Analysing the Influence of Macroeconomic Factors on Credit Risk in the UK Banking Sector

Macroeconomic factors have a critical impact on banking credit risk, which cannot be directly controlled by banks, and therefore, there is a need for an early credit risk warning system based on the macroeconomy. By comparing different predictive models (traditional statistical and machine learning algorithms), this study aims to examine the macroeconomic determinants’ impact on the UK banking credit risk and assess the most accurate credit risk estimate using predictive analytics. This study found that the variance-based multi-split decision tree algorithm is the most precise predictive model with interpretable, reliable, and robust results. Our model performance achieved 95% accuracy and evidenced that unemployment and inflation rate are significant credit risk predictors in the UK banking context. Our findings provided valuable insights such as a positive association between credit risk and inflation, the unemployment rate, and national savings, as well as a negative relationship between credit risk and national debt, total trade deficit, and national income. In addition, we empirically showed the relationship between national savings and non-performing loans, thus proving the “paradox of thrift”. These findings benefit the credit risk management team in monitoring the macroeconomic factors’ thresholds and implementing critical reforms to mitigate credit risk

Machine learning in concrete technology: A review of current researches, trends, and applications

Machine learning techniques have been used in different fields of concrete technology to characterize the materials based on image processing techniques, develop the concrete mix design based on historical data, and predict the behavior of fresh concrete, hardening, and hardened concrete properties based on laboratory data. The methods have been extended further to evaluate the durability and predict or detect the cracks in the service life of concrete, It has even been applied to predict erosion and chemical attaches. This article offers a review of current applications and trends of machine learning techniques and applications in concrete technology. The findings showed that machine learning techniques can predict the output based on historical data and are deemed to be acceptable to evaluate, model, and predict the concrete properties from its fresh state, to its hardening and hardened state to service life. The findings suggested more applications of machine learning can be extended by utilizing the historical data acquitted from scientific laboratory experiments and the data acquitted from the industry to provide a comprehensive platform to predict and evaluate concrete properties. It was found modeling with machine learning saves time and cost in obtaining concrete properties while offering acceptable accuracy

The use of machine learning algorithms in recommender systems: A systematic review

The final publication is available at Elsevier via https://doi.org/10.1016/j.eswa.2017.12.020 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.Natural Sciences and Engineering Research Council of Canada (NSERC) Ontario Research Fund of the Ontario Ministry of Research, Innovation, and Scienc

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

Creating public value in information and communication technology: a learning analytics approach

This thesis contributes to the ongoing global discourse in ICT4D on ICT and its effect on socio-economic development in both theory and practice. The thesis comprises five studies presented logically from chapters 5 to 9. The thesis employs Mixed Methods research methodology within the Critical Realist epistemological perspective in Information Systems Research. Studies 1-4 employ different quantitative research and analytical methods while study 5 employs a qualitative research and analytical method. Study 1 proposes and operationalizes a predictive analytics framework in Learning Analytics by using a case study of the Computer Science Department of the University of Jos, Nigeria. Multiple Linear Regression was used with the aid of the Statistical Package for Social Sciences (SPSS) analysis tool. Statistical Hypothesis testing was then used to validate the model with a 5% level of significance. Results show how predictive learning analytics can be successfully operationalized and used for predicting students’ academic performances. In Study 2 the relative efficiency of ICT infrastructure utilization with respect to the educational component of the Human Development Index (HDI) is investigated. A Novel conceptual model is proposed and the Data Envelopment Analysis (DEA) methodology is used to measure the relative efficiency of the components of ICT infrastructure (Inputs) and the components of education (Outputs). Ordinary Least Squares (OLS) Regression Analysis is used to determine the effect of ICT infrastructure on Educational Attainment/Adult Literacy Rates. Results show a strong positive effect of ICT infrastructure on educational attainment and adult literacy rates, a strong correlation between this infrastructure and literacy rates as well as provide a theoretical support for the argument of increasing ICT infrastructure to provide an increase in human development especially within the educational context. In Study 3 the relative efficiency and productivity of ICT Infrastructure Utilization in Education are examined. The research employs the Data Envelopment Analysis (DEA) and Malmquist Index (MI), well established non-parametric data analysis methodologies, applied to archival data on International countries divided into Arab States, Europe, Sub-Saharan Africa and World regions. Ordinary Least Squares (OLS) Regression analysis is applied to determine the effect of ICT infrastructure on Adult Literacy Rates. Findings show a relatively efficient utilization and steady increase in productivity for the regions but with only Europe and the Arab States currently operating in a state of positive growth in productivity. A strong positive effect of ICT infrastructure on Adult Literacy Rates is also observed. Study 4 investigates the efficiency and productivity of ICT utilization in public value creation with respect to Adult Literacy Rates. The research employs Data Envelopment Analysis (DEA) and Malmquist Index (MI), well established non-parametric data analysis methodologies, applied to archival data on International countries divided into Arab States, Europe, Sub-Saharan Africa and World regions. Findings show a relatively efficient utilization of ICT in public value creation but an average decline in productivity levels. Finally, in Study 5 a Critical Discourse Analysis (CDA) on the UNDP Human Development Research Reports from 2010-2016 is carried out to determine whether or not any public value is created or derived from the policy directions being put forward and their subsequent implementations. The CDA is operationalized by Habermas’ Theory of Communicative Action (TCA). Findings show that Public Value is indeed being created and at the core of the policy directions being called for in these reports.School of ComputingPh.D. (Information Systems

