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This study examined the influence of cultural norms and gender roles on the achievement of Sustainable Development Goals (SDGs) in Nigeria. Specifically, it investigated how cultural norms sustain gender inequality, assessed the effects of gender roles on women's participation in education, politics, and the economy, and evaluated the implications of gender inequality for SDG attainment. A quantitative descriptive survey design was adopted. The population comprised adult men and women in selected communities in Ogun State, Nigeria, from which a sample of 300 respondents was drawn using a multistage sampling technique. Data were collected using a 21-item structured Likert-scale questionnaire and analysed using descriptive statistics. Findings revealed that cultural norms significantly sustain gender inequality through male dominance in inheritance and leadership, stigmatization of women who challenge traditional expectations, and justification of discriminatory practices in the name of cultural preservation. The results further showed that gender roles restrict women's participation in key sectors, as household responsibilities limit educational opportunities, political structures constrain representation, and workplace practices favour men in leadership positions. Although some responses indicate emerging shifts in perceptions regarding women's leadership, traditional norms remain influential. The study also established that gender inequality negatively affects the attainment of SDGs, particularly in education, economic growth, and inclusive governance. The study concludes that persistent cultural and structural barriers continue to hinder sustainable development in Nigeria. It recommends the enforcement of gender-responsive policies, community-based sensitization involving traditional leaders, expansion of educational opportunities for girls, and institutional reforms to promote women's participation across all sectors

• Adepoju, A., & Olufemi, A. (2022). Gender, culture, and participation in community governance in Nigeria. Journal of African Development Studies, 18(2), 45–59. https://doi.org/10.1080/afds.2022.18.2 • African Development Bank. (2021). Africa gender index report 2021: Closing the gender gap in Africa. https: // www. afdb. org/ en/ documents / african - gender - index - report-2021 • Agbalajobi, D. T., & Olorunmola, A. (2021). Women's political participation and representation in Nigeria: Prospects and challenges. African Journal of Political Science, 15(1), 23–39. https://doi.org/10.1177/ajps.2021.1512 • Akinola, O. (2021). Patriarchy and cultural practices: Implications for gender equality in Nigeria. Gender and Society in Africa, 9(1), 72–88. https://doi.org/10.1080/gsa.2021.9.1 • Durojaye, E., & Owoaje, E. (2021). Policy interventions and cultural resistance to women's empowerment in Sub-Saharan Africa. African Journal of Reproductive Health, 25(4), 1–12. https:// doi.org/ 10.29063/ ajrh2021/v25i4 • Eagly, A. H. (1987). Sex differences in social behavior: A social-role interpretation. Lawrence Erlbaum Associates. • Edewor, P., & Aluko, Y. (2020). Inheritance rights and women's empowerment in Nigeria. African Sociological Review, 24(1), 59–78. https://doi.org/10.4314/asr.v24i1 • Ezeani, E. (2023). Gender equality and economic growth in Nigeria: An empirical analysis. Journal of Sustainable Development in Africa, 25(3), 11–25. https:// doi.org/ 10.1080/ jsda.2023.25.3 • Girls Not Brides. (2025). Child marriage atlas: Nigeria. https:// www. girlsnotb rides. org/ learning-resources/child-marriage-atlas/atlas/nigeria • Inter-Parliamentary Union. (2023). Data on women in parliament: Nigeria. https://data.ipu.org/parliament/NG/NG-LC01/data-on-women • Inter-Parliamentary Union. (2026). Data on women in parliament: Nigeria. https://data.ipu.org/parliament/NG/NG-LC01/data-on-women • National Population Commission (NPC) [Nigeria] and ICF. (2019). Nigeria demographic and health survey 2018. NPC and ICF. https://dhsprogram.com/pubs/pdf/FR359/FR359.pdf • Nussbaum, M. C. (2000). Women and human development: The capabilities approach. Cambridge University Press. • Okeke-Ihejirika, P., & Francis, S. (2022). Gender stereotypes and workplace leadership in Nigeria. Journal of Contemporary African Studies, 40(4), 612–629. https:// doi.org/ 10. 1080 /jcas.2022.40.4 • Olanrewaju, O., & Omotoso, S. (2022). Gender equality and sustainable development in Nigeria: Challenges and prospects. Journal of Sustainable Development, 15(4), 112–130. https://doi.org/10.5539/jsd.v15n4p112 • Onwuzuruigbo, I. (2021). Inclusive governance and women's representation in Nigeria. Nigerian Journal of Public Policy, 12(2), 101–120. • Oyesanya, O. (2022). Balancing cultural identity and gender equality in African societies. African Journal of Gender and Development, 14(1), 45–62. https:// doi.org/ 10.5897/ AJGD2022.0141 • Sen, A. (1999). Development as freedom. Oxford University Press. • UNESCO. (2022). Education for all global monitoring report: Gender summary. UNESCO. https: // unesdoc. unesco. org/ ark: / 48223/pf0000380265 • UNESCO. (2025). Global education monitoring report 2025: Gender report – Women lead for learning. https:// unesdoc. unesco. org/ ark:/ 48223/ pf0000393701 • UNDP. (2022). Human development report 2022: Uncertain times, unsettled lives. United Nations Development Programme. https://hdr.undp.org/content/human-development-report-2022 • United Nations. (2023). The sustainable development goals report 2023. https://unstats.un.org/sdgs/report/2023 • UN Women. (2023). Progress on the sustainable development goals: The gender snapshot 2023. United Nations Women. https:// www. unwomen. org/ en/ digital - library / publications / 2023 / 09 / progress-on-the-sustainable - development - goals - the-gender - snapshot - 2023 • UN Women. (2025). Progress on the sustainable development goals: The gender snapshot 2025. United Nations Women. https: // www. unwomen. org/en/ digital-library / publications / 2025 / 09 / progress - on - the -sustainable-development-goals-the-gender-snapshot-2025 • World Bank. (2021). Women, business, and the law 2021. World Bank Publications. https://openknowledge.worldbank.org/handle/10986/35094 • Yusuf, T., & Adebayo, K. (2021). Women and leadership exclusion in Nigeria: Cultural and political barriers. African Journal of Gender Studies, 13(2), 55–71. https:// doi.org/ 10.1080/ ajgs.2021.13.2

