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A Blockchain based Algorithm for Electronic Voting System
(International Journal of Computer Applications, 2021-09-21) Omogbhemhe M.I. Ambrose Alli University, Ekpoma
ABSTRACT The lack of fidelity in the present system of voting has made many researchers to advocate for a better system. This has resulted in developing peer-to-peer version of election voting system like the online voting system. This system would allow online voting to be done without the need of worrying about the authenticity of the result generated. This kind of digital signatures provide part of the solution, but the main benefits are lost if the verification and counting of the votes are entrusted to an organization. This paper proposed a solution to the counting and result authenticity problem faced while using the peer-to-peer system of voting that must be manage by a single organization. The solution is the development of a blockchain based voting system to achieving optimal vote’s fidelity in the election after voting. The network timestamps votes by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be altered without redoing the proof-of-work.
MACHINE LEARNING APPROACH FOR THE PREDICTION OF BLADDER CANCER STAGES BASED ON NEXT-GENERATION SEQUENCING DATA
(African Journal of Applied Research Vol. 12, No. 3 (2026), pp. 170-192, 2025-02-09) Prof. Imhenkuomon, A. from Ambrose Alli university, Ekpoma; and Omogbhemhe, M. I. from Ambrose Alli university, Ekpoma
ABSTRACT Purpose: The purpose of this paper is to apply Machine learning algorithms for the classification of various stages of bladder Cancer (BCa) based on RNA-Seq transcriptome per million(TPM) gene counts data and its corresponding pathological stages from the TCGA database. The objective is to assess classification performance across different stages. Design/Methodology/Approach: This study applied a computational research design on publicly available BCa gene expression data from The Cancer Genome Atlas (TCGA). Multiple supervised machine learning predictive modelling algorithms were trained and evaluated, with a nested crossvalidation design. A forward feature selection technique was used to select the best features for ML classifiers, in conjunction with 3-fold nested cross-validation (nCV), applied to binary classification using machine learning algorithms. The dataset preprocessing was carried out in two phases using the R and Python programming languages. Research Limitation: Reliance on downloaded data raises concerns about the data generator’s bias. Findings: This study suggests that TPM profiles of bulk RNA-seq samples are unreliable for separating adjacent stages of bladder cancer. These findings suggest that bulk transcriptomic data should not be used solely to inform treatment decisions for bladder cancer. Rather, it will be more informative to integrate molecular subtyping with multi-omics data or to make models that can directly predict clinical outcomes. Practical Implication: In practical terms, these findings suggest that bulk RNAseq TPM transcriptomic data should not be solely relied on for staging bladder cancer in clinical or predictive settings. Instead, more informative approaches such as combining molecular subtypes, integrating multi-omics data, or focusing on models that predict clinical outcomes are likely to provide greater value for decision-making and future research. Social Implication: This highlights the effect of over-relying on AI diagnostics that do not capture the full biological characteristics, which is essential for protecting patient safety. Originality/Value: This research examined the application of machine learning algorithms to predict bladder cancer stages using RNA-seq TPM gene-count NGS data from the TCGA database, a method that researchers have not previously considered. Keywords: Bioinformatics. bladder cancer. machine learning, next-generation sequencing
BIOMETRIC BANK ACCOUNT VERIFICATION SYSTEM IN NIGERIAN: CHALLENGES AND OPPORTUNITIES
((IJCSIS) International Journal of Computer Science and Information Security, Vol. 13, No. 6, June 2015, 2025-06-06) Prof. Omogbhemhe Izah mike from Ambrose Alli University, Ekpoma; Ibrahim Bayo Momodu from Ambrose Alli University, Ekpoma
ABSTRACT Due to the need for strong security for customer financial information in the banking sector, the sector has started the introduction of biometric fingerprint measures in providing securities for banking systems and software. In this paper, we have carefully explained the methodology of using this technology in banking sectors for customer verification and authentication. The challenges and opportunities associated with this technology were also discussed in this paper.
Model for Predicting Bank Loan Default using XGBoost
(International Journal of Computer Applications, 2021-10-20) Prof. Omogbhemhe M.I. of the department of computer science; Dr. Momodu I.B.A. of the department of computer science
ABSTRACT Loan default prediction is one of the most important and critical problem faced by many banks and other financial institutions as it has a huge effect on their survival and profit. Many traditional methods exist for mining information about a loan application and have been greatly studied and applied in the past. These methods seem to be underperforming as there have been reported increases in the amount of bad loans and defaulters among many financial institutions. In this paper, gradient boosting algorithm called XGBoost was used for loan default prediction. The prediction is based on a loan data from a leading bank taking into consideration data sets from both the loan application and the demographic of the applicant. Similarly, important evaluation metrics such as Accuracy, Recall, precision, F1-Score and ROC area of the analysis were used. The paper provides an effective basis for loan credit approval in order to identify risky customers from a large number of loan applications using predictive modeling. The full utilization of this model will assist financial institutions in knowing a risking customer that may default in loan payment before lending.
A Hybrid Machine Learning Model for Location-Specific Crop Recommendation Using Soil and Climate Parameters
(BENIN JOURNAL OF PHYSICAL SCIENCES BJPS Vol. 2(2), December, pg. 29-46 (2025), 2025-03-12) Dr. M. O. ODIGHI of the department of computer science; Prof. M. I. OMOGBHEMHE of the department of computer science
Abstract Accurate crop recommendation systems are essential for optimizing agricultural productivity and sustainability, yet existing approaches often fail to integrate diverse environmental factors and adapt to location-specific conditions. This study proposes a hybrid machine learning model that leverages soil and climate parameters through a threestage pipeline: Random Forest for feature selection, Extreme Gradient Boosting for robust prediction, and a lightweight Feed forward Neural Network for final decision-making. The model was evaluated on real-world datasets, demonstrating superior performance with an overall accuracy of 95.3%, precision of 95.1%, recall of 95.0%, F1-score of 95.1%, and a root mean squared error (RMSE) of 0.12. Ablation experiments reveal that excluding Random Forest feature selection reduces accuracy to 91.9%, omitting geospatial adaptation lowers accuracy to 92.6%, and replacing the neural network with logistic regression drops accuracy further to 89.2%. These results confirm the effectiveness of the hybrid architecture and the critical role of feature selection and geospatial adaptation in enhancing crop recommendation accuracy. This work presents a scalable and locationsensitive framework with significant potential to advance precision agricultur

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