https://www.um.edu.mt/library/oar/handle/123456789/38568 Sat, 23 May 2026 00:46:59 GMT 2026-05-23T00:46:59Z https://www.um.edu.mt/library/oar/handle/123456789/64686 Title: Representing protein sequences using K-Mers to augment CATH functional families Abstract: A major difficulty in determining protein structure from its sequence is finding a suitable, categorized protein sequence with which to compare the unknown protein sequence. Thus, this study explores an alternative method to Hidden Markov Models, using k-mers to accomplish protein function prediction. The proposed method makes use of the CATH database, which provided information on the evolutionary relationships of protein domains. The data in CATH was utilized by extracting k-mers from regions of proteins and mapped to the functional family they belong to and later were stored inside a graph database. The data within the graph database was utilized by comparing the k-mers mapped to functional families of known proteins, to the k-mers of a previously unknown sequence. Both techniques were evaluated by comparing the accuracy and speed of the results generated when the target sequences from two CAFA Challenges, CAFA 1and CAFA 2, were used as an input dataset to the programs. Results showed that when using a k-mer size of three, the proposed technique showed increase in performance but an average result when comparing accuracy. Moreover, region mapping of the k-mer approach was identical to that generated by the Hidden Markov Models. However, when increasing the k-mer value to four both accuracy and performance improved when compared to the 3-mer results. Finally, this study can be implemented in a distributed approach, where the workload and graph database are distributed over multiple servers. Also, conducting experiments at aiming to find an optimum k value for generating k-mers would be beneficial in increasing accuracy of the proposed approach. Description: B.SC.SOFTWARE DEVELOPMENT Mon, 01 Jan 2018 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/64686 2018-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/40348 Title: Investigating movement detection in unedited camera footage Abstract: Digital evidence collected from CCTVs can be of great aid in crime scene investigations. Investigators still manually review the video footage collected from a crime scene, which can be a very time consuming process, prone to human error and inefficient. The aim of this dissertation is to deliver a system that automates the process of detecting motion events within a video, so that the investigator can then analyse specifically parts of the video where the said events occur. The proposed system will also allow the investigator to modify the pre-processing and processing variables to suit their requirements, depending on the quality and type of video footage available. Additional functionalities offered by the system include filtering the motion events detected by colour and size, analysing and extracting features of regions where the motion event is detected, and finding association rules between objects that appear simultaneously in the video based on their colour. The data set that is used for evaluating the system is the Wallflower data set. Evaluation is conducted by using confusion matrices and comparing the results from the system with the true values of an oracle. The F1 Score measure is used to enable cross comparison between image sequences from the data set. The evaluation process was conducted over four different image sequences from the chosen data set, with the total number of frames evaluated amounting to 12,459 for each combination of pre-processing settings. The results obtained from the evaluation process show that the lowest and highest F1 Score across all image sequences used were those of 66.7% and 94.9% respectively. Evaluation has also shown that in order to get the best results, one must firstly identify the best pre-processing settings that best work for the specified video. Description: B.SC.SOFTWARE DEVELOPMENT Mon, 01 Jan 2018 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/40348 2018-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/40347 Title: A Swarm intelligence based approach to optimisation of machine learning problems using a parallelised framework and the cloud Abstract: In the recent years, the field of machine learning (ML) has become popular and has been utilised to achieve a wide variety of advances in many different areas. However, the information processing capability of certain ML models is dependent on a number of user-defined hyperparameters which need fine tuning for optimal performance. Due to the large sizes of the search spaces and datasets involved, algorithm complexity, long execution times and the high cost of processing, the use of brute force searching is not always a feasible approach. In these cases, finding a near-optimal solution given short time budgets is acceptable.The study describes an approach, based on ant swarm intelligence optimisation, for dealing with these types of combinatorial problems. A parallelisation approach was chosen and applied to the optimisation algorithms with the aim of identifying satisfactory performing ML model parameter configurations in a shorter time period. Finally, an investigation of the benefits and limitations of using cloud virtual machines for the experimentation phases of projects of this type was done during this study. The method has been tested on four classification problems having different sizes and features. The results show that the evolved ML models achieved satisfactory accuracy and generalisation ability while the optimiser applications were able to identify better performing parameter sets during training phase at earlier stages when compared to a purely random search in some cases. Description: B.SC.SOFTWARE DEVELOPMENT Mon, 01 Jan 2018 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/40347 2018-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/40346 Title: Natural user interfaces in 3D data visualization Abstract: Data visualization is a concept that has been around for centuries. Due to recent advancement in technology, one can easily see this concept being applied heavily in almost every field of work in the forms of computer applications that allows users to interact and view the data as they best see fit. Fluent interfaces for such applications become increasingly important the larger and more complex data gets, something WIMP GUIs have been seen to struggle with. The purpose of this study is to provide an insight on the possible benefits and limitations using natural user interfaces (Also known as POSTWIMP user interfaces) in 3D data visualization applications may provide. A data visualization application that visualizes data as 3D clusters was developed. The application provides 2 user interfaces, one WIMP, mouse and keyboard focused and the other speech and hand gesture recognition focused (POST-WIMP user interface). 15 participants were asked to perform a set of basic data visualization tasks, once with the WIMP user interface and another time with the POST-WIMP user interface and fill questionnaires regarding their experience using both interfaces. Results produced by the performance of the tasks and the questionnaires provided a better insight into the potential effectiveness and current limitations POST-WIMP user interfaces have when used for data visualization. Results showed that tasks performed with the POST-WIMP user interface took less time to be performed. However, participants made more mistakes when using the POST-WIMP user interface. It was shown that biggest contributor to the high error rate was the high learning curve the POST-WIMP user interface has compared to the WIMP user interface. This was further supported by the fact that when performing the final tasks, the participants’ error rates dropped slightly, regardless of the fact that the final tasks were more complex than the earlier ones. Description: B.SC.SOFTWARE DEVELOPMENT Mon, 01 Jan 2018 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/40346 2018-01-01T00:00:00Z