Depatment of Computer Science

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A Clinical Decision Support System for Predicting Psychotic Disorder using Random Forest Algorithm
(Published by Faculty of Engineering and Technology, Adeleke University. Ede. Osun State, 2023-05-19) Babatunde Roneke Seyi
Psychotic disorders exert a profound effect on one's perception of reality, cognitive abilities, emotions, and conduct. By precisely pinpointing individuals at risk or in the initial phases of a psychotic disorder, healthcare providers can promptly implement interventions, employ suitable treatment approaches, and offer essential support. This proactive approach holds the potential to impede the advancement of the disorder and mitigate its long-term impact on the individual's well-being. Earlier automated predictive systems are limited in accuracy of prediction, ease of use and complexity of computation. This work implements a Clinical Decision Support System (CDSS) utilizing the Random Forest algorithm. A dataset consisting of medical records from 500 carefully selected psychotic patients at Yaba Psychiatric Hospital in Lagos, Nigeria, spanning a period of five years (January 2010 to December 2014) was obtained. The dataset provides crucial insights into the demographic and clinical characteristics of the patients, enabling the Random Forest algorithm to capture relevant patterns and relationships associated with psychotic disorders. Following model training and evaluation, employing a range of evaluation metrics such as cross-validation, F1 score, recall score, and precision score, the predictive model achieves an impressive accuracy rate of approximately 95%. The implications of this research are profound. By harnessing the power of machine learning and the Random Forest algorithm, the CDSS holds great potential in significantly enhancing psychiatric diagnoses. The accuracy attained by the predictive model showcases its reliability and effectiveness as a decision support tool for healthcare professionals. This technology has the capacity to expedite diagnosis, enabling timely interventions and personalized treatment plans for patients with psychotic disorders.
A Comparative analysis of Texture feature and 3D colour Histogram for Content Based Image Retrieval.
(U6+ Consortium of African Universities Proceedings. Maiden Edition on Harnessing African Potentials for Sustainable Development, Calabar, Nigeria., 2019-09-15) Babatunde, R. S., and Ajao, J. F. (2019)
The visual content of an image can be used to search for similar images based on user interest from a large database, in the art known as Content Based Image Retrieval (CBIR). In CBIR systems, the user presents a query image to the system, the system obtains the feature vector, which is compared with certain image features of the images in the database. The adequacy of the feature vectors extracted for the retrieval of appropriate and exact image from the database is an open issue which calls for continual research and attention. This work involved the retrieval of similar images based on the content of the image by comparing the feature vectors of the queried image and those of the image database using Mahalanobis distance measure. Textural feature vectors of the images were obtained using local binary pattern and 3D colour histogram feature vectors were also extracted. The performances of these two different feature vectors were compared based on the distance metrics to determine their suitability. The similar images in the database are displayed along with their similarity distance value, in which the minimum distance is a metric for the matched images. The CBIR system was implemented using a locally acquired database of over 600 different images. Various images were used to query the CBIR system, which was able to successfully output similar images. The simulation result obtained revealed that textural feature vectors are more adequate in terms of speed and accuracy in content-based image retrieval than 3D colour histogram based on the images used in the experiment.
A comparison of Boosting techniques for Classification of Microarray data
(Published by Faculty of Computing and Information Systems, University of Ilorin, 2023-04-13) Babatunde Ronke Seyi
Context: The advancements in technology, particularly microarrays, have played a pivotal role in enhancing crucial aspects within the domains of genomics and bioinformatics. These advancements have significantly contributed to the enhancement of illness diagnosis, evaluation of therapy response in patients, and advancements in cancer research. Microarray data often exhibits a substantial likelihood of encompassing extraneous and duplicative factors, hence introducing noise into the dataset. Consequently, the process of scrutinizing the data to identify significant patterns for diagnosis can be quite daunting when employing conventional statistical approaches. Numerous studies are currently being conducted to enhance the analysis of microarray data, with the aim of enhancing performance and prediction accuracy at an accelerated pace. Most of these earlier methods are limited in their predictive capacity and are characterised by high computational time and algorithm complexity. Objective: This research addresses some of these issues by implementing the classification of microarray data using Boosting algorithms. Method: Benchmarked on a publicly available dataset, the microarray data was cleaned, normalised and salient features carrying essential information were obtained. Three state of the art boosting algorithms; AdaBoost, Gradient Boost, and XGBoost were used in classifying the microarray data and the performance result of each was compared. Results: The experimental findings indicate that XGBoost demonstrates superior performance compared to other boosting approaches, with a classification accuracy rate of 98.18% and training time of 11seconds. Conclusions: The novelty of the experiment compared to earlier work is evident in the training time reported which is an information not frequently explicit in other report of findings.
A Deep Neural Network-Based Yoruba Intelligent Chatbot System
(iSTEAMS, 2022-06-14) Babatunde, A.N., Oke, A.A., Balogun, B.F., AbdulRahman, T.A. & Ogundokun, R.O.
Two Artificial Intelligence software systems, Bot and Chatbot have recently debuted on the internet. This initiate a communication between the user and a virtual agent. The modeling and performance in deep learning (DL) computation for an Assistant Conversational Agent are presented in this research (Chatbot). The deep neural network (DNN) technique is used to respond to a large number of tokens in an input sentence with more appropriate dialogue. The model was created to do Yoruba-to-Yoruba translations. The major goal of this project is to improve the model's perplexity and learning rate, as well as to find a blue score for translation in the same language. Kares is used to run the experiments, which is written in Python. The data was trained using a deep learning-based algorithm. With the use of training examples, a collection of Yoruba phrases with various intentions was produced. The results demonstrate that the system can communicate in basic Yoruba terms and that it would be able to learn simple Yoruba words. The study result when evaluated showed that the system had 80% accuracy rate. Keywords: Chatbot, Natural Language Processing, Deep Learning, Artificial Neural Network, Yoruba Language
A Generalized Neuron Model (GNM) Based Human Age Estimation.
(Advances in Multidisciplinary & Scientific Research Journal., 2017-10-20) Babatunde, R.S. and Yakubu, I.A. (2017)
In real-world application, the identification characteristic of face images has been widely explored, ranging from National ID card, International passport, driving license amongst others. In spite of the numerous investigation of person identification from face images, there exists only a limited amount of research on detecting and estimating the demographic information contained in face images such as age, gender, and ethnicity. This research aim at detecting the age/age range of individual based on the facial image. In this research, a generalized neuron (GN), which is a modification of the simple neuron, is used, to overcome some of the problems of artificial neural network (ANN) and improve its training and testing performance. The GN is trained with discrete wavelet transform (DWT) features obtained after the application of Canny edge detection algorithm on the face Image. Validating the technique on FGNET face images reveals that the frequency domain features obtained using the DWT captures the wrinkles on the face region, which represents a distinguishing factor on the face as humans grow older. The empirical results demonstrates that the GN outperforms the simple neuron, with detection rate of 93.5%, training time of 96.30secs, matching time of 14secs and root mean square error of 0.0523. The experimental results suggest that the GN model performs comparably and could be adopted for detecting human ages.
