Okure Obot - Academia.edu (original) (raw)

Papers by Okure Obot

Health informatics journal, Apr 1, 2024

Physica scripta, Feb 19, 2024

Febrile diseases often exhibit overlapping symptoms, posing a challenge for their differential di... more Febrile diseases often exhibit overlapping symptoms, posing a challenge for their differential diagnosis. This challenge is particularly critical in pregnant women and children, where early and accurate diagnosis is vital to mitigate the elevated risk of maternal mortality prevalent in tropical and subtropical regions. Despite the commonality of fever as a symptom, the diverse range of potential co-morbidities necessitates an exploration of associated illnesses. This study employs the eXtreme Gradient Boosting (XGBoost) machine learning algorithm to classify febrile diseases' co-morbidities in pregnant women and children under 5 years. The dataset, comprising 1,350 records from selected health facilities across Niger-delta states in Nigeria, contributes to informed decisionmaking by physicians, ultimately enhancing healthcare provision. Evaluation results demonstrate the classifier's high precision (0.995) and recall (1.00) for the children dataset, while precision and recall of 1.00 are achieved for the pregnant women dataset. To facilitate model explanation and result interpretation, an eXplainable Artificial Intelligence (XAI) approach, specifically the SHapley Additive exPlanations (SHAP) method, is applied. The summary plot highlights upper and lower respiratory tract infections and malaria as the predominant diseases co-morbidities in children. In contrast, pregnant women exhibit upper and lower urinary tract infections, and malaria as the highest-ranking diseases comorbidity. These results underscore the potential of ML techniques in accurately classifying febrile conditions' comorbidities, contributing to the reduction of adverse health outcomes. The study's findings offer valuable insights for healthcare providers, enabling them to deliver more targeted and effective care to these vulnerable populations, thereby enhancing overall well-being.

World Journal of Applied Science & Technology, Nov 27, 2023

Cyber security is becoming more worrisome; malware is spreading by the day through proliferation ... more Cyber security is becoming more worrisome; malware is spreading by the day through proliferation and distribution of variants of known family signatures using obfuscation techniques. Mobile devices components such as central processing unit, memory, battery life, executable files and operating systems are constantly being attacked and rendered unusable. Attack agents are specifically evading detection, damaging mobile devices' executive files, stealing information, surcharging users for SMS sent and received without their knowledge or permission, and freezing applications for a ransom among others. This research work is keying into the fight against malware intrusion by designing and developing an intrusion detection system (IDS) using ensemble learning, boosting. Adaboost algorithm trains base classifiers (KNN and SVM) using network security laboratory-knowledge discovery in databases (NSL-KDD) dataset to build a more formidable classifier that will detect malware intrusion in mobile devices using cloud technology. The result obtained in this combination technique is 91.4% accurate with a bias (standard deviation) as low as 2.7%.

European journal of computer science and information technology, May 14, 2023

Conventionally, the knowledge of experts on the drilling features of a potential oil well is prac... more Conventionally, the knowledge of experts on the drilling features of a potential oil well is practically used to predict the volume of initial oil in place. Experts used different knowledge-based models such as volumetric, material balancing, analogy to predict the initial oil in place. In this study, 816 datasets were collected from Shell petroleum development company (SPDC) where the volumetric method is used for their prediction. These datasets were preprocessed and applied on two machine learning techniques of random forest and supervised vector regressor to predict the initial oil in place and the results obtained were compared with that obtained from SPDC.The results of computation using 4 principal features from the 9 features were closer to that obtained from SPDC than the computations using all the 9 features. The results of computations with random forest were also compared with that of supervised vector regressor. The results of random forest covary strongly (0.970) with the field results more than that of the support vector regressor (0.832). The uniqueness of this study is shown in the use of 4 predicting features (independent variables) to obtain prediction values that are very close to that obtained in the field with 9 features. This is obtained with random forest, so it can be recommended as a reliable machine technique for the prediction of initial oil in place in the Niger delta region.

Journal of Engineering Research and Reports, Apr 8, 2024

The stress of marking assessment scripts of many candidates often results in fatigue that could l... more The stress of marking assessment scripts of many candidates often results in fatigue that could lead to low productivity and reduced consistency. In most cases, candidates use words, phrases and sentences that are synonyms or related in meaning to those stated in the marking scheme, however, examiners rely solely on the exact words specified in the marking scheme. This often leads to inconsistent grading and in most cases, candidates are disadvantaged. This study seeks to address these inconsistencies during assessment by evaluating the marked answer scripts and the marking scheme of Introduction to File Processing (CSC 221

