Adolf Fenyi

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I was born in Cape-Coast in the Central Region of Ghana. I attained my first degree in the University of Cape-Coast, and the second degree in Kwame Nkrumah University of Science and Technology. I read Information Technology in both institutions. I am currently enrolled in the University of South Africa.

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Andrej Dujella

University of Zagreb

Hemin Koyi

Uppsala University

Jana Javornik

University of East London

Armando Marques-Guedes

UNL - New University of Lisbon

Graham Martin

University of Leicester

Gwen Robbins Schug

University of North Carolina at Greensboro

Gabriel Gutierrez-Alonso

University of Salamanca

John Sutton

Macquarie University

Eros Carvalho

Universidade Federal do Rio Grande do Sul

Kevin Arbuckle

Swansea University

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Papers by Adolf Fenyi

Colored Edge Detection Using Thresholding Techniques

Recent Advances in Computer Science and Communications

Background: In this research, a novel algorithm is formulated through the combination of gradient... more Background: In this research, a novel algorithm is formulated through the combination of gradient and adaptive thresholding. A set of 5 X 5 convolution kernels were generated to determine the gradients in the four main directions of the image. Objectives: The researcher converted the gaussian equation into a normalized kernel which was convolved with the gradients to suppress the impact of noise. Methods: The edges derived were partitioned into a set of 5 x 5 matrices. A weighted variance was calculated for each local window in the image. The pixel that generated the minimum variance was used for the segmentation process in each local window. The researcher then trimmed multiple pixel width edges into singles by developing a set of 5 X 5 structuring elements (SE). These elements were placed over the image to remove boundary pixels. In order to produce colored edges, the algorithm was executed over all the channels and the results were concatenated to produce the skeletal colored edg...

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Gaussian based Image Segmentation Algorithm

International Journal of Computer Applications, 2020

This study adopted the Gaussian equation to eliminate noisy pixels in a segmented image. The equa... more This study adopted the Gaussian equation to eliminate noisy pixels in a segmented image. The equation was converted into a convolution kernel and the values were normalized to attain a perfect result. In order to preserve every information in the image, the border pixels were padded with zeros before the image was convolved with the designed kernel. A variance was computed from the result obtained from the convolution operation and the pixel that obtained the minimum variance was selected as the threshold. In order to obtain the optimum threshold, the computed threshold was multiplied by the optimization constant which ranges from 0.1 to 1, and the resultant was considered as the final threshold for the segmentation process.

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Comparative Analysis of Advanced Encryption Standard, Blowfish and Rivest Cipher 4 Algorithms

International journal of innovative research and development, 2014

Cryptography is one of the main categories of computer security that converts information from it... more Cryptography is one of the main categories of computer security that converts information from its normal form into an unreadable form. The two main characteristics that identify and differentiate one encryption algorithm from another are its ability to secure the protected data against attacks and its speed and efficiency in doing so.Cryptography is required to transmit confidential information over the network. It is also demanding in wide range of applications which includes mobile and networking applications. Cryptographic algorithms play a vital role in providing the data security against malicious attacks. But on the other hand, they consume significant amount of computing resources like CPU time, memory, encryption time etc. Normally, symmetric key algorithms are preferred over asymmetric key algorithms as they are very fast in nature. Symmetric algorithms are classified as block cipher and stream ciphers algorithms. In this research project, I comparedthe AES and Blowfish al...

Comparative Analysis of Comparison and Non Comparison based Sorting Algorithms

by Michael Fosu and Adolf Fenyi

International Journal of Computer Applications, 2020

Sorting is one of the most important task in many computer applications. Efficiency becomes a big... more Sorting is one of the most important task in many computer applications. Efficiency becomes a big problem when the sorting involves a large amounts of data. There are a lot of sorting algorithms with different implementations. Some of them sort data by comparison while others don't. The main aim of this thesis is to evaluate the comparison and noncomparison based algorithms in terms of execution time and memory consumption. Five main algorithms were selected for evaluation. Out of these five, three were comparison based algorithms (quick, bubble and merge) while the remaining two were non-comparison based (radix and counting). After conducting an experiment using array of different data sizes (ranging from 1000 to 35000), it was realized that the comparison based algorithms were less efficient than the noncomparison ones. Among the comparison algorithms, bubble sort had the highest time complexity due to the swapping nature of the algorithm. It never stops execution until the largest element is bubbled to the right of the array in every iteration. Despite this disadvantage, it was realized that it is memory efficient since it does not create new memory in every iteration. It relies on a single memory for the swapping array operation. The quick sort algorithm uses a reasonable amount of time to execute, but has a poor memory utilization due to the creation of numerous sub arrays to complete the sorting process. Among the comparison based algorithms, merge sort was far better than both quick and bubble. On the average, merge sort utilized 32.261 seconds to sort all the arrays used in the experiment while quick and bubble utilized 41.05 and 165.11 seconds respectively. The merge algorithm recorded an average memory consumption of 5.5MB for all the experiment while quick and bubble recorded 650.792MB and 4.54MB respectively. Even though the merge sort is better than both quick and bubble, it cannot be compared to the non-comparison based algorithms since they perform far better than the comparison based ones. When the two groups were evaluated against execution time, the comparison based algorithms recorded an average score of 476.757 seconds while the non-comparison obtained 17.849 seconds. With respect to the memory utilization, the non-comparison based algorithms obtained 27.12MB while the comparison ones obtained 1321.681MB. This clearly reveals the efficiency of the non-comparison based algorithms over the comparison ones in terms of execution time and memory utilization.