Fair East Publishers: E-Journals
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Preserving culture in the digital age: media ethics and privacy for youth in Northeast India
No full textThe digital age has brought transformative changes to communication, learning, and cultural expression, presenting both opportunities and challenges for the youth of Northeast India. This paper explores the critical intersection of tradition and technology, focusing on preserving cultural integrity amidst rapid digital advancements. Northeast India, known for its diverse ethnicities, languages, and traditions, is navigating the complexities of media ethics and digital privacy to protect its cultural heritage. Social media and online platforms have emerged as powerful tools for cultural exchange, but they also pose risks of misrepresentation, cultural erosion, and privacy breaches. This study examines the role of media ethics in promoting accurate cultural narratives and fostering intercultural understanding. It also highlights the importance of digital privacy in safeguarding cultural practices and traditions. By analyzing challenges and proposing strategies, the paper emphasizes empowering youth as custodians of their heritage in the digital realm.
Keywords: Cultural Preservation, Digital Privacy, Media Ethics, Youth, Northeast India, Social Media
Impact of technology adoption on the financial services performance in Uzbekistan
No full textThis study explores the impact of technology adoption on the performance of financial services in Uzbekistan, with a focus on digital payments, mobile banking, and the broader fintech ecosystem. By analyzing data from 2017 to 2024, the study employs econometric models, including ARMA and regression analysis, to assess how technological innovations, such as mobile payment platforms and online banking penetration, affect the financial sector's growth, efficiency, and consumer accessibility. The findings highlight a significant positive correlation between technology adoption and financial services performance, particularly in terms of increased consumer engagement and the expansion of digital payment channels. The study also discusses policy implications for promoting further technological advancements to improve financial inclusion and sectoral growth.
Keywords: Financial Services, Fintech, Banking Sector, Microfinance and Electronic Payments
Improving team productivity and financial services efficiency with agile story points
No full textThis paper explores using Agile story points to enhance team productivity and efficiency in financial services, highlighting their benefits, challenges, and future implications. Story points are essential for estimating task complexity, risk, and effort, offering a flexible approach to sprint planning and resource allocation. The paper discusses the role of story points in improving communication among teams, increasing predictability, and ensuring timely customer delivery. Additionally, it examines the challenges associated with subjectivity in estimations, the influence of team dynamics, and the difficulties of scaling Agile practices in large financial institutions. Recommendations are provided for optimizing story point usage, scaling Agile across teams, and ensuring the necessary technological and organizational support for maximizing productivity. The findings emphasize that, with the right framework and leadership buy-in, Agile story points can drive significant improvements in efficiency within the highly regulated financial services sector.
Keywords: Agile Story Points, Team Productivity, Financial Services, Resource Allocation, Sprint Planning, Scaling Agil
Evaluating the effectiveness of risk-based auditing and sox compliance in preventing financial fraud: A case study of multinational corporations
No full textFinancial fraud remains a significant challenge for multinational corporations, leading to economic losses, reputational damage, and regulatory scrutiny. To combat financial misconduct, risk-based auditing and Sarbanes-Oxley Act (SOX) compliance have emerged as essential tools in fraud prevention, internal control enhancement, and financial reporting integrity. This study evaluates the effectiveness of risk-based auditing in fraud detection and assesses how SOX compliance has strengthened corporate governance in multinational corporations. By examining case studies and empirical data, this research highlights the impact of proactive risk assessments, internal control mechanisms, and audit committee oversight in mitigating financial risks. The study also explores challenges in SOX implementation, evolving regulatory trends, and the role of technology in strengthening audit effectiveness. Findings suggest that companies with strong risk-based auditing frameworks and SOX compliance measures demonstrate reduced fraud risks, enhanced transparency, and improved investor confidence. As corporate fraud schemes become increasingly sophisticated, the integration of predictive analytics, AI-driven risk monitoring, and global regulatory alignment will be critical in advancing fraud prevention efforts in the financial sector.
Keywords: Risk-Based Auditing, Sox Compliance, Financial Fraud Prevention, Internal Controls, Forensic Auditing, Corporate Governance, Regulatory Compliance, Fraud Risk Assessment, Multinational Corporations
AI and data-driven innovations in healthcare: Enhancing cancer detection, workforce optimization, and comprehensive care for people living with HIV
No full textThe integration of artificial intelligence (AI) and data-driven technologies is revolutionizing healthcare by enhancing diagnostic accuracy, optimizing workforce efficiency, and improving chronic disease management. This manuscript explores how AI-assisted imaging can improve early cancer detection, particularly in underserved areas, through advanced image recognition and predictive modeling. Additionally, the role of predictive analytics in optimizing healthcare workforce distribution is examined, highlighting its potential to enhance resource allocation, reduce clinician burnout, and improve patient outcomes. The manuscript also delves into the importance of lifestyle interventions in managing comorbidities among people living with HIV (PLWH), emphasizing the role of digital health technologies in promoting adherence to healthy behaviors. Finally, the paper discusses how data-driven decision-making can strengthen health systems, reduce disparities, and improve public health outcomes. By synthesizing these themes, this manuscript underscores the transformative potential of AI and data analytics in creating resilient, equitable, and efficient healthcare systems globally.