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Prior studies have shown that, after investing in recruitment, onboarding training/development, compensation, and other initiatives, organisations often experience high turnover among high-performing employees. Therefore, this study investigates the effect of talent management on employee retention of academic and non-teaching employees of the University of Ilorin, Kwara State, Nigeria. This was a cross-sectional survey study. The study participants are 3,391 University academic and non-teaching staff. Based on this, 358 samples were selected using the Taro Yamane formula, and the valid responses were analysed. Simple regression with a significance level of 0.05 was used to analyse data collected via a structured questionnaire. The result indicates that recruitment transparency does not have a statistically significant effect on employee retention, with a coefficient value (R= –0.018; t= –0.320; p= 0.749). In addition, competitive remuneration does not affect employee retention statistically significantly with a coefficient value (R=0.038; t=0.690; p=0.491). The study concludes that, in the context of the study, the practice of talent management will help address employee engagement initially, but, alone, is not enough to guarantee long-term retention. Therefore, adopting a holistic talent management structure that incorporates equitable pay, transparent recruitment, systematic career management, professional learning and development, and enabling leadership is recommended to increase long-term retention of academics and non-teaching staff. Keywords: Talent Management, Employee Retention, Academic/Non-teaching Staff

• Abbasi, S. G., Tahir, M. S., Abbas, M., & Shabbir, M. S. (2022). Examining the relationship between recruitment & selection practices and business growth: An exploratory study. Journal of Public Affairs (14723891), 22(2), 1-5. https://doi.org/10.1002/pa.2438 • Adewole, O. D., Rukevwe, O., Oluwatomisin, O. L., & Odunmbaku, D. O. (2025). Talent Management and Skill Development as Catalysts for Employee Retention: Evidence From Tech Entrepreneurship in Southwest, Nigeria. Journal of Entrepreneurship & Business, 13(2), 125-147. https://doi.org/10.17687/nq2n4z16 • Al-Dalahmeh, M., Héder-Rima, M., & Dajnoki, K. (2020). The effect of talent management practices on employee turnover intention in the information and communication technologies (ICTs) sector: Case of Jordan [Article]. Problems and Perspectives in Management, 18(4), 59-71. https://doi.org/10.21511/ppm.18(4).2020.06 • Aliyu, M.O. & Ambali, T. T. (2022). Analysis of psychological contracts and discretionary behaviour in Nigerian Academics: The role of the Academic Staff Union of Universities. International Journal of Economic Behaviour, 12(1), 5-25. DOI: https://doi.org/10.14276/2285-0430.3236 • Aliyu, M.O. (2024). Behavioural Impediments in Realising Decent Academic Citizenship: Evidence from Nigeria's Higher Education Sector. In: Iwu, C.G. (eds) Academic Citizenship in African Higher Education. Palgrave Macmillan, Cham. pp 109–128. https://doi.org/10.1007/978-3-031-63957-9_6 • Aljbour, A., Ali, M., & French, E. (2024). Talent management practices and the influence of their use on employee outcomes via perceived career growth [Article]. Employee Relations, 46(8), 1625-1647. https://doi.org/10.1108/ER-05-2023-0245 • Bhimavarapu, U. (2025). Enhancing employee engagement through data-driven HR practices. In Harnessing Business Intelligence for Modern Talent Management (pp. 127-163). https://doi.org/10.4018/979-8-3373-1942-1.ch006 • Bhuvaneswari, A., Tamilselvi, K., Sangitha, K., Selvi, K., Kumar, A. S., & Dhaneswar, S. (2025). Exploring the Impact of Talent Management Practices on Employee Retention and Promoting Decent Work and Economic Growth. Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025, • Ciff, T., Brouwer, A. E., Ponsioen, A., & Van Lieshout, H. (2024). Challenges and opportunities in the tight Dutch IT labour market [Article]. TECHNOLOGY IN SOCIETY, 77, Article 102541. https://doi.org/10.1016/j.techsoc.2024.102541 • Deogaonkar, A. (2026). Talent management in the age of AI: navigating digital dexterity, embeddedness, plateau, and flexibility fatigue. Management Research Review, 49(3), 297-312. https://research.ebsco.com/plink/26b9b45d-f1c4-3f18-accb-991927d38145 • Dogbe, C. S. K., Doku, P. A., Yeboah, E. E., & Appiah-Kubi, E. (2025). Talent management and employee retention: role of employee development [Article]. Journal of Organizational Effectiveness, 1-19. https://doi.org/10.1108/JOEPP-10-2024-0506 • Dunmade, E. O., Ajayi, M. A., & Obadare, G. O. (2022). Effective Succession Planning and Employees' Retention in Multi--Trex Integrated Food Plc, Lagos. Journal of Management & Social Sciences, 11(2), 1295-1314. https://research.ebsco.com/plink/e0e97d43-0c72-37d2-bff2-9a7385fa3e79 • Durakova, I. B., Endovitsky, D. A., & Sahakyan, M. A. (2025). Talent Management of Age Employees as a Condition for Sustainable Development of Higher Education in the ESG Trends. In Advances in Science, Technology and Innovation (Vol. Part F764, pp. 535-539). https://doi.org/10.1007/978-3-031-82210-0_87 • Fadi, S. (2025). Enhancing Organisational Performance through AI-Driven HRM Practices and Performance Metrics: Evidence from European Multinational Enterprises. Management & Production Engineering Review (MPER), 16(3), 1-14. https://doi.org/10.24425/mper.2025.156147 • Foley, T. Z. (2020). Talent Retention Strategies for Service Industry Managers Within Rust Belts [Dissertation/Thesis]. • Holban, C., & Bedrule-Grigoruţǎ, M. V. (2025). Talent Management Strategies for Generation Z: An Exploratory Study in Romanian Organisations [Article]. Studies in Business and Economics, 20(1), 304-318. https://doi.org/10.2478/sbe-2025-0018 • Jahan, U., & Kathiri, H. A. (2025). Talent management using technology to prevent the great resignation. In Aligning Talent Management and Organizational Innovation Goals (pp. 433-465). https://doi.org/10.4018/979-8-3373-0015-3.ch016 • Jibril, I. A., & Yesiltas, M. (2022). Employee Satisfaction, Talent Management Practices and Sustainable Competitive Advantage in the Northern Cyprus Hotel Industry [Article]. SUSTAINABILITY, 14(12), Article 7082. https://doi.org/10.3390/su14127082 • Karuppiah, S. P., Sriramakrishnan, R., Ezhil Bharathi, R. J., Anis Arokia Theresa, S., AswiyaFargath, S., & Kumaran, S. (2025). Employee Retention Analytics: Predictive Modeling for Workforce Stability and Talent Management. 