Literature Review on Big Data Analytics Methods

Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed

A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry

This paper presents a multi-stage model for accurate prediction of demand for dairy products (DDP) by the use of artificial intelligence tools including Multi- Layer Perceptron (MLP), Adaptive-Neural-based Fuzzy Inference System (ANFIS), and Support Vector Regression (SVR). The innovation of this work is the improvement of artificial intelligence tools with various meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), and Cultural Algorithm (CA). First, the best combination of factors with can affect the DDP is determined by solving a feature selection optimization problem. Then, the artificial intelligent tools are improved with the goal of making a prediction with minimal error. The results indicate that demographic behavior and inflation rate have the greatest impact on dairy consumption in Iran. Moreover, PSO still exhibits a better performance in feature selection in compare of newcomer meta-heuristic algorithms such as IWO and CA. However, IWO shows the best performance in improving the prediction tools by achieving an error of 0.008 and a coefficient of determination of 95%. The final analysis demonstrates the validity and reliability of the results of the proposed model, as it supports the simultaneous analysis and comparison of the outputs of different tools and methods

Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network

Hepatic granuloma develops in the early stage of liver cirrhosis which can seriously injury liver health. At present, the assessment of medical microscopic images is necessary for various diseases and the exploiting of artificial intelligence technology to assist pathology doctors in pre-diagnosis is the trend of future medical development. In this article, we try to classify mice liver microscopic images of normal, granuloma-fibrosis 1 and granuloma-fibrosis2, using convolutional neural networks (CNNs) and two conventional machine learning methods: support vector machine (SVM) and random forest (RF). On account of the included small dataset of 30 mice liver microscopic images, the proposed work included a preprocessing stage to deal with the problem of insufficient image number, which included the cropping of the original microscopic images to small patches, and the disorderly recombination after cropping and labeling the cropped patches In addition, recognizable texture features are extracted and selected using gray the level co-occurrence matrix (GLCM), local binary pattern (LBP) and Pearson correlation coefficient (PCC), respectively. The results established a classification accuracy of 82.78% of the proposed CNN based classifiers to classify 3 types of images. In addition, the confusion matrix figures out that the accuracy of the classification results using the proposed CNNs based classifiers for the normal class, granuloma-fibrosisl, and granuloma-fibrosis2 were 92.5%, 76.67%, and 79.17%, respectively. The comparative study of the proposed CNN based classifier and the SVM and RF proved the superiority of the CNNs showing its promising performance for clinical cases

Demand forecasting for a Mixed-Use Building using an Agent-schedule information Data-Driven Model

There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction

Розробка підсистеми прогнозування системи управління маркетинговою інформацією на промисловому підприємстві

The theoretical generalization, which is revealed in the development of conceptual and methodological principles and methodical provisions related to formation and functioning of the forecasting subsystem of the marketing information management system at an industrial enterprise, is presented.The market is a social phenomenon in which the availability of valuable marketing information reduces uncertainty, ensures the promptness of making managerial decisions, makes possible to avoid threats and creates a basis for increase in the efficiency of a production process and competitiveness. Therefore, the control of changes in the marketing environment requires the creation of a marketing information management system at an industrial enterprise, which is based on effective methods of collection and analysis of marketing information. Markets of industrial enterprises make possible to create and test progressive marketing information management systems.There are trends that cause worsening of prospects for economic growth at the current state of the marketing environment of industrial enterprises. Growth of these risks is facilitated by trends of globalization, informatization, social changes. Such an increase in business risks causes an increase of the role of forecasting. The classical concept of a marketing information management is enhanced and system is restructured and the creation of a subsystem of forecasting is improved. The methodological approach to the functioning of forecasting subsystems of marketing information systems of industrial enterprises based on the model of statistical forecasting of sales volume is offered.The proposed procedure to overcome a general lack of forecasting methods is related to the failure to take into account an inaccuracy of observations on which the forecast is based, – it is based on the use of fuzzy mathematical methods. It is shown on its basis how traditional forecasting methods can be successfully upgraded for the case when the initial data are given unclearlyИсследованы тенденции развития промышленных рынков и состояние научной мысли относительно определения понятия «система управления маркетинговой информацией». Установлена необходимость модернизации классической концепции построения системы управления маркетинговой информацией промышленного предприятия. Разработаны классификация информации, поступающей в подсистему прогнозирования и типы прогнозов, которые могут быть получены в подсистеме прогнозирования системы управления маркетинговой информацией промышленного предприятияДосліджено тенденції розвитку промислових ринків та стан наукової думки щодо визначення поняття «система управління маркетинговою інформацією». Встановлено необхідність модернізації класичної концепції побудови системи управління маркетинговою інформацією промислового підприємства. Розроблені класифікація інформації, яка надходить у підсистему прогнозування та типів прогнозів, які можуть бути отримані в підсистемі прогнозування системи управління маркетинговою інформацією промислового підприємств

Modifiers of patients' emergency department care-seeking behavior

Background: Inflow of patients to the emergency departments (ED) is increasing in many parts of the world, including Sweden. At the same time the number of EDs are decreasing. In addition to this, ED inflow is volatile. To some degree this volatility is explicable with variations over the hour of the day, day of the week and season, but a considerable portion of the ED inflow is yet to be explained in order to be able to predict the coming load on EDs. Aim: The overall aim of this thesis is to explore different factors modifying ED inflow. Methods: In four studies, different possible modifiers of ED inflow and modifiers of the patients’ decision to seek ED care was explored. In Study I, laypersons ability to triage trauma cases was investigated in a prospective survey study. In Study II – IV, retrospective observational studies were conducted. Studies II and III explored the impact of online health information seeking and the effect of news media reporting on ED inflow, respectively. In study II, a forecasting model was constructed, including website visits as explanatory variable, Study IV assessed the impact of callers’ sociodemographic background on advice from a telephone advice service (TAS) and compliance to those advices. Results: For Study I, 69 persons participated in the study, who in total triaged 52 % of the cases correctly. There was an over-triage (i.e. case triaged as more serious than it was) in 12.5 % and under-triage in 6.3 % of the cases. In Study II, correlation between a population’s number of visits to a regional website for health information and physical ED inflow was found. The forecasting model in Study II exhibited Mean Absolute Percentage Error of 4.8 %. In Study III, it was shown that news media reporting negativity, expressed as a numeric index, significantly correlated to and partially explained ED inflow. In Study IV, findings were that both the advices given to a caller by the TAS and the caller’s odds of complying to the advice were affected by sociodemographic factors, but that the compliance was also affected by the advice issued. Conclusions: This thesis shows that ED care-seeking behavior is modified by online health information, news media reporting, advices from the TAS and by the individual’s own sociodemographic background. This knowledge can be used to better understand ED care-seeking behavior and to construct better forecasting models of ED inflow