Keywords: Artificial Intelligence (AI), Data-Driven Healthcare, Early Cancer Detection, AI-Assisted Imagin
Leveraging data analytics for fraud detection: The future of financial risk mitigation and regulatory compliance
No full textThe increasing complexity of financial fraud schemes has necessitated the adoption of advanced data analytics, AI-driven fraud detection models, and forensic accounting tools to strengthen corporate fraud prevention and regulatory compliance. Traditional fraud detection techniques have proven inadequate in identifying sophisticated financial crimes, prompting organizations to integrate predictive analytics, machine learning algorithms, and real-time transaction monitoring systems to mitigate fraud risks. This paper examines how data analytics enhances financial risk mitigation, the role of AI in automating fraud detection, and the challenges associated with implementing data-driven fraud prevention models. Additionally, it explores the global impact of regulatory frameworks, such as the Sarbanes-Oxley Act (SOX) and international anti-money laundering directives, which drive the adoption of AI-powered risk assessment strategies. By analyzing case studies of multinational corporations that have implemented data-driven fraud detection mechanisms, this research highlights the effectiveness of forensic data analysis in improving corporate transparency and compliance. The findings suggest that AI and data analytics will continue to redefine financial fraud prevention, ensuring corporate integrity and investor confidence in an increasingly digitalized financial landscape.
Keywords: Fraud detection, Data Analytics, Ai-Driven Fraud Prevention, Financial Risk Mitigation, Regulatory Compliance, Predictive Analytics, Forensic Auditing, Machine Learning in Fraud Detection, Blockchain Technology in Fraud Prevention, Corporate Financial Security
A conceptual model for geospatial analytics in disease surveillance and epidemiological forecasting
No full textThe integration of geospatial analytics into disease surveillance and epidemiological forecasting has emerged as a crucial approach in understanding and mitigating the spread of infectious diseases. This study proposes a conceptual model that leverages geospatial data, artificial intelligence (AI), and machine learning (ML) to enhance real-time disease monitoring, outbreak prediction, and public health response. The model integrates multiple data sources, including satellite imagery, mobile health (mHealth) data, electronic health records (EHRs), and environmental variables, to provide a spatial-temporal understanding of disease patterns. The framework comprises four key components: (1) Data Acquisition and Integration, which gathers and harmonizes multi-source geospatial and epidemiological datasets; (2) Spatial-Temporal Analysis, where AI-driven models identify hotspots and predict disease spread; (3) Decision Support System, which provides real-time visualization and risk assessments for policymakers and healthcare providers; and (4) Intervention Optimization, which uses predictive modeling to enhance the efficiency of resource allocation and public health interventions. By incorporating advanced geographic information systems (GIS), deep learning, and cloud-based analytics, this model ensures a dynamic and adaptive surveillance mechanism capable of detecting emerging threats with high accuracy. The proposed framework is designed to address challenges such as data heterogeneity, privacy concerns, and computational scalability in large-scale disease monitoring efforts. Case studies, including recent pandemics such as COVID-19, demonstrate the potential of geospatial analytics in reducing response times and improving intervention strategies. Furthermore, the model highlights the significance of community engagement and ethical considerations in implementing geospatial disease surveillance systems. The study concludes that an AI-powered geospatial analytics approach enhances epidemiological forecasting by providing actionable insights, improving early warning systems, and strengthening public health resilience. Future research should focus on refining ML models, integrating real-time sensor data, and enhancing interoperability between health information systems for a more robust disease surveillance architecture.
