Forecasting

3
rd International Conference on Data Mining & Machine Learning (DMML 2022) will act as
a major forum for the presentation of innovative ideas, approaches, developments, and research
projects in the areas of Data Mining and Machine Learning. It will also serve to facilitate the
exchange of information between researchers and industry professionals to discuss the latest issues
and advancement in the area of Big Data and Machine Learning.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
Data Mining and Machine Learning.

Using a sample of dental students (N=373) from four Canadian dental schools, this longitudinal study determined whether the new Canadian Dental Association (CDA) structured interview was a predictor of clinical and academic performance. The new interview predicted clinical performance in the third and fourth years of dental school, but not academic performance. The Canadian Dental Aptitude Test (DAT) continued to predict first- and second-year academic performance, but not clinical performance in the senior years. A personality factor, "Conscientiousness," predicted clinical and academic performance to various degrees across the four years of dental school. A second personality factor, "Openness to Experience," predicted third-year academic performance. The results suggest that a combination of scores from the DAT, a valid measure of personality, and a well-designed structured interview will provide the best prediction of those applicants who will do well in both...

Purpose – Stock keeping unit (SKU) classifications are widely used in the field of production and operations management. Although many theoretical and practical examples of classifications exist, there are no overviews of the current literature, and general guidelines are lacking with respect to method selection for classifying SKUs. The purpose of this paper is to systematically synthesise the earlier work in this area, and to conceptualise and discuss the factors that influence the choice of a specific SKU classification.

Design/methodology/approach – The paper structurally reviews existing contributions and synthesises these into a conceptual framework for SKU classification.

Findings – How SKUs are classified depends on the classification aim, the context and the method that is chosen. In total, three main production and operations management aims were found: inventory management, forecasting and production strategy. Within the method three decisions are identified to come to a classification: the characteristics, the classification technique and the operationalisation of the classes.

Research limitations/implications – Drawing on the literature survey, the authors conclude with a conceptual framework describing the factors that influence SKU classification. Further research could use this framework to develop guidelines for real-life applications.

Practical implications – Examples from a variety of industries and general directions are provided which managers could use to develop their own SKU classification.

Originality/value – The paper aims to advance the literature on SKU classification from the level of individual examples to a conceptual level and provides directions on how to develop a SKU classification.

This longitudinal research aimed to determine the utility of psychosocial and eating characteristics in early teenage years in predicting eating attitudes and behaviors in early adulthood. Self-esteem, perfectionism, family function, and eating attitudes were measured at 12 years and eating attitudes and behavior were assessed at 19 years. Sixty-three women (71.6%) remained in the study over the 7 years. Bulimic attitudes in early teenage years were related to subsequent bulimic features, while restrictive attitudes were more closely related to later purging behaviors. Self-esteem and perfectionism were only weakly linked with subsequent eating. However, different perceived family characteristics at the first stage were associated with specific aspects of eating in early adulthood. There is consistency of eating characteristics across time, but psychosocial characteristics have a more limited predictive power. Future longitudinal studies should begin earlier in childhood if psychoso...

Patients with severe coronary artery disease with a clinical indication for revascularization but who are at high procedural risk because of patient comorbidities, complexity of coronary anatomy, and/or poor hemodynamics represent an understudied and potentially underserved patient population. Through advances in percutaneous interventional techniques and technologies and improvements in patient selection, current percutaneous coronary intervention may allow appropriate patients to benefit safely from revascularization procedures that might not have been offered in the past. The burgeoning interest in these procedures in some respects reflects an evolutionary step within the field of percutaneous coronary intervention. However, because of the clinical complexity of many of these patients and procedures, it is critical to develop dedicated specialists within interventional cardiology who are trained with the cognitive and technical skills to select these patients appropriately and to...

Every day in the news it seems like there are more and more stories about record-breaking weather. Kevin Kloesel is a Texas Ex and Director of the Oklahoma Climatological Survey, working right in the heart of Tornado Alley. He will talk about the science behind extreme weather events such as tornadoes and super storms, and how meteorologists deal with uncertainty in their forecasts. His talk will include demonstrations of extreme weather phenomena, and comparisons between shooting three-point baskets and climate change.

A hybrid neuro-symbolic problem solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation, it has been found that a hybrid case-based reasoning (CBR) system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The system employs a CBR model to wrap a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used in a different stage of the reasoning cycle of the CBR system to retrieve historical data, to adapt it to the present problem and to review the proposed solution. The results obtained from experiments, in which the system operated in a real environment, are presented.

In this paper, under the background of overall food security situation in the SAARC countries, attempts have been made to analyse the production behaviour along with the total seeds of two major food crops rice and wheat. This will help to draw up strategies and programmes for regional cooperation in ensuring food security and reducing hunger and malnutrition in the region. Forecasting of area, production, yield and total seed production will not only help to solve the food security problem but also seed security in these SAARC countries in future. In addition to descriptive statistics, the Box – Jenkins ARIMA modelling technique has been used to analyse the information from 1961 through 2010. The forecast shows that rice and wheat production for the year 2020 would be about 794 and 777 million tons respectively in the world. In-spite of increase in production the study reveals that the yield of rice and wheat in world would be 4.35 t/ha and 3.4 t/ ha in 2020 but the yield of these two crops in SAARC countries, barring one country in each, will remain far below the world projection. Thus, under the given remote possibility of horizontal expansion, the study emphasises the need for quantum jump in the per hectare yield of these two crops for this region. The study also advocates that good quality of seeds in good amount be made available to the farmers, otherwise the whole food security of this part of the Globe would be under tremendous risk.

The main purpose of this research paper is to explore and understand the nature of association and the possible existence of a short run and long run relationship between US Dollar, EURO, British Pound and Japanese Yen. To find out the relationship among currencies USD/INR, EUR/INR, GBP/INR and JPY/INR pairs are considered. The main idea is to know how these selected indicators are related to each other. The daily basis 2781 observations for all four variables from year 2007 to 2018 are taken into consideration. Data are collected from website of Reserve Bank of India. The stationarity of time series is checked and differentiated as per requirement. Johansen co-integration test to know the long run relationship between variables is used. The result shows that there is no co-integration equation among the variables. The short run relationship is examined with help of Vector Auto-regression (VAR) model and the short run relationship within different lags of variables has been identifi...

The concepts of causation and prediction are different, and have different implications for practice. This distinction is applied here to studies of the problem of student attrition (although it is more widely applicable). Studies of attrition from nursing courses have tended to concentrate on causation, trying, largely unsuccessfully, to elicit what causes drop out. However, the problem may more fruitfully be cast in terms of predicting who is likely to drop out. One powerful method for attempting to make predictions is rule induction. This paper reports the use of the Answer Tree package from SPSS for that purpose. The main data set consisted of 3978 records on 528 nursing students, split into a training set and a test set. The source was standard university student records. The method obtained 84% sensitivity, 70% specificity, and 94% accuracy on previously unseen cases. The method requires large amounts of high quality data. When such data are available, rule induction offers a ...