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Predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right solutions. Predictive analytics provides ideas on the occurrences of future downtimes and rejections thereby aids in taking preventive actions before abnormalities occur. Considering these advantages, predictive analytics is adopted in various diverse fields such as health care, finance, education, marketing, automotive, etc. Predictive analytics tools can be used to predict various behaviors and patterns, thereby saving the time and money of its users. Many open-source predictive analysis tools namely R, scikit-learn, Konstanz Information Miner (KNIME), Orange, RapidMiner, Waikato Environment for Knowledge Analysis (WEKA), etc. are freely available for the users. This chapter aims to reveal the best accurate tools and techniques for the classification task that aid in decision-making. Our experimental results show that no specific tool provides the best results in all scenarios; rather it depends upon the datasets and the classifier.

This chapter reviews traditional intelligence work, primarily how intelligence was perceived and conducted in the industrial economy. The review includes economic sectors with dedicated intelligence functions such as military, law enforcement, and national security. The review also includes secondary intelligence work in all other economic sectors. Looking across all these examples, the authors present a traditional life cycle model of intelligence work and highlight this traditional view of intelligence’s tactical and reactive approach. The chapter details the historical evolution and common intelligence elements in military, business, law enforcement, judicial forensics, national security, market, financial, medical, digital, and computer forensics.

Many psychological, cultural, and social barriers exist that impact Black male participation in the workforce. In this chapter, authors discuss the impact that mentorship, racism, society, culture, economics, and other pertinent factors have on the career development of Black men. This chapter examines programs and strategies that effectively address the career development needs of Black men. A review of counseling interventions and their applicability to career counseling with Black men are presented. Emerging trends in career development for Black men are also discussed. In addition, provided in this chapter are personal narratives given by the authors who contextualize their career development experiences through culturally-specific career development theoretical frameworks. Lastly, implications for research, counseling, counselor education, and policy, as well as recommendations for professional development are offered.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Engagement, motivation, and persistence are usually associated with positive outcomes. However, too much of it can overtax our psychophysiological system and put it at risk. On the basis of a neuro-dynamic personality and self-regulation model, we explain the neurobehavioral mechanisms presumably underlying engagement and how engagement, when overtaxing the individual, becomes automatically inhibited for reasons of protection. We explain how different intensities and patterns of engagement may relate to personality traits such as Self-directedness, Conscientiousness, Drive for Reward, and Absorption, which we conceive of as functions or strategies of adaptive neurobehavioral systems. We describe how protective inhibitions and personality traits contribute to phenomena such as disengagement and increased effort-sense in chronic fatigue conditions, which often affect professions involving high socio-emotional interactions. By doing so we adduce evidence on hemispheric asymmetry of motivation, neuromodulation by dopamine, self-determination, task engagement, and physiological disengagement. Not least, we discuss educational implications of our model.

Purpose – This chapter presents research on determinants of economic hardship and the effect of economic hardship on marital quality in two social contexts in Croatia: postwar recovery period (Study 1) and economic recession starting in 2009 to present (Study 2).

Methodology/approach – In Study 1 the sample consisted of 505 married couples (quota sample of Zagreb and neighboring villages). In Study 2 the sample consisted of 850 married couples (quota sample of Zagreb and 14 regions in Croatia). We have used the SPSS 18 Mixed Linear Model approach for data analysis. A number of variables representing individual characteristics of marital partners were entered as level 1. A number of variables representing marital dyad (duration of marriage, size of the family) were entered as level 2.

Findings – The variables of education, employment status, and size of the family turned out to be most predictive for economic hardship in both studies. Also, in both studies economic hardship turned out to be a very important predictor of marital quality.

Research limitations – The limitations of the studies are the absence of longitudinal approach and a probability sample.

Social implications – The studies carry important social implications showing that in the absence of government or community social support, partners’ social support could moderate negative effect of economic hardship on marital quality. We assume that this conclusion could be generalized to other social contexts as well.

Originality/value of chapter – The strength and originality of the studies was in multilevel approach in data analysis and treating marital partners as a dyad.

This chapter highlights an overemphasis and persistent bias in entrepreneurship pedagogy toward predictive logic that results in unidimensional instruction. In contrast, we explore how to teach a creative logic for entrepreneurial action. We argue that a more realistic and complete approach to teaching and pedagogy should include a creative logic that will augment existing methods focused on students’ research and analysis and balance these with taking explicit entrepreneurial action. Building upon social capital, networking, learning and real options theories, the chapter uses case studies and provides in-class exercises to illustrate our perspective and help researchers and instructors alike.

The purpose of this study is to determine which quantitative metrics are most representative of investor sentiment in the US equity markets. Sentiment is the aggregation of consumers', investors', and producers' thoughts and opinions about the future of the financial markets. By analyzing the change in popular economic indicators, financial market statistics, and sentiment reports, we can gain information on investor reactions. Furthermore, we will use machine learning techniques to develop predictive models that will attempt to forecast whether the stock market will go up or down based on the percent change in these indicators.

This chapter develops a predictive approach to Granger causality (GC) testing that utilizes k -fold cross-validation and posterior simulation to perform out-of-sample testing. A Monte Carlo study indicates that the cross-validation predictive procedure has improved power in comparison to previously available out-of-sample testing procedures, matching the performance of the in-sample F-test while retaining the credibility of post- sample inference. An empirical application to the Phillips curve is provided evaluating the evidence on GC between inflation and unemployment rates.