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Optimization algorithms appear in the core calculations of numerous Artificial Intelligence (AI) and Machine Learning methods and Engineering and Business applications. Following recent works on AI's theoretical deficiencies, a rigour context for the optimization problem of a black-box objective function is developed. The algorithm stems directly from the theory of probability, instead of presumed inspiration. Thus the convergence properties of the proposed methodology are inherently stable. In particular, the proposed optimizer utilizes an algorithmic implementation of the n-dimensional inverse transform sampling as a search strategy. No control parameters are required to be tuned, and the trade-off among exploration and exploitation is, by definition, satisfied. A theoretical proof is provided, concluding that when falling into the proposed framework, either directly or incidentally, any optimization algorithm converges. The numerical experiments verify the theoretical results on the efficacy of the algorithm apropos reaching the sought optimum. [ABSTRACT FROM AUTHOR]

The literature review highlights that previous studies have been identifying risk management as an essential tool for project management and could increase the chance of successfully meeting project objectives. In addition, as found from the reviewed literature, risk management has been seen as a tool of allowing the project team to communicate risk information, so as to enhance the decision-making process towards balancing threats and opportunities. Thus, this research aims to examine participants' views on the alignment of risk management, project management and organizational project success. Machine learning algorithms are employed to explore collective data from posts on twitter in order to obtain valuable knowledge about discussions regarding risk management, and project management. Additionally, the corresponding scientific literature obtained from Scopus database was analyzed utilizing bibliometric tools, in order to investigate diverse perceptions in academia and industry. Findings of this study will have implications for practitioners' perception of project risk management. [ABSTRACT FROM AUTHOR]

In this review paper, we computationally analyze a vast volume of published articles in the field of Adaptive Learning, as obtained by the Scopus Database. Particularly, we use a query with search terms targeting the area of Adaptive Learning Systems by utilizing a combination of specific keywords. Accordingly, we apply a multidimensional scaling algorithm to construct bibliometric maps for keywords, authors, and references. Subsequently, we present the computational results for the studied dataset, reveal significant patterns appearing in the field of adaptive learning and the inter-item associations, and interpret the findings based on the current state-of-the-art literature in the area. Furthermore, we demonstrate the time-series of the evolution of the research terms, their trends over time, as well as their prevalent statistical associations. [ABSTRACT FROM AUTHOR]

A recent study of property valuation literature indicated that the vast majority of researchers and academics in the field of real estate are focusing on Mass Appraisals rather than on the further development of the existing valuation methods. Researchers are using a variety of mathematical models used within the field of Machine Learning, which are applied to real estate valuations with high accuracy. On the other hand, it appears that professional valuers do not use these sophisticated models during daily practice, rather they operate using the traditional five methods. The Department of Lands and Surveys in Cyprus recently published the property values (General Valuation) for taxation purposes which were calculated by applying a hybrid model based on the Cost approach with the use of regression analysis in order to quantify the specific parameters of each property. In this paper, the authors propose a number of algorithms based on Artificial Intelligence and Machine Learning approaches that improve the accuracy of these results significantly. The aim of this work is to investigate the capabilities of such models and how they can be used for the mass appraisal of properties, to highlight the importance of sensitivity analysis in such models and also to increase the transparency so that automated valuation models (AVM) can be used for the day-to-day work of the valuer. [ABSTRACT FROM AUTHOR]

The importance of designing safe and economic structures in seismically active areas is of great importance. Thus, developing tools that would help in accurately predicting the dynamic properties of buildings is undoubtable a crucial objective. One of the parameters that significantly affects the seismic design of any structure is the fundamental period that is used to compute the seismic forces. It is well documented that the current design formulae for the prediction of the fundamental period of reinforced concrete buildings are simplistic and often fail to capture accurately their expected natural frequency. In addition, the design formulae do not have the ability to account for the soil-structure interaction (SSI) effect that, in some cases, significantly affects the natural frequency of buildings due to the additional flexibility induced by the soft soil. In this research work, a computationally efficient and robust 3D modeling approach is used for the modal analysis in order to investigate the accuracy of different design formulae in predicting the fundamental period of reinforced concrete buildings with and without SSI effects. In this context, 3D detailed modeling is used to generate a dataset that consists of 475 modal analyses, which is subsequently used to train and produce three predictive formulae using a higher-order, nonlinear regression modeling framework. The developed fundamental period formulae were validated through the use of 60 out-of-sample modal results and they are also compared to other existing formulae in the international literature and design codes. According to the numerical findings, the proposed fundamental period formulae are found to have superior predictive capabilities for the under-study types of buildings. • A parametric investigation on different fundamental period design code formulae is performed. • Soil-structure interaction is also accounted for in this study. • 3D detailed modeling is used to generate a dataset that consists of 475 modal analyses. Page 6 of 9. • Train different models using a higher-order, nonlinear regression modeling and the ANN method. • The proposed fundamental period formulae are found to have superior predictive capabilities. [ABSTRACT FROM AUTHOR]