iosr-JCE   Volume-27 ~ Issue-5 ~ Sep. - Oct. 2025

Abstract: This study analyzes the impact of security instability caused by the armed occupation of the M23 movement on the continuity of banking services in Goma and Bukavu, Democratic Republic of Congo (DRC). Using a multidisciplinary theoretical framework, human security, development under security constraints, business continuity, and financial systems in conflict zones, the research explores how conflict disrupts financial institutions and affects savers’ economic security. A mixed methodological approach, combining descriptive statistics and a multinomial logistic regression model (Logit), was applied to survey data from both cities. The results indicate that prolonged.....

Keywords: security instability, banking continuity, M23 conflict, financial resilience, inclusion finance.

[1]. (n.d.).
[2]. Addison, T. &. (2005). Post-conflict reconstruction in Africa. Journal of African Economies, 77–98.
[3]. Addison, T. &. (2007 & 2005). Post-conflict reconstruction in Africa.”. Journal of African Economies, 77–98.
[4]. Aker, J. C. (2010). Mobile phones and economic development in Africa. Journal of Economic Perspectives, 207-232.
[5]. B, & Herbane. (2010). Small business research: Time for a crisis-based view. international Small Business Journal, 43–64.

Abstract: The rate at which road accidents occur in recent times is highly alarming. In most cases this leads to loss of human lives and invaluable properties. Drinking of alcohol or smoking while driving, among others, can lead to road accidents. This is especially seen among drug or alcohol addicts. They smoke or drink even while driving and a lot of lives have been lost as a result. Efforts have been made by the state government to educate the road users on the importance of being careful while driving, yet several accidents are still recorded due to the fact that some drivers are addicts. Hence, there is a need to design a system that will monitor drivers’ attitude on the steering especially.......

Keywords: Microcontroller-Based, Alcohol monitoring, Accident Prevention, Internet of Things, Piezo Buzzer

[1] Okengwu, U. A., & Taiwo, A. A. “Design and implementation of in-vehicle alcohol detection and speed control system”, European Journal of Electrical Engineering and Computer Science 6(1), 1-10 (2022). https://doi.org/10.24018/ejece.2022.6.1.1234.
[2] Shrushti, V. “Alcohol detection system with alert notification application”, International Journal of Engineering Research & Technology 10(6), 123-130 (2021). ISSN: 2278-0181.
[3] Welcome, M. O., & Pereverzev, V. A. “Limit of blood alcohol concentration: A major problem to solve in Nigeria”, European Psychiatry 25(S1), 544 (2020). https://doi.org/10.1192/S030645272000544X. (Updated reprint from 2010).
[4] Ogazi, C., & Edison, E. “The drink driving situation in Nigeria”, Traffic Injury Prevention 13(2), 115-119 (2022). https://doi.org/10.1080/15389588.2022.2045678. (Updated edition).
[5] Koch, K., Maritsch, M., Van Weenen, E., Feuerriegel, S., Pfäffli, M., Fleisch, E., Weinmann, W., & Wortmann, F. “Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7(1), 1-25 (2023). https://doi.org/10.1145/3544548.3580975

Abstract: Understanding immune system dynamics requires models that can capture the complexity, imbalance, and stochastic nature of biological data. In this study, we develop an ensemble deep neural network framework to model immune interactions using simulated multimodal datasets integrating genomic, proteomic, and clinical parameters. The motivation stems from the persistent challenges of biological noise, data heterogeneity, and class imbalance that often reduce the reliability of conventional machine learning models in immunological prediction. Our approach combines multiple neural architecturesincluding convolutional, recurrent, and attention-based....

Keywords:
Ensemble Deep Learning; Computational Immunology; Multimodal Data Integration; Explainable Artificial Intelligence (XAI); Model Interpretability; Immune System Modeling; Biological Noise; Data Imbalance; SHAP Analysis; Neural Network Auditing; Immunotherapy Prediction; Precision Medicine; Biomedical Simulation; Feature Attribution; Deep Neural Architecture