A Hybridized Feature Extraction Model for Offline Yorùbá Document Recognition
(Asian Journal of Research in Computer Science, 2023-03-22) Jumoke F. Ajao a*, Rafiu M. Isiaka a and Ronke S. Babatunde
Document recognition is required to convert handwritten and text documents into digital equivalents, making them more easily accessible and convenient to store. This study combined feature extraction techniques for recognizing Yorùbá documents in an effort to preserve the cultural values and heritages of the Yorùbá people. Ten Yorùbá documents were acquired from Kwara State University’s Library, and ten indigenous literate writers wrote the handwritten version of the documents. These were digitized using HP Scanjet300 and pre-processed. The pre-processed image served as input to the Local Binary Pattern, Speeded-Up-Robust-Features and Histogram of Gradient. The combined extracted feature vectors were input into the Genetic Algorithm. The reduced feature vector was fed into Support Vector Machine. A 10-folds cross-validation was used to train the model: LBP-GA, SURF-GA, HOG-GA, LBP-SURF-GA, HOG-SURF-GA, LBP-HOG-GA and LBP-HOG-SURF-GA. LBP-HOG-SURF-GA for Yorùbá printed text gave 90.0% precision, 90.3% accuracy and 15.5% FPR. LBP-HOG-SURF-GA for Handwritten Yorùbá document showed 80.9% precision, 82.6% accuracy and 20.4% (FPR) LBP-HOG-SURF-GA for CEDAR gave 98.0% precision, 98.4% accuracy and 2.6% FPR. LBP-HOG-SURF-GA for MNIST gave 99% precision, 99.5% accuracy, 99.0% and 1.1% FPR. The results of the hybridized feature extractions (LBP-HOG-SURF) demonstrated that the proposed work improves significantly on the various classification metrics.
A Kohonen Self Organizing Map (KSOM) Technique for Classification of Electrocardiogram (ECG) Signals.
(Published by Faculty of Computing and Informatics. Ladoke Akintola University of Technology, Ogbomoso. Oyo State., 2024-03-15) Babatunde Ronke Seyi
Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases. Handling noisy ECG data, which is common in real-world situations makes accurate classification a critical task. Because ECG signals are faint and are quickly disrupted, classification accuracy can be poor, hence the need for improvement in the automatic ECG categorization system's recognition accuracy. The Kohonen Self Organising Map (KSOM) is known for its ability to cluster high-dimensional data in a low-dimensional space, hence its adoption in this research. The procedure employed include collection and pre-processing of diverse ECG data, including normal and abnormal cardiac rhythms. Inherent noise was removed from the data to ensure better-quality input data into the classification algorithm. A Kohonen SOM neural network (MiniSom model) was trained using the preprocessed ECG data. The KSOM organizes ECG signals into clusters on a topological map, preserving similarities and dissimilarities between different cardiac rhythms. Subsequently, the trained SOM serves as a reference model for classifying unseen ECG signals, indicating the corresponding cardiac rhythm. Benchmarked on two different dataset, evaluation of the classification performance of the technique was carried out. Cross-validation was done to assess the model's robustness and generalizability. Comparative analysis was conducted to measure the effectiveness and efficiency of the SOM-based approach against other common ECG signal classification techniques based on accuracy, precision, recall and fi-score. The result obtained shows that the average accuracy of 94.2%, precision of 83%, recall of 100% and f1-score of 91% achieved by the MiniSOM model outperformed the other models.
A 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.
A Machine Learning Approach to Dropout Early Warning System Modeling. International
(Journal of Advanced Studies In Computer Science And Engineering, 2019-10-18) Isiaka R.M., Babatunde R. S., Ajao F.J. and Abdulsalam S.O. (2019)
Dropout has been identified as a social problem with an immediate and long term consequences on the socioeconomic development of the society. A major element of a conceptual framework for dropout monitoring and control system is the prevention component. The component specified the need for automatic dropout early warning system. This paper presents the procedure for building the adaptive model, that is the core element for the realization of the prevention component of the framework. The model development is guided by the knowledge of the domain experts. An experimental approach was used to identify the K-Nearest Neighbors (KNN) as the best of the six algorithms considered for the adaptive models. Other algorithms explored are Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB) and Support Vector Machines (SVM). Historical dataset from a State University in the North Central region of Nigeria was used for building and testing the adaptive model. The average performance of the model on full attributes and the first year academic attributes compared favourably with 0.98 Precision, Recall and F1-Scores. Conversely, the Precision, Recall and F1-Scores on the entry attributes are 0.90, 0.95 and 0.92 respectively. The implication of this is that the academic performance is a very significant factor for dropping out of the educational system. The developed model can provide an early signal on the students with the propensity to dropout, thereby serving as an advisory system for the students, parents and school management towards curtailing the menace.
