online Banking System (SOBS) that integrates machine learning techniques with biometric recognition methods, including fingerprint scanning and facial recognition. By enhancing security protocols through advanced technologies, this model aims to mitigate risks associated with unauthorized access and bolster customer confidence in online banking services. The findings highlight the necessity for Moroccan banks to adopt innovative cybersecurity strategies that align with global best practices while addressing local challenges.
Keywords: Cybersecurity, Moroccan Banking System, Machine Learning, Biometric Recognition, Cloud Data Security
Transforming customer experience at scale: Deploying salesforce service cloud to modernize U.S. retail operations
Get PDFThe U.S. retail sector, a 75 billion annually due to poor customer experiences. We then examine Salesforce Service Cloud’s capabilities, including AI-driven case management, real-time analytics, automation, and integration with e-commerce platforms. Through use-case illustrations and performance metrics, we demonstrate how these features enable faster resolution times, improved customer satisfaction, and more efficient use of the retail workforce. Importantly, we highlight the platform’s role in enhancing employment efficiency for over 2.7 million Americans working in customer-facing roles, enabling frontline staff to shift from routine inquiries to high-value engagement. Finally, we position CRM modernization within the broader context of U.S. economic priorities, referencing Executive Order 14058 and other federal mandates that call for customer experience transformation. Our findings support the conclusion that Service Cloud is more than a software solution—it is critical infrastructure for ensuring that the U.S. retail sector remains competitive, resilient, and aligned with the digital economy of the future.
Keywords: Customer Experience, Salesforce, Retail Sector, Digital Commerce
The artificial intelligence governance framework for finance: A control-by-design approach to algorithmic decision-making in accounting
Get PDFThe rapid integration of artificial intelligence (AI) into financial and accounting systems has redefined decision-making processes, creating both opportunities for efficiency and risks related to transparency, bias, and regulatory compliance. Traditional governance mechanisms often lag behind technological innovation, resulting in accountability gaps in algorithmic decision-making. This paper introduces the Artificial Intelligence Governance Framework for Finance (AIGF-F), a control-by-design model aimed at embedding governance, risk management, and ethical oversight directly into AI-driven accounting systems. The framework emphasizes proactive governance through three core dimensions: algorithmic transparency, embedded control mechanisms, and adaptive regulatory alignment. It incorporates auditability features such as algorithmic audit trails, explainability protocols, and fairness metrics to ensure that AI outputs remain accountable to stakeholders. By adopting a control-by-design philosophy, the AIGF-F moves governance from a reactive supervisory function to an integral component of system architecture, minimizing risks before they materialize. The framework also highlights the role of augmented human oversight, ensuring that accountants and auditors remain central in interpreting AI-driven insights and validating ethical boundaries. Furthermore, the model demonstrates how financial institutions can balance innovation with compliance by integrating dynamic monitoring tools and continuous feedback loops that adjust controls in response to evolving data environments and regulatory landscapes. For practitioners, the AIGF-F offers a structured approach to implementing AI responsibly in areas such as financial reporting, auditing, fraud detection, and compliance monitoring. For regulators, it provides a scalable framework for establishing adaptable supervisory structures capable of keeping pace with algorithmic complexity. Ultimately, this study positions AI governance not as a barrier but as an enabler of sustainable financial transformation. By embedding governance into design, the AIGF-F enhances trust, accountability, and resilience in AI-enabled accounting systems, contributing to the broader discourse on ethical and responsible digital finance.
Keywords: Artificial Intelligence Governance, Control-By-Design, Algorithmic Decision-Making, AI In Accounting, Financial Reporting, Auditability, Explainability, Fairness Metrics, Compliance Monitoring, Ethical AI. 
Loan approval prediction using machine learning techniques
Get PDFLoans are the primary revenue generator for banks because they earn interest income from the credit, they extend through lending products. However, defaults on these loans can significantly impact profits. By identifying borrowers likely to default, banks can mitigate risk and reduce non- performing loans in their portfolio. This makes the study of this phenomenon very important. Previous research has shown there are many methods to study loan default prediction, which is essential for maximizing profits. However, comparing the nature and performance of different techniques is critical for reliability. The project focuses on leveraging machine learning techniques to enhance the efficiency and accuracy of loan approval processes in financial institutions. By analyzing a dataset comprising various applicant attributes and historical loan data, predictive models are developed to assess the likelihood of loan repayment or default. This project will stand out by using multiple feature engineering techniques such as Binning and Bucketing, Polynomial, Interaction Features to enhance the dataset. Through multiple feature engineering, model evaluations and ensembling techniques, the project aims to provide a comprehensive solution for automating and optimizing loan approval decisions.
