All works in alphabetical order of title.
25 works
Prof. Eunice E. Omonzejie, BA, Ed. (Ife), Grenoble-France), MA (Benin), PhD) (Benin)
AFRICAN MIGRANT MASCULINITIES, MODERNITIES AND CULTURAL IDENTITIES: DECODING CONTEMPORARY REALITIES IN FRANCOPHONE FICTION. IT IS AN INAUGURAL LECTURE DELIVERED BY PROF. ENICE OMONZEJIE. A PROFESOR OF METROPOLITAN FRENCH AND FRANCOPHONE LITERATURES AND CULTURES.
ENGR. PROF. VINCENT OPE-OLUWA AFENYE AKPAIDA B. ENGR., ENG. (BENIN) PhD (EKPOMA)
THE SIGNIFICANCE OF PATH LOSS IN OUR LIVES AND ITS REFLECTION OF DIVINE PRESENCE
PROF. AZUKA ROMANU B.SC, PhD (EKPOMA), PGDE (NONUN) ND, PGD BIBLE THEOLOGY (NAST, EWU)
MICRO-OGRGANISMS ARE AMONG THE OLDEST FORMS OF LIFE ON EARTH. LONG BEFORE HUMANS, ANIMALS, OR PLANTS EXISTED, THESE TINY ORGANISMS WERE ALREADY SHAPING THE PLANET.
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.
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
ABSTRACT Building intelligent software that can effectively detect accident scene with the help of Google map has suffered set back because of the poor ability of the currently used software to effectively detect, identify and classify accident scene images from non accident scene images. Hence there is need for a better technique of implementing this software. In this paper, Convolutional neural networks (CNN) which is a part of deep learning algorithm was used to provide a better classification technique that any software to be developed for the purpose of detecting accident scene image can adopt. The algorithm was tested on 4000 accident scene images with other kind of images (cats and dogs) by adopting models of other researchers. In this paper, classification accuracy and Mean Squared Error (MSE) were used to evaluate the algorithm in identifying accident scene images accurately. The result was further presented using a graph of MSE against a number of trained epochs. The result of the experiment shows accuracy in the image classification and identification
THIS LECTURE INTEGRATES TWO INTERRELATED NARRATIVES: MY PERSONAL JOURNEY IN BECOMING AN OBSTETRICS AND GYNAECOLOGIST, AND THE HISTORICAL EVOLUTION OF THE DEPARTMENT OF OBSTETRICS AND GYNAECOLOGY AT IRRUA SPECIALIST TEACHING HOSPITAL (ISTH), IN COLLABORATION WITH THE COLLEGE OF MEDICINE, AMBROSE ALLI UNIVERSITY, EKPOMA.
THE CONCEPT OF GEOPHYSICS AS A TOOL FOR SUSTAINABLE ENVIRONMENT STEWARDSHIP IN NIGERIA, WE WITNESS THE TANGIBLE CONSEQUENCES OF NEGLECTING THIS SUBTERRANEAN WORLD FROM DEVASTATING BUILDING COLLAPSE IN URBAN AREAS TO THE SILENT POISONING OF OUR WATER SOURCES.
Abstract Purpose: The study was conducted to appraise user education delivery among library staff for enhanced information resource accessibility in Kogi State University, Lokoja; and in order to achieve the aim of the study, five specific objectives guided the study. Design/Methodology/Approach: Descriptive design was used for the study. A sample of 202 respondents which was made up of 62 staff and 140 users in, drawn out of a population size of 62 and (20,182) where the subject of the study. A total of two hundred and two copies of questionnaire were administered and one hundred and ninety one (191) copies representing (94.5%) were correctly filled and returned.Data were analyzed using Mean, percentages and frequencies. Findings: The findings show that user education contribute reasonably in enhancing information resource accessibility in Kogi State University Library as it promotes the value of library and its resources in the university (62%) and equips users with information literacy and search skills (61%) to a high extent. Challenges were agreed be associated with effective delivery of user education among academic librarian in Kogi State University Library, among which included lack of well-trained librarians of (57%) and inadequate number of staff (62%). Originality/Value: It was recommended that user education programme should be stepped up to include all strategies that facilitate the accessibility of information resources in Kogi State University Libraries through effective user education programme. Keywords: User Education Delivery; Library Staff; Information Resource Accessibility; Kogi State
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.
