9 results on '"Adigun, Oyeranmi"'
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2. MACHINE LEARNING TECHNIQUES FOR PREDICTION OF COVID-19 IN POTENTIAL PATIENTS
- Author
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Adigun, Oyeranmi, primary, Rufai, Mohammed Mutiu, additional, Okikiola, Folasade Mercy, additional, and Olukumoro, Sunday, additional
- Published
- 2023
- Full Text
- View/download PDF
3. Hybrid Cloud Storage Techniques Using Rsa And Ecc
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Musa, Ugbedeojo, primary, Adebiyi, Marion O., additional, Adigun, Oyeranmi, additional, Adebiyi, Ayodele A., additional, and Aremu, Charity O., additional
- Published
- 2023
- Full Text
- View/download PDF
4. Емпіричний аналіз вебометричного рейтингу в секторі політехнічної освіти Нігерії
- Author
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Yekini, Nureni Asafe, Adigun, Oyeranmi, and Akinwole, Agnes Kikelomo
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transparency ,рейтинг ,покращення вебометричного рейтингу ,видимість ,General Engineering ,visibility ,політехніка ,webometrics ,ranking ,ICT impact ,вебометрика ,polytechnics ,ResearchGate ,вплив ІКТ ,Google Scholar ,прозорість ,improve webometric ranking - Abstract
The January 2022 edition of webometric ranking placed Yaba College of Technology as number one from 152 polytechnics in Nigeria. The ranking weight is 66 for country ranking, 8162 world ranking, Impact, Openness, and Excellence of 9698, 4558, and 7190 respectively. The negative variation and low webometric ranking of Yaba College of Technology that happened to be the first higher institution of learning in Nigeria with the slogan the first and still the best is a point of concern and motivates this research work. This research work collected data to evaluate the indicators for webometric ranking among the students and staff of Yaba College of Technology, a total of 346 were sampled students 44.51 % and Staff 55.49 %. The discussion and analysis of data obtained revealed that the poor webometric ranking is due to inadequacy of the necessary ICT infrastructure to encourage robust web presence; non-availability of up-to-date and scanty content on the Polytechnics website; Non-frequent usage of the Polytechnic website by the staff and students of the Polytechnic; the inadequate number of external networks (subnets) links with Polytechnic website; insufficient number of the top-cited publications in high impact Journals from the staff of the Yaba College of Technology; and Scanty number of the profile of staff from the Polytechnic on Google Scholar and ResearchGate, etc. among others. This research work opined that low webometric ranking could result in the following negative impact on the polytechnics lowering the esteem of the Polytechnic in the eyes of stakeholders, potential students and funding agencies, academic exchange with reputable institutions from other parts of the world for teaching, learning and research may writhe. The consequence of our findings recommendations was made to improve webometric ranking in future., Вебометричний рейтинг за січень 2022 року поставив Технологічний коледж Яба на перше місце серед 152 політехнічних закладів Нігерії. Показник рейтингу становить 66 для рейтингу країни, 8162 для світового рейтингу, вплив, відкритість і досконалість 9698, 4558 і 7190, відповідно. Негативні варіації та низький вебометричний рейтинг Технологічного коледжу Яба, який став першим вищим навчальним закладом у Нігерії з гаслом «перший і все ще найкращий», викликають занепокоєння та є стимулом для цієї дослідницької роботи. У цій дослідницькій роботі було зібрано дані для оцінки показників для вебометричного рейтингу серед студентів і персоналу Технологічного коледжу Яба, загальна кількість яких 346, серед відібраних 44,51% студентів і 55,49% персоналу. Обговорення та аналіз отриманих даних виявили, що низький вебометричний рейтинг спричинений неадекватністю необхідної інфраструктури інформаційно-комунікаційних технологій для заохочення надійної присутності в Інтернеті; відсутність актуального та мізерного контенту на сайті Політехніки; Нечасте використання сайту Політехніки співробітниками та студентами Політехніки; недостатня кількість зв'язків зовнішніх мереж (підмереж) із сайтом Політехніки; недостатня кількість найбільш цитованих публікацій у резонансних журналах від співробітників Технологічного коледжу Яба; і незначна кількість профілів співробітників Політехніки на Google Scholar і ResearchGate тощо. У цій дослідницькій роботі було висловлено думку, що низький вебометричний рейтинг може призвести до наступного негативного впливу на політехніку: зниження поваги політехніки в очах зацікавлених сторін, потенційних студентів та фінансових установ, академічний обмін з авторитетними установами з інших частин світу для викладання, навчання та дослідження можуть згортатися. Наслідком наших висновків були зроблені рекомендації щодо покращення вебометричного рейтингу в майбутньому.
