Stock market prediction in financial and commodity markets is a major challenge for speculators, investors, and companies but also profitable with an accurate prediction. Thus, obtaining accurate prediction results be...
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Stock market prediction in financial and commodity markets is a major challenge for speculators, investors, and companies but also profitable with an accurate prediction. Thus, obtaining accurate prediction results becomes extremely important especially while the stock market is essentially volatile, nonlinear, complicated, adaptive, nonparametric and unpredictable in nature. This study aims to forecast the opening and closing stock prices of 42 firms listed in Istanbul Stock Exchange National 100 Index (ISE-100) using well-known machine learning methods, Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) models and deep learning algorithm, Long Short Term Memory (LSTM) by comparing their forecasting performances. The analysis includes 9 years of data from 01.01.2010 to 01.01.2019. For each firm 2249 data for the opening and 2249 for the closing stock prices were established as daily data sets. Forecasting performance of these methods was evaluated by applying different criteria for each model: root mean squared error (RMSE), mean squared error (MSE) and R-squared (R2). The results of this study show that MLP and LSTM models become advantageous in estimating the opening and closing stock prices comparing to SVM model.
The use of agents to develop network based applications is well accepted but its wide scale adoption by the mainstream *** is yet to be *** is clear that simpler and easier-to-use tools are required for agent based ne...
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ISBN:
(纸本)0780370104
The use of agents to develop network based applications is well accepted but its wide scale adoption by the mainstream *** is yet to be *** is clear that simpler and easier-to-use tools are required for agent based network computing to become *** paper considers a number of approaches that can facilitate such simplification and examines their implementation in an easy to use system called SASH that uses the high level interpretative language *** performance implications of using such a high level language are considered.
In many schools and colleges, teachers record the students' attendance in an attendance register manually. Later this data is entered in to a computer and the aggregate percentage of students' attendance is ca...
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ISBN:
(纸本)9781509046218
In many schools and colleges, teachers record the students' attendance in an attendance register manually. Later this data is entered in to a computer and the aggregate percentage of students' attendance is calculated. This method results in duplication of work and increases manpower requirements. In this paper, an attendance recording and consolidation system (ARCS) is implemented using Arduino and Raspberry Pi microcontroller boards. Arduino is used to implement the attendance recording device (ARD), whereas Raspberry Pi is used as a Web server. ZigBee technology is used for transmitting the attendance from the ARD to the server. A web page is created by using PHP language to display the aggregate percentage of attendance of each student corresponding to various classes in the institution. The advantages of this method are user friendly, secure and affordable.
With the increasing degree of informatization in today's society,the presentation of problems has become more complex,which puts forward higher requirements for people's ability to solve *** is a popular langu...
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With the increasing degree of informatization in today's society,the presentation of problems has become more complex,which puts forward higher requirements for people's ability to solve *** is a popular language recently,and it is very popular among developers because of the many mature libraries that are encapsulated in *** can use related libraries in python and use open-source related libraries for algorithm *** main purpose of this paper is to study the optimization platform of the model based on *** paper mainly analyzes the characteristics of the python language and the structure of python programming,and uses the relevant database of python to realize the modeling *** experiment shows that the accuracy of the decision tree model is 96.94 %,the accuracy of the KNN classification model is 89.05%.
Home automation means controlling of home functions and features automatically and sometimes remotely using one or more computers. An automated home is also called as a smart home. Speech based home automation uses hu...
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ISBN:
(纸本)9781509046218
Home automation means controlling of home functions and features automatically and sometimes remotely using one or more computers. An automated home is also called as a smart home. Speech based home automation uses human voice commands to operate the electrical appliances in the home. It is very useful for human beings especially for elderly and physically handicapped people. In this paper, we present the implementation details of two schemes for speech based home automation and control. The first scheme uses the Bluetooth technology for controlling of electrical appliances when we are at home. It uses a HC-05 Bluetooth module and Arduino Bluetooth controller mobile application for switching on or off the appliances. The second scheme uses GSM/GPRS technology for controlling the electrical appliances. The developed system also alerts the user about any intrusion into the house when we are away from the home. This system is implemented on ARM11 Raspberry Pi microcontroller board. python integrated development environment (IDE) is used for developing the necessary software. Relays and bulbs are used as load to demonstrate the working of the prototype system. Home automation system gives accessibility, comfort, energy efficiency, security by providing control and monitoring of appliances, security surveillance.
This paper presents a power system analysis tool, called DOME, entirely based on python scripting language as well as on public domain efficient C and Fortran libraries. The objects of the paper are twofold. First, th...
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ISBN:
(纸本)9781479913022
This paper presents a power system analysis tool, called DOME, entirely based on python scripting language as well as on public domain efficient C and Fortran libraries. The objects of the paper are twofold. First, the paper discusses the features that makes the python language an adequate tool for research, massive numerical simulations and education. Then the paper describes the architecture of the developed software tool and provides a variety of examples to show the advanced features and the performance of the developed tool.
The inclusion of skewed statistical distributions in the stochastic process for composing music contributes to clarify the effect of some pitch patterns for generating new pieces of music. The aim of this study was to...
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The inclusion of skewed statistical distributions in the stochastic process for composing music contributes to clarify the effect of some pitch patterns for generating new pieces of music. The aim of this study was to compare and measure the effect of using skewed statistical distributions instead of using the common uniform distribution applied in Markov chains. We applied an explorative data analysis related to the Shannon entropy and the Monte Carlo approach, to generate and compare different stochastic realizations associated with a particular exponential and uniform distributions and two types of note selection based on intervals. Findings suggested that the presence of an exponential statistical distribution may generate a wide range of entropy values that could be associated with the diversity concept in complex systems. On the other hand, the presence of the uniform distribution may generate a narrow range of entropy values possibly associated with less diverse behaviors in simple systems. Therefore, the use of skewed statistical distributions, in particular the exponential, in the stochastic process for musical composition sets ground for the emergence of articulated musical patterns.
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