Springer eBooks, 2014

The influence of noise and reverberation in Digital Forensic voice evidence can conceal the ident... more The influence of noise and reverberation in Digital Forensic voice evidence can conceal the identification, verification and processing of crime data. Computationally, the efficiency in processing speech signals largely depends on the integrity and authenticity of audio/voice recordings. Our interest is on improving integrity, vis-a-vis the intelligibility of speech signals. We achieved this in four folds. First, a speech quality enhancement technique that cleans and rebuilds defective speech data for quality Forensic analysis is proposed by exploring an optimal estimator for the magnitude spectrum, where the Discrete Fourier Transform (DFT) coefficients of clean speech are modelled by a Laplacian distribution and the noise DFT coefficients are modelled using a Gaussian distribution. Second, an automatic speech pre-processing algorithm for phoneme segmentation of raw speech data, capable of iteratively refining Hidden Markov Model (HMM) speech labels for improved intelligibility is introduced. Third, a simulation of the distortion from a quantised R-bit and computation of the Signal-to-Noise Ratio (SNR) for the signal to quantisation noise is carried out for the purpose of managing speech signal distortions. Fourth, an investigation of the effect of confused phonemic and tone bearing unit features on the intelligibility of speech is presented to assist Forensic experts decode voice disguise or language “barriers” that may impede proper Forensic voice analysis. Results obtained in this investigation reveal a future of prospects in the field of Forensic intelligence and is most likely to reduce unnecessary setbacks during Forensic analysis.

Springer eBooks, Oct 14, 2022

Typhoid fever is a global health problem, which seems neglected, but is responsible for significa... more Typhoid fever is a global health problem, which seems neglected, but is responsible for significant levels of morbidity in many regions of the world, with about 12 million cases annually, and about 600,000 fatalities. Diagnosis of typhoid poses a great deal of challenge because its clinical presentation is confused with those of many other febrile infections such as malaria, yellow fever, etc. In addition, most developing countries do not have adequate bacteriology laboratories for further investigations. Decision support systems have been known to increase the efficiency and effectiveness of the diagnosis process, in addition to improving access; however, most existing decision support models for diagnosis of diseases have largely focused on 'non-tropical' conditions. An effective decision support model for diagnosis of tropical diseases can only be developed though the engineering of experiential knowledge of physicians who are experts in the management of such conditions. In this study, we mined experiential knowledge of twenty-five tropical disease specialist physicians to develop a decision support system based on the Analytic Hierarchy Process (AHP). The resulting model was tested based on 2044 patient data. Our model successfully determined the occurrence (or otherwise) of typhoid fever in 78.91% of the cases, demonstrating the utility of AHP in the diagnosis of typhoid fever.

Modern Applied Science, Aug 30, 2017

Maintaining healthy organization-customers relationship has positive influence on customers' beha... more Maintaining healthy organization-customers relationship has positive influence on customers' behavioral tendencies as regards preference to products and services, buying behavior, loyalty, satisfaction, and so on. To achieve this, an in-depth analysis of customers' characteristics and purchasing behavioral trend is required. This paper proposes a hybrid unsupervised learning framework consisting of k-means algorithm and self-organizing maps (SOMs) for customer segmentation and behavior analysis. K-means algorithm was used to partition the entire input space of customers' transaction dataset into 3 and 4 disjoint segments based on customers' frequency (F) and monetary value (MV). SOM provided visualization of the underlying clusters and discovered customers' relationships in the dataset. Interaction of F and MV clusters resulted in 12 sub-clusters. An in-depth analysis of each sub-cluster was also performed and appropriate customer relationship management (CRM) strategies established for each sub-cluster. Discovered knowledge will guide effective allocation of resources to each customer cluster and other organizational decision support functions much required by CRM systems.

Expert Systems With Applications, Mar 1, 2011

The purpose of this study is to make the case for the utility of decision support systems (DSS) i... more The purpose of this study is to make the case for the utility of decision support systems (DSS) in the diagnosis of malaria and to conduct a case comparison of the effectiveness of the fuzzy and the AHP methodologies in the medical diagnosis of malaria, in order to provide a framework for determining the appropriate kernel in a fuzzy-AHP hybrid system. The combination of inadequate expertise and sometimes the vague symptomatology that characterizes malaria, exponentially increase the morbidity and mortality rates of malaria. The task of arriving at an accurate medical diagnosis may sometimes become very complex and unwieldy. The challenge therefore for physicians who have limited experience investigating, diagnosing, and managing such conditions is how to make sense of these confusing symptoms in order to facilitate accurate diagnosis in a timely manner. The study was designed on a working hypothesis that assumed a significant difference between these two systems in terms of effectiveness and accuracy in diagnosing malaria. Diagnostic data from 30 patients with confirmed diagnosis of malaria were evaluated independently using the AHP and the fuzzy methodologies. Results were later compared with the diagnostic conclusions of medical experts. The results of the study show that the fuzzy logic and the AHP system can successfully be employed in designing expert computer based diagnostic system to be used to assist non-expert physicians in the diagnosis of malaria. However, fuzzy logic proved to be slightly better than the AHP, but with non-significant statistical difference in performance.