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- 2023
5. Empirical analysis of webometric ranking in Nigeria polytechnics education sector
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Yekini, Nureni Asafe, primary, Adigun, Oyeranmi, additional, and Akinwole, Agnes Kikelomo, additional
- Published
- 2022
- Full Text
- View/download PDF
6. Detection of Fracture Bones in X-ray Images Categorization
- Author
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Rufai Mohammed, Aigbokhan Edwin, Babatunde Ronke, and Adigun Oyeranmi
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Economics and Econometrics ,Materials science ,Categorization ,business.industry ,Materials Chemistry ,Media Technology ,X ray image ,Fracture (geology) ,Forestry ,Pattern recognition ,Artificial intelligence ,business - Abstract
Fractured bone detection and categorization is currently receiving research attention in computer aided diagnosis system because of the ease it has brought to doctors in classification and interpretation of X-ray images. The choice of an efficient algorithm or combination of algorithms is paramount to accurately detect and categorize fractures in X-ray images, which is the first stage of diagnosis in treatment and correction of damaged bones for patients. This is what this research seeks to address. The research design involves data collection, preprocessing, segmentation, feature extraction, classification and evaluation of the proposed method. The sample dataset were x-ray images collected from the Department of Radiology, National Orthopedic Hospital, Igbobi-Lagos, Nigeria as well as Open Access Medical Image Repositories. The image preprocessing involves the conversion of images in RGB format to grayscale, sharpening and smoothing using Unsharp Masking Tool. The segmentation of the preprocessed image was carried out by adopting the Entropy method in the first stage and Canny edge method in the second stage while feature extraction was performed using Hough Transformation. Detection and classification of fracture image employed a combination of two algorithms; K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) for detecting fracture locations based on four classification types: (normal, comminute, oblique and transverse).Two performance assessment methods were employed to evaluate the developed system. The first evaluation was based on confusion matrix which evaluates fracture and non-fracture on the basis of TP (True Positive), TN (True negative), FP (False Positive) and FN (False Negative). The second appraisal was based on Kappa Statistics which evaluates the type of fracture by determining the accuracy of the categorized fracture bone type. The result of first assessment for fracture detection shows that 26 out of 40 preprocessed images were fractured, resulting to the following three values of performance metrics: accuracy value of 90%, sensitivity of 87% and specificity of 100%. The Kappa coefficient error assessment produced accuracy of 83% during classification. The proposed method can find suitable use in categorization of fracture types on different bone images based on the results obtained from the experiment.
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- 2020
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7. Classification of Diabetes Types using Machine Learning
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Adigun, Oyeranmi, primary, Okikiola, Folasade, additional, Yekini, Nureni, additional, and Babatunde, Ronke, additional
- Published
- 2022
- Full Text
- View/download PDF
8. Modified Genetic Algorithm Parameters to Improve Online Character Recognition
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Adigun, Oyeranmi, primary, Omidiora, Elijah, additional, and Rufai, Mohammed, additional
- Published
- 2016
- Full Text
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9. Implication of Variation in Ant Colony Optimization Algorithm (ACO) Parameters on Feature Subset Size.
- Author
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Babatunde, Ronke S., Isiaka, Rafiu M., Adigun, Oyeranmi J., and Babatunde, Akinbowale N.
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ANT algorithms ,HEURISTIC algorithms ,SUBSET selection ,FILTERS & filtration ,PHEROMONES - Abstract
Purpose: The purpose of this research is to apply ant colony optimization (ACO), a nature inspired computational (NIC) technique to achieve optimal feature subset selection, for the purpose of data dimensionality reduction, which will remove redundancy, provide a reduced storage space, improve memory utilization and subsequently, classification task. Design/Methodology/Approach: The ACO feature subset selection process was filter-based, accomplished by using correlation coefficient as the heuristic information ɳ which guides the search to determine the pixel (feature) attractiveness. The intensity of the pixel represents the pheromone level τ. Also, ρ, φ, α and βare key parameters which were tuned to various arbitrary values at each iteration to determine the best combination of parameters which can result in a reduced feature subset (window size). The procedure was simulated in MATLAB 2012. Findings: The study has been able to demonstrate that experimental results obtained by tuning the parameters of ACO at each run gave a reduced feature subset (window size), at parameter settings of α (0.10), β (0.45), ρ (0.50), φ (1.0) which was salient for efficient face recognition thereby demonstrating the effectiveness and superiority of filter-based over wrapper-based feature selection. Research Limitation: The major limiting factor in this research was time and materials which hindered the further testing and implementation of innovative computational intelligence. Originality/Value: This research work is valuable because it gives the implication of the variation of the ACO parameters which determines the extent to which the optimal solution construction that results in a reduced feature subset can be realized. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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