BIBLIOMETRIJSKA ANALIZA UMJETNE INTELIGENCIJE U POSLOVNOJ EKONOMIJI

Invention of artificial intelligence (AI) is certainly one of the most promising technological advancements in modern economy. General AI reaching singularity makes one imagine its disruptive influence. Once invented it is supposed to surpass all human cognitive capabilities. Nevertheless, narrow AI has already been widely applied encompassing many technologies. This paper aims to explore the research area of artificial intelligence with the emphasis on the business economics field. Data has been derived from the records extracted from the Web of Science which is one of the most relevant databases of scientific publications. Total number of extracted records published in the period from 1963-2019 was 1369. Results provide systemic overview of the most influential authors, seminal papers and the most important sources for AI publication. Additionally, using MCA (multiple correspondence analysis) results display the intellectual map of the research field.Otkriće umjetne inteligencije zasigurno predstavlja jednu od najvažniji tehnoloških inovacija moderne ekonomije. Opća umjetna inteligencija koja može dosegnuti singularitet ima potencijal kreirati novu tehnološku arenu. Jednom otkrivena smatra se da će nadmašiti sve ljudske kognitivne sposobnosti. Nadalje, specifična umjetna inteligencija već je otkrivena i primijenjena u brojnim sustavima. Ovaj rad nastoji istražiti područje umjetne inteligencije s naglaskom primjene u ekonomiji. Podaci su derivirani na osnovi zapisa Web of Science baze jednog od najrelevantnijih izvora znanstvenih radova. Ukupan broj ekstrahiranih zapisa u periodu 1963-2019 bio je 1369. Rezultati čine sustavan pregled najutjecajnijih autora, radova te izvora publikacija. Dodatno, koristeći MCA kreirana je intelektualna mapa istraživačkog područja

The total assessment profile, volume 2

Appendices are presented which include discussions of interest formulas, factors in regionalization, parametric modeling of discounted benefit-sacrifice streams, engineering economic calculations, and product innovation. For Volume 1, see

Data Mining

Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment

An overview of the main machine learning models - from theory to algorithms

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn the context of solving highly complex problems, Artificial Intelligence shows an exponential growth over the past years allowing the Machine Learning to augment and sometimes to outperform the human learning. From driverless cars to automatic recommendation on Netflix, we are surrounded by AI, even if we do not notice it. Furthermore, companies have recently adopted new frameworks in their routines which are mainly composed by algorithms able to solve complex problems in a short period of time. The growth of AI technologies has been absolutely stunning and yes, it is only possible because a sub-field of AI called Machine Learning is growing even faster. In a small scale, Machine Learning may be seen as a simple system able to find patterns on data and learn from it. However, it is precisely that learning process that in a large scale will allow machines to mimic the human behavior and perform tasks that would eventually require human intelligence. Just for us to have an idea, according to Forbes the global Machine Learning market was evaluated in $1.7B in 2017 and it is expected to reach almost$21B in 2024. Naturally, Machine Learning has become an attractive and profitable scientific area that demands continuous learning since there is always something new being discovered. During the last decades, a huge number of algorithms have been proposed by the research community, which sometimes may cause some confusion of how and when to use each one of them. That is exactly what is pretended in this thesis, over the next chapters we are going to review the main Machine Learning models and their respective advantages/disadvantages

Benchmarking environmental machine-learning models: methodological progress and an application to forest health

Geospatial machine learning is a versatile approach to analyze environmental data and can help to better understand the interactions and current state of our environment. Due to the artificial intelligence of these algorithms, complex relationships can possibly be discovered which might be missed by other analysis methods. Modeling the interaction of creatures with their environment is referred to as ecological modeling, which is a subcategory of environmental modeling. A subfield of ecological modeling is SDM, which aims to understand the relation between the presence or absence of certain species in their environments. SDM is different from classical mapping/detection analysis. While the latter primarily aim for a visual representation of a species spatial distribution, the former focuses on using the available data to build models and interpreting these. Because no single best option exists to build such models, different settings need to be evaluated and compared against each other. When conducting such modeling comparisons, which are commonly referred to as benchmarking, care needs to be taken throughout the analysis steps to achieve meaningful and unbiased results. These steps are composed out of data preprocessing, model optimization and performance assessment. While these general principles apply to any modeling analysis, their application in an environmental context often requires additional care with respect to data handling, possibly hidden underlying data effects and model selection. To conduct all in a programmatic (and efficient) way, toolboxes in the form of programming modules or packages are needed. This work makes methodological contributions which focus on efficient, machine-learning based analysis of environmental data. In addition, research software to generalize and simplify the described process has been created throughout this work

Style anomalies on the Toronto Stock Exchange : a univariate, multivariate, style timing and portfolio sorting analysis

Includes bibliographical references.A growing body of empirical evidence has found inconsistencies in the Capital Asset-pricing Model (CAPM) of Sharpe (1964), Lintner (1965), and Black (1972) and Ross's (1976) Arbitrage Pricing Theory (APT). Numerous attempts to explore the validity of these theories of modern finance have led to the identification of various firm specific attributes that explain the cross-sectional variation of returns. These attributes have appropriately been termed 'style anomalies '.This thesis investigates the existence and exploitability of style anomalies for the shares comprising the Toronto Stock Exchange (TSX) for the period 31 January 1989 to 31 July 2005. The investigation is divided into four areas of research. First, a methodology similar to Fama and Macbeth (1973) is used to explore the cross-sectional relationships between some 904 firm-specific attributes and the unadjusted and risk adjusted monthly returns of equities constituting the S&P TSX Composite Index. A myriad of uncorrelated style anomalies are found to persist before and after controlling for systematic risk, and are categorized as either size, growth, momentum, value, liquidity and bankruptcy (risk) effects. The most significant attributes from each respective style group include: Price, eighteen month change in net tangible asset value, price change over twelve months, twelve month change in price to net tangible asset value, three month change in the absolute volume ratio and interest cover before tax. Multivariate testing confirms the ability of anomalies to explain excess returns. In and out sample cross sectional tests show inconsistent anomaly persistence, raising the question of whether they are perhaps perennial in nature. Second, the predictability of style payoffs is examined through the analysis of autocorrelation and six style timing models. Strong positive autocorrelation at lower orders for the majority of style payoffs suggests that the ability to time payoffs is possible. The six month moving average timing model shows the best forecasting skill, followed by twelve month and eighteen month moving average models. Third, the presence of firm specific attributes among three classified sectors namely: Basic materials, Cyclicals and Non-Cyclicals are compared. Risk, value and liquidity based anomalies dominate the Basic Materials shares. Liquidity effects stand out within the Cyclicals group, and the Non-Cyclicals sectors exhibit value and size effects. The ability to exploit all style-based anomalies after accounting for transaction costs is evaluated using a portfolio sorting methodology. The tests illustrate that increased exposure to the anomalies has delivered substantially higher returns with lower volatility than a buy and hold approach using an equally weighted all share benchmark. These abnormal returns are confirmed after adjusting for systematic risk. Further testing shows that the attributes, rather than loading on those attributes, are better at explaining share returns. Finally, the seasonal nature of Canadian equity returns is investigated. A six month strategy of "Selling in June and going away till December" provides the most optimal returns. The calendar month tests find January, February and December to be the strongest months of the year. Attribute payoffs seem to show vague seasonal tendencies

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions

Multivariate Analysis in Management, Engineering and the Sciences

Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field