PROFESSOR CLETUS ENORELENFORH NCE (ABRAKA), B.Sc. Ed. (HONS) (UNILAG). M.Sc. (EKPOMA), Ph.D (BENIN)
PREAMBLE “He (God) raiseth up the poor out of the dust and lifteth up the beggar from the dunghill, to set them among princes, and to make them inherit the throne of glory...” I Samuel 2:8a My Acting. Vice-Chancellor sir, I am delighted and honoured for the privilege given to me to deliver the 116th lecture in the series of inaugural lectures of Ambrose Alli University, Ekpoma. Who could have imagined that I would one day stand before this distinguished audience to deliver an inaugural lecture as a professor of mathematics, due to my academic background? My parents were not educated, but loved education. It might interest you to know that in my first West African School Certificate Examination (WASC) in 1977, I got an F9 in mathematics. Despite my excellent performance and much fascination with the subject throughout primary school till final year in secondary school, the result of “expo 77” WASC examination may have revealed my weakness(es). However, I was determined not to let this initial failure derail my dreams of going to the university and becoming a mathematician but I took responsibility for my failure and didn't blame any teacher. A few years later, during my teaching practice in 1981, I returned to St. John Bosco Grammar School, Ubiaja, the school where I failed mathematics in my first WASC examination attempt, to teach the very subject I once failed. Also in 1982, I taught mathematics to the first set of students of Ewatto Grammer School, my students in both institutions performed excellently well in their final examination. Some of them are professors today as a testimony of what God could do in the life of a man who gives his/her life to Christ. In, my first seminar presentation during my Ph.D programme in 2000/2001, my supervisor remarked that I may not be able to make significant contribution in my chosen area of research (Numerical Analysis). I did not allow his assertion deter me. After submitting my first manuscript from my Ph.D work for publication, my supervisor (Prof. F.O. Otunta) of blessed memory, called me one night to say, and I quote “Cletus, this is good; your work has been published in a very reputable journal. You are doing very well!”. After my final oral defence, I published 12 different articles from my Ph.D research, in prominent journals, including the Journal of Applied Mathematics and Computation (Elsevier), which continues to be ranked among the top journals in computational mathematics globally. Today, I stand as the 4th inaugural lecturer in the Department of Mathematics, and 116th in the history of Ambrose Alli University, Ekpoma. My career has been dedicated to teaching and research in mathematics with special reference to Numerical Analysis and Computational Mathematics. My Acting Vice Chancellor sir, having taught mathematics from primary school through to the university level, this lecture therefore provides a profound opportunity for me, as a professor, to share my teaching, research experiences and new insights with both the university community and the general public. Thus, the choice of my lecture title “Everything is Mathematics: Numerical Tools, My Language.”
PROFESSOR JULIUS IDIALU IKHAREHON B.Sc. (Hons), MBA (Uniport), M.Phil. (Ife), Ph.D. (Ife), FIMC, MAMN, FBSE, MSM Professor of Strategic Management and Corporate Governance; PROFESSOR JULIUS IDIALU IKHAREHON
I return all glory and thanks to Almighty God for His grace, mercy and faithfulness in my life and academic journey. I stand before this distinguished audience with deep gratitude and humility to deliver the 155th Inaugural Lecture of Ambrose Alli University, Ekpoma. I am especially privileged to be able to deliver this lecture as the 2nd in the Department of Business Administration and the 7th in the Faculty of Management Sciences. An inaugural lecture is not just an occasion to count one's publications or celebrate one's academic career. It enables me to describe in my own words the questions that have shaped my scholarship, the evidence that has informed it, the limits of that evidence, and the contribution I hope to make to knowledge and society. The question, which is the subject of this lecture, has been with me throughout my educational career. I started at the level of business organization, asking how does corporate responsibility relate to organisational performance? I became interested in strategic management and corporate governance later on how organisations set direction, allocate assets, exercise authority, manage competing interests and sustain performance. From there the question naturally widened. Can the determinants of organisational effectiveness shed light on the ways in which nations become capable of sustainable development? I am not saying that a country is only a big business, it obviously is not. The state has constitutional powers and authority, sovereignty and coercive powers, public responsibilities and political institutions that have no direct corporate counterpart. I ask not to simply graft wholesale corporate governance onto government but rather to explore how its underlying principles of strategic direction, accountability, stakeholder coordination, institutional capability, long-term value creation and sustainability can shed light on public governance when thoughtfully adapted to the public realm.
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
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.
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.
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.
