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The rapid journey from central planning to euro area (EU) membership stress-tested the social learning processes of the former transition economies (FTEs). The desire for a higher standard of living, to be anchored to the West and to enter the EU spurred major reform waves and led to the very rapid introduction of institutions that had evolved as best-practice in highly developed countries. Although social learning accompanied this process, in many FTEs it was not fast enough to keep pace with the rapid reforms, leaving new  institutions with social norms that were not sufficiently strong to maintain them. As a result, widespread reform reversals emerged in the region. Such reform reversals appeared as formal reversals, which changed legislation, and behavioral reversals, which eroded the quality of an institution by materially changing the way it worked. It was frequently the interaction of reversals in different sectors that created a full-blown reform reversal episode, with the financial sector particularly prone to behavioral reversals, both in public and private institutions. External anchors such as the Washington institutions played a dominant role in shaping the transition process. The EU and EU accession acted as a strong anchor that could prevent or reverse formal reform reversals in areas covered by EU law, but could play a much weaker role in the case of behavioral reversals. The ultimate solution to prevent reform reversals is to accelerate social learning processes that strengthen the national ownership of reforms. It is also important to focus on the quality and internal coherence of reforms and newly created institutions. [ABSTRACT FROM AUTHOR]

This paper deals with the comparative analysis of prediction classifiers in the blended learning environment. The model proposed in this paper predicts students' final grades based on activities within different educational environments. A comparative study of classifier performance has been performed in order to determine the classifier most suitable for multiclass feature dataset. Important results for different classes have been obtained using different classifiers, and the majority vote scheme is subsequentially used to form an ensemble based on Naïve Bayes, Hidden Naïve Bayes, J48 decision tree and Random Forest. According to experimental evaluation, there is a significant improvement of proposed model's precision and accuracy regarding the students' grades prediction in blended learning environment scenario. The major contribution of the research presented in this paper is an efficient multi‐class prediction model applicable to aforementioned environment. [ABSTRACT FROM AUTHOR]

Basis test paths is a method that uses a graph contains nodes as a representation of codes and the lines as a sequence of code execution steps. Determination of basis test paths can be generated using a Genetic Algorithm, but the drawback was the number of iterations affect the possibility of visibility of the appropriate basis path. When the iteration is less, there is a possibility the paths do not appear all. Conversely, if the iteration is too much, all the paths have appeared in the middle of iteration. This research aims to optimize the performance of Genetic Algorithms for the generation of Basis Test Paths by determining how many iterations level corresponding to the characteristics of the code. Code metrics Node, Edge, VG, NBD, LOC were used as features to determine the number of iterations. J48 classifier was employed as a method to predict the number of iterations. There were 17 methods have selected as a data training, and 16 methods as a data test. The system was able to predict 84.5% of 58 basis paths. Efficiency test results also show that our system was able to seek Basis Paths 35% faster than the old system. [ABSTRACT FROM AUTHOR]

The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms. [ABSTRACT FROM AUTHOR]

The use of data mining within the higher education context has, increasingly, been gaining traction. A parallel examination of the accuracy, robustness and utility of the algorithms applied to data mining is argued as a necessary step toward entrenching the use of EDM. This article provides a comparative analysis of various classification algorithms within an Open Distance Learning institution in South Africa. The study compares the performance of the ZeroR, OneR, Naïve Bayes, IBk, Simple Logistic Regression and the J48 in classifying students within a cohort over an eightyear time span. The initial results appear to show that, given the data quality and structure of the institution under study, the J48 most consistently performed with the highest levels of accuracy. [ABSTRACT FROM AUTHOR]

Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy. [ABSTRACT FROM AUTHOR]

Nowadays, there has been recent interest in applying machine learning techniques to the neurodegenerative disorders. Dementia is one such emerging global health issue and its early detection can be very helpful. A comparative analysis of four machine learning algorithms i.e., J48, Naïve Bayes, Random Forest and Multilayer Perceptron is presented in this paper. For the attribute reduction, the CFSSubsetEval is used. It has been shown that J48 is performing best among all the algorithms for the detection of Dementia. [ABSTRACT FROM AUTHOR]

