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.

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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

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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

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