2025 IEEE 4th World Conference on Applied Intelligence and Computing, AIC 2025, • Labolo, M. (2021). The effect of talent management, employee recognition and compensation fairness on organisational performance [Article]. Global and Stochastic Analysis, 8(2), 213-226. https://www.scopus.com/pages/publications/85119691630?origin=resultslist • Leal-Solis, A., Sanchez Gonzalez, M. J., & Nieves-Pavon, S. (2026). Business Management of Human Capital in the Hotel Sector: Organisational Resources and Talent Retention from a Job Demands-Resources Perspective [Article]. SUSTAINABILITY, 18(2), Article 599. https://doi.org/10.3390/su18020599 • Lenz, J., & Burbach, R. (2025). Talent Management in Small- and Medium-Sized Enterprises (SMEs). In International Encyclopedia of Business Management (pp. Vol1:736-Vol731:740). https://doi.org/10.1016/B978-0-443-13701-3.00183-3 • Leontes, N. I. (2024). Talent management and employee retention in the South African Higher education landscape. International Journal of Research in Business & Social Science, 13(6), 303-318. https://doi.org/10.20525/ijrbs.v13i6.3532 • Li, L. (2025). Employee segmentation and compensation optimisation with adaptive clustering. Journal of Computational Methods in Sciences & Engineering, 1. https://doi.org/10.1177/14727978251364453 • Mabaso, C. M., & Mathebula, S. (2025). Total rewards for attracting and retaining Millennials in the workplace post-COVID-19 [Article]. SA Journal of Human Resource Management, 23, Article 2855. https://doi.org/10.4102/sajhrm.v23i0.2855 • Magaji, N., Owolabi, T., Ibhiedu, A., Otegbade, T., & Adedokun, A. (2025). Talent Management and Employee Retention in Nigeria Deposit Money Bank: Evidence from Abeokuta, Ogun State. Journal of System & Management Sciences (JSMS), 15(2), 324-346. https://doi.org/10.33168/JSMS.2025.0220 • Makumbe, W. (2025). Talent management and intention to stay in the mining industry: a moderation mediation model of workplace flexibility and organisational commitment. Rajagiri Management Journal, 19(4), 289-302. https://doi.org/10.1108/RAMJ-02-2025-0042 • Manimaran, B., Kulandai, A., Bacdayan, P., & Parayitam, S. (2025). Exploring the relationship between talent management, job satisfaction, performance and organisational citizenship behavior: evidence from manufacturing industry in India [Article]. TQM Journal. https://doi.org/10.1108/TQM-03-2025-0171 • Mapuranga, R., Mukuze, K., & Maravanyika, A. (2024). Talent management in the digital age: Leveraging technology to enhance public sector recruitment and retention in Zimbabwe. In Digital Transformation in Public Sector Human Resource Management (pp. 259-278). https://doi.org/10.4018/979-8-3693-2889-7.ch014 • Mathur, S., & Srivastava, N. (2024). Analysis the Influence of Talent Management Practices on Employee Retention: Mediating Role of Job Satisfaction [Article]. International Research Journal of Multidisciplinary Scope, 5(2), 275-289. https:// doi. org/ 10.47857 /irjms. 2024. v05i02. 0451 • Mokoena, W., Schultz, C. M., & Dachapalli, L. A. P. (2022). A talent management, organisational commitment and employee turnover intention framework for a government department in South Africa [Article]. SA Journal of Human Resource Management, 20, Article a1920. https://doi.org/10.4102/sajhrm.v20i0.1920 • Permana, Y., Ambarwati, R., & Azahraty. (2025). Talent Management in the Digital Era: Utilising Technology for Recruitment and Retention at PT Arutmin Indonesia. International Journal of Economics (IJEC), 4(1), 150-156. https://doi.org/10.55299/ijec.v4i1.841 • Phan, M. D., Nguyen, T. M. T., Duong, N. A., & Nguyen, T. T. (2022). Employee Retention and Talent Management: Empirical Evidence from Private Hospitals in Vietnam [Article]. JOURNAL OF ASIAN FINANCE ECONOMICS AND BUSINESS, 9(6), 343-362. https:// doi. org/ 10.13106 /jafeb .2022. vol9. no6.0343 • Sitaniapessy, S. S., Armanu, & Kurniawati, D. T. (2023). The Effect of Talent Management and Perceived Organizational Support on Employee Retention Mediated by Organisational Commitment. International Journal of Social Service & Research (IJSSR), 3(8), 1910-1918. https://doi.org/10.46799/ijssr.v3i8.470 • Theodorsson, U., Gudlaugsson, T., & Gudmundsdottir, S. (2022). Talent Management in the Banking Sector: A Systematic Literature Review [Review]. Administrative Sciences, 12(2), Article 61. https://doi.org/10.3390/admsci12020061 • Tucmeanu, E. R., & Almasan, C. M. (2024). Digital Transformation of Workforce Management: Artificial Intelligence-driven Strategies for Talent Acquisition and Retention. Economics, Management & Financial Markets, 19(4), 58-78. https://doi.org/10.22381/emfm19420244 • Wenting, L., Hussain, W. M. H. W., Xinlin, J., Na, M., & Alam, S. S. (2024). Analysing the Impact on Talent Acquisition and Performance Management: HR and Data Analysis [Article]. Journal of Organizational and End User Computing, 36(1). https://doi.org/10.4018/JOEUC.342603 • Yamoah, E. E., Yeboah, I. A., & Nyala, D. N. (2024). Human Resource Practices and Employee Retention: The Moderating Effect of Job Engagement. SEISENSE Business Review, 4(1), 200-216. https://doi.org/10.33215/agg17288