The wide acceptance of decision tree classifiers lies with their fast performance and simple nature. The J48 group of decision tree classifiers are widely used for classification and decision-making process. In this paper, three popular J48 group classifiers, namely J48, J48Consolidated and J48Graft are evaluated using both binary and multi-class datasets across thirteen performance matrices, which is unique in its area. In order to come across a versatile classifier, the evaluated results of these classifiers are nourished to a prominent multi-criteria decision-making module called Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for suitable rank allocation. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates the effect of public policies enforcing workplace pension plan provision on participation rates. Using British data covering almost two decades (1992–2009), I identify the potential opt‐in rate among private sector workers who have not been offered a workplace pension plan, had they been offered the opportunity to join such a saving scheme. I find that universal provision of workplace pension schemes alone could generate a major impact on pension coverage. [ABSTRACT FROM AUTHOR]

In this article, we evaluate the influence that employment promotion measures designed for people with disabilities have on the latter’s labour market participation through the use of Propensity Score Matching analysis. In particular, we consider an Italian law—Law 68 of March 12, 1999—whose aim is to regulate and promote the employment of disabled people. For our analysis we use the information contained in the ISFOL Participation, Labour, Unemployment, Survey 2008 and 2010. The results show that the Law 68/99 has a positive but very low impact on the participation of disabled people in the labour market. Policy considerations are discussed. [ABSTRACT FROM AUTHOR]

We evaluate the effect of a preschool health intervention aimed at reducing infections through improved hygiene practices and training of the staff. The large-scale design enables us to study heterogeneous effects with respect to several child and preschool characteristics that are related to the level of illness-related absence. We find no reduction, on average, in children's illness-related absence. This conclusion holds across different age groups of preschool children, and for preschools with varying levels of absence before the intervention. We find some suggestive evidence that the intervention may have induced effects operating in different directions: while improved hygiene practices may have reduced illness, stricter guidelines regarding absence during infections seems to have contributed to an increased absence level, resulting in an overall zero effect. Overall, our results suggest that reducing absence through improved hygiene practices is not easily accomplished in a child care setting. [ABSTRACT FROM AUTHOR]

Labor market segmentation refers to a salient divide between secure and insecure jobs and is related to problems in important areas, including macro-economic efficiency, workers' well-being and repercussions for social cohesion. EU-28 countries have started a new wave of labor market reforms in the aftermath of the 2008/2009 crisis to tackle a number of issues, including labor market segmentation. This particularly concerns reforms in: (1) employment protection, i.e. dismissal protection and restrictions on fixed-term contracts; (2) unemployment benefit generosity and coverage; and (3) the intensity of active labor market policies. The paper provides an overview of reform patterns and tries to assess whether and to what extent these reforms have led to less dualized, more 'flexicure' labor markets in terms of dismissal protection, the provision of unemployment benefits and access to ALMPs. In particular, we will provide some evidence on potential changes in hirings on temporary contracts. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of collective minimum wages on youth employment for 14 sectors of the Austrian economy, taking into account the possibility of nonlinearity. We find that when the real index of minimum wage rises above a certain point, negative employment effects for young individuals (aged between 19 and 25 years) can be expected in Austria. In five of seven market sectors the minimum wage lies above the optimal level that maximizes the employment of young workers. [ABSTRACT FROM AUTHOR]

This paper evaluates the efficiency of Active Labour Market Policies (ALMPs) in the European Union (EU). The paper first reviews the main trends governing the evolution of the European Social Fund (ESF) since its creation. The ESF promotes public expenditure in ALMPs in order to foster social cohesion across the EU. In order to test to what extent this strategy can be backed up by facts, we estimate the impact of public expenditure on ALMPs on the employment rate using panel data from 28 European countries (1985–2011), taking into account the endogeneity of the explanatory variables and the dynamic behaviour of their relationship. Results support the hypothesis that expenditure in ALMPs is more beneficial for employment than aggregate public expenditure. In addition, we show that periphery countries observe a larger efficiency of their ALMPs. These results support the recent policy strategy undertaken by the European Commission to raise the budget devoted to ESF in Member States experiencing higher unemployment rates. [ABSTRACT FROM AUTHOR]

