Approaches to the choice of tools for constructing a methodology for learning to work with Big Data

The article actualizes the need for teaching students to work with Big Data technologies. Big Data is a promising and fundamental industry that requires a large number of qualified specialists in various fields. The aim of the work is to describe the concept of determining a set of hardware, software, algorithmic and methodological tools (taking into account the contingent of students and the capabilities of the educational institution) for building a methodology for teaching a discipline related to the study of Big Data processing methods. There are two main sectors of stakeholders who need specialists in the field of Big Data. A detailed comparative analysis of software solutions that support Big Data processing is carried out. The article describes the methodology for constructing a course for teaching students technologies for processing and analyzing Big Data. A plan for organizing a lecture course and laboratory practice with consideration of subtasks is proposed for students to perform during training. The composition and methodology of independent work of students in the discipline related to the study of Big Data, using a learning management system such as Moodle, are discussed. An example of implementing data processing by means of the RapidMiner Studio package using a multi-layer neural network training algorithm using the error back propagation method is presented.

Building robust neural networks using different loss functions

The paper considers the problem of building robust neural networks using different robust loss functions. Applying such neural networks is reasonably when working with noisy data, and it can serve as an alternative to data preprocessing and to making neural network architecture more complex. In order to work adequately, the error back-propagation algorithm requires a loss function to be continuously or two-times differentiable. According to this requirement, two five robust loss functions were chosen (Andrews, Welsch, Huber, Ramsey and Fair). Using the above-mentioned functions in the error back-propagation algorithm instead of the quadratic one allows obtaining an entirely new class of neural networks. For investigating the properties of the built networks a number of computational experiments were carried out. Different values of outliers’ fraction and various numbers of epochs were considered. The first step included adjusting the obtained neural networks, which lead to choosing such values of internal loss function parameters that resulted in achieving the highest accuracy of a neural network. To determine the ranges of parameter values, a preliminary study was pursued. The results of the first stage allowed giving recommendations on choosing the best parameter values for each of the loss functions under study. The second stage dealt with comparing the investigated robust networks with each other and with the classical one. The analysis of the results shows that using the robust technique leads to a significant increase in neural network accuracy and in a learning rate.

ESTIMATING STOCK RETURNS USING ARTIFICIAL NEURAL NETWORKS: AN EXPERIMENTAL DESIGN WITH AN EVIDENCE FROM IRAQ STOCK EXCHANGE

The research aims to estimate stock returns using artificial neural networks and to test the performance of the Error Back Propagation network, for its effectiveness and accuracy in predicting the returns of stocks and their potential in the field of financial markets and to rationalize investor decisions. A sample of companies listed on the Iraq Stock Exchange was selected with (38) stock for a time series spanning (120) months for the years (2010_2019). The research found that there is a weakness in the network of Error Back Propagation training and the identification of data patterns of stock returns as individual inputs feeding the network due to the high fluctuation in the rates of returns leads to variation in proportions and in different directions, negatively and positively.

RLIM: A Recursive and Latent Infection Model for COVID-19 Prediction and Turning Point in United States

Initially found at Hubei, Wuhan and identified as a novel virus of coronavirus family by WHO, COVID-19 has spread worldwide with an exponentially speed, causing millions of death and public fear. Currently, COVID19 has brought a secondary wave within U.S., India, Brazil and other parts of the world. However, its transmission, incubation, and recovery processes are still unclear from the medical, mathematical and pharmaceutical aspects. Classical Suspect-Infection-Recovery model has limitations to describe the dynamic behavior of COVID-19. Hence, it becomes necessary to introduce a recursive, latent model to predict the number of future COVID-19 infected cases in U.S. In this article, a dynamic model called RLIM based on classical SEIR model is proposed to predict the number of COVID-19 infections with a dynamic secondary infection rate ω in assumption. An intermediate state called SI is introduced between recovery and infection statues to record the number of secondary infected cases from a latent period of recovery. Compared with other models, RLIM fits historical recovery cases and utilizes them to predict future infections. Because RLIM utilizes multiple information sources, and provides error back propagation schematics, it is reasonable to assert that its predictions are more accurate and persuasive. Projections of four U.S. COVID19 states show that with the secondary infectious rate ω varies from 0.01 to 0.3 within a latent period of 14 days chosen, RLIM can predict the newly infected number from January 15 to February 15, 2021 with AFER lower to 14%. It also successfully estimates the turning point of New Yorks infections in January 2021, based on current data records.

