School of Electrical Engineering and Technology (SEET)

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School of Electrical Engineering and Technology (SEET)

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A Comparison of Strategies for Missing Values in Data on Machine Learning Classification Algorithms
(IEEE, 2019) Makaba, T.; Dogo, E.
Dealing with missing values in data is an important feature engineering task in data science to prevent negative impacts on machine learning classification models in terms of accurate prediction. However, it is often unclear what the underlying cause of the missing values in real-life data is or rather the missing data mechanism that is causing the missingness. Thus, it becomes necessary to evaluate several missing data approaches for a given dataset. In this paper, we perform a comparative study of several approaches for handling missing values in data, namely listwise deletion, mean, mode, k-nearest neighbors, expectation-maximization, and multiple imputations by chained equations. The comparison is performed on two real-world datasets, using the following evaluation metrics: Accuracy, root mean squared error, receiver operating characteristics, and the F1 score. Most classifiers performed well across the missing data strategies. However, based on the result obtained, the support vector classifier method overall performed marginally better for the numerical data and naïve Bayes classifier for the categorical data when compared to the other evaluated missing value methods.
A Face Recognition-Based Intruder Detection System for Automatic Door Control
(IEC, 2023-03-21) Abdullahi Daniyan, Michael O. Michael
In recent years, theft and unauthorized access to private areas in homes and communities has become a growing concern, leaving individuals feeling insecure about their lives and properties. To address this problem, this paper proposes a solution that features a facial recognition system to prevent entry by unauthorized individuals. The system uses an ESP32 Camera module and the Arduino Integrated Development Environment (IDE) to capture and store facial biometric details through facial recognition techniques. The data is saved in a database that is accessible through a web interface enabled by HOTSPOT connection. The camera's Internet Protocol (IP) address also allows for live streaming as an added feature. An ATmega328 microcontroller on the Arduino IDE receives signals from the ESP32 camera, process the data and operate the door accordingly. When the ESP32 Camera recognizes a face, it sends a signal to the microcontroller to open the door. If the face is not recognized, the door is kept locked to prevent entry by intruders. The proposed solution effectively identifies intruders and those with authorized access but grants access only to authorized individuals in its database. This ensures a secure environment for homes and communities, providing peace of mind to individuals who have long been worried about theft and unauthorized access. Results demonstrate that the proposed facial recognition system is has been able to provide a secure environment for homes and communities by denying entry to intruders and granting access only to recognized individuals.
A Joint Optimization Scheme for Enhanced Breast Cancer Diagnosis using Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO)
(International Conference of the Faculty of Science, 2025-01-14) Ahmed, Y.E., Abdullahi, I.M., Maliki, D., & Akogbe, M. A
One of the leading diseases globally is cancer and breast cancer is not exempted. The objective of the WHO Global Breast Cancer Initiative (GBCI) is to reduce global breast cancer mortality by 2.5% per year, thereby averting 2.5 million breast cancer deaths globally between 2020 and 2040. The three pillars toward achieving these objectives are: health promotion for early detection; timely diagnosis; and comprehensive breast cancer management. In this study we propose an early and comprehensive detection technique in combating breast cancer diagnosis by combining the strength of both PSO (Particle Swarm Optimization) and BPSO (Binary Particle Swarm Optimization) to achieve optimal solution. The results obtained indicated the superiority of the Hybrid PSO-BPSO model in detection over an existing solution by achieving an accuracy of 98.82% on both the WBCD and WDBC datasets.
A Model for an Enterprise Automated RFID-Based Pay and Park System
(ceur-ws.org, 2016) Dogo, E. M.; Ahmed, A.; Adelakun, M. O.
Traffic management is one of the challenging problems in urban cities as vehicle owners look for where to park and queue to pay for rented car parks usually on an hourly basis. Therefore, the choice of a suitable, reliable and flexible architecture for Radio Frequency Identification (RFID) based pay and park system readily comes to mind. It is assumed in this work that the parking lot is already known and secured by the vehicle owner in a closed car park; this paper therefore seeks to address and automate the billing system for enterprise car parks. To achieve this, a reliable and accurate enterprise star topology networked RFID based system, that computes the amount to be paid by a user which is calculated based on the time the user enters and exits the park, and the amount the park owner is charging at a particular point in time is proposed for the automated pay and park system. The system comprises of both software and hardware components integrated together. The developed prototype system is able to grant authorized users access to the park within 30ms after verification and open the barrier in 30ms whenever the emergency button is pressed for safety consideration.
