DSpace Angular :: Browsing by Author "Jimada-Ojuolape, Bilkisu"

Browsing by Author "Jimada-Ojuolape, Bilkisu"

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A chi-square-SVM based pedagogical rule extraction method for microarray data analysis
(Institute of Advanced Engineering and Science (IAES), 2020) Salawu, Mukhtar Damola; Arowolo, Micheal Olaolu; Abdulsalam, Sulaiman Olaniyi; Isiaka, Rafiu Mope; Jimada-Ojuolape, Bilkisu; Olumide, Mudashiru Lateef; Gbolagade, Kazeem A.
Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms.
Comparative analysis of the reliability assessment of commercial and residential feeders in the power distribution utility of Nigeria
(e-Prime - Advances in Electrical Engineering, Electronics and Energy, 2024) Adesina, Lambe Mutalub; Ogunbiyi, Olalekan; Jimada-Ojuolape, Bilkisu
Reliability studies serve as valuable tools for assessing and optimizing system performance. Utilities with higher reliability indices are more likely to achieve break-even points due to significantly reduced downtime. This paper explores a comparative assessment of two 11 kV feeders supplying electricity to residential and commercial customers, addressing concerns about distribution system reliability in Nigeria and its impact on the country’s GDP. The study involves a comprehensive reliability analysis, utilizing a flowchart to outline procedural steps and employing the ETAP Software program for data analysis collected over a one-month period from a power utility company. The data encompass operational parameters such as day-hourly consumption, outage records, and network equipment data. Results indicate higher reliability indices in the commercial feeder compared to the residential feeder, with the Customer Average Interruption Duration Index (CAIDI) being lower in the com- mercial feeder. The research underscores the significance of reliability assessment in improving operational efficiency, facilitating maintenance planning, and enhancing customer satisfaction.
Composite Reliability Impacts of Synchrophasor-Based DTR and SIPS Cyber–Physical Systems
(IEEE Systems Journal, 2022) Jimada-Ojuolape, Bilkisu; Teh, Jiashen
The integration of smart infrastructures that facilitate two-way communication via information and communi- cation technology (ICT) has enhanced the efficiency and sustain- ability of the power network. However, technologies, such as dy- namic thermal rating (DTR) systems and system integrity protec- tion schemes (SIPS), have disadvantages, including infrastructure failures, cyber–physical interdependencies, and cyber intrusions. These disadvantages contribute to the reduced reliability of the net- work. Comprehensive exploration of the reliability impacts of mul- tiple smart grid technologies on a single power network has not been conducted. A DTR and SIPS system integrating wide area monitor- ing functions using phasor measurement units (PMUs) is presented in this study, and the effect of ICT contingencies on network relia- bility is investigated. A Monte Carlo simulation approach and a sce- nario reduction technique are utilized in this article. This approach is evaluated on the IEEE-RTS to demonstrate the advantages of ICTs. The approach is suitable for different network topologies, in which the difference lies in the number of required PMUs. Results demonstrate that a reliable SIPS can minimize the aging process while slightly influencing load curtailment despite the increase in overall network aging due to DTR. PMU functional failures result in load loss due to loss of observability despite their high availability.
Conceptual Design and Turbine Selection for a Micro Hydropower System Using Multi-Criteria Analysis
(Nigerian Journal of Engineering, 2023) Jimada-Ojuolape, Bilkisu; Balogun, Monsurat; Adesina, Lambe
Nigeria has grappled with a persistent electricity supply challenge characterized by surging demand, inadequate maintenance of power generation infrastructure, and a host of other issues. This ongoing predicament has given rise to frequent power outages, compelling citizens to resort to expensive alternatives like petrol and diesel generators. Moreover, a substantial number of Nigerian households rely on motorised boreholes for their water supply. Considering these circumstances, this study proposes an innovative solution in the form of a micro hydropower system that harnesses the existing tank-borehole setups within homes to generate electricity. The primary objective of this research is to define the specifications of the central subsystem, namely the turbine, by employing the multi-criteria analysis method, thereby facilitating the practical implementation of the proposed energy generation scheme. In particular, this study delves into an evaluation of two turbine options: Pelton and crossflow turbines. The results of the analysis revealed that the crossflow turbine emerges as the most suitable choice for this application, leading to the exclusion of other turbine alternatives such as the Francis and Turgo turbines.
Customer Churn Prediction in Banking Industry Using K-Means and Support Vector Machine Algorithms
(International Journal of Multidisciplinary Sciences and Advanced Technology, 2020) Abdulsalam, Sulaiman Olaniyi; Arowolo, Micheal Olaolu; Jimada-Ojuolape, Bilkisu; Saheed, Yakub Kayode
This study proposes a customer churn mining structure based on data mining methods in a banking sector. This study predicts the behavior of customers by using clustering technique to analyze customer’s competence and continuity with the sector using k-means clustering algorithm. The data is clustered into 3 labels, on the basis of the transaction in and outflow. The clustering results were classified using Support Vector Machine (SVM), an Accuracy of 97% was achieved. This study enables the banking administrators to mine the conduct of their customers and may prompt proper strategies as per engaging quality and improve proper conducts of administrator capacities in customer relationship.
Detection and Confirmation of Electricity Thefts in Advanced Metering Infrastructure by Long Short- Term Memory and Fuzzy Inference System Models
(NIGERIAN JOURNAL OF TECHNOLOGICAL DEVELOPMENT, 2024) Otuoze, Abdulrahman; Mustafa, M; Sultana, U; Abiodun, E; Jimada-Ojuolape, Bilkisu; Ibrahim, O; Avazi-Omeiza, I; Abdullateef, A
The successful implementation of Smart Grids heavily relies on energy efficiency, particularly through the Advanced Metering Infrastructure (AMI) and Smart Electricity Meters (SEM). However, cyber-attacks pose a threat to SEM, with electricity theft being a primary motivation. Despite the valuable data provided by SEM for analytical purposes, existing methods to identify theft involve cumbersome and costly on-site inspections. This research proposes an electricity theft detection model using the Long Short-Term Memory (LSTM) network. The model employs a collective anomaly approach, defining prediction errors through a threshold and forecast horizon. Suspicious consumption profiles are analysed, and a fuzzy inference system (FIS) implemented in MATLAB 2021b is used to model security risks based on these profiles. The study utilizes energy consumption data from four diverse consumer profiles (consumers 1, 2, 3, and 4) to develop consumer-specific LSTM models for detection and an FIS model for confirmation. Tampered consumer data is identified and confirmed based on selected AMI parameters. While all consumers exhibit suspicious profiles at times, only consumers 2 and 3 are confirmed as engaging in electricity theft. This research provides a robust approach to detecting and verifying fraudulent consumption profiles within the context of AMI, offering a more reliable dimension to theft detection and confirmation.
Development of a GSM based Vehicle Demobilizer and Tracking System
(ABUAD Journal of Engineering Research and Development (AJERD), 2023) Jimada-Ojuolape, Bilkisu; Spencer, Oluwafunto; Balogun, Monsurat
Vehicle hijacking remains a pervasive global issue, posing heightened risks, especially when owners face armed hijackers. Concurrently, the disposal of old mobile phones contributes significantly to the escalating electronic waste (e-waste) challenge, emphasizing the imperative of responsible recycling practices. Addressing these concerns, this paper introduces a GSM-based vehicle demobilizer and tracking system, utilizing a cost-effective mobile phone connected to the device through a SIM card and operating with an NE 555 timer IC in bistable mode. In the event of a hijacking, a call to the mobile phone triggers the device, halting the vehicle. In the prototype design presented in this paper, the result of the output is represented by an LED to show that the circuitry actually works. When the device was tested, the LED illuminated when a call was placed to the mobile phone connected to the device which signifies that the circuit works and can achieve its purpose. The resulting design also shows that the vehicle owner can prevent his car from being stolen away even after it has been hijacked without self-endangerment and within a short time frame.
Enhancing Automated Face Recognition with Makeup Detection: A CNN-Based Approach
(COVENANT JOURNAL OF ENGINEERING TECHNOLOGY, 2024-05-20) Balogun, Monsurat O.; Jimada-Ojuolape, Bilkisu; Odeniyi, Latifat A.
This study delves into the complex issue posed by facial makeup, which has the potential to significantly alter the appearance of individuals, posing a challenge to automated face recognition systems, as well as age and beauty estimation methods. A model solution aimed at automatically detecting makeup in facial images to improve the accuracy of recognition systems was proposed in this work. The approach revolves around utilizing a sophisticated model that harnesses a feature vector encapsulating crucial aspects of facial attributes including shape, texture, and color. Employing an advanced Convolutional Neural Network (CNN) architecture, the model detects the presence of makeup by analyzing key facial landmarks such as eye distance, nose width, eye socket depth, cheekbones, jawline, and chin. Experiments were performed on a dataset consisting of 200 facial images to assess the effectiveness of the proposed method. The model achieved a validation accuracy of 100%, demonstrating its robustness in makeup face detection. Notably, the computational runtime for the validation process was 1 minute and 40 seconds, underscoring the efficiency of the proposed approach. Moreover, an innovative adaptive pre-processing strategy that capitalizes on makeup information to enhance the performance of facial recognition systems was developed. This strategy aims to optimize the recognition process by leveraging insights gained from makeup detection. By integrating this adaptive pre-processing step, further advancements in the accuracy and reliability of facial recognition technology, particularly in scenarios where makeup may confound traditional recognition methods, are envisioned.
