Computer Engineering
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Computer Engineering
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Item Performance Evaluation of Sun Tracking Control Systems using IMC and PID Controllers(4th International Engineering Conference (IEC 2022) Federal University of Technology, Minna, Nigeria, 2022) Madaki, I. D.; Folorunso, T. A.; Bala, J. A.; Adedigba, A. P.; Dogo, E. M.The inadequate supply of electricity for illumination has made many industries, organizations, and households resort to alternative energy sources, one of which includes solar energy. In comparison to other renewable sources of energy, the idea of employing photovoltaic panels for solar energy conversion into electrical energy remains a widespread choice. However, the amount of power a solar panel can produce is reduced due to the sun's constant shift in angle with respect to the earth. In this work, we evaluate the performance response of the STS using the transient response, Integral Absolute Error (IAE) and Integral Square Error (ISE) of the IMC and PID controller using MATLAB. The results obtained shows that, the PID outperforms the IMC in terms of IAE, ISE and Rise time, while the IMC outperforms the PID in terms of Settling time and system overshoot.Item 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.Item Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection(IEEE, 2020) Dogo, E. M.; Nwulu, N. I.; Twala, B.; Aigbavboa, C. O.Imbalanced class distribution and missing data are two common problems and occurrences in water quality anomaly detection domain. Learning algorithms in an imbalanced dataset can yield an overrated classification accuracy driven by a bias towards the majority class at the expense of the minority class. On the other hand, missing values in data can induce complexity in the learning classifiers during data analysis. These two problems pose substantial challenges to the performance of learning algorithms in real-life water quality anomaly detection problems. Hence, the need for them to be carefully considered and addressed to achieve better performance. In this paper, the performance of a range of several combinations of techniques to deal with imbalanced classes in the context of binary-imbalanced water quality anomaly detection problem and the presence of missing values is extensively compare. The methods considered include seven missing data and eight resampling methods, on ten different learning state-of-the-art classifiers taking into account diversity in their learning philosophies. The different classifiers are evaluated using stratified 5-fold cross-validation, based on three performance evaluation metrics namely accuracy, ROC-AUC and F1-measure. Further experiments are carried out on nineteen variants of homogeneous and heterogeneous ensemble techniques embedded with resampling and missing value strategies during their training phase as well as an optimized deep neural network model. The experimental results show an improvement in the performance of the learning classifiers, especially when dealing with the class imbalance problem (on the one hand) and the incomplete data problem (on the other hand). Furthermore, the neural network model exhibit superior performance when dealing with both problems.Item Impact of Gaussian Noise on the Optimization of Medical Image Registration(2024) Sokomba, A. Z.; Dogo, E. M.; Maliki, D.; Abdullahi, I. M.Gaussian noise often poses a significant challenge to medical image registration, impacting the accuracy and reliability of alignment across varying imaging modalities. The research investigates the effect of Gaussian noise on medical image registration by comparing four optimization techniques: a direct approach, an optimization using FMINCON, a multiscale approach, and a combined optimization strategy that integrates FMINCON and the multiscale approach. The comparative analysis assesses each method's robustness against Gaussian noise, evaluating registration accuracy through three key similarity metrics: Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). The results reveal that while each approach demonstrates a degree of resilience to noise, the combined optimization method significantly outperforms the others, achieving the lowest MSE, highest PSNR, and superior SSIM. These findings suggest that the combined approach effectively enhances the optimization process by leveraging the strengths of both FMINCON and multiscale frameworks, thus providing a more accurate and noise-resistant solution for medical image registration. The analysis highlights the necessity of image filtering techniques to mitigate noise interference and improve the image registration process in clinical applications.Item 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.