Today, graphology is seen as an experimental field of science that is dedicated to suggest ideas about diseases, profession choices, mood and characteristics of a person by investigating his/her handwriting. Graphology is in cooperation with medicine, psychology, sociology or other disciplines that are based on observation. Graphology is used for staff recruitment in business, diagnoses in medicine, identification of criminals in forensics, choosing a profession in education, guidance and counseling and other practices at every level of social structure. It is quite interesting that the number of the scientific studies on graphology is limited around the world, that there are no specific institutions providing education of graphology and that institutions except for a few international corporations do not benefit from graphology at all. In terms of demographic properties, many statistical and mathematical analyses investigate similar and different variables. Especially, differences regarding gender have become subject to research. Therefore, detecting gender through handwriting can give pace to research in other disciplines. Moreover, the research can be useful in any field where gender detection is needed. This study fulfills two objectives. The first one is to find out whether a writer can identify his/her own handwriting. The second objective is to detect the gender of a writer of a text with the help of graphology and computer sciences. The impact of the study is reflected in the fact that findings can be used in fields where gender detection is needed, and that the detection is done with the help of expert and intelligent systems. At the end of the study, gender detection was performed for the individuals by making use of 133 attributes. Then, a decision tree and lists of rules were created with some algorithms. The purpose was to detect the gender of the person by making a character analysis of the handwriting with the help of decision tree formation methods in data mining. The analysis showed that it is possible to detect the gender of a person with the use of the specified attributes. The study reached a success level of 93.75% with ID3 algorithm. [ABSTRACT FROM AUTHOR]

The impact of financial crises on the youth unemployment rate (YUR), compared to the total unemployment rate (UR), is estimated for a panel of OECD countries over the period 1981–2009, using bias-corrected dynamic panel data estimators of short- and long-run coefficients. Both YUR and UR are found highly persistent. Also, short- and long-run effects of financial crises on YUR are significantly large, respectively, some 1.9 and 1.5–1.7 times higher than the short- and long-run effects on UR. Similar results are found for the unemployment impacts of GDP growth lagged 1 year and institutional variables. These results are robust to various dynamic specifications. [ABSTRACT FROM AUTHOR]

Agriculture is the backbone of India. The ultimate goal of farming is the cultivation and perfection of human beings. Agriculture acts as a mediator between nature and the human community. Soil is the boon for agriculture and chemical fertilizer is the curse for soil. Food produced by these artificial fertilizers causes many challenges to human lives. Autism Spectrum Disorder is the third most common developmental disorder. The first ever survey shows that more than 10 million children in India suffer from autism. Data Mining is one of the phases involved in Knowledge Discovery in Databases. The goal of Data mining is the discovery of patterns and knowledge from large datasets. There are a number of machine learning techniques such as classification, clustering, association rule etc. involved in data mining. Classification belongs to supervised learning technique of data mining. This research work focuses on finding whether agriculture has any impact on autism. This paper shows the literature review based on the significance of prediction and classification based data mining algorithms in the field of agriculture and autism spectrum disorder [ABSTRACT FROM AUTHOR]

Labour informality is one of the most serious challenges facing a developing economy like India with large-scale poverty and decent work deficits. This study has inspected possible heterogeneity within informal employment among the non-cultivator workers. Multinomial Logit was applied to find out the determinants of participation in different components of informal employment. Significant heterogeneity within the informal employment on poverty, age, gender, socio-religious communities, educational attainment and industrial classification was observed. There is coexistence of voluntary and involuntary informal employment. Given the diversity of employment, one-size-fits-all policy design may adversely affect sustainable and inclusive growth in India. [ABSTRACT FROM PUBLISHER]

In recent years, due to significant developments in online shopping and the widespread use of e‐commerce, competition among companies has increased considerably. As a result, product reviews have become a primary factor in consumers' decision making, which has given rise to a market for fraudulent reviews about real products and services. In this study, the authors propose a model using a multiple classifier system to identify deceptive negative customer reviews, which they validated with a dataset of hotel reviews from TripAdvisor. The proposed model used five classifiers by following the majority voting combination rule – namely, libLinear, libSVM, sequential minimal optimisation, random forest, and J48 – the first two of which represent different implementations of support vector machines. Ultimately, the model provided remarkable results that demonstrate improvement upon approaches reported in the literature. [ABSTRACT FROM AUTHOR]

Empirical evidence on the effect of minimum wages on youth employment is inconclusive, with studies pointing to negative, positive or insignificant effects. In trying to explain some of the conflicting evidence, this research paper examines synergies of minimum wages with other labour market institutions using an unbalanced panel dataset of 19 OECD countries over 1985-2013. Institutions that enforce labour market rigidity, such as unemployment benefits and union density, are found to exacerbate the negative effect of minimum wages on youth employment, while government expenditure on training programmes for the unemployed dampen it. This finding of significant synergy effects indicates that panel data models which omit interactive terms between minimum wages and institutions might be misspecified. In addition, the analysis suggests that the negative effect of minimum wages is most severe in rigid labour markets with high unemployment benefits and union density. Therefore, policymakers need to consider the full spectrum of institutions they face before adjusting minimum wages. [ABSTRACT FROM AUTHOR]