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Cybercrime also known as 'yahoo-yahoo' has emerged as a critical challenge affecting Nigerian youth, evolving into ritualistic (yahoo Plus) forms. Driven by unemployment, poverty, and eroding moral values, cybercrime persists despite the 2015 Cybercrimes Act and EFCC enforcement, damaging Nigeria's international reputation and perpetuating poverty cycles. Religious institutions (churches, mosques, African Traditional Religion centers) serve as moral custodians with unique influence over youth values, offering alternative success pathways through moral education and mentorship. However, empirical evidence of faith-based interventions at local government levels remains limited. This mixed-methods study examined religious leaders' roles in curbing cybercrime among 297 youths in Odogbolu Local Government Area, Ogun State, using surveys and interviews with Christian, Islamic, and ATR leaders. Findings identify unemployment, peer influence, and materialism as primary drivers, with males and younger respondents showing greater tolerance. Religious institutions actively combat cybercrime through youth seminars, Friday khutbahs, and communal counseling, though impact remains moderate due to economic hardship and social media influences. The study concludes that sustainable reduction requires integrated approaches combining religious moral guidance with economic interventions and multi-stakeholder collaboration, providing localized empirical evidence for policymakers and community stakeholders. Keywords: Cybercrime, Christian Religion Leaders, Islamic Religion Leaders, Traditional Religion Leaders, Youth

