Human thyroid cancer is the most commonly occurring cancer of the endocrine gland having good survival rate, but some patients show recurrence with an invasive phenotype and treatment failures. The mechanisms behind this invasive phenotype are not well understood in TC. Previously our group has identified a pro-migratory role of relaxin-like peptides in thyroid cancer that is mediated by S100A4. We have observed in human TC cells that extracellular S100A4 induces migration and activates ERK1/2, JNK/SAPK and NFkB signaling pathways. Employing immunohistochemistry and immunofluorescence we have identified the expression of RAGE in human TC primary cells, cell lines, and in tumor tissues but not in normal thyroid tissues. We showed that S100A4 binds to RAGE in TC cells and that RAGE and its cytoplasmic partner Dia-1 mediate the S100A4-induced migration of TC cells. This study identified a crucial role of RAGE in TC cell migration induced by S100A4.

Most of the modern cities are today facing tremendous traffic congestions, which is a consequence of an increasing usage of private motor vehicles in the cities. Public transport plays a crucial role to reduce this traffic, but to be an attractive alternative to the use of private motor vehicles the public transport needs to provide services that suit the citizens requirements for travelling. A system that can provide transit agencies with rapid feedback about the usage of their transport network is the Automatic Passenger Counting (APC) system, a system that registers the number of passengers boarding and alighting a vehicle. Knowledge about the passengers travel behaviour can be used by transit agencies to adapt and improve their services to satisfy the requirements, but to achieve this knowledge transit agencies needs to know how to use an APC system. This thesis investigates how a transit agency can make use of an APC system. The research has taken place in Melbourne where Yarra Trams, operator of the tram network, now are putting effort in how to utilise the APC system. A theoretical framework based on theories about Knowledge Discovery from Data, System Development, and Human Computer Interaction, is built, tested, and evaluated in a case study at Yarra Trams. The case study resulted in a software system that can process and model Yarra Tram's APC data. The result of the research is a proposal of a framework consistingof different steps and events that can be used as a guide for a transit agency that wants to make use of an APC system.

This master thesis deals with automation process of a data mining in the LISp-Miner program. The aim of this thesis is to create an automated process that analyzes analytical questions in the data about traffic accidents in the Czech Republic using a LMCL scripting language and LM Exec module. Theoretical part of thesis describes the process of knowledge discovery in databases and most widely used methodology. It also describes the relevant topics for the work with LISp-Miner. The practical part is focused on description of traffic accidents in the Czech Republic, a description of the used data, creation and evaluation of analytical questions and especially a description of created scripts. The output of the thesis is a group of scripts and manual how to use them again, so they can be reused for analysis of actual data on traffic accidents not only in the Czech Republic, if they have the same data structure.

The thesis deals with the valuation of real estates in the Czech Republic using statistical methods. The work focuses on a complex task based on data from an advertising web portal. The aim of the thesis is to create a prototype of the statistical predication model of the residential properties valuation in Prague and to further evaluate the dissemination of its possibilities. The structure of the work is conceived according to the CRISP-DM methodology. On the pre-processed data are tested the methods regression trees and random forests, which are used to predict the price of real estate.

Diploma thesis " The real role of knowledge mining " is divided into two major parts, the theoretical and the practical. The practical part describes the basic concepts of data mining, various methods and types of tasks used for knowledge discovery in databases and algorithms used in this area . Main focus is devoted to the CRISP -DM methodology and to various stages of knowledge discovery from databases. This methodology will be later used as the basis for practical part of the thesis while other less known methods used for data mining won`t be neglected. List of paid and free software which can be used for knowledge mining in databases is presented at the end of theoretical part. The second part of the thesis is focused on the practical step by step application of the CRISP -DM methodology, which contains real data from the field of mobile communications. Data mining task used in practical part is the behavioral prediction of mobile carrier customers. Supporting the practical part of the thesis, IBM SPSS Modeler was used as a main software for knowledge mining. Key words: data mining, knowledge disvocery in databases. Churm management, prediction, CRISP-DM.

Throughout last decades, knowledge discovery from databases as one of the information and communicaiton technologies' disciplines has developed into its current state being showed increasing interest not only by major business corporates. Presented diploma thesis deals with problematique of data mining while paying prime attention to its practical utilization within business environment. Thesis objective is to review possibilities of data mining applications and to decompose implementation techniques focusing on specific data mining methods and algorithms as well as adaptation of business processes. This objective is subject of theoretical part of thesis focusing on principles of data mining, knowledge discovery from databases process, data mining commonly used methods and algorithms and finally tasks typically implemented in this domain. Further objective consists in presenting data mining benefits on the model example that is being displayed in the practical part of the thesis. Besides created data mining models evalution, practical part contains also design of subsequent steps that would enable higher efficiency in some specific areas of given business. I believe previous point together with characterization of knowledge discovery in databases process to be considered as the most beneficial one's of the thesis.