A Secure Electronic Voting System Using Multifactor Authentication and Blockchain Technologies.
(2022) Olaniyi, O.M.; Dogo, E.M.; Nuhu, B.K.; Treiblmaier, H.; Abdulsalam, Y.S.; Folawiyo, Z.
This chapter presents a distributed e-voting system that solves the problems of vote-rigging, voter impersonation, and vote falsification, all of which are prevalent in traditional paper ballot systems. In general, the digitization of democratic decision-making is convenient, fast, and cost-saving but can become a gateway for electoral fraud if not properly secured. Authentication and the simultaneous achievement of confidentiality, integrity, and availability represent major challenges toward establishing e-voting as a reliable means of democratic decision-making. In this chapter, a combination of multifactor authentication (MFA) and blockchain techniques is used to secure electronic voting. MFA hampers the compromising of voters’ identities and allows for easy verification, while blockchain technology protects the integrity of the votes and ensures the verifiability of the cast votes. Combining a facial recognition algorithm and RFID authenticates and authorizes voters to participate in the election process. A smart contract implemented on an Ethereum network provides the required measures of integrity and verifiability for secure e-voting. Performance evaluations of the proposed approach show that the MFA yielded a 0.1% false acceptance rate and a 0.8% false rejection rate for 100 voters, respectively. This illustrates that the proposed technique can solve issues of authentication and integrity, thereby paving the way for free, fair, and credible e-democratic decision-making in digitally enabled voting scenarios.
A Smart Real-time Attendance System using Smart Data Filtering and Selection Techniques
(2024-04-09) Ibrahim M. A., Maliki, D., I. A. Dauda, A. Y. Ogaji, S. Yakubu
Cooperate organizations, firms, companies, and educational institutions in Nigeria and the whole world are concerned about attendance of students and employees as the case may be, student overall performance is affected by it. In order to provide solutions for attendance management systems, a variety of techniques and technologies were used in the development of the attendance systems. However, most of these systems lack the flexibility of use and appropriate resource management. This paper presents the development of a smart real-time attendance system that uses smart data filtering and selection techniques to parse user-defined attendance instructions, optimize performance, and improve efficiency and flexibility. This system also employs a multi-factor approach in terms of security engaging the use of RFID technology and fingerprint biometrics to manage attendance records. Also, the system uses a wireless (Wi-Fi) communication approach for real-time communication. The performance of the system was mainly evaluated in terms of throughput, latency, and accuracy showing an average delay of 3 seconds per student, 21.95Mbps average throughput, and zero percent false acceptance.
Abdullahi, I. M., Salawu B. T., Maliki D., Nuhu B K., & Aliyu, I. (2017). Development of an Artificial Neural Network Model for Daily Electrical Energy Management. Proceedings of the 2nd International Engineering Conference, (IEC 2017), Federal University of Technology Minna, Nigeria, pp 120-125.
(Federal University of Technology Minna, 2017-05-07) Abdullahi, I. M., Salawu B. T., Maliki D., Nuhu B K., & Aliyu, I
Efficient monitoring and control of electrical energy do not only prevent fire out-breaks caused by electrical appliances, but can also reduce excessive billings and prevent electrical installations. Most Energy Management Systems (EMS) for remote controlling of electrical appliances rely mostly on sensors, data and GSM networks which are un-reliable or even un-available in most part of developing world, this makes them less reliable. Therefore, there is need for an intelligent system that can manage electrical consumption intelligently using user-appliance interactive pattern over a period of time for intelligent control of users’ appliances in his/her absence. The model parameters (number of neurons and training algorithms) that affects its performance were first investigated and adopted. The performance of the developed model was evaluated using Regression analysis (R) and Mean Square Error (MSE) using ANN and Simulink tool boxes in Matlab R2015b. A good model can be used for real time control when deployed. Also, Scale Conjugate Gradient (SCG) training algorithm should also be used because of its high performance for pattern recognition problems. This work will go a long way in efficiently controlling household electrical appliances in the absence of the users thereby preventing fire disasters caused by electrical appliances, reducing the tariffs of consumers while increasing lifespan of electrical installations.
Accessing Imbalance Learning Using Dynamic Selection Approach in Water Quality Anomaly Detection
(MDPI, 2021) Dogo, E. M.; Nwulu, N. I.; Twala, B.; Aigbavboa, C.