• Abdulrazaq, M. (2023). Islamic organizations and the fight against cyber fraud in Northern Nigeria. Journal of Islamic Studies in Nigeria, 15(2), 45–62. • Adebisi, O., & Lawal, T. (2022). Interfaith collaboration and moral reach of religious institutions in Nigeria. Journal of Community Development, 12(3), 45–62. • Adebanjo, J. (2021). Cybercrime prosecutions under Nigeria's Cybercrimes (Prohibition, Prevention, etc.) Act of 2015: A review of cases and implications for youth employment. Nigerian Law Journal, 18(2), 112–128. • Adebanjo, J., & Yusuf, A. (2022). Psychological impact of Yahoo Plus on victims of cyber fraud. Journal of Cyber Victimology, 8(1), 78–94. • Adeboye, P. (2023). Faith-based rehabilitation programs for ex-cybercriminals: Outcomes of church-led vocational training. Journal of Faith-Based Social Services, 15(4), 203–219. • Adekunle, T. (2023). Cybercrime networks in Nigerian university communities: Informal mentoring and illicit digital activities. Higher Education in Nigeria, 22(1), 34–49. • Adesina, K. (2023). Churches and ethical standards: Reinforcing moral education among Nigerian youths. Journal of Religious Ethics, 9(3), 88–104. • Adetunji, R. (2023). Policy evaluation of the Cybercrimes (Prohibition, Prevention, etc.) Act of 2015: Enforcement effectiveness and challenges. Nigerian Policy Review, 14(1), 67–83. • Adegoke, N., Adejumo, O., & Oyeniyi, T. (2024). International reputation and the stigmatization of Nigerian citizens due to cybercrime proliferation. International Journal of Cybersecurity and Society, 6(1), 34–51. • Adegoke, T., & Salawu, B. (2023). Spiritual elements in cyber fraud: A study of university students and correctional facility inmates. African Journal of Criminology, 11(2), 156–172. • Adewale, B., & Adegoke, S. (2020). Peer pressure and the allure of quick wealth in youth cybercrime. Journal of Youth Studies, 17(4), 445–462. • Adewale, T., & Mustapha, A. (2023). Moral agency among Nigerian youths under economic pressure: A volitional perspective. Journal of Moral Development, 8(2), 123–139. • Adewumi, F. (2022). Masculinity narratives and cybercrime rationalization among male youths in Nigeria. Gender and Development Studies, 13(4), 178–195. • Adeyemi, K., & Obi, C. (2023). Faith-based programs with vocational components: Behavioral change outcomes among at-risk youth. Journal of Youth Development, 16(2), 89–107. • Adeyemi, L., & Ogunwale, T. (2022). Unemployment and cyber fraud perception among Nigerian youths. Economic and Social Review, 19(3), 267–284. • Ajayi, R. (2023). Glorification of wealth through fraudulently acquired means in digital spaces. Digital Culture and Society, 7(2), 56–71. • Ali, M. (2023). Religious leaders as ethical frameworks providers for youth crime prevention. Journal of Religious Leadership, 12(1), 45–63. • Aquinas, T. (1274). Summa theologica (Fathers of the English Dominican Province, Trans.). Benziger Bros. • Augustine, S. (430). City of God (M. Dods, Trans.). T. & T. Clark. • Babatunde, A., & Makanju, O. (2019). Historical roles of religious leaders in addressing societal vices in Nigeria. African Journal of Religious Studies, 7(3), 112–128. • Bandura, A. (1977). Social learning theory. Prentice Hall. • Bello, K., & Ogunwale, T. (2024). Emotional and psychological distress among youths involved in cybercrime. Mental Health and Society, 6(1), 34–52. • Central Bank of Nigeria. (2023). Financial sector cybersecurity measures and fraud prevention strategies. Author. • Chinonso, P., & Okafor, M. (2021). Structural limitations of faith-based interventions in Nigeria. Development Studies Quarterly, 9(4), 78–94. • Economic and Financial Crimes Commission. (2023). Annual report on cybercrime enforcement and international reputation impact. Author. • Eze, C. (2024). State government efforts to combat cybercrime: A survey of cybersecurity agencies. Nigerian Governance Review, 11(2), 156–174. • Eze, M., & Iwu, C. (2023). Faith institutions leveraging social media for moral campaigns among digitally active youth. Journal of Digital Religion, 5(3), 201–218. • Eze, P., & Ojo, D. (2022). Male youth socialization into cybercrime in Southern Nigeria: Provider-role pressures and peer networks. Masculinity Studies, 8(2), 89–107. • Kant, I. (1785). Groundwork of the metaphysics of morals (M. Gregor, Trans.). Cambridge University Press. • National Bureau of Statistics. (2023). Nigeria youth unemployment statistics and labor market trends. Author. • Nigerian Communications Commission. (2024). Telecommunications collaboration with regulatory bodies on fraudulent website shutdowns. Author. • Nwosu, C., & Eze, O. (2022). Religious and family moral instruction versus unemployment: Competitive influence on youth values. Family and Society, 14(3), 267–283. • Ockham, W. (1347). Summa totius logicae [Manuscript]. • Ogwezzy, M. C. (2021). Weak law enforcement and the normalization of fraudulent behaviour among Nigerian youths. Journal of Criminal Justice in Nigeria, 9(2), 78–94. • Ogunlade, F. (2020). Churches, sermons, and youth programs: Guiding youths toward lawful behaviors. Journal of Christian Education, 16(4), 112–128. • Ogunleye, T. (2024). Educational abandonment and long-term career consequences of youth cybercrime involvement. Education and Employment Studies, 11(1), 45–62. • Ogunmefun, A., Akinpelu, A., & Aluko, O. (2025). Socio-economic drivers of cybercrime among Nigerian youth. Contemporary Social Problems, 13(1), 34–51. • Ogunyemi, A. (2021). Ritualistic components of Yahoo Plus: In-depth interviews with former cybercriminals. African Traditional Religion and Modernity, 7(2), 178–195. • Ojedokun, U. (2018). Peer influence and fraud participation among university students in Nigeria. Journal of Higher Education Crime, 6(3), 89–107. • Ojedokun, U., & Eraye, M. (2012). Internet access, digital tools, and cybercrime accessibility among Nigerian youths. Digital Divide Studies, 4(2), 56–71. • Ojedokun, U., & Hassan, B. (2022). Normalization of cybercrime within youth digital spaces: Self-reinforcing trends across age cohorts. Youth and Digital Culture, 10(4), 267–284. • Okeshola, F., & Adenaike, F. (2018). Erosion of moral and religious values and youth cybercrime in Nigeria. Moral Education Quarterly, 6(1), 34–51. • Okonkwo, R., & Okafor, C. (2022). Economic deprivation and weakened moral frameworks as predictors of cybercrime involvement among Nigerian youths. Crime and Poverty Studies, 8(3), 156–172. • Olawale, T., & Ibrahim, A. (2023). Community-based religious programming effectiveness: Coordination with family structures and local governance. Community Development Journal, 18(2), 89–107. • Olowu, K., & Fashion, A. (2022). Peer socialization and economic deprivation: Accelerators of youth entry into cybercrime. Journal of Deviant Behavior, 14(4), 445–462. • Olumide, Y., & Adebayo, T. (2021). Financial implications of cyber fraud in the Nigerian banking sector. Banking and Finance Review, 12(2), 78–94. • Owolabi, T. (2024). Yahoo Plus as distortion of traditional values: Extreme practices for financial success. African Values and Modernity, 9(1), 34–52. • Owolabi, T. (2024). Use of African Traditional Religion symbols in Yahoo Plus rituals: Interviews with spiritualists and cybercriminals. Traditional Religion Today, 11(3), 201–218. • Salami, A., & Adebayo, F. (2023). Gender patterns in moral disapproval of cybercrime across Nigerian communities. Gender Studies Nigeria, 7(4), 112–128. • Tade, S. (2019). Cyber fraud awareness and involvement across six tertiary institutions in Nigeria. Higher Education and Crime, 7(2), 178–195. • Tade, S. (2023). Intersection of cybercrime and ritual practices among Nigerian youth: A six-state survey. Journal of Ritual Studies, 15(1), 89–107. • Uche, C. (2017). Societal glorification of wealth and normalization of fraudulent behaviour among young Nigerians. Sociology of Money and Morality, 5(3), 267–284. • Ugochukwu, P., & Nweze, C. (2023). Religious institutions integrating digital literacy: Stronger behavioral influence over youth cybercrime attitudes. Religion and Technology, 6(2), 123–139.

