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James, Gabriel Gregory
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Computer Science & IT

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Optimizing Business Intelligence System Using Big Data and Machine Learning James, Gabriel Gregory; P, Oise G; G, Chukwu E; A, Michael N; F, Ekpo W; E, Okafor P
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.631

Abstract

The Business Intelligence (BI) and Data Warehouse (DW) system deployed in the Nigerian National Petroleum Corporation should provide cooperate decision makers with real-time information to help them identify and understand key business factors to make the best decisions for the situation at any given time. The relentless collection of data from user interactions have introduced both a high level of complexity, as well as a great opportunity for businesses. In addition to connecting not just people, but also machines to the internet, and then collecting data from these machines via sensors would result in an unimaginable repository of data. This ever-increasing collection of data is known as Big Data. Integrating this with existing Business intelligence systems and deep analysis using Machine Learning algorithms, Big Data can give useful insights into business problems and perhaps even to make suggestions as to when and where future problems will occur (Predictive Analysis) so that problems can be avoided or at least mitigated. This paper targets at developing a system capable of optimizing a business intelligence using big data and machine learning approach. The design of a system to optimize the Business Intelligence System using Machine Learning and Big Data at NNPC was successfully carried out. The System was able to automatically analyze the sample report under NNPC permission to use and it generated expected predictive outputs which serves as a better guide to managers. When applying Deep Learning, one seeks to stack several independent neural network layers that, working together, produce better results than the already existing shallow structures.
Predictions of Criminal Tendency Through Facial Expression Using Convolutional Neural Network James, Gabriel Gregory; Okafor, Peace Chiamaka; Chukwu, Emenike Gabriel; Michael, Nseobong Archibong; Ebong, Oscar Aloysius
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.635

Abstract

Criminal intention is a critical aspect of human interaction in the 21st-century digital age where insecurity is on the high side as a major global threat. Kidnapping, killings, molestation of all sorts, gender-based violence, terrorism, and banditry are the trends of criminality in our nation, as such, there is a need to effectively explore innovative means to identify and cope with this evil menace in our society. The facial positioning of humans can tell their evil intention even if they pretended to smile with the evil in their minds. In normal instances, it may be very difficult to predict the heart of man, but with the trending information technology like image processing, the state of a human face could be used as a means to read their tendencies. This paper proposes a deep learning model based on the FER2013 dataset through the implementation of a CNN model that predicts criminal tendencies with the help of facial expressions. With this goal in mind, we explore a new level of image processing to infer criminal tendency from facial images through a convolutional neural network (CNN) deep learning algorithm in other to discriminate between criminal and non-criminal facial images. It was observed that CNN was more consistent in learning to reach its best test accuracy of 90.6%, which contained 8 convolutional layers. To increase the accuracy of this model, several procedures were explored using Random Search from the Keras tuner library, testing out various numbers of convolutional layers and Adam optimizer. It was also noticed that applying the dissection and visualization of the convolutional layers in CNN reveals that the shape of the face, eyebrows, eyeball, pupils, nostrils, and lips are taken advantage of by CNN to classify the images.
Oil and Gas Pipeline Leakage Detection using IoT and Deep Learning Algorithm Ekong, Anietie P.; James, Gabriel Gregory; Ohaeri, Ifeoma
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.652

Abstract

Pipeline leaks are a frequent occurrence in oil and gas infrastructure worldwide. Though leak detection systems are expected to be installed on all pipelines in the near future, relying on human efforts to physically monitor these pipelines is and will continue to be challenging. Though today's leak detection techniques are not able to completely stop leaks from occurring or to detect most leaks, they are essential in lessening their effects. Despite recent developments toward solving this problem, the solution still falls short of expectations. This research presents an approach to pipeline leak detection by leveraging on the exceptional abilities of Convolutional Neural Network (CNN) and Internet of Things (IoT). A comprehensive dataset on oil and pipeline leakage is collected, and the CNN model is developed and trained with the collected dataset. Thereafter, the trained model is integrated into the monitoring system to provide notifications of leaks. The model is adaptable and scalable and its performance, as evaluated, shows an improvement over existing systems with an accuracy of 97% hence well suited for deployment in various pipeline networks for the overall improvement of safety environment in the oil and gas sector.
Co-Authors A, Michael N Chukwu, Emenike Gabriel E, Okafor P Ebong, Oscar Aloysius Ekong, Anietie P. F, Ekpo W G, Chukwu E Michael, Nseobong Archibong Ohaeri, Ifeoma Okafor, Peace Chiamaka P, Oise G