OAR@UM Collection:
https://www.um.edu.mt/library/oar/handle/123456789/76579
2024-03-28T17:22:54ZFormal model extraction from informal descriptions
https://www.um.edu.mt/library/oar/handle/123456789/77097
Title: Formal model extraction from informal descriptions
Abstract: The translation of natural language specifications to model-based specification can be defined
as the translation from unstructured to structured system specification. There is always the
possibility of introducing translation errors in this step such as human errors. This project aims
to explore and study this translation process in light of proposing an automated mapping
paradigm to produce structured models from unstructured natural textual descriptions of
system behaviour through the analysis of natural language parts and their mapping to specific
formalised model notation.
Description: B.Sc. IT (Hons)(Melit.)2020-01-01T00:00:00ZEnhancing an existing patient dashboard with the use of Internet of Things
https://www.um.edu.mt/library/oar/handle/123456789/76929
Title: Enhancing an existing patient dashboard with the use of Internet of Things
Abstract: Internet of Things has now become a common trend in the healthcare sector as, with the help of
different devices, multiple problems and tasks can be either solved or automated [1]. The aim of this
dissertation was to enhance a current web application used at Mater Dei Hospital called the patient
dashboard by improving the overall user experience for the medical practitioners while also
introducing case studies that make use of Internet of Things. The patient dashboard project was
piloted in August 2018 and is currently being used by over 1000 medical practitioners. It is used to
connect multiple modules from all departments to store all patient data on a single screen such as
medication and blood tests. Many case studies were viewed and analysed to identify where IoT is
being used in healthcare and how it can be further implemented within the patient dashboard
[2]. Usability studies were also conducted at Mater Dei Hospital on the present patient dashboard to
get a better understanding of how and where IoT is being used at the hospital. Results from this study
have shown that overall; the application offers a wide-variety of functionality, however, the medical
practitioners pointed out certain UI drawbacks that impact the amount of steps required to carry out
a task. Upon getting further clarification, the case studies with the most scores given by the
participants were designed and implemented creating prototypes to be evaluated later on in the
study. The features introduced made use of IoT devices which include QR readers, barcode readers
as well as NFC reading and writing. To evaluate this study, another usability review was then carried
out on the prototypes developed. The participants found the new UI to be less time consuming (to
access certain features) but also relatively clear and easy to read. Last but not least, when attempting
the features that made use of IoT devices, medical practitioners were greatly satisfied with the
performance and how much they facilitated conditions.
Description: B.Sc. IT (Hons)(Melit.)2020-01-01T00:00:00ZMovie recommendations using machine learning algorithms
https://www.um.edu.mt/library/oar/handle/123456789/76928
Title: Movie recommendations using machine learning algorithms
Abstract: This research attempts to evaluate machine learning technology in a movie recommendation
scenario. Previous research in the area has mostly used the MovieLens100K dataset and Mean
Absolute Error (MAE) accuracy calculation mechanism; hence for comparison purposes, this
research will apply these in its case studies. A review of previous literature shows that good
accuracies could be obtained using various methods, however details about internal workings
or execution speed are not always given. For this reason, standard machine learning
technologies identified via a literature review have been individually examined in a consistent
way that would then allow a fair comparison of their accuracy and performance.
Furthermore, this research also proposes two novel machine learning technologies, specifically
designed for Movie Recommendation. The Matrix technology can rate any kind of movie, even
those that it has never encountered in its training while achieving decent accuracy and
execution speed. The Movie Centric technology, on the other hand, concentrates on movies it
has been trained on and generalises its viewers, achieving a better performance than the
Matrix one both in terms of speed as well as MAE. Both technologies can work with just the
viewer’s gender as opposed to the group of viewer parameters utilised by other standard
technologies.
This research has concluded that the Random Forest Classifier provides the best MAE/speed
compromise between the standard technologies and the Gradient Boosting Classifier provides
the best MAE at the expense of speed. The novel Movie Centric algorithm proposed
outperforms the Random Forest Classifier both in speed as well as MAE using only viewer
gender. However, it does not reach the accuracy levels of the Gradient Boosting Classifier
although it executes much faster.
Description: B.Sc. IT (Hons)(Melit.)2020-01-01T00:00:00ZAutomatic crime information gathering and data analytics from online news reports
https://www.um.edu.mt/library/oar/handle/123456789/76927
Title: Automatic crime information gathering and data analytics from online news reports
Abstract: One of the major challenges faced by law enforcement is that of the prioritisation and rostering of
resources, maximising chances of having the right resources at the right place and at the right time.
This research proposes a hybrid machine learning technology which uses a set of customised
crawlers to gather data on a daily basis from newspaper articles. Articles that deal with criminal
offences are identified, analysed and their inherent details extracted using Natural Language
Processing (NLP) Technology. Articles coming from different sources are converged using a
standardised format that allows the details of the criminal act (such as crime, location, time,
criminal, etc.) to be easily accessed. Related data such as population, literacy etc. are also extracted
from other sources using dedicated web crawlers and cross referenced with the criminal events
themselves. Web crawling is automated using a special bot designed to initiate the crawling
processes regularly.
A visualisation engine is being proposed to allow users to quickly and effectively browse the
criminal event database using a feature rich search engine enabling specific parameters to be easily
identified and depicted. Representations include geographical/calendar heat maps, graphs, etc.
Previous research in similar areas has utilised various machine learning techniques with different
success rates. This research aims to study the effectiveness of K-Means and DBSCAN [87] based
technologies when applied to crime prediction. K-Means uses a purely statistical past-data based
model to attempt to predict the incidence of crime; while DBSCAN uses clustering techniques
which could include other datasets in addition to past criminal event data.
Various datasets has been used to evaluate the performance of the proposed technology; with
encouraging results. The Precision/Recall/F-Measure technique used in previous studies [85], [96],
has been utilised to compute the F-Measure of both techniques. Moreover, geographically
different regions (Malta and Boston) where used to evaluate different crime patterns. While the
large number of possible prediction configurations make it very difficult to cover all the possible
scenarios, both techniques performed quite well, with the K-Means based one being slightly more
accurate when predicting recurring crimes. Predictions of monthly instances of specific crimes
were achieved with a combined (NLP + Prediction) F-Measure of 0.78 which compares very
favourably with other studies, even those who only covered prediction on a ready-made dataset
without any NLP related inaccuracies.
Description: B.Sc. IT (Hons)(Melit.)2020-01-01T00:00:00Z