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The integration of machine learning (ML) with the Internet of Things (IoT) technologies represents a critical advancement in the development of intelligent systems. This paper explores the cooperative interplay between ML and IoT to create systems that autonomously process data, make decisions, and optimize operations across diverse domains. With appli-cations ranging from healthcare and transportation to smart cities and industrial automation, the cooperation of ML and IoT has revolutionized the efficiency and scalability of modern systems. The paper reviews state-of-the-art ML techniques and their applications, proposes an architecture to facilitate this coop-eration, and discusses the results and implications of integrating these technologies. This study aims to provide a comprehensive understanding of ML-IoT cooperation, address challenges, and offer a framework for future innovation. Keywords : Machine Learning, Internet of Things, Intel-ligent Systems, Autonomous Operations, Predictive Analytics, Smart Cities, Industrial Automation, Healthcare, Reinforcement Learning, Edge Computing

• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. • Cakir, B., Engin, A., & Kibar, D. (2021). Development of an IIoT-based condition monitoring system for predictive maintenance. Computers & Industrial Engineering. • De Benedetti, A., Epifani, F. M., & Giani, A. (2018). Anomaly detection for predictive maintenance in IoT-enabled photovoltaic systems. Neurocomputing. • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. • Huang, Y., Liu, R., & Wang, P. (2020). IoT-based multi-sensor data fusion for mechanical fault prediction. Simulation Modelling Practice and Theory. • Jiang, H., et al. (2020). ML-based early detection of cardiac anomalies. Biomedical Engineering Online, 19, 101–112. • Jiang, X., Guo, M., & Xu, Z. (2022). Electrical-spatio temporal graph convolutional network for intelligent predictive maintenance in IoT. IEEE Transactions on Industrial Informatics. • Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). Springer. • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In F. Pereira, C. J. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems (Vol. 25, pp. 1097–1105). • Lin, Y., et al. (2021). Smart grids in smart cities: Leveraging IoT and machine learning. Energy Informatics, 4(1), 15–28. • Ong, S., Zhang, K., & Saad, N. M. B. (2022). Deep-reinforcement-learning-based predictive maintenance model for effective resource management in industrial IoT. IEEE Internet of Things Journal. • Souza, J., Oliveira, M., & Soares, R. (2021). Deep neural networks for fault classification in rotating machinery using vibration signals. Computers & Industrial Engineering. • Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press. • Wu, J., Zhao, S., & Li, Y. (2018). K-PdM: A cluster-based hidden Markov model framework for predictive maintenance in IIoT. IEEE Access. • Zhang, C., et al. (2023). Predictive maintenance in IoT systems using machine learning models. Journal of Industrial Informatics, 12(3), 201–218. • Zhang, Q., Wang, J., & Yang, K. (2018). IoT for predictive maintenance: Challenges and opportunities. IEEE Internet of Things Journal.

