Investigations on chemometric approaches for diagnostic applications utilizing various combinations of spectral and image data types

In the presented work, several data fusion and machine learning approaches were explored within the frame of the data combination for various measurement techniques in biomedical applications. For each of the measurement techniques used in this work, the data was ana-lyzed by means of machine learning. Prior to applying these machine learning algorithms, a specific preprocessing pipeline for each type of data had to be established. These pipelines made it possible to standardize the data and to decrease sample-to-sample variations which originate from the instability of devices or small deviations in the sample preparation or measurement routine. The preprocessed data sets were used for various analyses of biological samples. Separate data analyses were performed for microscopic images, Raman spectra, and SERS data. However, this work mainly focused on the application of data fusion methods for the analy-sis of biological tissues and cells. To do so, different data fusion pipelines were constructed for each task, depending on the data structure. Both low-level (centralized) and high-level (distributed) data fusion approaches were tested and investigated within in this work. To demonstrate centralized and distributed data fusion, two examples were implemented for tissue investigation. In both examples, a combination of Raman spectroscopic and MALDI spectrometric data were analyzed. One example demonstrated centralized data fusion for the analysis of the chemical composition of a mouse brain section, and the other example employed distributed data fusion for liver cancer detection. Other data fusion examples were demonstrated for cell-based analysis. It was demonstrated that leukocyte cell subtype identification can be improved by a centralized data fusion of Raman spectroscopic data and morphological features obtained from microscopic images of stained cells. The last example presented in this work demonstrated a sepsis diagnostic pipeline based on the combination of Raman spectroscopic data and biomarkers. Besides the measured values, the demographic information of the patient was included in the analysis process for considering non-disease-related variations. During the construction of data fusion pipelines, such issues as unbalanced data contribu-tion, missing values, and variations that are not related to the investigated responses were faced. To resolve these issues, data weighting, missing data imputation, and the introduc-tion of additional responses were employed. For further improvement of analysis reliability, the data fusion pipelines and data processing routine were adjusted for each study in this work. As a result, the most suitable data fusion approach was found for every example, and a combination of the machine learning methods with data fusion approaches was demon-strated as a powerful tool for data analysis in biomedical applications

A context-aware model to improve usability of information presented on mobile devices

Online information access on mobile devices is increasing as a result of the growth in the use of Internet-enabled handheld (or pocket-size) devices. The combined influence of recent enabling technologies such as Web 2.0, mobile app stores and improved wireless networks have driven the increase in online applications that allow users to access various types of information on mobile devices regardless of time and location. Examples of such applications (usually shortened to app) include: social media, such as FacebookTM App and TwitterTM App, banking applications such as (Standard Bank South Africa)TM Mobile Banking App and First National Bank (FNB) BankingTM App, and news application such as news 24TM App and BBCTM News App. Online businesses involved in buying, selling and business transaction processing activities via the Internet have exploited the opportunity to extend electronic commerce (e-commerce) initiatives into mobile commerce (m-commerce). Online businesses that interact with end user customers implement business to consumer (B2C) m-commerce applications that enable customers to access and browse product catalogue information on mobile devices, anytime, anywhere. Customers accessing electronic product catalogue information on a mobile device face a number of challenges such as a long list of products presented on a small screen and a longer information download time. These challenges mainly originate from the limiting and dynamic nature of the mobile apps operating environment, for example, dynamic location, bandwidth fluctuations and, diverse and limited device features, collectively referred to as context. The goal of this research was to design and implement a context-aware model that can be incorporated into an m-commerce application in order to improve the presentation of product catalogue information on m-commerce storefronts. The motivation for selecting product catalogue is prompted by literature which indicates that improved presentation of information in m-commerce (and e-commerce) applications has a positive impact on usability of the websites. Usable m-commerce (and e-commerce) websites improve efficiency in consumer behaviour that impacts sales, profits and business growth. The context-aware model aimed at collecting context information within the user environment and utilising it to determine optimal retrieval and presentation of product catalogue in m-commerce. An integrated logical context sensor and Mathematical algorithms were implemented in the context-aware model. The integrated logical context sensor was responsible for the collection of different types of predetermined context information such as device specification or capabilities, connection bandwidth, location and time of the day as well as the user profile. The algorithms transformed the collected context information into usable formats and enabled optimal retrieval and presentation of product catalogue data on a specific mobile device. Open-source implementation tools were utilised to implement components of the model including: HTML5, PhP, JavaScript and MySQL database. The context-aware model was incorporated into an existing m-commerce application. Two user evaluation studies were conducted during the course of the research. The first evaluation was to evaluate the accuracy of information collected by the context sensor component of the model. This survey was conducted with a sample of 30 users from different countries across the world. In-between the context sensor and main evaluation surveys, a pilot study was conducted with a sample of 19 users with great experience in mobile application development and use from SAP Next Business and Technology, Africa. Finally an overall user evaluation study was conducted with a sample of 30 users from a remote area called Kgautswane in Limpopo Province, South Africa. The results obtained indicate that the context-aware model was able to determine accurate context information in real-time and effectively determine how much product information should be retrieved and how the information should be presented on a mobile device interface. Two main contributions emerged from the research, first the research contributed to the field of mobile Human Computer Interaction. During the research, techniques of evaluating and improving usability of mobile applications were demonstrated. Secondly, the research made a significant contribution to the upcoming field of context-aware computing. The research brought clarity with regard to context-aware computing which is lacking in existing, current research despite the field’s proven impact of improving usability of applications. Researchers can utilise contributions made in this research to develop further techniques and usable context-aware solutions

XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas

Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI

XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas

Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI