Browsing by Author "Popoola, Damilola David"

Now showing 1 - 9 of 9
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    A KNN and ANN model for predicting heart diseases
    (Explainable Artificial Intelligence in Medical Decision Support Systems, 2024-07-03) Abdulsalam, Sulaiman Olaniyi; Arowolo, Micheal Olaolu; Udofot, Enobong Chidera; Sanni, Ayodeji Matthew; Popoola, Damilola David; Adebiyi, Marion Olubunmi
    The heart is the single most important organ in the human body. Patients, professions, and medical systems are all bearing the brunt of heart failure’s devastating effects on contemporary society. Since cardiac arrest may well be demonstrated as a better understanding or conceivably go unobserved, particularly in the vast population of clients that have other cardiovascular disorders, the true prevalence of heart failure is likely to be underestimated, accounting for only 1–4% of all hospitalized patients as test procedures in developed nations.A person with heart failure has a heart that is unable to circulate sufficient blood through the body, but the term“heart failure” does not explain why this happens. The clinical picture is confusing since there are several possible causes of heart problems, many of which are diseases in and of themselves. Many cases of heart failure can be avoided if the underlying medical conditions that cause them are identified and treated promptly. The study and prediction of cardiac conditions must be precise because numerous diseases have been connected to the cardiovascular system. The resolution of this problem requires intensive online research on the relevant topic. Since incorrect illness prognoses are a leading cause of death among heart patients, learning more about effective prediction algorithms is crucial. This research utilizes K-nearest neighbor (KNN) and artificial neural network (ANN) to assess cardiovascular diseases using data collected from Kaggle. The highest accuracy (96%) was achieved by ANN trained with the standard scalar. Medical experts, specialists, and academics can all benefit greatly from this study. Based on the results of this study, cardiologists will be able to make more knowledgeable decisions about the inhibition, analysis, and handling of heart disease
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    A Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis
    (Journal of Advances in Mathematical & Computational Sciences, 2022-12-04) Isiaka, Rafiu Mope; Babatunde, Ronke Seyi; Ajao, Jumoke Falilat; Yusuff, Shakirat Ronke; Popoola, Damilola David; Arowolo, Michael Olaolu; Adewole, Kayode
    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.
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    DATA INTEGRITY USING CAESAR CIPHER AND RESIDUE NUMBER SYSTEM
    (ProQuest Dissertations Publishing, 2019-06-30) Popoola, Damilola David
    Data security ensures a secure communication through a digital medium. The data needs to be protected from unauthorized access and transmitted to the intended receiver with confidentiality and integrity. Various schemes have been proposed over the years towards ensuring that data sent over a digital medium is hard to understand by an external body, by transforming the data from plaintext to a gibberish form known as a cipher text. Although the cipher text have been able to make data hard to understand by a third party, the irregular representation attributed to it serves as a means through which eavesdropper’s attention is drawn to such data. Therefore, this research presents a method that incorporates both substitutional and transposition cipher in the encryption of data. It also incorporates an additional stage to the traditional Caesar cipher with creation of two character tables, firstly to substitute the plaintext to Yoruba characters and secondly to substitute the Yoruba Characters to Numbers which will allow the implementation of RNS to change the representation of the characters from gibberish form to a matrix. The transposition was done with Residue Number System using the moduli set {22n-1, 22n, 22n+1}. The proposed scheme was able to produce the cipher text as residues in the form of matrix which will make messages sent over a network attain low suspicion from attackers.
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    Development of an Intrusion Detection System in Web Applications Using C-Means and Decision Tree Algorithm
    (International Journal of Applied Science and Technology, 2023-03-01) Isiaka, Rafiu Mope; Popoola, Damilola David
    Intrusion detection is extremely important for online applications and for determining whether there has been a hostile entrance into the website. The aim of this research is to provide a machine learning technique for detecting intrusion in a web application. Machine learning models such as C-means, Decision Tree and Support Vector Machine were utilized to create an intrusion detection system. The study used the CIC-IDS 2018 intrusion dataset (Friday-Working Hours-Afternoon-Ddos.pcap ISCX). The data was initially sent to Decision tree and SVM which had accuracy of 99.97% and 99.77%, respectively. The raw data was next transferred into the c-means clustering approach, which had an accuracy of 99.99%. The goal of the clustering technique used is to improve the system’s accuracy, and the results were assessed using performance metrics like accuracy, sensitivity, precision, specificity, F1-score as well as accuracy comparison of the results obtained with the state of the art.
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    Development of an Intrusion Detection System in Web Applications Using C-Means And Logistic Regression Algorithm
    (Journal of Innovation Research & Advanced Studies, 2022-06-30) Popoola, Damilola David; Arowolo, Michael Olaolu
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    Enhancing Image Security Using Data Encryption Standard, Discrete Wavelet Transform Watermarking Residue Number System and Gaussian Filtering
    (International Journal of Advanced Research in Multidisciplinary Studies, 2022-06-01) Asaju, Bolaji; Popoola, Damilola David; Gbolagade, Kazeem Alagbe
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    Residue Number System Based Convolution Neural Network Algorithm
    (International Journal of Nature And Science Advance Research, 2022-06-20) Akanbi, Abdulrahman Akanbi; Popoola, Damilola David; Gbolagade, Kazeem Alagbe
    A number of positively convergent elements have aided the development of Deep Learning. The efficiency of floating point operations is highly optimized in modern microarchitectures. A whole area of research has emerged around quantized models, which reduce by orders of magnitude the amount of required memory, with a particular focus on quantized convolution neural networks. However, there is still a need to rethink how these quantized models can then be accelerated efficiently. The research starts by recognizing that inference in convolution neural networks is fundamentally a Digital Signal Processing (DSP) task. Memory, computation, and power utilization were expensive, in a different but similar way to how they are on modern mobile platforms, whose budget is set by their battery capacity. It is therefore of utmost importance to provide an alternative solution to this problem. This research introduces Residue Number System Architecture to the process to take advantage of the limited - but not binary - range of values that the operands can assume during the convolution operation in a
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    Secure Message Transmission using Caesar Cipher and Residue Number System
    (African Scholars Journal of Science Innovation & Tech. Research, 2022-06-01) Popoola, Damilola David; Gbolagade, Kazeem Alagbe
    Secure communication across a digital medium is ensured by data security. The data should be guided against availability by unrecognised users and communicated to the right recipient in a secure and confidential manner. Although the cipher text can make data difficult for a third party to interpret, the irregular representation given to it acts as a means of drawing an attacker's attention to such data. As a result, through the design of a character swap table to swap characters to numbers and the use of RNS to convert the numbers to residues, this research proposes a scheme that integrates additional phases to the standard Caesar cipher. The proposed system was able to generate the cipher text as residues, ensuring that the message remains undetectable and difficult to crack.
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    Towards adoption of information and communication technology in higher education – a structural equation model approach
    (International Journal of Learning Technology, 2021-06-02) Isiaka, Rafiu Mope; Babatunde, Ronke Seyi; Eiriemiokhale, Kennedy Arebamen; Popoola, Damilola David