A 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.
A New Hash Function Based on Chaotic Maps and Deterministic Finite State Automata
(IEEE, 2020-06-16) Moatsum Alawida; Je Sen Teh; Damilare Peter Oyinloye; Musheer Ahmad; Rami S Alkhawaldeh
In this paper, a new chaos-based hash function is proposed based on a recently proposed structure known as the deterministic chaotic finite state automata (DCFSA). Out of its various configurations, we select the forward and parameter permutation variant, DCFSAFWP due to its desirable chaotic properties. These properties are analogous to hash function requirements such as diffusion, confusion and collision resistance. The proposed hash function consists of six machine states and three simple chaotic maps. This particular structure of DCFSA can process larger message blocks (leading to higher hashing rates) and optimizes its randomness. The proposed hash function is analyzed in terms of various security aspects and compared with other recently proposed chaos-based hash functions to demonstrate its efficiency and reliability. Results indicate that the proposed hash function has desirable statistical characteristics, elevated randomness, optimal diffusion and confusion properties as well as flexibility.
A novel approach to outliers removal in a noisy numeric dataset for efficient Mining.
(Published by Faculty of Computing and Information Systems, University of Ilorin., 2016-02-16) Ajiboye, A. R, Adewole, K. S., Babatunde, R. S. and Oladipo, I. D. (2016):
Data pre-processing is a key task in the data mining process. The task generally consumes the largest portion of the total data engineering effort while unveiling useful patterns from datasets. Basically, data mining is about fitting descriptive or predictive models from data. However, the presence of outlier sometimes reduces the reliability of the models created. It is, therefore, essential to have raw data properly pre-processed before exploring them for mining. In this paper, an algorithm that detects and removes outliers in a numeric dataset is proposed. In order to establish the effectiveness of the proposed algorithm, the clean data obtained through the implementation of the proposed approach is used to create a prediction model. Similarly, the clean data obtained through the use of one of the existing techniques is also used to create a prediction model. Each of the models created is simulated using a set of untrained data and the error associated with each model is measured. The resulting outputs from the two approaches reveal that, the prediction model created using the output from the proposed algorithm has an error of 0.38, while the prediction model created using the cleaned data from the clustering method gives an error of 0.61. Comparison of the errors associated with the models created using the two approaches shows that, the proposed algorithm is suitable for cleaning numeric dataset. The results of the experiment also unveils that, the proposed approach is efficient and can be used as an alternative technique to other existing cleaning methods.
A novel smartphone application for early detection of habanero disease.
(Scientific Report., 2024-01-23) Babatunde, R. S., Babatunde A. N, Ogundokun R.O, Yusuf O.K, Sadiku P.O and Shah M.A (2024)
Habanero plant diseases can significantly reduce crop yield and quality, making early detection and treatment crucial for farmers. In this study, we discuss the creation of a modified VGG16 (MVGG16) Deep Transfer Learning (DTL) model-based smartphone app for identifying habanero plant diseases. With the help of the smartphone application, growers can quickly diagnose the health of a habanero plant by taking a photo of one of its leaves. We trained the DTL model on a dataset of labelled images of healthy and infected habanero plants and evaluated its performance on a separate test dataset. The MVGG16 DTL algorithm had an accuracy, precision, f1-score, recall and AUC of 98.79%, 97.93%, 98.44%, 98.95 and 98.63%, respectively, on the testing dataset. The MVGG16 DTL model was then integrated into a smartphone app that enables users to upload photographs, get diagnosed, and explore a history of earlier diagnoses. We tested the software on a collection of photos of habanero plant leaves and discovered that it was highly accurate at spotting infected plants. The smartphone software can boost early identification and treatment of habanero plant diseases, resulting in higher crop output and higher-quality harvests.