In India each family had five to six children but from last decade's low fertility is growing rapidly in both urban and rural areas of India. Economic and social modernization in India with gain in education, obesity and age led to lower fertility in every region of India. In this paper Data mining techniques like statistical analysis, classification, Naives bayes, J48 has been used to analyse and mine knowledge on significant factors causing infertility in women. Even though there are many factors were causing infertility in women, but only three factors Age, Obesity (BMI) and Education taken for analysis. Samples are taken from both rural and urban areas of Indore. Out of several independent attributes collected about outpatients, only three attributes consider being significant have been taken up for preliminary study. The aim of the study is to assess both in the rural and urban areas they said factors in the light of fertility in women. Common attributes have been considered among an equal sample size of fertile and infertile outpatients of rural and urban areas. The result of the study shows that the attributes considered are significant in determining fertility of women both individually and together. In the research found that the education effects age of the marriage of women and age influence body mass index (BMI) and obesity triggers changes in hormonal levels which is the cause of the infertility in urban area's whereas in rural area's the percentage of infertility is low due to education and the age of the marriages is earlier compare to urban areas. [ABSTRACT FROM AUTHOR]

Prediction of agriculture yield with the help of data mining tools and techniques is one of the emerging research domains from past couple of years. This paper focuses on studying the effects of rainfall and relative humidity on three different stages of wheat crop and thus generating rules about crop yield using decision tree induction technique. A comparison of different classification algorithms is also done to discover the best rules about wheat crop yield with maximum accuracy and least errors. These study findings will help the farmers in future to predict their wheat crop yield in advance before crop growing season. [ABSTRACT FROM AUTHOR]

Currently, Medical care has not completely appreciated the possible advantages to be acquired from Big Data Analytics. The evolving areas of efficient utilization of Analytics are predictive analytics and traceability. The Big Data Analytics abilities and possible benefits are provided in healthcare to formulate far better data-driven analytics techniques for MERS-CoV disease. This paper is basically giving emphasis on the MERS-CoV . It is an airborne illness which develops simply and has large death rate. It could be the acute respiratory syndrome brought on by betacoronavirus and belongs to the corona viruses household, which is responsible for producing gentle to reasonable colds. The initial situation of the illness was reported from Saudi Arabia. Recently, the outbreak of MERS-CoV infections triggered global attention to Saudi Arabia. Relating to the records, males are far more susceptible than girl, especially following the age of 40. Due to the understanding and early diagnosis the incidence is slipping gradually. There is no unique therapy for the MERS-CoV till now. As a representative case study the Data Mining techniques are proposed that can be used in order to better realize the security and the chance of recovery from MERS-CoV infections. In addition unsupervised filtering is used for better performance and accuracy of predictive Models. [ABSTRACT FROM AUTHOR]

This paper evaluates the extent of wage rigidities in Colombia over a period, 2002–2014, in which the fall in unemployment was relatively slow with respect to sustained economic growth. Following Holden and Wulfsberg (2009), we compute a measure of downward real wage rigidity (DRWR) of 12.09%, four times bigger than their aggregate estimate for the OECD economies. Moreover, in contrast to the evidence for the advanced economies, the determinants of such rigidities show no connection to the wage bargaining system. Amid the absence of effective labor market institutions to make rigidities less prevalent, economic growth appears as the most powerful mechanism to ward them off. Under this light, we provide a stylized description of the wage setting rule in Colombia, compare it with the common one in the advanced economies, and call for a far-reaching reform of the Colombian wage bargaining setup. [ABSTRACT FROM AUTHOR]

This paper uses Census IPUMS data from 1970 to 2000 and ACS data from 2010 to estimate the impact of oil booms and busts on wages and human capital formation in the USA. The paper finds that the oil boom between 1970 and 1980 was associated with a slower growth in the relative demand for skills in the oil and gas sector and regions where the sector had a large presence. The oil boom led to a sharp rise in real wages and a modest decline in college wage premium in oil-rich regions in the USA. Using a synthetic cohort approach, the paper finds that relative to cohorts who went to high school in the pre-oil boom period, the cohort reaching high school age during the oil boom was about 1-2% points less likely to have a college degree by 2000 and 2010. [ABSTRACT FROM AUTHOR]

The identification of useful patterns from a medical dataset benefits early diagnosis. Classification techniques in data mining are famous for medical data prediction. Decision Tree (J48) and Navie Bayes are the most popular and frequently used algorithms in the field of prediction analysis. The efficiency of such prediction algorithms can be improved with better pre-processing approaches. This paper aims at finding optimum features using permutation combination of input data attributes for improving the classifier accuracy. The Pima Indian Diabetes Dataset from UCI repository is used for experimentation. The performance of J48 and Navie Bayes has been tested for different combination of features. [ABSTRACT FROM AUTHOR]