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Suicide is a leading cause of death worldwide, particularly among young adults, and remains a major challenge to global prevention efforts. Currently, there are no clinically available imagings, laboratory, or biomarker-based methods to aid in diagnosis or identify individuals at risk of suicide. As a result, suicide detection is increasingly relying on artificial intelligence (AI) platforms that can analyze large datasets to develop risk algorithms capable of predicting suicide trends and identifying at-risk individuals or groups. By analyzing social media posts, medical records, and data from mobile devices, multidisciplinary approaches that integrate diverse data sources and AI techniques can help identify individuals at risk, enabling timely interventions. Keywords : Suicide Prevention, Young Adults, Artificial Intelligence, Machine Learning, Neural Network, Suicide Prediction, Mental Health, NLP

• Fazel, S., Runeson B., & Roppu A.H. (2020). Suicide, The New England journal of Medicine, 380 (3), 2066-274. • Chan M.K.Y. (2026). Predicting Suicide following self-harm: systematic review of risk factors and risk soales. The British journal al psychiatry, 20j(4), 277-283. • Marks, M. (2019), Artificial intelligence based suicide predication yole journal of law and technology, 21, 98-151. • Yao, H., cher J.H. & XU, Y.f. (2020). Patients with mental health dis-orders in the Covid-19 epidemic. The lancet Psychiatry, 7(4), ezl. • Beviouiguet, S., Coutel, P., Larsen, M.E., Walter, M. & vaiva, G. (2018). Suicide pereventron - towards integrative, innovative and individualized brief contact interventions. European Psychiatry, 47, 25-26. • Abd-alrazaq, A., Alsaad, R., Alhuwail, D., Ahmed, A-, Mealy P.M., Latifi, S., Aziz, S., Damesh, R., Alabed Alrazak, M., Sheikh, J. & Househ, M. (2021). Artificial intelligence for mental healthcare : clinical applications, barriers, facilitators, and drtificial wisdom. Biological Psytiatry : cognitive Neuro Science and Neuro imaging, 6(9), 856-864

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The global silver market between 2015 and 2025 has undergone a profound structural transformation, evolving from a traditionally cyclical precious metal market into a strategically critical industrial ecosystem. This paper expands the existing analysis by integrating predictive analytics, data mining models, and structural economic theory to explain the unprecedented silver price supercycle observed during 2020–2025. Using econometric modeling, machine learning techniques, and sentiment mining, the study demonstrates that silver's price surge is not a speculative anomaly but the outcome of chronic supply inelasticity intersecting with accelerating technology-driven demand. The findings position silver as a form of industrial infrastructure rather than merely a financial hedge, with significant implications for policymakers, investors, and sustainable technology planners. Keywords : Silver Super-cycle, Predictive Analytics, Structural Deficit, Data Mining, Green Energy, Industrial Metals, Machine Learning

• Acemoglu, D., & Restrepo, P. (2020). Artificial intelligence and jobs. Journal of Economic Perspectives, 34(3), 30–50. • Baur, D. G., & Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. The Financial Review, 45(2), 217–229. • Chen, L., & Zhao, Y. (2025). Comparative analysis of deep learning models for silver price prediction: CNN, LSTM, GRU and hybrid approaches. Akdeniz İİBF Journal. https://dergipark.org.tr/en/pub/auiibfd/issue/83172/1404173 • Forti, V., Baldé, C. P., Kuehr, R., & Bel, G. (2024). The Global E-waste Monitor 2024: Quantities, flows, and the circular economy potential. United Nations Institute for Training and Research (UNITAR). • Frankel, J. A. (2014). Effects of speculation and interest rates in a "carry trade" model of commodity prices. Journal of International Money and Finance, 42, 88–112.Graedel, T. E., et al. (2015). Criticality of metals and metalloids. Proceedings of the National Academy of Sciences, 112(14), 4257–4262. • Gielen, D., Boshell, F., & Saygin, D. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38–50. • Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press. • International Energy Agency (IEA). (2024). Renewables 2024: Analysis and forecast to 2030. IEA Publications. https:// www. iea. org / reports/renewables-2024 • Kurniasari, D. (2025). Forecasting silver prices using the Long Short-Term Memory (LSTM) method. • Liu, Y., Nie, L., & Li, Z. (2022). Commodity price prediction using hybrid deep learning models. Applied Energy, 306, 117985. • Oxford Economics & The Silver Institute. (2025). Silver, the next generation metal: Demand forecast across key technology sectors. The Silver Institute. https:// silverinstitute. org /wp-content/uploads/2025/12/Silver_The- Next-Generation-Metal_DECEMBER-Release.pdf • Reuter, M. A., et al. (2013). Metal recycling: Opportunities, limits, infrastructure. UNEP Report. • The Silver Institute. (2025). World silver survey 2025. Washington, D.C.: The Silver Institute. https://silverinstitute.org/silver-supply-demand/ • U.S. Geological Survey (USGS). (2025). Interior Department releases final 2025 list of critical minerals. USGS News. https:// www. usgs. gov / news / science - snippet / interior - department - releases - final - 2025 - list - critical - minerals • United States Geological Survey. (2025). Mineral Commodity Summaries: Silver. USGS. International Renewable Energy Agency. (2024). Renewable Capacity Statistics 2024. IRENA. • University of Lampung Repository. http://repository.lppm.unila.ac.id/54482/ • World Bank. (2020). Minerals for climate action: The mineral intensity of the clean energy transition. World Bank Group. • World Silver Survey. (2025). World Silver Survey 2025. Silver Institute, Washington, DC. International Energy Agency. (2024). Solar PV Global Supply Chains. IEA Publications. • Zhang, Y., Li, X., & Wang, S. (2023). Forecasting precious metal prices using LSTM neural networks. Expert Systems with Applications, 213, 118947.