Enhancing Human Identification Systems Through Bi-modal Fusion Using Negative Selection Algorithm
(COVENANT JOURNAL OF ENGINEERING TECHNOLOGY, 2024-02-27) Balogun, Monsurat O; Jimada-Ojuolape, Bilkisu; Odeniyi, Latifat A
The Negative Selection Algorithm (NSA) is a computational technique inspired by the human immune system and widely used in various fields like intrusion detection, network security, data mining, and pattern recognition. However, its effectiveness in human identification has not been thoroughly explored. This study focuses on utilizing NSA for human image classification, specifically in a bi-modal system combining physiological traits (faces and fingerprints) and behavioral traits (signatures and voices), as well as a uni-modal system using all features. The research collected 2400 images from 200 individuals, pre-processed images, and salient features selected for easy classification. NSA was used for image classification in both bi-modal and uni-modal systems. The results demonstrated NSA's effectiveness, particularly in the bi-modal system. The biometric system that fused behavioral traits exhibited high accuracy, with true positive and true negative rates of 141% and 144%, respectively, and an overall accuracy of 95%. The system is based solely on physiological traits and achieved slightly lower accuracy rates at 89%. Furthermore, among the uni-modal systems, the voice-based system stood out with a true positive rate of 131% and an accuracy of 88.33%. These findings emphasize the advantages of combining different biometric traits, showcasing the potential for increased accuracy in identification systems. The study highlights NSA's role in enhancing classification accuracy, suggesting the developed biometric systems could significantly improve the performance and reliability of various integrated identification systems.
Feasibility of Wind Energy Utilization for Sustainable Power Generation in Ilorin, Kwara State, Nigeria's North-Central Region
(ABUAD Journal of Engineering Research and Development (AJERD), 2024-05-16) Balogun, Monsurat O; Jimada-Ojuolape, Bilkisu; Taiwo, James Ayo; Olusi, Titilayo
The escalating energy demands across Nigeria, especially in remote rural areas, have outpaced the capacity of the national electricity grid, necessitating the development of independent and sustainable energy sources. Among the renewable options, wind energy stands out as a promising solution. This study focuses on assessing the potential of wind energy in Ilorin, located in Kwara State, within Nigeria's north-central region. Utilizing data collected from 2007 to 2021 by the Nigerian Meteorological Agency, the research examines monthly average wind speeds at two specific coordinates in Ilorin, considering variations in air density. The study utilizes a 15-year set of monthly average wind velocities obtained from the Nigerian Meteorological Agency (NiMet) Headquarters in Abuja, measured at a height of 10 meters above ground level. By employing the 2-coefficient Weibull statistical model and extrapolation principles across different altitudes ranging from 150 to 900 meters above ground level, the study reveals distinct seasonal patterns of wind speeds ranging from 1.1 to 5.1 m/s in Ilorin. Furthermore, wind power density values ranging from 6.7 to 39.20 W/m2 are identified, with optimal wind attributes observed at altitudes exceeding 900 meters. These findings provide valuable insights for assessing the feasibility of wind energy utilization and designing efficient systems in Nigeria's north-central regions, aiding in the sustainable energy transition.
Impact of the Integration of Information and Communication Technology on Power System Reliability: A Review
(IEEE Access, 2020) Jimada-Ojuolape, Bilkisu; Teh, Jiashen
There has been a progressive development in the synthesis of Information and Communication Technologies (ICTs) in power networks recently. ICT systems have become a vital part of every aspect of our daily lives and its integration into the electric power system has become paramount. ICTs support efficient incorporation of activities of all stakeholders of the power system to certify a more cost-effective and sustainable power system. The power system will exhibit intelligent monitoring and control, bidirectional communication between stakeholders and power system elements, security and safety of supply and self-healing qualities. However, asides from the vast benefits ICTs, their implementation within the power network come with some drawbacks which include element failures, failures due to interdependencies as well as vulnerabilities to cyber-attacks. These drawbacks can impact the reliability of the power network negatively. The objective of this paper is to investigate the impact of ICTs integration on the reliability of power networks in terms of empirical validation of standard reliability indices. This study groups the findings into four perspectives, including the effects of cyber power interdependencies, ICT infrastructure failures, cyber-attacks and environmental conditions. As expected, results show that failures and maloperations in the ICT network have adverse effects on system reliability and careful considerations need to be made to dampen these shortcomings.
