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Article22 May 2018

Towards the digitalization of Restaurant Business Process for Food Ordering in Nigeria Private University: The Design Perspective. A Study of Samuel Adegboyega University Edo State Nigeria

Awojide, Simon1 , Omogbhemhe, I.M.2 , Awe, O.S. 2 and Babatope T.S

Abstract- The use of information and Communication Technology (ICT) has improved a lot of businesses by Digitalizing their business processes. Traditional (manual) Ordering of food in a Restaurant in the University Environment has been in practice for long, which brought about piles of Challenges. Digitalizing the Restaurant business processes in Restaurant was the focus of this paper. In view of this, the system developed in this paper was able to solve the challenges of the existing ordering of Food in the restaurant. This paper has provided an overview Food Ordering system (FOS) and has provided a digital design for such system. It also discusses the advantages associated with integrating the system across the Restaurant Business. The FOS developed in this work, when fully implemented, will go a long way to addressing the problem faced by the restaurant management in the area of managing their customer’s food ordering, management of customer’s information and having quick access to information. Index Terms- ICT, Food Ordering, Restaurant Busi

Article2026

Monitoring CSM in Northern Nigeria: A Logistic and OLS Regression Approach

Sarah Oluwatosin Elakhe, Rofiyat Patience Braimah, Idemudia Waziri Edokpa and Odunayo Joseph Braimah

Abstract Cerebrospinal meningitis (CSM) remains a major public health challenge in sub-Saharan Africa. This study analyzed records of 390 suspected CSM patients (2020–2023) from Aminu Kano Teaching Hospital, Nigeria, using sex, age, and month as explana tory variables. Logistic regression and ordinary least square (OLS) models were fitted to identify epidemic patterns. Findings re veal that children under five are most vulnerable, with infections peaking during Harmattan months. Males are more prone than females. Logistic regression explained 4.3% of variation compared to 3.2% for OLS, indicating better predictive accuracy. Age was not significant, while sex and month showed significant negative effects. The study highlights key demographic and seasonal risk factors for CSM in Northwestern Nigeria.

Article9 May 2018

Towards the digitalization of Restaurant Business Process for Food Ordering in Nigeria Private University: The Design Perspective. A Study of Samuel Adegboyega University Edo State Nigeria

Awojide, Simon, Omogbhemhe, I.M. , Awe, O.S. and Babatope T.S

Article26 July 2018

Web Service Framework for Automating Academic Clearance Processing System

Omogbhemhe Izah Mike Department of Computer Science Ambrose Alli University, Ekpoma

ABSTRACT Clearance, as it applies to the education sector, is a process whereby the sector verify the authenticity of information provided by the student either at the point of entering the university or at the point of graduation. This kind of system is a student information management system whose main task is to process and manage student clearance data. This clearance exercises subject students to move from one office to another using a particular form (clearance form) in verifying their information by signing this form by officers assigned to these offices. Clearance processing in Nigeria tertiary institutions is unnecessary delayed due to high human involvement in the process. These has resulted in many students fainted during the exercise, some students missed their lectures, test and some students’ academic information (result computation) being delayed. This paper argues that the most excellent way for this automation is the establishment of service oriented web system. Consequently, this paper proposed and designed a web service framework suitable for implementing automated clearance processing system in Nigerian tertiary institutions

Article21 September 2021

A Blockchain based Algorithm for Electronic Voting System

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.

Article9 February 2025

MACHINE LEARNING APPROACH FOR THE PREDICTION OF BLADDER CANCER STAGES BASED ON NEXT-GENERATION SEQUENCING DATA

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

Article6 June 2025

BIOMETRIC BANK ACCOUNT VERIFICATION SYSTEM IN NIGERIAN: CHALLENGES AND OPPORTUNITIES

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.

Article20 October 2021

Model for Predicting Bank Loan Default using XGBoost

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.

Article12 March 2025

A Hybrid Machine Learning Model for Location-Specific Crop Recommendation Using Soil and Climate Parameters

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

Article7 February 2011

On Communication Protocols that Compute Almost Privately

Marco Comi; Bhaskar DasGupta; Michael Schapira et al.

A traditionally desired goal when designing auction mechanisms is incentive compatibility, i.e., ensuring that bidders fare best by truthfully reporting their preferences. A complementary goal, which has, thus far, received significantly less attention, is to preserve privacy, i.e., to ensure that bidders reveal no more information than necessary. We further investigate and generalize the approximate privacy model for two-party communication recently introduced by Feigenbaum et al.[8]. We explore the privacy properties of a natural class of communication protocols that we refer to as "dissection protocols". Dissection protocols include, among others, the bisection auction in [9,10] and the bisection protocol for the millionaires problem in [8]. Informally, in a dissection protocol the communicating parties are restricted to answering simple questions of the form "Is your input between the values αand β(under a predefined order over the possible inputs)?". We prove that for a large class of functions, called tiling functions, which include the 2nd-price Vickrey auction, there always exists a dissection protocol that provides a constant average-case privacy approximation ratio for uniform or "almost uniform" probability distributions over inputs. To establish this result we present an interesting connection between the approximate privacy framework and basic concepts in computational geometry. We show that such a good privacy approximation ratio for tiling functions does not, in general, exist in the worst case. We also discuss extensions of the basic setup to more than two parties and to non-tiling functions, and provide calculations of privacy approximation ratios for two functions of interest.