A predictive system for Parkinson Disease using Generative Adversarial network (GAN).
(Published by Federal University of Wukari (FUW), 2023-12-16) Babatunde Ronke Seyi
Parkinson's disease (PD) symptoms often overlap with those of other neurological disorders, making an early diagnosis difficult or even impossible. To tackle this problem, this study suggests a unique approach that makes use of Generative Adversarial Networks (GANs). Using a variety of datasets, GANs create synthetic medical data that includes PD-related clinical and demographic characteristics. The algorithm undergoes a rigorous training process to improve prediction accuracy. The generated data is assessed by a discriminator, which makes accurate PD predictions possible. Thorough measurements and statistical analysis verify the system's efficacy. The work demonstrates the revolutionary potential of GANs, especially in addressing data limitations for early Parkinson's disease diagnosis. The early PD detection efficacy of the technology is demonstrated by the very accurate predictive model. This study offers a sophisticated and useful method for early identification of Parkinson's disease (PD) by combining modern machine learning. It also promises improved patient outcomes by prompt neurology and healthcare interventions.
A scheme for Resilient Routing in Residue Number System based Software Defined Networks
(KWASU PRESS AND PUBLISHING, 2021-04-01) Oke Afeez Adeshina, Akinbowale Nathaniel Babatunde, Oloyede Abdulkarim Ayopo, Kazeem Alagbe Gbolagade
In recent times, the advances in internet technology and the rise of network traffic, owing to relentless creation of rigorous and mission-critical applications, has been giving serious attention to resilient routing in Software Defined Network (SDN). The evolution has made it possible for extensive use of SDN to respond to the current requirements of modern networking. Residue Number System (RNS) been used to minimize latency in routing tables for Openflow protocol. However, majority of network elements remain vulnerable to failures, especially transmission devices and links. It has been shown that proactive approaches to link recovery in RNS based SDN can select alternate path during link failures, however, the challenges of fast failure recovery still exist with large data path labels for both primary and alternate routes. Furthermore, the problem of early backup path failure before primary path still remains a challenge. The paper presents an approach that is intended to improve SDN routing and reduces the computational in RNS based SDN. The proposed scheme utilizes the shortest path re-route algorithm and a reactive mechanism to respond to link failure when it occurs. The proposed scheme which will be implemented as a prototype using the mininet emulator and floodlight controller is intended to effectively route packets especially when there are link failures for RNS based SDN. Keywords: Residue Number System (RNS); Software Defined Network (SDN); resilience; OpenFlow; traffic engineering; controller
A Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis
(Journal, Advances in Mathematical & Computational Sciences, 2022-08-19) 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
A Systematic Review of Feature Dimensionality Reduction Techniques for Face Recognition Systems.
(Journal of Digital Innovations & Contemp Res. In Sc., Eng & Tech., 2017-09-07) Babatunde, R.S. (2017)
In this research, a comparison of the various techniques used for face recognition is shown in tabular format to give a precise overview of what different authors have already projected in this particular field. A systematic review of 40 journal articles pertaining to feature dimensionality reduction was carried out. The articles were reviewed to appraise the methodology and to identify the key parameters that were used for testing and evaluation. The dates of publication of the articles were between 2007 and 2015. Ten percent (10%) of the articles reported the training time for their system while twenty four percent (24%) reported their testing time. Sixty seven percent (67%) of the reviewed articles reported the image dimension used in the research. Also, only forty eight percent (48%) of the reviewed articles compared their result with other existing methods. The main emphasis of this survey is to identify the major trade-offs of parameters and (or) metrics for evaluating the performance of the techniques employed in dimensionality reduction by existing face recognition systems. Findings from the review carried out showed that major performance metrics reported by vast amount of researchers in this review is recognition accuracy in which eighty six percent (86%) of the authors reported in their experiment.
Adaptive Neuro Fuzzy Inference System (ANFIS) Based Detection of Cervical Cancer.