The aim of this work is to analyse the situation of persons with disabilities in the Italian labour market, with a view to providing guidelines to promote their inclusion both in the labour market and in society. For this purpose, we propose a two-step analysis focusing on Italian regions for the period 2006-2009. In the first phase, we use the Data Envelopment Analysis method to evaluate regional efficiency in providing employment for persons with disabilities. Cluster analysis is then applied to regional efficiency scores and economic policy variables in order to identify 'policy clusters of regions'. Our results show that it is necessary both to focus on the residual work ability of persons with disabilities and to develop a social integration culture on the demand side of the labour market. Moreover, a structural reform of disability benefit systems is required in order to promote a culture of social inclusion. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze a flexicurity index for disabled people by using Italian regional data. To this purpose, the empirical results are based on a composite index denoted as Mazziotta-Pareto Index. Our results show that Northern Italy regions show a higher flexicurity degree than Southern Italy ones. In addition, by estimating an augmented matching function, we verify that flexicurity increases the probability of finding employment for a disabled person. In particular, we test that the flexicurity indicator that gives more weight to the economic independence of disabled people represents the indicator that most favors the labour matching process. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the impact of flexicurity upon regional labour market performance in the employment of disabled people. To this purpose, we estimate a matching function by means of a two-regime spatial autoregressive model on Italian regions for the period 2006-2011. Our results highlight the importance of spatial interactions in the matching process of disabled people, which enable Northern-Central Italy regions to find a job for a number of disabled persons five times higher than the one employed by Southern Italy regions. [ABSTRACT FROM AUTHOR]

Introduction: This paper is part of a research project aimed to construct a predictive model of students´ academic performance, and as result of an iterative process of experimentation and evaluation about the pertinence of some data mining techniques. Methodology: this paper was written in 2016 in the University El Bosqué, Bogotá, Colombia and it´s presents a comparative analysis of the performance and relevance of the J48 and RandomForest algorithms, in order to identify the most influential demographic and ICFES scores variables, as well as the classification rules, to predict the first year academic performance of the Engineering Faculty students, in Universidad El Bosque in Bogotá, Colombia. Results: The analysis process was carried out on 7644 students´ records, and it was developed in two phases: Firstly the data needed to feed the mining process was extracted and prepared. Secondly the data mining process itself was implemented through preprocessing data and executing the classification algorithms available in Weka. Some significant variables and rules to predict academic performance are found, according to the studied population characteristics. Conclusions: Academic risk as the cause of the desertion phenomenon must be studied as a phenomenon itself. Establishing its causes allows for the creation of preventive strategies for the accompaniment of students through their process, aimed to mitigate the risk of both phenomena. [ABSTRACT FROM AUTHOR]

While India is among the world's largest producers and exporters of apparel products, the sector has not performed to its potential. This study analyzes why this might be so from the perspective of the structure of production in the sector, the most striking aspect of which is a firm size distribution heavily dominated by small firms. Using nationally representative firm-level and labor force survey data, we argue that the dominance of firms operating at scales too small to apply modern production and management technologies is one proximate reason for India's relative underperformance. Further, we note that India's labor regulations and the associated enforcement regime are important policy drivers of Indian firms' tendency to avoid placing 'too many workers under one roof'. [ABSTRACT FROM AUTHOR]

The article discusses various papers published in this issue including one by Sawada on how Asian households make decisions in response to a variety of disasters, one by Meng on the impact of the 2008 Labor Contract law on migrant workers' labor market outcomes in China, and one by Kondo on the link between elderly health care and labor supply.

This paper presents the results of using a commercial pulsimeter as an electrocardiogram (ECG) for wireless detection of cardiac alterations and stress levels for home control. For these purposes, signal processing techniques (Continuous Wavelet Transform (CWT) and J48) have been used, respectively. The designed algorithm analyses the ECG signal and is able to detect the heart rate (99.42%), arrhythmia (93.48%) and extrasystoles (99.29%). The detection of stress level is complemented with Skin Conductance Response (SCR), whose success is 94.02%. The heart rate variability does not show added value to the stress detection in this case. With this pulsimeter, it is possible to prevent and detect anomalies for a non-intrusive way associated to a telemedicine system. It is also possible to use it during physical activity due to the fact the CWT minimizes the motion artifacts.