University of Ibadan Journals
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Modelling of Components of Building Structures Using Discrete Structures of Computer Science
Full text linkBuildings are essential physical structures which serve several purposes including residential accommodation, factories, hospitals, laboratories, offices, schools, as well as religious places of worship such as church, mosque, shrine etc. A building is an aggregate of components such as roof, window, door and arch. From the perspective of computer graphics, each of these components can be formed from a point and a line. Several models dealing with aspects of building structures have been developed in the literature. Two of the popular generic models are the Tits Building model and the evolutionary computation (bioinformatics) model. While the former uses Discrete Structures of Computer Science (DSCS), specifically, set theory, graph theory and lattice theory, the latter is strictly based on genes derived from nucleotides in DNA Computing. In this paper, a novel DSCS model is presented using the qualitative equivalence behavior of a set of autonomous ordinary differential equations (ODEs). In the model, ODEs of the same kind are classified based on the phase portraits (i.e. geometrical shapes) generated from their equilibrium points. The model successfully presents a good representation of (aspects) of components of buildings, with focus on a door, and can be potentially simulated into computer software.
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Development of A Honeyed Advanced Encryption Standard Algorithm
Full text linkAsoro, B. O. Emezue,
H. C. Osunade, O.
[email protected]
[email protected],
[email protected]
Department of Computer Science,
University of Ibadan, Ibadan, Nigeria
Abstract
The rate at which Information is transmitted over the Internet (which is an open channel to all) keeps increasing in recent times. It has become very important for users to make use of encryption schemes that will protect their information from unauthorized parties. This research work presents an easy and safe encryption algorithm for data security since data encrypted with this system cannot be read, edited or copied through illegal access. It provides a middle ground for both speed and security of data regardless of the key strength used for encryption of data. With the use of this improved Honeyed Advanced Encryption Standard (HAESA), attackers will only access plausible looking messages called honeyed messages. These honeyed messages are meant to thwart the effort of any illegal access to this system. Honeyed messages are provided by the user of this system. The strength of HAESA lies in the fact that only the actual seeds are encrypted with AES after it must have been encoded with DTE to messages. On decryption, the message will only be displayed to the intended recipient with the right key. From the evaluation results, it was noted that this algorithm assured faster data encryption and decryption time. This research work made use of parameters like throughput and spee
Assessment of Selected Data Mining Classification Algorithms for Analysis and Prediction of Certain Diseases
Full text linkAbstract
Medical science generates large volumes of data stored in medical repositories that could be useful for extraction of vital hidden information essential for diseases diagnosis and prognosis. In recent times, the application of data mining to knowledge discovery has shown impressive results in disease analysis and prediction. This study investigates the performance of three data mining classification algorithms, namely decision tree, Naïve Bayes, and k-nearest neighbour in predicting the likelihood of the occurrence of chronic kidney disease, breast cancer, diabetes, and hypothyroid. The datasets which were obtained from the UCI Machine were split into 60% for training and 40% for testing on the one hand and 70% for training and 30% for testing on the other hand. The performance parameters considered include classification accuracy, error rate, execution time, confusion matrix, and area under the curve. Waikato Environment for Knowledge Analysis (WEKA) was used to implement the algorithms. The findings from the analysis showed that decision tree recorded the highest prediction accuracy followed by the Naïve Bayes and k-NN algorithm while k-NN recorded the minimum execution time on the four datasets. However, k-NN also has the largest average percentage error recorded on the datasets. The findings, therefore, suggest that the performance of these classification algorithms could be influenced by the type and size of datasets
An Enhanced Web Page Recommendation System Using Hidden Markov Model and Page Rank Technique
Full text linkAbstract
The rapid expansion of the World Wide Web (WWW) has created an opportunity to disseminate and gather information online. There is an increasing need to study the behaviour of web users to serve them better by reducing the access latency using efficient web prediction technique. Markov Models have been widely used for predicting next web page request from the users’ navigational behaviour recorded in the web log. This usage-based technique can be combined with the structural properties of the web pages to achieve better prediction accuracy. This study combines both Markov Model and Page ranking, which considers the structural properties of the Web. In order to create an efficient prediction model, the original data was preprocessed in the form that can be used for unsupervised learning. The pre-processed data was then analyzed using unsupervised learning K-means clustering algorithm. To increase the efficiency of Hidden Markov Model (HMM), efficient ranking algorithm was used to identify the most relevant page in clusters. i.e. PageRank. The HMM was then used to predict users web navigation path. Briefly, results from the study shows that Hidden Markov model and Clustering can work together and provide better
prediction results without compromise to accuracy though with a trade-off in time complexity, HMM is more accurate for predicting navigational paths thereby enhancing web page recommendation.
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