Impacts of Communication Network Availability on Synchrophasor-Based DTR and SIPS Reliability
(IEEE Systems Journal, 2021) Jimada-Ojuolape, Bilkisu; Teh, Jiashen
The modern electricity network has become smarter and more reliable because of the addition of information and com- munication technology. Dynamic thermal rating systems (DTR) allow existing transmission lines to be operated much closer to their limits. However, line outages after DTR implementation could threaten network security. Thus, system integrity protec- tion schemes (SIPS), which are designed to ensure security, are useful to forestall impending contingencies that could arise from DTR implementation. Furthermore, synchrophasors can improve wide-area monitoring and protection capabilities. However, the reliability of these technologies considering their communication network availability is yet to be studied in a unified framework. Therefore, a novel framework of synchrophasor-based DTR and SIPS is presented while examining eight DTR network topologies and two SIPS schemes simultaneously. A sequential Monte Carlo simulation-based technique is proposed to simulate the effect of the topologies on system-wide reliability. This method is tested on the IEEE-RTS. Results show an improvement of 93.27% between DTR communication Schemes 1 and 8 in load curtailment. More impor- tantly, the article reveals an improvement of 99.15% in the load curtailment value in scenarios where SIPS is deployed compared with the case where SIPS is not deployed, highlighting the benefits of DTR and SIPS hybrid implementation.
Reliability Enhancement of Synchrophasor-Based DTR system considering N-1 contingency for PMU placement
(IEEE, 2022-01-03) Jimada-Ojuolape, Bilkisu
The cyber power network has become more intelligent and efficient because of the integration of information and communication technology (ICT) infrastructure. By combining intelligent infrastructure such as phasor measurement units (PMUs), smart sensors, and other two-way communication and monitoring capabilities, this enhances reliability and sustainability. They do, however, have some disadvantages, such as component and communication network failures, as well as cyber breaches, which might compromise the existing network's reliability. Dynamic Thermal Rating (DTR) systems which is a smart grid technology, improves existing line ratings by allowing them to be increased based on several parameters without violating line safety requirements. Thus, this paper presents a DTR system that uses PMUs to implement Wide Area Monitoring (WAM) functions and investigates the impact of PMU failures and line outages on cyber-physical network reliability. The study uses a Monte Carlo simulation- based method to carry out the investigation on the IEEE RTS while considering the impacts of seven and thirteen PMUs. The test network is modified to emphasize the benefits of ICTs and the findings show that using PMUs enhances reliability greatly. While the use of seven PMUs offers reliability improvement by 53.15%, the use of 13 PMUs offers more significant improvement (by 95.79%) because a single line outage will not affect network observability.
Securing the grid: A comprehensive analysis of cybersecurity challenges in PMU-based cyber-physical power networks
(Electric Power Systems Research, 2024) Jimada-Ojuolape, Bilkisu; Teh, Jiashen; Lai, Ching-Ming
The threat of cyber-attacks poses a considerable risk to Phasor Measurement Unit (PMU) applications within Cyber-Physical Power Networks (CPPNs), leaving them susceptible to malicious attacks such as false data in- jection, time synchronization manipulation, and DoS attacks. Existing studies in the area of cyber security in CPPNs primarily address broader network security issues, overlooking specific challenges in networks with features like PMU integration, highlighting the urgent need to explore vulnerabilities in PMU-based networks for comprehensive understanding. In this study, a thorough analysis is conducted, exploring various cyber-attacks targeting PMU-based CPPNs. These attacks are categorized into data integrity, communication network vul- nerabilities, device spoofing and tampering, and coordinated cyber-physical attacks and subsequently, discussed in detail, underscoring the pressing need for robust cybersecurity measures. The study not only provides valuable insights but also outlines future research directions, empowering power system researchers and operators to enhance the resilience of PMU-based CPPNs against evolving cyber threats. Like the need for more reliability studies that investigate impacts of cyber-attacks in PMU-based CPPNs. The findings offer a good foundation for devising effective planning and fortification strategies to navigate the challenges posed by emerging cyberse- curity threats.