Keywords: Loans approval, Machine Learning
Prediction of genetic biomarkers from RNA-SEQ datasets of prostate cancer
Get PDFA type of cancer known as prostate cancer (PC) manifests itself in the tissues of a man’s prostate gland. A walnut-sized gland between the penis and the bladder is prostate cancer! Prostate cancer symptoms include difficulty urinating, a decrease in the force of the urine stream, blood in the urine and semen, bone discomfort, weight loss, and erectile dysfunction. Four factors are associated with Prostate cancer: advanced age, race, family history, and obesity. Men aged 50 and older frequently develop prostate cancer. This project used different machine learning models to analyze the data gathered. The machine learning that was considered were: Random Forest, K-Nearest Neighbors, Gradient Boosting, Linear Discriminant Analysis (LDA), Support Vector Machines, and Naïve Bayes Classification. Prostate cancer, as well as ovarian and breast cancer, have all been related to BRCA1 and BRCA2 mutations. When a man has this mutation and is 65 or older, the patient is in danger of progressing to a more severe stage of the illness. As PC and breast cancer are connected to BRCA1 and BRCA2, patients with a family history of PC and breast cancer have a 60% likelihood of acquiring PC. More than 200 genes were examined, and HOXB13 G84E was shown to be a high-risk gene in PC patients. Doctors look for biomarkers to assist in detecting various malignancies and disorders. Biomarkers can be used to examine how a patient’s body reacts to a course of treatment for their ailment. It was challenging to decide which model would produce the best performance while choosing. Five A variety of machine learning models were chosen. The association between CACNG3 and ZNF263 in the prostate cancer setting raises the possibility of a functional connection between these two genes. It might suggest a joint function in encouraging carcinogenesis or regulating critical cancer-related pathways. The RandomForest ML model was the most effective one for this assignment. A machine learning system called random forest consists of various decision trees. Its primary goal is to solve all classification and regression problems. The genes KRT33A and CACNG3 were discovered to have a significant association with prostate cancer and disease development in this study. Overall, this thorough analysis highlights the complex dynamics of prostate cancer, covering its causes, detection techniques, genetic risk factors, and creative machine-learning applications.
Keywords: Prostate Cancer, Genetic Biomaker, RNA-Seq, Machine Learning
Wearable health technology: A critical review of devices, data accuracy, and clinical relevance
No full textWearable health technology has emerged as a dynamic force in modern healthcare, offering innovative solutions for monitoring health metrics, enhancing clinical decision-making, and improving patient outcomes. This critical review comprehensively explores the multifaceted landscape of wearable health technologies, addressing key aspects, including data accuracy, clinical relevance, privacy and security, regulatory considerations, and future directions. Evaluation of data accuracy and clinical relevance highlights the pivotal role of wearable device data in healthcare. However, challenges in regulation and ethical data use persist. Privacy and security concerns emphasize the need for robust safeguards in an increasingly interconnected healthcare ecosystem. Regulatory frameworks, both domestically and internationally, shape the safety and effectiveness of these devices. Emerging trends in wearable health technology promise advanced sensors, artificial intelligence, and broader applications. Collaborative efforts among stakeholders will be crucial to harness the transformative potential of wearables, ultimately shaping a future where personalized and data-driven healthcare is the norm.
Keywords: Wearable Health Technology, Data Accuracy, Clinical Relevance, Privacy and Security, Regulatory Guidelines
Disinformation in the digital era: The role of deepfakes, artificial intelligence, and open-source intelligence in shaping public trust and policy responses
No full textThis study investigates the role of deepfake and open-source intelligence (OSINT) in enabling disinformation campaigns and their societal consequences. Using the Deepfake Detection Challenge (DFDC) dataset for technical evaluation, social media datasets for OSINT network and sentiment analysis, and public opinion data from the Global Disinformation Index, the study applied machine learning classification, network analysis, sentiment analysis, and interrupted time series (ITS) analysis. The technical assessment achieved a detection accuracy of 0.73, precision of 0.75, and recall of 0.70, identifying areas for enhancement in identifying synthetic media. OSINT analysis revealed pivotal amplifiers of disinformation, with User1 having a degree centrality of 0.263 and betweensess centrality of 0.135. Sentiment analysis showed an average sentiment score of -0.085, while ITS analysis documented a significant 9.76-point decline in public trust post-disinformation events. Recommendations include developing adaptive AI detection systems, implementing global regulatory measures, fostering public media literacy, and encouraging ethical OSINT practices.
Keywords: Deepfakes, Artificial Intelligence, Disinformation Campaigns, Open-Source Intelligence, Public Trust
The role of streaming services in digital content consumption: Insights from Uzbekistan
No full textThis study explores how the emergence of legal distribution channels affects digital piracy. We use online video-on-demand services as a proxy for legal distribution channels and investigate how increased satisfaction with this service influences the consumption of pirated content. Data for this study were collected through an online survey of a representative sample of 332 participants and analyzed using the Structural Equation Modeling approach. The findings indicate that greater use of subscription-based video-on-demand services significantly reduces the consumption of pirated video content. Additionally, system features such as content richness, system quality, and recommendation mechanisms are crucial in shaping user satisfaction with such services. Based on these findings, this study recommends streaming service providers and policymakers to further promote legal digital consumption and reduce piracy.
Keywords: Digital Piracy, Structural Equations Model, Technology Acceptance Model, Uzbekistan, Video-on-Demand Service