[1]. Athaya T, Ripan RC, Li X, Hu H. Multimodal deep learning approaches for single-cell multi-omics data integration. Brief Bioinform. 2023 Sep 20;24(5):bbad313. doi: 10.1093/bib/bbad313. PMID: 37651607; PMCID: PMC10516349.Fabris, A. (2023). Fairness and Bias in Algorithmic Hiring (survey). arXiv 2023. https://arxiv.org/pdf/2309.13933
[2]. Jin W, Yang Q, Chi H, Wei K, Zhang P, Zhao G, Chen S, Xia Z, Li X. Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers. Front Immunol. 2022 Dec 1; 13:1025330. doi: 10.3389/fimmu.2022.1025330. PMID: 36532083; PMCID: PMC9751999.
[3]. A. Maslova, R.N. Ramirez, K. Ma, H. Schmutz, C. Wang, C. Fox, B. Ng, C. Benoist, S. Mostafavi, & Immunological Genome Project, Deep learning of immune cell differentiation, Proc. Natl. Acad. Sci. U.S.A. 117 (41) 25655-25666, https://doi.org/10.1073/pnas.2011795117 (2020).
[4]. Yang M et al. Multimodal data deep learning method for predicting symptomatic pneumonitis in combined radiotherapy and immunotherapy. Frontiers in Immunology. 2025
[5]. Maria Hügle, Gabriel Kalweit, Thomas Huegle, Joschka Boedecker A Dynamic Deep Neural Network For Multimodal Clinical Data AnalysisarXiv:2008.06294 [cs.LG](or arXiv:2008.06294v1 [cs.LG] for this version)https://doi.org/10.48550/arXiv.2008.06294..

Abstract: Modern technologies like the cloud, AI, and the Internet of Things (IoT) are swiftly changing the maritime sector digitally. This makes navigation, freight management, and operations more efficient. Marine operations are becoming more and more reliant on digital technology, which makes them more vulnerable to a range of cybersecurity problems.Some of these hazards include data breaches that affect important marine infrastructure, attacks on navigation systems, and people getting into networks on board without permission....

Keywords:
Cybersecurity, DoS, Real time Maritime application, IMO

[1]. Akhtar, Z. B. (2025). Artificial Intelligence (AI) Meets Blockchain: Transforming Industries For The Next Digital Era. Interdisciplinary Systems For Global Management, 1(1), 59-75.
[2]. M. Gupta And D. Gupta, “Enhancing Tokenomics: A Comparative Study Of Token Burning And Liquidity Pool Strategies For Sustainable Ecosystem Growth,” Sci. J. Metaverse Blockchain Technol., Vol. 3, No. 1, Pp. 8–26, 2025, Doi: 10.36676/Sjmbt.V3.I1.54.
[3]. Saidu, Y., Shuhidan, S. M., Aliyu, D. A., Aziz, I. A., & Adamu, S. (2025). Convergence Of Blockchain, Iot, And AI For Enhanced Traceability Systems: A Comprehensive Review. IEEE Access.
[4]. Rahman, M. S., Hossain, M. S., Rahman, M. K., Islam, M. R., Sumon, M. F. I., Siam, M. A., & Debnath, P. (2025). Enhancing Supply Chain Transparency With Blockchain: A Data-Driven Analysis Of Distributed Ledger Applications. Journal Of Business And Management Studies, 7(3), 59-77.
[5]. Reem, C. S. A., Chowdhury, M. A. H., Ashrafudoulla, M., & Ha, S. D. (2025). Leveraging Blockchain And AI For Biofilm Control In Food Processing Environments. Comprehensive Reviews In Food Science And Food Safety, 24(5), E70261.

Abstract: This research paper offers a detailed examination of the shadow banking sector in emerging economies in general, and the Non-Banking Financial Company (NBFC) in India in particular. It discusses how NBFCs have emerged as important financial intermediaries, bridging the credit gaps left by mainstream banks and supporting the growth story of the economy. The paper studies the nuances of the Indian NBFC crisis, triggered by the default of Infrastructure Leasing & Financial Services (IL&FS) in 2018, and its contagion impact on the financial system....

Keywords:
Shadow Banking, Non-Banking Financial Companies (NBFCs), Emerging Economies, India, Financial Crisis, Systemic Risk, Regulation, IL&FS, Asset-Liability Mismatch.

[1]. Acharya, V. V., Richardson, M., Van Nieuwerburgh, S., & White, L. J. (Eds.). (2018). Restoring Financial Stability: How To Repair A Failed System. John Wiley & Sons.
[2]. Caprio, G., Jr., Demirgüç-Kunt, A., & Levine, R. (2007). Rethinking Bank Regulation: Till Angels Govern. Cambridge University Press.
[3]. Financial Stability Board. (2014). Global Shadow Banking Monitoring Report 2014.
[4]. Gorton, G., & Metrick, A. (2012). Regulating The Shadow Banking System. Brookings Papers On Economic Activity, 2012(1), 261-312.
[5]. Mcculley, P. (2007). Teton Reflections. PIMCO Global Central Bank Focus..