Utilización del Machine Learning en la industria 4.0

Los mayores crecimientos económicos vienen impulsados por grandes innovaciones tecnológicas, como la máquina de vapor, la electricidad o el motor de combustión interna. Las empresas, por su parte, tratan de aprovechar estas revolucionarias tecnologías para crear nuevos modelos de negocio y generar altos beneficios con mínimo coste. Actualmente, nos encontramos en la cuarta revolución industrial, donde una de las tecnologías más importantes es la inteligencia artificial. En concreto, el aprendizaje automático o Machine Learning surge como un subcampo de la inteligencia artificial que da a las computadoras la habilidad de aprender sobre algo para lo que no han sido explícitamente programadas. En el presente Trabajo Fin de Máster se introducen los fundamentos del Machine Learning bajo el contexto de la Industria 4.0, se explican los diferentes tipos de problemas que es capaz de resolver y se exponen casos reales de aplicación en la industriaDepartamento de Organización de Empresas y Comercialización e Investigación de MercadosMáster en Ingeniería Industria

An integrated framework for learning analytics

Low retention rates have been an ongoing concern, especially among educational institutions amidst expanding their student base and catering to large and diverse student cohorts. Increasing retention rates without lowering academic standards poses many challenges. The traditional teaching techniques using a one-size-fits-all approach appear to be less effective, and the size and diversity of cohorts demand innovative teaching techniques allowing for adaptive and personalized coaching and learning. In this thesis, we propose a novel, adaptive and integrated analytics framework for learning analytics to address the key concerns of educational institutions. The proposed framework comprises three layers: (1) the conceptual layer which is a context-agnostic and generic analytics layer including descriptive, predictive, and prescriptive techniques; (2) the logical layer or the context-specific learning analytics processes layer that specializes the conceptual layer in the context of education; ten key learning analytics processes are formalized, implemented, and linked to the conceptual layer components; finally, (3) the physical layer that is concerned with education-oriented application implementations and is a context-specific components/algorithmic implementation of the logical layer processes. Our proposed framework, however, is not limited only to the learning and teaching environment. As a proof of concept, we chose the education context and applied our framework on it. The three-layered integrated learning analytics framework proposed allows domain-agnostic elements defined in the conceptual layer to be realized by domain-specific processes in the logical layer, and implemented through existing and new components in the physical layer. Please note that the learning analytics is not confined to the education context alone. The framework, therefore, can be customized for different domains making the approach more widely applicable. An adaptive and innovative approach in the physical layer named the personalized prescriptive quiz (PPQ) is introduced as a demonstration of education-oriented applications assisting the educational institutions. The novel agile learning approach proposed combines descriptive, predictive and prescriptive analytics to create a personalized iterative and incremental approach to learning. The PPQ allows students to easily analyze their current problems (especially, identifying their misconceptions), predict future results, and benefit from personalized intervention tasks. The enhanced PPQ incorporating difficulty and discrimination indexes, run-time question selection, and a hybrid iterative predictive model can be more beneficial and effective for personalized learning. The results demonstrate a significant improvement in student academic performance after applying the PPQ approach. In addition, students claimed that the PPQ helped them elevate their self-esteem and improve student experience which may eventually lead to improved retention rates