Keywords: Geospatial Analytics, Disease Surveillance, Epidemiological Forecasting, Artificial Intelligence, Machine Learning, Geographic Information Systems, Public Health, Spatial-Temporal Analysis, Outbreak Prediction, Decision Support Systems
Prevalence of Multiple Sexual Partners and Associated Factors among Undergraduates of Ebonyi State University Abakaliki
Full text linkRelationships, sex, marriage and family life, have taken a new dimension towards multiple sexual partnerships among adolescents and youth across the globe particularly schools/universities where the menace has taken a center stage causing a significant public health concern. This study focused on multiple sexual partners and associated factors among undergraduate’s student of Ebonyi State University. The study adopted cross-sectional survey research design to draw 308 respondents that participated in the study. Semi-structured questionnaire titled: Prevalence of Multiple Sexual Partner and Associated Factor Questionnaires (PMSPAFQ) was used as instrument for data collection. Data were analyzed using frequencies, percentages, mean and standard deviation. In order to describe predictor variables and their association with the multiple sexual partners, Analysis of Variance (ANOVA) was used to test the hypothesis of no significant difference in the risk factors of multiple sexual partners. The level of significant was set at P<0.05. Result: the prevalence of multiple sexual partners among undergraduates was 71.7%. The most common risk factors were the use of alcohol and other drugs (3.00 + 0.97) and the highest consequences of engaging in multiple sexual partners is unintended pregnancies (x= 2.96). There is a significant difference in the risk factors and multiple sexual partners at P<0.05. This means that risk factors such as peer pressure, use of alcohol and other drugs, and being from a disadvantaged socioeconomic background is associated with multiple sexual partners at P<0.05. The study found the protective factors of multiple sexual partners among adolescents and youths to include comprehensive sex education programs, counseling services, and awareness campaigns against multiple sexual partners. Conclusion: the study concludes that there is need to prioritize the protective factors as discovered in this study for intervention and preventive effort of MSP.
Keywords: Multiple Sexual Partners, Prevalence, Factors, University, Ebonyi State
Community-Level infectious disease education and adherence model for resource-limited settings
Full text linkInfectious diseases remain a leading cause of morbidity and mortality in resource-limited settings, exacerbated by inadequate health literacy, poor adherence to preventive measures, and constrained healthcare infrastructure. This paper proposes the Community-Level Infectious Disease Education and Adherence Model (CIDEAM), an integrated, culturally sensitive framework designed to enhance disease prevention, treatment adherence, and health outcomes in underserved populations. The model emphasizes participatory education strategies, community health worker engagement, and context-appropriate communication channels to bridge knowledge gaps and foster behavioral change. Drawing from evidence-based interventions and socio-behavioral theories, CIDEAM incorporates peer-led workshops, visual and audio educational tools in local languages, and interactive community dialogues to improve understanding of disease transmission, symptoms, treatment protocols, and prevention methods. It also integrates adherence monitoring mechanisms such as mobile health (mHealth) reminders, household follow-up visits, and social support groups to reinforce compliance with medical regimens, particularly for chronic and high-burden infections like tuberculosis, HIV/AIDS, and malaria. The model accounts for socio-economic determinants of health by incorporating strategies to address stigma, misinformation, and barriers to accessing healthcare services, thereby promoting trust and collaboration between communities and healthcare providers. Implementation in resource-limited contexts is supported by capacity-building initiatives for community health workers, partnerships with local leaders, and the leveraging of low-cost digital technologies. Pilot studies suggest that CIDEAM can significantly improve treatment adherence rates, reduce infection incidence, and strengthen community resilience against outbreaks. The model’s adaptability ensures relevance across diverse cultural and epidemiological contexts, making it a scalable and sustainable solution for global health initiatives targeting infectious disease control in marginalized populations. By integrating education, adherence support, and socio-cultural considerations, CIDEAM offers a pathway to reducing infectious disease burdens while empowering communities to take ownership of their health outcomes.
Keywords: Community Health Education, Infectious Disease Prevention, Treatment Adherence, Resource-Limited Settings, Health Literacy, Mhealth, Community Engagement, Behavioral Change, Disease Control, Global Health
Argumentative techniques used in argumentative essay-writing by French students at colleges of education level in Ghana: A case study in three colleges of education in Ghana
Full text linkThis study looks at argumentative essay-writing by students of French at the colleges of education in Ghana. It identifies and analyzes argumentative techniques as well as difficulties found in the students' essays and proposes solutions to them. The research instrument used to collect the data is an argumentative essay test in French. A systematic sampling technique was used to select forty (40) Level 200 students of three (3) colleges of education teaching French as a foreign language namely Bagabaga College of Education, Tamale, Wesley College of Education, Kumasi and Mount Mary College of Education, Somanya. The study shows that textual argumentative techniques such as stance taking either by agreement or refusal, defense of stance and synthesis characterized the essays. Also, most of the students lacked textual argumentative technique competence. Methodological techniques suggested as solutions to these problems include but are not limited to the following: systematic and thorough teaching of the concepts of argumentation, cohesion and argumentative approaches at the colleges of education. It is also recommended that, in class, teachers should introduce/adopt practical activity-based training on argumentative techniques examined in this article and regular exercises on argumentative essay-writing which lead to the development of competence in argumentative techniques.
Keywords: Argumentation, Argumentative Essay, Argumentative Techniques. Argumentative Essay, Argumentative Techniques