Browsing by Author "Takyi, Kate"

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    An improved man-in-the-middle (MITM) attack detections using convolutional neural networks
    (Multidiciplainary Science Journal, 2024-08) Iddrisu, Mohammed; Takyi, Kate; Gyening, Rose-Mary Owusuaa Mensah; Peasah, Kwame Ofosuhene; Banning, Linda Amoako; Agyemang, Kwabena Owusu; 0000-0002-8087-5207
    The increasing reliance on digital communication networks has made information security a critical concern for individuals, organizations, and governments worldwide. Man-in-the-middle (MITM) attacks are significant, prevalent, and damaging concerning cyber-attacks. Detecting MitM attacks is complex due to their stealthy nature and the sophisticated methods employed by attackers. There is the need for researchers to address this issue using current and novel methods like artificial intelligence. In this paper, an improved MitM attack detection approach using the Convolutional Neural Network (CNN) deep learning algorithm is developed, resulting in an overall detection accuracy of 0.986%. The results confirms that the proposed model is very efficient in comparision to other proposed solutions by other authors.
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    Cocoa beans classification using enhanced image feature extraction techniques and a regularized Artificial Neural Network model
    (Elseviere, 2023-07) Opoku, Eric; Gyening, Rose-Mary Owusuaa Mensah; Appiah, Obed; Takyi, Kate; Appiahene, Peter; 0000-0002-8087-5207
    Cut-Test technique employs visual inspection of interior coloration, compartmentalization, and defects of beans for effective classification of cocoa beans. However, due to its subjective nature and natural variations in visual perception, it is intrinsically limited, resulting in disparities in verdicts, imprecision, discordance, and time-consuming and labor-intensive classification procedures. Machine Learning (ML) techniques have been proposed to address these challenges with significant results, but there is still a need for improvement. In this paper, we propose a color and texture extraction technique for image representation, as well as a generalized, less complex Neural Network model, to help improve the performance of machine classification of Cut-Test cocoa beans. A total of 1400 beans were classified into 14 grades. Experimental results on the equal cocoa cut-test dataset, which is the standard publicly available cut-test dataset, show that the novel extraction method combined with the developed Artificial Neural Networks provides a more homogeneous classification rate for all grades, obtaining 85.36%, 85%, 83%, and 83% for accuracy, precision, recall, and F1 measure, respectively. The proposed model outperforms other ML models, such as Support Vector Machines, Decision Trees, Random Forests, and Naïve Bayes, on the same dataset. Additionally, the proposed ANN model demonstrates relatively better generalization when compared with earlier work by Santos on the same dataset. The proposed techniques in this work are robust on the cut-test dataset and can serve as an accurate Computer-Aided Diagnostic tool for cocoa bean classification.
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    The use of knapsack 0/1 in prioritizing software requirements and Markov chain to predict software success
    (Springer, 2023-09) Armah, Isaac Aduhene; Hayfron‑Acquah, James Ben; Takyi, Kate; Gyening, Rose‑Mary Owusuaa Mensah; Eshun, Michael; 0000-0002-8087-5207
    Requirements prioritization is one of the most valuable aspects of software engineering. This is primarily due to the fact that resources, be it time, skillset, or budget, are limited. Existing complex methodologies, such as ana lytical heuristic process (AHP) and planning game, face low adoption in the industry, promoting the need for more accessible techniques. This research introduces a novel con tribution to software engineering by ofering a simple and scalable approach to requirement prioritization (RP) and software acceptance prediction. The proposed approach con sists of two key methods, knapsack 0/1 and Markov, to opti mize RP and predict software acceptance respectively. By considering constraints, organizations can make enlightened decisions on handling requirements and optimize their mini mum viable product. The results showcase signifcant time efciency, with an average worst-case time of 5.645s for 10,000 requirements and an upper bound of 0.023s for the Markov prediction. This study aims to provide practitioners with a practical solution for prioritizing requirements and predicting software outcomes from user acceptance tests. By simplifying the process and ofering compelling time complexity, this approach contributes to the enhancement of software development practices.