Browsing by Author "Yusuff, S. R."
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- ItemA Prediction Model for Bitcoin Cryptocurrency Prices(Springer, Cham, 2022-04-20) Aorowolo, M. O.; Ayegba, P.; Yusuff, S. R.; Misra, SCryptocurrencies like Bitcoin are a contentious and difficult technological innovation in today's financial system. With huge improvements in financial markets, machine learning and Artificial Intelligence aided trading have piqued interest in recent years. This study suggests a predicting model for blockchain bitcoin cryptocurrency prices and its profitability trading strategies using machine learning algorithms (ICA-Firefly and SVMs). For the prediction analysis of Bitcoin cryptocurrency data, this study combines ICA-Firefly with SVM algorithms. The model was tested on a large dataset of 2,194 samples, and its performance was analyzed in terms of evaluation metrics. In evaluation to state of the art, the ICAFirefly with L-SVM and Sigmoid SVM classification approach performs well on the bitcoin sample dataset, with an accuracy of 95% and 97%, respectively. The ICA-Firefly with the SVM model can be adopted as a viable financial system sustainability management strategy.
- ItemA Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis(An International Pan-African Multidisciplinary Journal of the SMART Research Group International Centre for IT & Development (ICITD) USA © Creative Research Publishers, 2022-12-04) Isiaka, R. M.; Babatunde, R. S.; Ajao, J. F.; Yusuff, S. R.; Popoola, D. D.; Arowolo, M. O.; Adewole, K. S.Following the identification of Coronavirus Disease 2019 (COVID-19) in Wuhan, China in December 2019, AI researchers have teamed up with a health specialist to combat the virus. This study explores the medical and non-medical areas of COVID-19 that AI has impacted: the prevalence of the AI technologies adopted across all stages of the pandemic, the collaboration networks of global AI researchers, and the open issues. 21,219 papers from ACM Digital, Science Direct and Google Scholar were examined. Adherence to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and utilizing the PICO (population, intervention, comparison, and outcome) paradigm, guided the inclusion of researches in the review. Tables and graphs were utilized to display the results. Analysis revealed that AI has impacted 4 molecular, 4 clinical, and 7 societal areas of COVID-19. Deep Learning among other AI technologies was traced to all aspects of the pandemic. 2173 authors and co-authors were traced to these achievements, while 32 of the most connected 51 authors were affiliated with institutions in China, 18 to the United States, and 1 to Europe. The open issues identified had to do with the quality of datasets, AI model deployment, and privacy issues. This study demonstrates how AI may be utilized for COVID-19 diagnosis, prediction, medication and vaccine identification, prognosis, and contact person monitoring. This investigation began at the beginning of the epidemic and continued until the first batch of vaccinations received approval. The study provided collaboration opportunities for AI researchers and revealed open issues that will spike further research toward preparing the world for any future pandemic
- ItemAn Evaluation of performance of k-NN and Mahalanobis Distance on Local Binary Pattern Feature representation for Access Control in Face Recognition System.(The Creative Research Networks for The African Society for Information & Communication Technology, USA, 2017-03) Yusuff, S. R.; Balogun, F. B.; Babtunde A. N.; Babatunde, R. S.A vast majority of the existing face recognition techniques encounter misclassifications when there is a large variation of the subject in terms of pose, illumination and expression as well as artefact such as occlusion. With the usage of Local Binary Pattern (LBP), local feature representation which preserves local primitives and texture information, robust against illumination, expression and occlusion was realized in the feature extraction process. Mahalanobis distance and k-NN classifier were employed for recognition as basis for comparison. An assessment of both algorithms was considered. It was observed that Mahalanobis distance measure outperformed the k-NN classifier for access control and recognition system due to its high recognition rate (98.4%), genuine acceptance rate (98.37) and its stern adherence to both FAR and FRR (0.0144, 1.568) than kNN with 94.9% and (0.392 and 2.895) respectively. This result demonstrates the robustness of Mahalanobis distance classifier for use access control system
- ItemExamining students’ perception of surveillance technology and its impact on privacy in educational institutions(Emerald Publishing Limited, 2026-04-13) Durodolu O. O.; Shuaibu A. J.; Babatunde V. O.; Yusuff, S. R.Purpose – The ubiquity of digital surveillance technologies is at the forefront of debates on balancing collective security while ensuring individual privacy. Ethical concerns of individual privacy, freedom and misuse are at the crux of ongoing global discourse, with the integration of artificial intelligence into surveillance further increasing the intrusive nature of these technologies. This study examined students’ perception of surveillance technology and its impact on privacy. Questions articulated from the problematic bordered around the level of awareness, perception on the impact of surveillance technology, attitude towards surveillance and beliefs in the regulation of surveillance in educational institutions among others. Design/methodology/approach – This study was premised on the pragmatic paradigm involving the integration of both quantitative and qualitative approaches. Data were collected from 450 respondents using a structured questionnaire. Findings – Findings revealed a significant association between students’ awareness of surveillance technologies and their frequency of observing them on campus (χ² = 47.80, df = 6, p < 0.001; mixed perceptions of privacy with most respondents (54.9%) feeling their privacy was moderately respected, a notable minority expressing concern over inadequate protection, and nearly a quarter (24.2%) reporting behavioural changes due to being watched; and a statistically significant positive relationship between trust in institutional data handling and confidence in data protection (ρ = 0.144, p < 0.01).The qualitative responses revealed that students acknowledged the usefulness of surveillance for safety, but insisted on clear boundaries regarding scope, usage and access. This study concluded that surveillance in education was a double-edged phenomenon that enhances safety and order but raised several challenges that called for deliberate governance strategies. It was recommended that educational institutions developed clear communication strategies on the use of surveillance technologies and accompany such with privacy protection policies. Originality/value – The findings contribute to academic debates on digital ethics, governance and the balance between security and privacy in learning environments, while offering actionable recommendations for institutions to design communication strategies and privacy policies that safeguard both safety and individual rights.
