FUPRESpace
Welcome to FUPRESpace, The Institutional Repository of Federal University of Petroleum Resources. A collection of theses, articles, books, videos, images, lectures, papers, data sets, and all types of digital content originating from the Federal University of Petroleum Resources, Nigeria. This repository is managed by the University Library
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- This contains the intellectual works of the faculty members in the College of Computing
- This contains the intellectual works of the faculty members in the College of Engineering and Technology
- This contains the intellectual works of the faculty member in the College of Maritime and Offshore Studies.
- This contains the intellectual works of the faculty members in the College of Science
- This contains the intellectual works of the staff of the University Library
Recent Submissions
A Comparative Analysis Performance of Data Augmentation on Age-Invariant Face Recognition Using Pretrained Residual Neural Network
(Journal of Theoretical and Applied Information Technology, 2021) ABERE, REUBEN; OKOKPUJIE, KENNEDY; OFOCHE, JOYCE C.; BIOBELEMOYE, BASUO J.; OKOKPUJIE, IMHADE PRINCESS
There has been an immense improvement in face recognition research. Unfortunately, the accuracy of face recognition systems recognizing the same person over time due to ageing is open research. Minor geometric changes in the face that occur due to ageing contribute to face recognition systems' inaccuracy. Researchers,
over subsequent years, have come up with methods to improve the performance of Age Invariant Face Recognition (AIFR) systems, the most recent one being the use of Convolutional Neural Network (CNN) to create face recognition models. The pre-trained residual network (ResNet) is trained and tested using a heterogeneous database to actualize this improvement. The heterogeneous database consists of images from 82 Caucasian subjects in the FG-Net database and 11 African subjects. These obtained images were augmented using geometric transformation and Noise to increase the amount of data for training. Afterwards, a model robust is developed. The Sliding Window framework was used to detect the faces fed into CNN for training and testing. After getting the results from our classification model, an analysis was carried out on the classification models of both the original dataset and augmented datasets. It was observed that the model performed remarkably with the noise-injected dataset and performed worst with the geometric transformation database.
Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost
(Journal of Computing Theories and Applications, 2024) Abere, Reuben Akporube et. al.
Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) synthetic minority oversampling technique (SMOTE), and (d) SMOTE-edited nearest neighbor (SMOTEEN). Both tree-based ensembles were constructed and trained to assess how well they performed with the chi-square feature selection mode. The result shows that RF achieved F1 0.9898, Accuracy 0.9973, Precision 0.9457, and Recall 0.9698 for the default, RUS, SMOTE, and SMOTEEN resampling, respectively. Xgboost outperformed Random Forest with F1 0.9945, Accuracy 0.9984, Precision 0.9616, and Recall 0.9890 for the default, RUS, SMOTE, and SMOTEEN, respectively. Studies support that the use of SMOTEEN resampling outperforms other
schemes; while, it attributed XGBoost enhanced performance to hyper-parameter tuning of its decision trees. Retention strategies of recency-frequency-monetization were used and have been found to curb churn and improve monetization policies that will place business managers ahead of the curve of churning by customers.
Assessment of Asymmetric Mangrove Restoration Trials in Ogoniland, Niger Delta, Nigeria: Lessons for Future Intervention
(Ecological Restoration, 2016) Nenibarini Zabbey; Franklin B.G. Tanee
Mangrove restoration has been undertaken with varying degrees of success in many tropical and subtropical marine shorelines around the globe. However, mangrove reforestation in the Niger Delta, Africa’s largest delta and mangrove belt is, at best, rudimentary. Here, we present floristic results on two opportunistic artificial mangrove regeneration case studies aimed at restoring mangrove swamps damaged by oil pollution (Bodo Creek) and colonized by invasive Nypa
fruticans (nypa palm) (Kono Creek) in Ogoniland, eastern Niger Delta, Nigeria. Nursery raised seedlings of the delta’s dominant Rhizophora racemosa were planted 1 m apart in zigzag fashion at both locations. Planting at the oil-polluted site was preceded by soil quality investigation and bio-stimulation with fertilizer, whereas at Kono Creek, there was no addition of fertilizer before and after planting. A 3-year post planting evaluation of survival rate, growth, and girth parameters showed better performance of mangroves at the Bodo Creek restoration than at the Kono Creek restoration, with survival rates of 72% and 12%, respectively. In sharp contrast to the Bodo Creek restoration, few stands of the planted mangroves at the Kono Creek restoration had started producing propagules. Investigations of soil quality, and where necessary, followed by remedial treatment, particularly augmenting key nutrients, are critical precursors of successful artificial mangrove regeneration.
BEHeDaS: A Blockchain Electronic Health Data System for Secure Medical Records Exchange
(Journal of Computing Theories and Application, 2024) Abere, Reuben Akporube et. al.
Blockchain platforms propagate into every facet, including managing medical services with professional and patient-centered applications. With its sensitive nature, record privacy has become imminent with medical services for patient diagnosis and treatments. The nature of medical records has continued to necessitate their availability, reachability, accessibility, security, mobility, and confidentiality. Challenges to these include authorized transfer of patient records on referral, security across platforms, content diversity, platform interoperability, etc. These, are today – demystified with blockchain-based apps, which proffers platform/application services to achieve data features associated with the nature of the records. We use a permissioned-blockchain for healthcare record management. Our choice of permission mode with a hyper-fabric ledger that uses a world-state on a peer-to-peer chain – is that its smart contracts do not require a complex algorithm to yield controlled transparency for users. Its actors include patients, practitioners, and health-related officers as users to create, retrieve, and store patient medical records and aid interoperability. With a population of 500, the system yields a transaction (query and https) response time of 0.56 seconds and 0.42 seconds, respectively. To cater to platform scalability and accessibility, the system yielded 0.78 seconds and 063 seconds, respectively,
for 2500 users.
Two-Echelon Inventory Model With Service Consideration and Lateral Transshipment
(WSEAS TRANSACTIONS on SYSTEMS, 2021) ZELIBE, SAMUEL CHIABOM; BASSEY, UNANAOWO NYONG
This paper considers a two-echelon inventory system with service consideration and lateral transshipment. So far, researchers have not extensively considered the use of lateral transshipment for such systems. Demand arrivals at both echelons follow the Poisson process. We introduce a continuous review base stock policy for the system in steady state, which determined the expected level for on-hand inventory, expected lateral transshipment level and expected backorder level. We showed that the model satisfied convexity with respect to base stock level. Computational experiments showed that the model with lateral transshipment performed better that the model without lateral transshipment.