Scholarly Publication
Permanent URI for this collection
Browse
Recent Submissions
- ItemEXPERIMENTAL MEASUREMENT OF SIGNALS ATTENUATION OF SOBI FM AT VHF ALONG ILORIN - JEBBA ROAD, NIGERIA(Nigerian Institute of Physics, 2022-12-22) Ajani, A. S., Egebeyale, G. B., Yusuf, O. A., Aliu T. O., Aremu O. A., Odeyemi, C. S. and Oyero, O. PThe study of propagation loss and fade margin of 101.9MHz (Sobi FM) signals along Ilorin (Lat 80541 & Long 40551E) – Jebba (Lat 90111N & Long 40821E) road at very high frequency (VHF) band was done experimentally using spectrum analyzer. Alongside theoretical calculation to determine the attenuation was also done using Friis and Free Space attenuation equations. The attenuation and fade margin were measured regularly at intervals of two kilometers (2 km) from the base station up to a total of sixty kilometers (60 km) along the chosen axis. The analytical models were obtained in form of polynomial equations for received power, measured attenuation and the fade margin of the signals. The calculated results correlated with the measurements (Correlation coefficient value R2 = 1), but gave deviation when compared with the measurements. The variance of the results for the existing formula with the measurements was adjudged to hills, valleys, trees and bends along the links. The highest value of the fade margin obtained (75.89 dB) did not exceeded the receiver’s sensitivity of 80 dB, which implies that the radio-signals can be improved, by increasing the height and the power of transmitting antenna. This research provides information on improving Sobi FM transmission coverage and a guide to upgrade or redirect their transmission signals appropriately.
- ItemBack Propagation Neural Network and Chicken Swarm Optimization for Yoruba Indigenous Food Recognition.(University of Ibadan Journal of Science and Logics in ICT Research., 2023-07-13) Agbeniyi, M.T., Ajao, J. F., and Babatunde, R.S. (2023)Back Propagation Neural Networks (BPNNs) are widely used to model complex systems in a steady state. However, BPNNs have a slow convergence rate. Several meta-heuristic algorithms have been used to speed up its convergence. Though, the modifications increased the number of parameters which affect the convergence rate, but more work needs to be done on BPNN in order to improve the performance of BPNN. This work introduced Chicken Swarm Optimization (CSO) technique to improve the performance of BPNNs in order to recognize Yoruba indigenous food. 1251 varieties of Yoruba indigenous foods such as Amala ogede, Iyan gbere, Puguru, Akara, Dele, Ekuru, Monu, Aseke, Abari, Sapala, and Egbo were prepared. The prepared foods were captured using a digital camera and were digitalized. The digitalized foods were subjected to preprocessing using Image resizing, Morphological, Edge scaling, Histogram, normalization and Sobel edge. All images were subjected to feature extraction using Local Binary Pattern (LBP) and the feature vectors gotten were optimized with Chicken Swarm Optimization (CSO) algorithm. The result of the optimized parameters were classified using Back Propagation Neural Network (BPNN). The recognition accuracy using BPNN yields 82.22 %, 87.59 %, 83.67 %, and 85.34 % for the four categories of food respectively, whereas BPNN-CSO yields 85.34 %, 91.90 %, 91.02 %, and 90.23 %, respectively. Sensitivity using BPNN yields 87.96%, 92.22%, 90.22%, 90.13% while BPNN-CSO delivers 90.13%, 97.78%, 97.74%, 96.71% respectively using recognition time and sensitivity standard term that are included in the result table. It was observed that, the recognition accuracy, sensitivity, specificity, and false positive ratio values of BPNN-CSO gives improved performance result compared to BPNN.
- ItemA Neuro-Fuzzy-based Approach to Detect Liver Diseases(Published by University of Lahore, Lahore, Pakistan. Pakistan Engineering Council (PEC)., 2024-03-22) Babatunde, R. S., Babatunde, A. N., Balogun, B. F., Abdulahi, A. T., Umar, E., Mohammed, S. B., Ajiboye, A. R., Obiwusi, K. Y., and Oke, A. A. (2024):The liver is a crucial organ in the human body and performs vital functions essential for overall health, including metabolism, immunity, digestion, detoxification, and vitamin storage. Detecting liver diseases at an early stage poses challenges due to the liver's ability to function adequately despite partial damage. Early detection is crucial as liver diseases have significant clinical and socio-economic impacts, affecting other organ systems and requiring timely intervention to improve patient survival rates. Classical diagnostic methods for liver disorders may not always produce better results, thus necessitating more advanced and accurate diagnostic systems. Intelligent systems like predictive modeling and decision support systems, have shown promising results in recent years in disease detection and are aiding medical practitioners. In this research, a neuro fuzzy-based system integrating neural networks and fuzzy logic (FL) was implemented. Based on the risk factors present in the dataset that was used to benchmark the algorithm, this system offered a classification accuracy of 97% which is comparable with existing systems in the literature. This study creates a neuro-fuzzy system for early liver disease identification, solving diagnostic issues and offering healthcare improvements. The proposed system is validated by presenting the simulation results.
- ItemA Logistic Regression-Based Technique for Predicting Type II Diabetes.(Published by Academic City University College, Accra, Ghana., 2024-01-29) Babatunde, R.S., Babatunde, A.N., Balogun, B.F., Abdulrahman, T.A., Umar, E., Ajiboye, R.A., Mohammed, S.B., Oke, A.A. and Obiwusi, K.Y. (2024)In recent years, diabetes has emerged as one of the main causes of death for people. The spread of unhealthy foods, sedentary lifestyles, and eating habits have all contributed to the annual increase in the incidence of diabetes. A diabetes prediction model can help with clinical management decision-making. Diabetes prevention may be aided by being aware of potential risk factors and early detection of high-risk individuals. Numerous diabetes prediction models have been created. The size of the data set to be used was an issue in earlier research, but more recent studies have incorporated the use of high-quality, trustworthy data sets, such as the Vanderbilt and PIMA India data sets. Recent research has demonstrated that a few variables, including glucose, pregnancy, body mass index (BMI), the function of the diabetic pedigree, and age, can be used to predict Type II diabetes. Machine learning models of these parameters can be used to accurately predict the chance of the disease occurring as it was investigated in this study. In order to predict Type II diabetes, this study used the machine learning method Logistic Regression.
- ItemPredicting Student Academic Performance Using Artificial Neural Network.(LASU Journal of Research and Review in Science. 8(1): 1-7. Published by Lagos State University., 2021-03-23) Olaniyan, O. M., Oloyede, A. O., Aremu, I. A., Babatunde, R. S., and Adeyemi, B.M. (2021)Abstract Introduction: Predicting student academic performance plays an important role in academics. Classifying students using conventional techniques cannot give the desired level of accuracy, while doing it with the use of soft computing techniques may prove to be beneficial. Aim: This study aims to accurately predict and identify student academic performance using an Artificial Neural Network in educational institutions. Materials and Methods: Artificial Neural network was employed to compute the performance procedure over the MATLAB simulation tool. The performance of the Neural Network was evaluated by accuracy and Mean Square Error (MSE). This tool has a simple interface and can be used by an educator for classifying students and distinguishing students with low achievements or at-risk students who are likely to have low performance. Results: Findings revealed that the Neural network has the highest prediction accuracy by (98%) followed by the decision tree by (91%). Support vector machine and k-nearest neighbor had the same accuracy (83%), while naive Bayes gave lower prediction accuracy (76%). Conclusion: This work has helped to analyze the capabilities of an Artificial Neural Network in the accurate prediction of students’ academic performance using Regression and feed-forward neural network (FFNN) as evaluation metrics.