Surveys on the reliability impacts of power system cyber–physical layers
(Sustainable Cities and Society, 2020) Jimada-Ojuolape, Bilkisu; Teh, Jiashen
Information and communication technology (ICT) is a vital addition to modern society living and is a crucial feature of the smart grid. It boosts the power system with the advantage of using intelligent infrastructure to aid monitoring, protection, bidirectional communication, supply safety, security and self-healing characteristics. ICT infrastructures are integrated into the power network by using technologies, such as active distribution net- works, smart cities and societies, dynamic line ratings, special protection schemes and demand-side management programmes. Deployment of these technologies has beneficial impacts on the reliability of power systems. However, these infrastructures are naturally prone to failures and cybersecurity issues because of Internet of things standards, which can further jeopardise system reliability. Various studies have examined the effect of these technologies on the reliability of power systems whilst ignoring that the associated ICTs could be un- available. Thus, this work comprehensively reviews studies that go beyond component-based reliability as- sessment and accounts for the impact of ICT integrations on system-wide reliability whilst explicitly considering the effects of malfunctions of the cyber system. Moreover, the paper presents quantitative and qualitative in- formation about the impact of ICT deployed with various smart grid technologies and applications on the re- liability of modern power systems.
Synchrophasor-Based DTR and SIPS Cyber-Physical Network Reliability Effects Considering Communication Network Topology and Total Network Ageing
(IEEE Access, 2023) Jimada-Ojuolape, Bilkisu; Teh, Jiashen; Alharbi, Bader
The lifespan of overhead transmission conductors is influenced by various factors, including design, maintenance, and operating conditions. Prolonged exposure to high temperatures, particularly near maximum design values, can significantly age transmission lines (TLs). The dynamic thermal rating (DTR) system, an advanced technology that increases TL capacities, can contribute to these stringent conditions. DTR overlays a cyber layer with communication and control systems onto the physical network, transforming it into a cyber-physical network. Currently, the increased risk of TL ageing associated with DTR, and the cyber layer’s functionality lack a unified framework for quantification. This study introduces a novel framework that considers the cyber layer’s network topology and its impact on TL ageing due to DTR implementation. It also proposes the implementation of system integrity protection schemes (SIPS) within the framework. SIPS detects abnormal conditions from DTR deployment, like excessive line loading, and takes corrective actions to optimize DTR system’s performance. The study employs a Sequential Monte Carlo Simulation (SMCS) technique on a modified cyber-physical IEEE RTS-79. The study reveals that topologies like double ring and mesh, cause higher line ageing indices compared to line and star topologies. ETNA values remain relatively stable across the network topologies within a year, suggesting minimal short-term impact on transmission line ageing. However, cumulative effects over several years can potentially reduce lifespan. SIPS can slow network ageing by up to 5hr/yr, but their contingencies can cause the opposite effect. Nonetheless, a reliable SIPS can maintain an ETNA value of 101hr/yr without increasing load curtailment.
Synchrophasor-Based Dynamic Thermal Rating System for Sustainable Cyber- Physical Power Systems
(CRC press, Tailor and Francis group, 2023) Jimada-Ojuolape, Bilkisu; Teh, Jiashen; Arowolo, Olaolu Micheal
Using Artificial Intelligence to Predict Animal Behaviour in Food Webs
(Transactions on Machine Learning and Artificial Intelligence, 2017) Aduragba, Tahir; Ahmed, Abdulkadir; Jimada-Ojuolape, Bilkisu; Ajani, Ayodeji; Adedoyin, Yinka
Overfishing of species in the marine life has caused oceans to become deserts at a fast pace. The population of specific species such as Cod and Haddock has reduced over the years. This has affected countries that hugely depend on them as a source of food. This study used Dynamic Bayesian Network (DBN) to predict animal behaviour in a food web. Two independent biomass surveys from the North Sea were used to learn predictive models and test them on the Northern Gulf Ocean. The resulting predictive model is expected to unveil useful information about what affects the population of fishes in the Northern Gulf Ocean. In addition, the predictive model was used to make predictions into the future about the effects of tampering with the population of specific species of fish in the same region. The focus was on the Cod species in the George’s Bank in relationship to species network in their food web. Looking at their biomass states and the effects it has on the hidden dependence when there is a change in their biomass states. Also, the different predictive models were used to evaluate species in the George’s Bank based on their performance. The result from the experiment shows that there is a hidden dependence, which is responsible for the collapse of species (Cod); due to the temperature or salinity of the ocean.

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