Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

Do Optic Disc Drusen Cause Unilateral Nyctalopia?

This is a case report of a patient with unilateral nyctalopia in whom ipsilateral optic disc drusen were the only finding despite extensive investigation

Are countries with higher levels of mental health cases experience higher divorce rates?

This paper aims to determine if spouses’ mental health can be a factor affecting the divorce rate of marriage. A regression analysis is carried out to determine how the percentage of mental health cases in a country’s population affects the divorce rates of a country, while controlling the effects of labour force participation and income. The data from the selected 20 countries are collected from reputable world organizations selected. The results obtained from the regression analysis show that mental health has a marginally significant association with divorce rate and the association between income index and divorce rate is statistically significant

Mining health knowledge graph for health risk prediction

Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients’ health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health

Research review on time series forecasting of gold price movement

This paper propose different techniques used in forecasting on time series analysis. Various techniques had been used in the field of time series forecasting from the traditional Box-Jenkins approach to the most popular neural network technique nowadays. However, there is no specifically the best method to deal with time series forecasting as the application of different time series forecasting methods has their own requirements and restrictions. In determining the movement of gold price, there are a lot of different methods being implemented by various authors to propose their models. Various time series forecasting method have been discussed in this paper which consists of several journal articles that related to gold price and some of the data mining techniques in time series forecasting retrieved from Google Scholar in this review

Financial Preparedness And Retirement Planning Among Emplyees Provident Fund (Epf) Contributors

Established in 1951, the Employees Provident Fund scheme made it compulsory for Malaysian workforce to save for their retirement. However, many of the employee provident fund contributors found that their Employees’ Provident Fund (EPF) savings are not enough to sustain them through retirement. Thus, this study was conducted to identify factors that affect an EPF contributor's decision to have planned savings for retirement besides employee provident fund savings and to analyse factors that contribute to EPF contributor's financial preparedness for retirement. These constitutes the first two objectives of this study. In the third objective, the study uses descriptive statistical analysis to explore the relationship between socio-demographic characteristic and preferred retirement saving channels. A total of 500 questionnaires were administered to those working in Kuala Lumpur and Selangor area. The likelihood of having a personal retirement saving plan and the categories of retirement financial preparedness were analysed using Logistic regression model and Multinomial logistic model respectively

The Influence of Parole Case Characteristics and Construal Level on Parole Decisions and Perceived Humanness

Despite the low rate of discretionary parole release in New York, much is still unknown about the processes underpinning parole decisions. The present paper delves into how aggravating and mitigating parole case characteristics (e.g. institutional behavior) relate to parole decisions and the perceived humanness of parole applicants. The paper also examines how construal level can moderate the above relationships. Finally, a moderated mediation model outlining the pattern of these relationships is posited and tested. 122 New York residents were recruited online and randomly assigned to read either an abstractly or concretely construed transcript for an interview with a parole applicant. Participants then completed a questionnaire asking how they perceived the case’s characteristics, whether they would grant or deny parole, their decision certainty, their preferred specific parole disposition and the perceived humanness of the applicant. Results showed that cases perceived to have more mitigating characteristics were associated with greater certainty in granting parole, more lenient specific dispositions, and more perceived humanness. Additionally, for cases perceived to have more aggravating characteristics, abstract (versus concrete) construals led to greater certainty in granting parole. However, construal level did not moderate the relationships involving specific disposition or perceived humanness. Lastly, the posited model was partially supported, such that the direct effect of case characteristics on decision certainty was moderated by construal level; however, construal level did not moderate the indirect effect through perceived humanness. These findings lay the groundwork for more extensive parole research and have implications for parole applicants preparing for parole reviews