METHODOLOGY FOR DETERMINING THE TIMING OF THE MANUFACTURE OF NEW ORDERS IN SINGLE PRODUCTION AND REDUCING THE RISKS OF PRODUCTION ACTIVITIES

The article is devoted to the development and testing of the methodology for assigning the priority of technological operations for a number of orders and assessing the risks of setting the price and deadlines for new orders, taking into account it’s complexity and priority. It is noted the main problems of high-tech single production and the risks they create. To solve the identified problems on the basis of the analysis, a complex method was chosen, consisting of a combination of the use of simulation modeling and neural network modeling. The neural network model is based on the statistics of the production times of various parts in this area. The type of neural network model selected by the Fitting app is trained by the network using the Levenberg-Marquardt error back propagation algorithm. The simulation model of the production site is built in the Tecnomatix Plant Simulation program. As a result, thanks to the developed methodology, it became possible to obtain information about a new order before it was directly introduced into production, to diversify risks before they caused damage, as well as to improve reputation. In conclusion, as an example, the addition of a new part to other parts already being produced is given, and the time of its production is calculated.

Artificial Neural Network Approach for Solving Fuzzy Fractional order initial value problems under H-differentiability

This paper aims to solve the celebrated Fuzzy Fractional Differential Equations (FFDE) using an Artificial Neural Network (ANN) technique. Compared to the integer order differential equation, the proposed FFDE can better describe several real application problems of various physical systems. To accomplish the aforementioned aim, the error back propagation algorithm and a multi-layer feed forward neural architecture are utilized using the unsupervised learning in order to minimize the error function as well as the modification of the parameters such as weights and biases. By combining the initial conditions with the ANN, output provides an appropriate approximate solution of the proposed FFDE. Then, two illustrative examples are solved to confirm the applicability of the concept as well as to demonstrate both the precision and effectiveness of the developed method. By comparing with some traditional methods, the obtained results reveals a close match that confirms both accuracy and correctness of the proposed method.

Research on residual GM optimization based on PEMEA-BP correction

With the advantages of small samples and high accuracy, Grey Model (GM) still has two major problems need to be addressed, high input data requirements and large margin of error. Hence, this paper proposes an algorithm based on Populational Entropy Based Mind Evolutionary Algorithm-Error Back Propagation Training Artificial Neural Algorithm to modify GM residual tail, which will not only keep the advantages of GM, but also expand its scope of use to various non-linear and even multidimensional objects. Meanwhile, it can avoid defects of other algorithms, such as slow convergence and easy to fall into the local minimum. In small samples data experiments, judging from SSE, MAE, MSE, MAPE, MRE and other indicators, this new algorithm has significant advantage over GM, BP algorithm and combined genetic algorithm in terms of simulation accuracy and convergence speed.

Drug Stock Prediction at Balige HKBP Hospital Using Adaptive Neuro-Fuzzy Inference System

Currently the purchases of drugs at Instalasi Farmasi RSU (IFRS) HKBP Balige are based on the examination of the amount of drugs usage. The purchases of drugs based on the examination of the amount of drugs usage cause frequent unplanned drugs purchases that must be hastened (cito) and purchases to other pharmacies. The purchases of cito and purchases to other pharmacies will inflict a financial loss to the patients, because when IFRS makes drugs purchases of cito or to other pharmacies, the cost of the drugs will be more expensive. Therefore, in this research, a prediction of drugs stock in IFRS HKBP Balige using Adaptive Neuro Fuzzy Inference System (ANFIS) will be carried out. ANFIS is a combination of Least Square Estimator (LSE) and Error Back Propagation (EBP) algorithms. ANFIS consists of forward pass and the backward pass learning. The sample data used to predict drugs stock in this research is data of drugs sales at the IFRS HKBP Balige from 2013 to 2015. From the results of drugs stock prediction research with ANFIS, obtained that number of errors of ANFIS model is 5.52%. Based on MAPE accuracy level evaluation, number of errors have an excellent rate so that it can be concluded that the predicted results of the drugs stock are good.