Browsing by Author "Oyewola, David Opeoluwa"

Now showing 1 - 4 of 4
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    Application of Deep Learning Techniques and Bayesian Optimization with Tree Parzen Estimator in the Classification of Supply Chain Pricing Datasets of Health Medications
    Oyewola, David Opeoluwa; Dada, Emmanuel Gbenga; Omotehinwa, Temidayo Oluwatosin; Emebo, Onyeka; Oluwagbemi, Olugbenga Oluseun (MDPI, 2022-10-10)
    From the development and sale of a product through its delivery to the end customer, the supply chain encompasses a network of suppliers, transporters, warehouses, distribution centers, shipping lines, and logistics service providers all working together. Lead times, bottlenecks, cash flow, data management, risk exposure, traceability, conformity, quality assurance, flaws, and language barriers are some of the difficulties that supply chain management faces. In this paper, deep learning techniques such as Long Short-Term Memory (LSTM) and One Dimensional Convolutional Neural Network (1D-CNN) were adopted and applied to classify supply chain pricing datasets of health medications. Then, Bayesian optimization using the tree parzen estimator and All K Nearest Neighbor (AllkNN) was used to establish the suitable model hyper-parameters of both LSTM and 1D-CNN to enhance the classification model. Repeated five-fold cross-validation is applied to the developed models to predict the accuracy of the models. The study showed that the combination of 1D-CNN, AllkNN, and Bayesian optimization (1D-CNN+AllKNN+BO) outperforms other approaches employed in this study. The accuracy of the combination of 1D-CNN, AllkNN, and Bayesian optimization (1D-CNN+AllKNN+BO) from one-fold to 10-fold, produced the highest range between 61.2836% and 63.3267%, among other models.
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    Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
    Dada, Emmanuel Gbenga; Oyewola, David Opeoluwa; Joseph, Stephen Bassi; Emebo, Onyeka; Oluwagbemi, Olugbenga Oluseun (MDPI, 2022-11-27)
    Public health is now in danger because of the current monkeypox outbreak, which has spread rapidly to more than 40 countries outside of Africa. The growing monkeypox epidemic has been classified as a “public health emergency of international concern” (PHEIC) by the World Health Organization (WHO). Infection outcomes, risk factors, clinical presentation, and transmission are all poorly understood. Computer- and machine-learning-assisted prediction and forecasting will be useful for controlling its spread. The objective of this research is to use the historical data of all reported human monkey pox cases to predict the transmission rate of the disease. This paper proposed stacking ensemble learning and machine learning techniques to forecast the rate of transmission of monkeypox. In this work, adaptive boosting regression (Adaboost), gradient boosting regression (GBOOST), random forest regression (RFR), ordinary least square regression (OLS), least absolute shrinkage selection operator regression (LASSO), and ridge regression (RIDGE) were applied for time series forecasting of monkeypox transmission. Performance metrics considered in this study are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE), which were used to evaluate the performance of the machine learning and the proposed Stacking Ensemble Learning (SEL) technique. Additionally, the monkey pox dataset was used as test data for this investigation. Experimental results revealed that SEL outperformed other machine learning approaches considered in this work with an RMSE of 33.1075; a MSE of 1096.1068; and a MAE of 22.4214. This is an indication that SEL is a better predictor than all the other models used in this study. It is hoped that this research will help government officials understand the threat of monkey pox and take the necessary mitigation actions.
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    Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10)
    Dada, Emmanuel Gbenga; Oyewola, David Opeoluwa; Joseph, Stephen Bassi; Emebo, Onyeka; Oluwagbemi, Olugbenga Oluseun (Hindawi, 2023-10-13)
    The importance of facial expressions in nonverbal communication is significant because they help better represent the inner emotions of individuals. Emotions can depict the state of health and internal wellbeing of individuals. Facial expression detection has been a hot research topic in the last couple of years. The motivation for applying the convolutional neural network-10 (CNN-10) model for facial expression recognition stems from its ability to detect spatial features, manage translation invariance, understand expressive feature representations, gather global context, and achieve scalability, adaptability, and interoperability with transfer learning methods. This model offers a powerful instrument for reliably detecting and comprehending facial expressions, supporting usage in recognition of emotions, interaction between humans and computers, cognitive computing, and other areas. Earlier studies have developed different deep learning architectures to offer solutions to the challenge of facial expression recognition. Many of these studies have good performance on datasets of images taken under controlled conditions, but they fall short on more difficult datasets with more image diversity and incomplete faces. This paper applied CNN-10 and ViT models for facial emotion classification. The performance of the proposed models was compared with that of VGG19 and INCEPTIONV3. The CNN-10 outperformed the other models on the CK + dataset with a 99.9% accuracy score, FER-2013 with an accuracy of 84.3%, and JAFFE with an accuracy of 95.4%.
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    Using Deep 1D Convolutional Grated Recurrent Unit Neural Network to Optimize Quantum Molecular Properties and Predict Intramolecular Coupling Constants of Molecules of Potential Health Medications and Other Generic Molecules
    Oyewola, David Opeoluwa; Dada, Emmanuel Gbenga; Emebo, Onyeka; Oluwagbemi, Olugbenga Oluseun (MDPI, 2022-07-18)
    A molecule is the smallest particle in a chemical element or compound that possesses the element or compound’s chemical characteristics. There are numerous challenges associated with the development of molecular simulations of fluid characteristics for industrial purposes. Fluid characteristics for industrial purposes find applications in the development of various liquid household products, such as liquid detergents, drinks, beverages, and liquid health medications, amongst others. Predicting the molecular properties of liquid pharmaceuticals or therapies to address health concerns is one of the greatest difficulties in drug development. Computational tools for precise prediction can help speed up and lower the cost of identifying new medications. A one-dimensional deep convolutional gated recurrent neural network (1D-CNN-GRU) was used in this study to offer a novel forecasting model for molecular property prediction of liquids or fluids. The signal data from molecular properties were pre-processed and normalized. A 1D convolutional neural network (1D-CNN) was then built to extract the characteristics of the normalized molecular property of the sequence data. Furthermore, gated recurrent unit (GRU) layers processed the extracted features to extract temporal features. The output features were then passed through several fully-connected layers for final prediction. For both training and validation, we used molecular properties obtained from the Kaggle database. The proposed method achieved a better prediction accuracy, with values of 0.0230, 0.1517, and 0.0693, respectively, in terms of the mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE).