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Item Leveraging user session for personalized e- commerce recommendation(2024-07) Onibonoje, S.; Ojo, A.The advent of the internet has propelled many shopping activities online, leading to the rapid growth of e- commerce. This shift has revolutionized the shopping experience, offering unparalleled convenience with anytime, anywhere access via computers and internet connectivity. Moreover, the vast array of easily accessible choices empowers buyers to make well-informed decisions. Numerous websites have emerged to provide e-commerce services, catering either as a complement to physical stores or as standalone businesses. However, the abundance of offerings often leads to information overload for buyers, making product searches time-consuming and frustrating. Personalized e-commerce recommendations alleviate this challenge by guiding users to relevant products swiftly, enhancing the overall shopping experience and ultimately boosting product sales. The study focuses on creating a session-based recommendation system for e-commerce websites, leveraging Recurrent Neural Networks with LSTM architectures to analyze sequential user behavior and browsing context for personalized product recommendations. The research methodology encompasses data collection and preprocessing, where data was splitted into training, testing and validation set. The model was efficiency was evaluated using precision, recall and mean reciprocal rank with the result showing considerable promise for recommendation. This research makes a substantial contribution by suggesting tailored options, users are more likely to find suitable products, leading to increased satisfaction and repeat purchases, thereby benefiting e-commerce platforms.Item Forecasting Nigerian equity stock returns using long short-term memory technique(2024) Ojo, A. K.; Okafor, I. J.Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. The model was evaluated using performance metrics and compared with other deep learning models like Artificial and Convolutional Neural Networks (CNN). The experimental results showed that the LSTM model can predict future stock market prices and returns with over 90% accuracy when trained with a reliable dataset. The study concludes that LSTM models can be useful in predicting financial time-series-related problems if well-trained. Future studies should explore combining LSTM models with other deep learning techniques like CNN to create hybrid models that mitigate the risks associated with relying on a single model for future equity stock predictions.Item Improvement on emotional variance analysis technique (EVA) for sentiment analysis in healthcare service delivery(Foundation of Computer Science FCS, New York, USA, 2024-05) Agada, V. E.; Ojo, A. K.This research introduces an innovative approach to improving sentiment analysis in healthcare service delivery by integrating Emotion and Affect Recognition (EAR) techniques into Emotional Variance Analysis (EVA). Leveraging logistic regression, the modifications, including adjusting confidence thresholds and utilizing the Rectified Linear Unit (ReLU) function, aim to address high polarity and enable real-time analysis. The methodology outlines a systematic process for EAR integration, offering practical insights for healthcare practitioners. In this study, additional datasets, including the Healthcare Patient Satisfaction Data Collection, the 9 Popular Patient Portal App Reviews for November 2023, and the HCAHPS Hospital Ratings Survey, are incorporated to enhance the robustness and reliability of the approach. The results across three healthcare centers demonstrate the effectiveness of this augmented approach, with comparisons against existing models using performance metrics. While showcasing promising potential, further research is needed to explore scalability and generalizability.Item Weather forecasting using deep learning and seasonal autoregressive integrated moving average model(2024-04) Ojukotimi, O. O.; Ojo, A. K.A number of industries, including mining, agriculture, transportation, and disaster relief, rely heavily on weather forecasting. Time-series trends have been well-captured by conventional forecasting models like SARIMA (Seasonal Auto Regressive Integrated Moving Average). Deep learning methods have become effective instruments for identifying complex patterns and raising predicting precision in recent years. This paper suggests a method to improve the accuracy of weather forecasting by utilizing SARIMA models and deep learning. To capture spatial and temporal correlations in meteorological data, the integration of the SARIMA model and Long Short-Term Memory (LSTM) networks of deep learning architecture is investigated. The Nigeria Metrological Agency (NIMET) provided 20 years' worth of temperature, humidity, wind, and rainfall data for this study. According to the evaluation results, the LSTM had an RMSE of 41.00 for the features of the training dataset, whereas the proposed SARIMA had RMSEs of 0.59 for rainfall, 23.99 for temperature, 1.23 for wind, and 24.47 for relative humidity. This demonstrates unequivocally that SARIMA outperformed the LSTM model.Item SMS spam detection and classification to combat abuse in telephone networks using natural language processing(2023) Oyeyemi, D. A.; Ojo, A. K.In the modern era, mobile phones have become ubiquitous, and Short Message Service (SMS) has grown to become a multi-million-dollar service due to the widespread adoption of mobile devices and the millions of people who use SMS daily. However, SMS spam has also become a pervasive problem that endangers users' privacy and security through phishing and fraud. Despite numerous spam filtering techniques, there is still a need for a more effective solution to address this problem [1]. This research addresses the pervasive issue of SMS spam, which poses threats to users' privacy and security. Despite existing spam filtering techniques, the high false-positive rate persists as a challenge. The study introduces a novel approach utilizing Natural Language Processing (NLP) and machine learning models, particularly BERT (Bidirectional Encoder Representations from Transformers), for SMS spam detection and classification. Data preprocessing techniques, such as stop word removal and tokenization, are applied, along with feature extraction using BERT. Machine learning models, including SVM, Logistic Regression, Naive Bayes, Gradient Boosting, and Random Forest, are integrated with BERT for differentiating spam from ham messages. Evaluation results revealed that the Naïve Bayes classifier + BERT model achieves the highest accuracy at 97.31% with the fastest execution time of 0.3 seconds on the test dataset. This approach demonstrates a notable enhancement in spam detection efficiency and a low false-positive rate. The developed model presents a valuable solution to combat SMS spam, ensuring faster and more accurate detection. This model not only safeguards users' privacy but also assists network providers in effectively identifying and blocking SMS spam messages.Item Performance evaluation of classification algorithms on academic performance of postgraduate students(2023-02) Okunlola, O. A.; Ojo, A. K.Educational data mining has contributed to enhancing student academic performance by way of enabling stakeholders in academic institutions to have a pre-knowledge of the risks and dangers ahead and how to mitigate them. Prediction algorithms perform differently on dataset, and so, the need to develop models using different prediction algorithms and evaluating the result of such predictions is very important in order to be sure the best algorithm for a particular dataset is used. This work employed four classifiers: K-Nearest-Neighbour, Neural Network, Naïve Bayes and Decision Tree to model and, evaluated their models to know the performance of each on the target dataset. Their results were evaluated based on the various performance metrics. The results showed that Decision Tree had the highest accuracy on the dataset with test accuracy of 48.25% and therefore is the most suitable out of the four classifiers for performing prediction modelling on the dataset. Naïve Bayes is the second-best prediction model that can be used for predicting academic performance with an accuracy of 36.25%., followed by Neural Network with accuracy of 32.5 % and then K-Nearest Neighbour with accuracy of 32.5% but with lower precision, recall and area under Receiver Operating Characteristic curve.Item K-nearest neighbors Bayesian approach to false news detection from text on social media(Modern Education and Computer Science Press, 2022-08) Ogunsuyi, O. J.; Ojo, A. K.Social media usage has increased due to the rate at which technologies are emerging and it is less likely to detect false news/information manually as it aims to capture the human mind. The spread of false news can cause havoc; therefore, detection of false news becomes paramount where almost everyone has access to social media. Our proposed system optimizes the false news detection process. The system combines advantages of two textual feature extraction methods and two machine learning algorithms for text classification. Basic pre-processing methods were employed. Feature extraction was carried out using Term Frequency-Inverse Document Frequency with Word2Vector. K-Nearest Neighbour (KNN) and Naïve Bayes (NB) algorithms are combined to give KNN Bayesian. The most available systems made use of a single feature extraction method but in our system, two feature extraction methods are combined. The evaluation metrics used were accuracy, precision, recall, f1score and KNN Bayesian performed better than KNN. To further evaluate our model, the Area under the Curve-Receiver Operator Characteristics (AUC-ROC) revealed that AUC of KNN Bayesian ROC curve is higher than that of KNN.Item A model for conflicts’ prediction using deep neural network(2021-10) Olaide, O. B.; Ojo, A. K.Conflict is part of human social interaction, which may occur from a mere misunderstanding among groups of settlers. In recent times, advanced Machine Learning (ML) techniques have been applied to conflict prediction. Strategic frameworks for improving ML settings in conflict research are emerging and are being tested with new algorithm-based approaches. These developments have given rise to the need to develop a Deep Neural Network model that predicts conflicts. Hence, in this study, two Artificial Neural Network models were developed, the dataset which was extracted from https://www.data.worlduploaded by the Armed Conflict Location and Event Data Project (ACLED), in four separate CSV files (January 2015 to December 2018). The dataset for the year 2015 has 2697 instances and 28 features, for 2016 was 2233 with the same feature, for 2017 has 2669 instances with the same features, and 2018 has 1651 instances. After the development of the models: the baseline Artificial Neural Network achieved an accuracy of 95% and a loss of 5% on the training data and an accuracy of 90% and 10% loss on the test set. The Deep Neural Network Model achieved 98% accuracy and 2% loss on the training set, with 89% accuracy and 11% loss on the test set. It was concluded that to further improve the prediction of conflict, there is a need to address the issue of the dataset, in developing a better and more robust model.Item Predictive analysis in solar kiln drying of wood using recurrent neural networks(Foundation of Computer Science FCS, New York, USA, 2021-06) Ojo, A. K.; Amoo-Onidundu, C. E.Prediction in data mining, is a technique used in predicting results or outcomes of future occurrence in reference to existing information. Several predictive models have been developed for different fields of study. In solar kiln drying experiment, as a result of dependence on nature for its operation, outcomes of drying process is unstable and varies with weather variability. Although predictive models have been developed for wood drying experiments, there is very limited information on the use of Neural Networks for predicting outcomes in solar kiln drying of wood. In this work, Long Short-term Memory model, a special type of Recurrent Neural Network was adopted for prediction in solar kiln drying of wood. Data collected on external (atmospheric) and internal conditions of a solar kiln sited at from Forestry Research Institute of Nigeria was used for this study. Daily ambient and internal temperature and relative humidity were used as input data. The closeness of relationship between the experimental and predicted values (Mean Square Error, MSE = 0.97; 30.4) and (Squared Correlation, R2=0.68, 0.85) for Temperature and Relative Humidity respectively revealed that the model had a good agreement with data. The Equilibrium Moisture Content (EMC) of internal solar kiln environment which influences the outcome of drying was considered. The EMC of internal solar kiln environment was predicted for the next 730 days and suitability of the model for prediction was examined giving an MSE value of 0.2 and r2 value of 0.87. The findings of this study suggest a viable model for predicting drying outcomes under varying weather conditions.Item Improved model for facial expression classification for fear and sadness using local binary pattern histogram(2020) Ojo, A. K.; Idowu, T. O.In this study, a Local Binary Pattern Histogram model was proposed for Facial expression classification for fear and sadness. There have been a number of supervised machine models developed and used for facial recognition in past researches. The classifier requires human effort to perform feature extraction which has led to unknown changes in the expression of human face and incomplete feature extraction and low accuracy. This study proposed a model for improving the accuracies for fear and sadness and to extract features to distinguish between fear and sadness. Images of different people of varying ages were extracted from two datasets got from Japanese female facial expression (jaffe) dataset and Cohn cade got from Kaggle. In other to achieve an incremental development, classification was done using Linear Support Vector Machine (LSVM) and Random Forest Classifier (RFC). The accuracy rates for the LSVM models, LSVM1 and LSVM2 were 88% and 87% respectively while the RFC models, RFC1 and RFC2, were 81% and 82% respectively.