Automatic anomaly detection monitoring plays a vital role in water utilities’ distribution systems to reduce the risk posed by unclean water to consumers. One of the major problems with anomaly detection is imbalanced datasets. Dynamic selection techniques combined with ensemble models have proven to be effective for imbalanced datasets classification tasks. In this paper, water quality anomaly detection is formulated as a classification problem in the presences of class imbalance. To tackle this problem, considering the asymmetry dataset distribution between the majority and minority classes, the performance of sixteen previously proposed single and static ensemble classification methods embedded with resampling strategies are first optimised and compared. After that, six dynamic selection techniques, namely, Modified Class Rank (Rank), Local Class Accuracy (LCA), Overall-Local Accuracy (OLA), K-Nearest Oracles Eliminate (KNORA-E), K-Nearest Oracles Union (KNORA-U) and Meta-Learning for Dynamic Ensemble Selection (META-DES) in combination with homogeneous and heterogeneous ensemble models and three SMOTE-based resampling algorithms (SMOTE, SMOTE+ENN and SMOTE+Tomek Links), and one missing data method (missForest) are proposed and evaluated. A binary real-world drinking-water quality anomaly detection dataset is utilised to evaluate the models. The experimental results obtained reveal all the models benefitting from the combined optimisation of both the classifiers and resampling methods. Considering the three performance measures (balanced accuracy, F-score and G-mean), the result also shows that the dynamic classifier selection (DCS) techniques, in particular, the missForest+SMOTE+RANK and missForest+SMOTE+OLA models based on homogeneous ensemble-bagging with decision tree as the base classifier, exhibited better performances in terms of balanced accuracy and G-mean, while the Bg+mF+SMENN+LCA model based on homogeneous ensemble-bagging with random forest has a better overall F1-measure in comparison to the other models.
An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things
(MDPI Sensors, 2019-03-21) Oyewobi S. Stephen; Gerhard Hancke; Adnan M. Abu-Mahfouz; Adeiza J. Onumanyi
The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches
An improved resampling approach for particle filters in tracking
(IEEE, 2017-11-06) Yu Gong; Sangarapillai Lambotharan; Abdullahi Daniyan
Resampling is an essential step in particle filtering (PF) methods in order to avoid degeneracy. Systematic resampling is one of a number of resampling techniques commonly used due to some of its desirable properties such as ease of implementation and low computational complexity. However, it has a tendency of resampling very low weight particles especially when a large number of resampled particles are required which may affect state estimation. In this paper, we propose an improved version of the systematic resampling technique which addresses this problem and demonstrate performance improvement.
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
(i-Manager, 2018) DOGO, E. M.; NWULU, N. I.; NKONYANA, T.; OLANIYI, O. M.; AIGBAVBOA, C. O.
This paper reviews scholarly articles on the application of blockchain technology for secure electronic voting (e-voting). Furthermore, the feasibility of using blockchain technology to replace the existing manual or semi-digitized voting system in developing countries with Nigeria as a case study is performed. To analyse the current state and preparedness of adopting Blockchain Enabled E-voting (BEEV) system in Nigeria, this paper employs the qualitative SWOT (Strengths, Weaknesses, Opportunities, and Threats) and PEST (Political, Economic, Social, and Technological) analysis approach. This evaluation leads us to identify internal and external factors and the strategic direction in adopting BEEV in Nigeria. It is the authors' opinion that this approach could also be tailored to evaluate situations of other developing countries.
Bluetooth Assisted Misplaced Object Finder Using DFRobot Arduino Integrated with Android Application
(2024) Dogo, E. M.; Emeni, B.; Nuhu, B. K.; Ajao, L. A.
Finding lost or misplaced items can be time-consuming and frustrating. Yet, this is common and occurs to many individuals daily and globally. This paper has developed a system that allows users to locate their misplaced or lost items by leveraging the capabilities of Bluetooth technology and a microcontroller-based control system. The DFRobot Bettle BLE Arduino microcontroller is the main component for communication and control. By interfacing the microcontroller with an LED and a buzzer, the system provides visual and auditory signals to assist in locating the target device or item. The search pro-cess is initiated through an Android application, through establishing a Blue-tooth connection between the microcontroller and the target device, permitting the exchange of signals for tracking purposes. When the device is within range, the LED indicator illuminates, and the buzzer produces audible alerts, guiding the user to the device’s location. The application also provides an estimated distance of the object using Bluetooth signal strength. Tests carried out on the system proved its effectiveness in terms of quick response to signals and reliability in both indoor and outdoor environments.
Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks
(IEEE, 2020-10-13) Gbadamosi, Safiu Abiodun,; Hancke, Petrus Gerhard; Abu-Mahfouz Adnan M.
Narrowband Internet of Things (NB-IoT) is a type of low-power wide-area (LPWA) technology standardized by the 3rd-Generation Partnership Project (3GPP) and based on long-term evolution (LTE) functionalities. NB-IoT has attracted significant interest from the research community due to its support for massive machine-type communication (mMTC) and various IoT use cases that have stringent specifications in terms of connectivity, energy efficiency, reachability, reliability, and latency. However, as the capacity requirements for different IoT use cases continue to grow, the various functionalities of the LTE evolved packet core (EPC) system may become overladen and inevitably suboptimal. Several research efforts are ongoing to meet these challenges; consequently, we present an overview of these efforts, mainly focusing on the Open System Interconnection (OSI) layer of the NB-IoT framework. We present an optimized architecture of the LTE EPC functionalities, as well as further discussion about the 3GPP NB-IoT standardization and its releases. Furthermore, the possible 5G architectural design for NB-IoT integration, the enabling technologies required for 5G NB-IoT, the 5G NR coexistence with NB-IoT, and the potential architectural deployment schemes of NB-IoT with cellular networks are introduced. In this article, a description of cloud-assisted relay with backscatter communication, a comprehensive review of the technical performance properties and channel communication characteristics from the perspective of the physical (PHY) and medium-access control (MAC) layer of NB-IoT, with a focus on 5G, are presented. The different limitations associated with simulating these systems are also discussed. The enabling market for NB-IoT, the benefits for a few use cases, and possible critical challenges related to their deployment are also included. Finally, present challenges and open research directions on the PHY and MAC properties, as well as the strengths, weaknesses, opportunities, and threats (SWOT) analysis of NB-IoT, are presented to foster the prospective research activities
Combating Road Traffic Congestion with Big Data: A Bibliometric Review and Analysis of Scientific Research
(Springer, 2021) Dogo, E.M.; Makaba, T.; Afolabi, O.J.; Ajibo, A.C.
Road traffic congestion is one of the challenging problems confronting city dwellers globally. It is majorly caused by either one or a combination of recurrent congestion, nonrecurrent congestion, and precongestion conditions in urban road networks. This chapter performs a bibliometric analysis and reviews the volume of literature linking big data with combating road traffic congestion between 2011 and 2020. The review employs a quantitative analysis of bibliometric science mapping tool to highlight features that affect knowledge accumulation. The chapter also reviews the intellectual structure of knowledge based on total publications and citations. The key scholars, documents, affiliations, regions, data, and algorithms that influenced the development of this research area are analyzed. The results of documents co-citation evaluation show that the key research clusters are salient elements linked with the development and deployment of connected and autonomous vehicles (CAVs) technology. These research clusters are traffic flow prediction, congestion and accidents alert systems, security and privacy mitigation, vehicle emission profiles, travel time estimation, optimization of vehicular routing, journey planning and congestion prediction, and travel and parking guidance. Finally, the chapter presents the way forward and future research direction for sustainable road traffic management in the context of smart city initiatives leveraging on big data.
Comparative Analysis of Machine Learning Algorithms for Eccentricity Fault Classification in Salient Pole Synchronous Machine
(IEEE, 2024-03-22) Shejwalkar, Ashwin; Yusuf, Latifa; Ilamparithi, Thirumarai Chelvan
The paper performs a comparative study of Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for the classification of Static Eccentricity (SE) and Dynamic Eccentricity (DE) faults in a Salient Pole Synchronous Machine (SPSM). The SPSM was subjected to varying SE and DE severities, unbalanced source voltages, and load conditions. Stator and field current data were measured, and various time-domain and frequency-domain features were extracted from the above-mentioned data. Both networks were fed these features and compared based on classification accuracy, robustness, and computational complexity.