- ItemGender recognition using local binary pattern and Naive Bayes classifier(Journal of computer Science and its Application, 2016) Babatunde, R. S.; Abdulsalam, S. O.; Yusuff, S. R.; Babatunde, A. N.Human face provides important visual information for gender perception. Ability to recognize a particular gender is very important for the purpose of differentiation. Automatic gender classification has many important applications, for example, intelligent user interface, surveillance, identity authentication, access control and human-computer interaction amongst others. Gender recognition is a fundamental task for human beings, as many social functions critically depend on the correct gender perception. Consequently, real-world applications require gender classification on real-life faces, which is much more challenging due to significant appearance variations in unconstrained scenarios. In this study, Local Binary Pattern is used to detect the occurrence of a face in a given image by reading the texture change within the regions of the image, while Naive Bayes Classifier was used for the gender classification. From the results obtained, the gender correlation was 100% and the highest accuracy of the result obtained was 99%. The system can be employed for use in scenarios where real time gender recognition is required.
- ItemHuman-Computer Interaction-Based Yorùbá Language using Recurrent Neural Network of Deep Learning(Faculty of Computing and Information Technology and Faculty of Natural and Applied Science, Sule LAmido University, Kafin Hausa, 2025-03) Babatunde A. N.; Ajao J. F.; Babatunde R. S.; Olawuyi H. O.; Yusuff, S. R.Background: This paper presents the development of a Human-Computer Interaction-Based Yorùbá to English Language Translator using a Recurrent Neural Network (RNN), specifically a Bidirectional Gated Recurrent Unit (GRU) encoder-decoder model. This study presents a novel contribution to existing studies by systematically addressing the setbacks of Yoruba tonal variations with larger corpus and offering a user -friendly interface both underexplored in prior research. Aim: The research aims to develop a machine translation system for translating Yoruba to English and English back to Yoruba applying Bidirectional Gated Recurrent Unit (GRU) algorithm. Methodology: 10054 instances of bilingual Yorùbá and English parallel corpus were collected from Kaggle. The dataset was split into 80% and 20% for training and testing respectively. The Bidirectional GRU model was trained to learn the relationships between the languages with optimization techniques such as gradient descent and backward propagation. Results: The model was evaluated using the BLEU score achieving 93% and 96% accuracy on the training and test sets respectively indicating its robustness in translating between Yorùbá and English and vice versa. A user-friendly web interface was integrated for easy real-time translation. The final system was deployed on a cloud server for remote access. The findings demonstrates that models lik e the Bidirectional GRU can ef fectively handle Yorùbá’s linguistic complexities, improving translation accuracy and facilitating cross-cultural communication. The system implemented in this paper performs well with standardized text but its performance may reduce for high idiomatic expressions. Notwithstanding, the system holds promise for applications in education, multilingual content localization, and assistive communication tools, particularly for Yorùbá-speaking communities.
- ItemOffline yoruba word recognition system based on capsule neural network(Ilorin Journal of Computer Science and Information Technology, 2022-06-30) Ajao, J. F.; Olorunmaiye, E; Mabayoje, M. A.; Yusuff, S. R.; Bajeh, A.The need for Digital Document archiving has resulted in several efforts in Image Document Analysis, where scanned historical/ indigenous documents can be stored in digitized form and made readily accessible and available for future use. Visual system in human uses an highly space variant in sampling, coding, processing and understanding of variations in human handwritings, which possess lots of challenges or difficulty for machine to understand. Consequently, it is important to preserve our indigenous heritage values by way of digitization. In this regard, the research work models a Yoruba Script Recognition System using Capsnet, based on the peculiar features of Yoruba alphabet. Yoruba bird names were gathered from the Yoruba dictionary and were written by 20 indigenous literate writers in 50 different writing styles. This was used to model HYSR system. The training time and testing after training different epoch produces 89.2222 and the testing time generated is 0.8667. The model accuracy gives 85.49%, which shows a promising result.
- ItemYORÙBÁ CHARACTER RECOGNITION SYSTEM USING CONVOLUTIONAL RECURRENT NEURAL NETWORK(Ugur SEN, 2022-09-01) Ajao. J. F.; Yusuff, S. R.; Ajao, A. O.Abstract: Handwritten recognition systems enable automatic recognition of human handwritings, thereby increasing humancomputer interaction. Despite enormous efforts in handwritten recognition, little progress has been made due to the variability of human handwriting, which presents numerous difficulties for machines to recognize. It was discovered that while tremendous progress has been made in handwritten recognition of English and Arabic languages, very little work has been done on Yorùbá handwritten characters. Those few works, in turn, made use of Hidden Markov Model (HMM), Support Vector Machine (SVM), Bayes theorem, and decision tree algorithms. To integrate and save one of Nigeria's indigenous languages from extinction, as well as to make Yorùbá documents accessible and available in the digital world, this research work was undertaken. The research presents a convolutional recurrent neural network (CRNN) for the recognition of Yorùbá handwritten characters. Data were collected from students of Kwara State University who were literate Yorùbá writers. The collected data were subjected to some level of preprocessing such as grayscale, binarization, and normalization in order to remove perturbations introduced during the digitization process. The convolutional recurrent neural network model was trained using the preprocessed images. The evaluation was conducted using the acquired Yorùbá characters, 87.5% of the acquired images were used for the training while 12.5% were used to evaluate the developed system. As there is currently no publicly available database of Yorùbá characters for validating Yorùbá recognition systems. The resulting recognition accuracy was 87.2% while the characters with under dot and diacritic signs has low recognition accuracy