International Journal of Business Information Systems, 2020

Studies in health technology and informatics, 2010

A neuro-fuzzy decision support system is proposed for the diagnosis of heart failure. The system ... more A neuro-fuzzy decision support system is proposed for the diagnosis of heart failure. The system comprises; knowledge base (database, neural networks and fuzzy logic) of both the quantitative and qualitative knowledge of the diagnosis of heart failure, neuro-fuzzy inference engine and decision support engine. The neural networks employ a multi-layers perception back propagation learning process while the fuzzy logic uses the root sum square inference procedure. The neuro-fuzzy inference engine uses a weighted average of the premise and consequent parameters with the fuzzy rules serving as the nodes and the fuzzy sets representing the weights of the nodes. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. An experimental study of the decision support system was carried out using cases of some patients from three hospitals in Nigeria with the assistance of their medical personnel who collected patients&amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; data over a period of six months. The results of the study show that the neuro-fuzzy system provides a highly reliable diagnosis, while the emotional and cognitive filters further refine the diagnosis results by taking care of the contextual elements of medical diagnosis.

Tropical Medicine and Infectious Disease, Nov 25, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

A study of the orthodox practice of diagnosing hepatitis revealed that inexactness in the diagnos... more A study of the orthodox practice of diagnosing hepatitis revealed that inexactness in the diagnostic results has led several patients into abusing therapies. This prompted a further study into how this could be resolved. In this regard, effort was made for medical doctors to specify some linguistic labels while taking history and performing medical examinations on the patients. The effort

Journal of Engineering Research and Reports, Jul 15, 2023

Machine learning is a form of artificial intelligence that is applicable in all fields of study. ... more Machine learning is a form of artificial intelligence that is applicable in all fields of study. It incorporates many algorithms used in carrying out various tasks such as classification, predictions, estimations, comparisons, approximations, optimization and selections. In estimating original oil in place, which affords the explorationist the foresight on the total amount of crude oil that is potentially in reservoir. Machine learning is found to perform reserves estimation with speed and accuracy where insufficient data are available. These among other attributes of Machine Learning motivated a systematic literature review of studies undertaken between 2010 and 2021 and explore the strengths and limitations reported in the studies. In the oil industry, different types of data are gathered from subsurface and surface in order to know the reservoir hydrocarbon potential. Sensorsare known to be able to collect these data in large quantity, analyse and

Bio-Algorithms and Med-Systems, Sep 1, 2013

In this article, we present the conventional method and neuro-fuzzy model for the diagnosis and t... more In this article, we present the conventional method and neuro-fuzzy model for the diagnosis and therapy of heart disease. The neuro-fuzzy system provides a basis for creating a decision support system that has a learning ability and the capacity to deal with vagueness and unstructuredness in disease management. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. These filters further refine the diagnosis and therapy processes by taking care of the contextual elements.

Springer eBooks, 2009

A neuro-fuzzy expert system is proposed for the diagnosis of heart failure. The system comprises;... more A neuro-fuzzy expert system is proposed for the diagnosis of heart failure. The system comprises; knowledge base (database, neural networks and fuzzy logic) of both the quantitative and qualitative knowledge of the diagnosis of heart failure, neuro-fuzzy inference engine and decision support engine. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. An experimental study of the decision support system was carried out using cases of some from three hospitals in Nigeria with the assistance of their medical personnel who collected patients’ data over a period of six months. The results of the study show that the neuro-fuzzy system provides a highly reliable diagnosis, while the emotional and cognitive filters further refine the diagnosis results by taking care of the contextual elements of medical diagnosis.

International journal of quantitative research in education, 2021

International Journal of Medical Engineering and Informatics, 2008

The application of the conventional symbolic rules found in knowledge base technology to the mana... more The application of the conventional symbolic rules found in knowledge base technology to the management of a disease suffers from its inability to evaluate the degree of severity of a symptom and by extension, the degree of the illness. Fuzzy logic technology provides a simple way to arrive at a definite conclusion from vague, ambiguous, imprecise and noisy data (as found in medical data) using linguistic variables that are not necessarily precise. In order to achieve this, a study of a knowledge base system for the management of diseases was undertaken. The root sum square of drawing inference was employed to infer the data from the rules developed. This resulted in the establishment of some degrees of influence on the diseases. Using malaria as a case study, a system that uses Visual Basic .Net development environment was developed and the results of the computations are presented in this research.