The aim of the thesis was to study and describe data mining methodology CRISP-DM. From the collected database of calls to the call center a prediction was performed, based on CRISP-DM methodology. In phase of test situation modeling four different testing methods were used: the k-NN, neural network, linear regression and super vector machine. The input attributes importance for further prediction was evaluated based on different selections. The results and findings may provide data for further more accurate forecasts in the future; not only in number of calls but also other indicators relevant to the call center.

This master’s thesis will look into the use of data mining in the area of segmentation and the prediction of onboarding candidates of a recruitment agency. The obtained results should serve to make company processes more effective concerning the processing of orders, and should also facilitate a more personal approach to candidates. The first chapter includes imperetive theoretical bases from the studies of Business Intelligence, data warehouses, data mining and marketing. Thereafter an analysis of the current state is presented with a focus on the capture of the key processes in processing and order. The last chapter looks at the proposed solution and implementation on the platform Microsoft SQL Server 2014. To conclude there are proposals of utilizing data mining in direct marketing.

Magister Commercii - MCom / Introduction: Diabetes mellitus is a global health problem with a high mortality rate. Self-management is an essential part of diabetes management and it includes self-care behaviour tasks such as healthy eating, being active and taking prescribed medication. In the current digital age, the use of technology for self- management of the disease is an important consideration. As a first step towards this, individuals have to first accept and use the technology. However, the literature indicates low levels of technology use amongst diabetic patients in environments with low socio- economic indicators and amongst minority groups. Previous studies suggest that there are many factors that influence technology acceptance such as economic, social and cultural factors. Mobile health (m-health) received recognition in healthcare literature in recent years and are known for delivering effective and efficient interventions to patients with chronic conditions such as diabetes. An investigation into m-health acceptance for diabetes management is vital as it impacts the achievement of development goals, including the United Nations’ SDG 3. This research posits that the culture of patients is a possible reason for the low acceptance and use of technology. Research based on the proliferation of culture as a determinant for diabetes self-management at an individual level is limited, especially in the South African context. The main research question pursued in the study reported in this thesis is How does culture influence m-health acceptance of diabetic patients in disadvantaged communities? Research design and methodology: Using an interpretivist paradigm, a case study research design provided the basis to collect data from 20 diabetes patients in Mitchells Plain and Strandfontein. The theoretical model that was used as a lens for investigation comprised a juxtaposition of Hofstede’s cultural dimensions and Unified- Theory of Acceptance and Use of Technology 2 (UTAUT2). The analysis of the qualitative data was undertaken with Atlas Ti, using a thematic content analysis process. Results: Eight themes emerged from the data and key results of the study indicate that opinions towards medical practitioners, which reflects power distance has a positive impact on users and non-users. Diabetic patients comply with the opinions of their doctors as they fear disagreeing with them. As such, this may result in having a positive influence on a participant’s ability to adopt and use mobile applications. Caregiver influence, which reflects femininity, has a negative influence on users as a result of diabetic patients being responsible for taking care of their family and others are both home carers and providers for their families. This indicates that patients are more concerned with the quality of their life and family than with the adoption mobile applications. Future work: It is recommended that research should be conducted in other areas in the Western Cape, specifically in the Cape flats to see whether the same sorts of results will be achieved in different communities. This could help policymakers and application developers tailor mobile applications for this target population.

Deer Vehicle Collisions (DVCs) are a global problem that is not only resulting in seriousinjuries to humans but also results in loss of human and deer lives. Deer are more active and less attentive during the mating and hunting seasons. Roadside deer activity such as feeding and strolling along the roadside has a significant correlation with DVCs. To mitigate DVCs, several strategies were used that include vegetation management, fences, underpasses and overpasses, population reduction, warning signs and animal detection systems (ADS). These strategies vary in their efficacy. These strategies may help to reduce DVCs. However, they are not always easily feasible due to false alarms, high cost, unsuitable terrain, land ownership, and other factors. Thus, DVCs are increasing due to the increase in number of vehicles and the absence of intelligent highway safety and alert systems. Detecting deer in real-time on our roads is a challenging problem. Thus, this research work proposed a deer detection and movement DDM technique to automate DVCs mitigation system. The DDM combines computer vision, artificial intelligent methods with deep learning techniques. DDM includes two main deep learning algorithms 1)onestage deep learning algorithm based on Yolov5 that generates a detection model(DM) to detect deer and 2) deep learning algorithm developed by python toolkit DeepLabCut to generate movement model(MM) for detecting the movement of the deer. The proposed method can detect deer with 99.7% precision and succeeds to ascertain if the deer is moving or static with an inference speed of 0.29s. The proposed method can detect deer with 99.7% precision and using DeepLabCut toolkit on the detected deer we can ascertain if the deer is moving or static with an inference speed of 0.29s.