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Social infrastructure constitutes a vital component of overall infrastructure, as it encompasses facilities and services that directly enhance the quality of human life. The present study seeks to identify and analyse the key components of social infrastructural development in India over the period from 1971 to 2011, using secondary data sources. The assessment of social infrastructural development is carried out across three major dimensions i.e. education, health and financial infrastructure represented by a total of 27 indicators. The standardised composite index was calculated using Principal Components Analysis (PCA). The study reveals that in 1971, the education and health sectors jointly accounted for the greatest spatial variation in the development of social infrastructure in India. By 2011, however, the health sector and its associated indicators emerged as the dominant contributors to spatial disparities, leading to a relative decline in the influence of the education sector. Although the number of districts classified under very low and low levels of social infrastructural development declined over the study period, nearly two-thirds of the country continued to exhibit low or very low levels of development in 2011, underscoring the need for greater attention from regional planners and policymakers. Keywords: Social Infrastructure, Regional Development, Principal Components Analysis (PCA), Spatial Inequality, India

• Hotelling, H. (1933). Analysis of complex statistical variable into principal component. Journal of Educational Psychology, 24(7), 498-520. https://doi.org/10.1037/h0070888. • Joshi, B. M. (1990). Infrastructure and economic development in India. New Delhi: Ashish Publishing House. • Kapil, A. (2010). Infrastructure and economic development. New Delhi: Deep and Deep Publication Pvt. Ltd. • Kumar, S., Thakur, B. R., & Kumar, M. (2022). Physical infrastructure: Spatial differences and magnitude of development in India. Pacific Business Review International, 14(7), 89-96. • Mishra, O. P., Mishra, M. & Shukla, R. P. (1991). Planning for social infrastructure: A case study of tehsil colonel ganj, District Gonda Uttar Pradesh. Geographical Review of India, 53(3), 30-50. • Parthasarathy, R. (1997). The new paradigm for financing infrastructure projects and services. Asian Transport Journal, 33-41. • Rosenstein, P. N. (1943). Problems of industrialization of eastern and southeastern Europe. The Indian Economic Journal, 53, 202-211. • Sharma, P. (1995). Regional inequalities in the process of socio-economic development. Annals, National Association Geographers India,15(4), 35. • Singh, R. (2009). Trends in regional disparities in India since independence: A geographical analysis. Doctoral dissertation, Punjab Geographer, 132. • Thakur, B. R. & Sharma, D. D. (2010). Development of infrastructure in tribal areas of Himachal Pradesh: A geographical analysis. Transactions of Institute of Indian Geographers, 32(1), 93-104.

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Small enterprises are critical drivers of economic growth, employment generation, and innovation in India. However, limited access to formal finance often constrains their growth. This study examines the role of bank-provided microfinance in supporting small enterprises in Mumbai, focusing on challenges in accessing credit and the impact of microfinance on enterprise performance. Primary data were collected from 50 small business owners using structured surveys and interviews, supplemented by secondary sources. Results indicate that while accessibility challenges are not statistically significant, bank microcredit has a substantial positive impact on revenue, profitability, technology adoption, operational expansion, product development, and market competitiveness. The study highlights the effectiveness of bank microfinance in promoting enterprise growth and provides policy implications for enhancing financial inclusion among small businesses. Keywords: Small Enterprises, Microfinance, Bank credit, Financial Inclusion, Mumbai

• Bhanot, D., & Bhapat, S. (2014). Financial sustainability of Indian microfinance institutions: Portfolio quality, return on assets, and staff productivity. International Journal of Financial Studies, 2(1), 45–60. https://doi.org/10.3390/ijfs2010045 • Care International. (2015a). Microfinance and small enterprise development: A global perspective. Care International Publications. • Field, E., Pande, R., & Rigol, N. (2014). Microfinance and firm performance: Evidence from India. American Economic Journal: Applied Economics, 6(3), 72–102. https://doi.org/10.1257/app.6.3.72 • Finch, J., & Kocieniewski, M. (2022). Microfinance interventions and borrower vulnerability: Context matters. Journal of Development Economics, 158, 102765. https: // doi. org / 10. 1016 / j. jdeveco. 2022. 102765 • Gutiérrez-Nieto, B., & Serrano-Cinca, C. (2019). Microfinance, financial inclusion, and social justice: Recent developments. Journal of Business Ethics, 156(1), 113–129. https: // doi. org/ 10. 1007 / s10551 - 017 - 3597 - 4 • Imoisi, A. I., & Godstime, I. (2014). Microfinance and entrepreneurship development. International Journal of Academic Research in Business and Social Sciences, 4(12), 34–45. https:// doi. org/ 10. 6007/ IJARBSS / v4-i12/1276 • Makorere, M. (2014). Loan characteristics and enterprise performance: Evidence from microcredit borrowers. African Journal of Business Management, 8(10), 408–417. https://doi.org/10.5897/AJBM2014.7223 • Zaby, S. (2019). The evolution of microfinance institutions and the impact on financial inclusion. Journal of Small Business and Enterprise Development, 26(5), 673–690. https: // doi. org/ 10. 1108 / JSBED - 06 - 2018 - 0180