(Published by LAUTECH, Ogbomoso. Oyo State, 2018-11-22) Babatunde, R. S. and Muhammad-Thani S. (2018):
Cancer occurs when cells in an area of the body grows abnormally. Cervical cancer is a cancer that is known to be the second most deadly cancer affecting women worldwide which emerges from the cervix of a woman. The normal cervix has two main types of cells: squamous cells, that protect the outside of the cervix and glandular cells. Cervical cancer is caused by abnormal changes in either of these cell types in the cervix. Pap smear screening is the most successful attempt of medical science and practice for the early detection of cervical cancer. However, the manual analysis of the cervical cells (pap smear) is time consuming, laborious and error prone. In this research, detection of cervical cancer was done using Adaptive Neuro-Fuzzy Inference System (ANFIS), a hybrid intelligent system which combines the fuzzy logic qualitative approach and adaptive neural network capabilities. The system was modelled to predict the probability of cervical cancer as either high probability or low probability based on the input risk factors of cervical cancer as contained in the dataset used. The efficiency of the ANFIS in predicting cervical cancer was tested using a dataset collected from Leah Foundation, a non-governmental organization in Nigeria, having a total of 250 patient’s data. The dataset contains eight risk factors of cervical cancer, which serves as the input variables. The output is the classified risk factors each patient is prone to. A patient with low level (represented as 1) is classified to be of no or less probability of having cervical cancer, while a patient with high level (represented as 2) is classified to be a victim of high probability of having cervical cancer. The result of this system shows that the system can be adopted by medical practitioners as well as for individual use.
An Enhanced Differential Homomorphic Model using N-Prime Scheme for Privacy Preservation
(Faculty of Engineering, Federal University Oye-Ekiti, 2023-03-01) Hafiz O. Sanni, Rafiu M. Isiaka, Akinbowale N. Babatunde and Muhammed K. Jimoh
In preserving individual privacy in data publishing, several efforts have been made by scholars globally to develop an individual privacy-preserving model and hybridized models which harness the strength of the individual model to increase privacy preservation in data Publishing (PPDP). The Differential homomorphic model (DHM) was among the hybridized models developed that combine differential and homomorphic models. Though is one of the state-of-the-art hybridization methods for privacy preservation because of the Differential model and Homomorphic model strengths of the two hybridized models which are the ability to prevent composition problems and database attacks respectively. However, applying this model is challenging because of the high computational complexities due to the modular exponentiation problem available in the pailler encryption scheme used in DHM. In this research, an N-PRIME homomorphic encryption scheme was proposed to replace the Pailler encryption scheme in the differential homomorphic model (DHM). The designed model was 51% faster than the existing model (Differential Homomorphic Model) in terms of computation time and 48.5% faster when generating the graphical data set, though the designed model consumed 4% more storage space than the existing model. Keywords- Homomorphic, Differential, Privacy Preservation, Database Security, N-prime, Hybridized Model, Database Attack.
An enhanced image based mobile deep learning model for identification of Newcastle poultry disease
(Published by School of Mathematics and Computing, Kampala International University, 2024-06-11) Damilare Andrew Omideyi, Rafiu Mope Isiaka, Ronke Seyi Babatunde
An enhanced mobile deep learning model based on images is presented in this paper to identify Newcastle poultry disease. A dataset of manually annotated and labeled images of the disease was utilized to pre-train an image-based Convolutional Neural Network (CNN). An Android smartphone app was developed to communicate with the model. A local server was integrated with the generated model to do image classification. A mobile application was developed and made available, enabling users to upload a fecal photograph to a website housed on the streamlet server and obtain the model's processed findings. The user regains control over their health status. The model achieved an accuracy of 95% on the test set and was able to correctly identify specific instances of Newcastle poultry disease. The paper discusses the advantages of a mobile-based approach in comparison to traditional methods of identification and proposes the model as an effective low-cost solution for farmers and researchers.

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