Comparative Analysis of Macro Femto Networks Interference Mitigation Techniques
(IJWMT, 2022-12-20) Katfun Philemon Dawar; Abraham U. Usman; Bala Alhaji Salihu; Michael David; Supreme Ayewoh Okoh; Ajiboye, Johnson Adegbenga
When interference is reduced, the benefits of using a macrocell and femtocell heterogeneous network (Macro-Femto) heterogeneous network (HetNet) can be increased to their full potential. In this study, Enhanced Active Power Control (EAPC), Active Power Control (APC), and Power Control (PC1) interference mitigation strategies are applied, and their performances in uplink and downlink transmission of 5G Non-Stand-Alone (NSA) architecture are compared. According to the findings of a MATLAB simulation, the EAPC technique utilized a lower amount of transmit power for the Macro User Equipment (MUE), the Home User Equipment (HUE), and the femtocell logical node (Hen-gNB), in comparison to the APC and PC1 techniques. While PC1 approach required less en-gNB transmission power. The MUE, HUE, hen-gNB, and en-gNB throughput of the EAPC approach was much higher. This work will enable wireless system designers and network engineers know the appropriate technique to utilize to achieve desired Quality of Service (QoS) while conserving network resources
Data association using game theory for multi-target tracking in passive bistatic radar
(IEEE, 2017-06-20) Yu Gong; Abdullahi Daniyan; Abdulrazaq Aldowesh; Sangarapillai Lambotharan
We investigate a game theoretic data association technique for multi-target tracking (MTT) with varying number of targets in a real passive bi-static radar (PBR) environment. The radar measurements were obtained through a PBR developed using National Instrument (NI) Universal Software Radio Peripheral (USRP). We considered the problem of associating target state-estimates-to-tracks for varying number of targets. We use the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter to perform the multi-target tracking in order to obtain the target state estimates and model the interaction between target tracks as a game. Experimental results using this real radar data demonstrate effectiveness of the game theoretic data association for multiple target tracking.
Deploying A Standalone Facial and Emotion Recognition Classroom Management System on Resource-Constrained.
(Izmir Turkiye, 2024-12-20) Abdullahi, I.M., Maliki, D., Abdulqudus, A., Abraham, S.A, & Ibrahim, M.
In recent times, it has been proven in most industries that deep learning can play a huge part in the development and automation of processes otherwise performed manually by humans alone. The trend however has encountered more of a shift and tend towards transfer learning where standalone systems can be built on weights that have been extensively trained to be use-case agnostic. This project seeks to address the problem of student truancy. The methodology applied is a combination of a deep learning use-case agnostic weight embedding obtained from a popular network called Face net. Recognition is performed by computing facial distances using the weight embedding. Also addressed is the common reliance on internet for functionality present in most modern-day systems by deploying all the resources necessary on a resource-constrained development board. Emotions during class are also analyzed to improve classroom experience which will be displayed on a web application dashboard powered by artificial intelligence back-end. The results obtained show an above average recognition rate of 0.63 with emotional recognition accuracy of 0.72. The implications of these results are that accurate attendance can be taken in an organization with minor increments to the system such as increased computational capabilities.
Deployment of an Electronic-based Approach for Fruits Juice Ingredient Analysis
(International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) Landmark University, 2024-04-04) Kufre Esenowo Jack; Lanre Joseph Olatomiwa; Yahaya Asizehi Enesi; Grace Idowu Olaleru; Nnaemeka Emmanuel Ugwuogor; Babawuya Alkali
This paper considers the deployment of an electronic-based fruits juice ingredient analysis. Most of the fruits juice products available in the market now contain water in large quantities than the active ingredients. This design attempts to respond to the end user's complaints. By putting on this design, it is expected to serve as a quality check and control for our teeming enterprising fruit substance producers. The system was designed and simulated using proteus and implemented using hardware electronic components. The system uses an infrared transmitter, fruit sample handler, and infrared receiver to realize its design. The instrument was calibrated with natural pineapple juice with 60% of water content. The outputs of this device were displayed using a cathode ray oscilloscope and voltmeter respectively. Five different samples of fruit juice were analyzed namely: A, B, C, D, and E. Results showed that all fruit juice contains a reasonable quantity of water which is not regarded as an adulteration since it is the natural content of the fruit. However, water content above 60% may be considered as much. It is recommended that fruit juice producers employ this system for their quality and control checks. Moreover, further research should take into consideration, the colour and viscosity of different fruit juices with a view to seeing how the system can analyze them, while the output should incorporate a microcontroller for an intelligent analysis and digital display.
Design and Implementation of a Wireless Patient Health Monitoring System
(IEEE, 2019) O. Manzombi, O.; Dogo, E. M.; Nwulu, N. I.
This paper presents the design and implementation of an IoT wireless patient's health monitoring system. The system can be used to continuously monitor the body temperature and pulse rate of a patient located in a hospital room using biomedical sensors. The temperature and pulse rate values are taken from the sensors and processed by an Arduino Uno. Furthermore, they are sent wirelessly via RF communication using a 433 MHz transmitter and receiver kit. The readings are encoded and sent to the receiver where they are decoded and displayed on an LCD screen. Finally, the temperature and pulse rate values are also displayed and stored online using an Arduino Ethernet Shield 2 for future analysis.