International Journal of Emerging Technology and Advanced Engineering
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171
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1
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Published By IJETAE Publication House

2250-2459

Author(s):  
Satria Wiro Agung ◽  
◽  
Kelvin Supranata Wangkasa Rianto ◽  
Antoni Wibowo

- Foreign Exchange (Forex) is the exchange / trading of currencies from different countries with the aim of making profit. Exchange rates on Forex markets are always changing and it is hard to predict. Many factors affect exchange rates of certain currency pairs like inflation rates, interest rates, government debt, term of trade, political stability of certain countries, recession and many more. Uncertainty in Forex prediction can be reduced with the help of technology by using machine learning. There are many machine learning methods that can be used when predicting Forex. The methods used in this paper are Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Support Vector Regression (SVR). XGBOOST, and ARIMA. The outcome of this paper will be comparison results that show how other major currency pairs have influenced the performance and accuracy of different methods. From the results, it was proven that XGBoost outperformed other models by 0.36% compared to ARIMA model, 4.4% compared to GRU model, 8% compared to LSTM model, 9.74% compared to SVR model. Keywords— Forex Forecasting, Long Short Term Memory, Gated Recurrent Unit, Support Vector Regression, ARIMA, Extreme Gradient Boosting


Author(s):  
Robert Cerna Duran ◽  
◽  
Brian Meneses Claudio ◽  
Alexi Delgado

The increase in garbage production today is due to the exponential growth of the population worldwide, due to the fact that thousands of tons of garbage are generated daily around the world, but the mismanagement that gives them has become an environmental problem since 33% of all the garbage generated is not recycled, for that reason it is estimated that within the next three decades the amount of waste worldwide will increase to 70%. That is why in the present research work it is proposed to make an intelligent system based on the Internet of Things (IoT) that allows monitoring the garbage containers in real time representing with percentages the state of these containers and these can be collected in time by garbage trucks, and thus avoid the increase of garbage in the streets and the various types of problems that these would cause. As a result, it was obtained that the System does comply with the established conditions because it allows to monitor in real time representing by percentages the state of the garbage container, which indicates 40% as almost full and 80% indicates that it is already available for collection. Finally, it is concluded that using the Garbage Container Monitoring System will allow to better optimize the collection process and, in addition, the problems that are usually perceived today due to the amount of garbage that are registered in the streets will decrease. Keywords-- Internet of Things; Intelligent system; Real time; Environmental Problem; Monitoror; Percentage.


Author(s):  
Dan Alva Castillo ◽  
◽  
Brian Meneses Claudio ◽  
Alexi Delgado

Citizen insecurity is a reality with which we must coexist, the cities of Latin America are among the most violent and insecure in the world. According to the statistics of the National Police of Peru, they report that by 2017 the crimes of theft or robbery were the most common because they had a monthly average of 15348 complaints, equivalent to 66.9% of the total crimes nationwide. The INEI (National Institute of Statistics and Informatics) revealed that in the same year the district of Carabayllo obtained 1.85% of the total complaints in Metropolitan Lima, occupying the 17th place in the ranking of districts with the highest number of complaints for this crime. That is why in the present research work a way to counteract these criminal acts was proposed, the first thing is to be located within the operating range of the RF module, so that the remote transmission control can activate it, the RF module will be connected to the power outlet and the siren. The siren will oversee persuading the criminal, in addition to alerting the neighbors about the events that are happening. It was obtained as a result that the system fulfills its purpose, it can be alerted in real time about some attempted theft or at the instant of a threat situation, only by pressing the button of the remote control we can persuade the criminals either by scaring them with the sound or with the help of the neighbors. Keywords-- Citizen insecurity, RF Module, Siren, Remote Control


Author(s):  
Muhammad Zuhri Infusi ◽  
◽  
Gede Putra Kusuma ◽  
Dewi Annizah Arham

Local Government Revenue or commonly abbreviated as PAD is part of regional income which is a source of regional financing used to finance the running of government in a regional government. Each local government must plan Local Government Revenue for the coming year so that a forecasting method is needed to determine the Local Government Revenue value for the coming year. This study discusses several methods for predicting Local Government Revenue by using data on the realization of Local Government Revenue in the previous years. This study proposes three methods for forecasting local Government revenue. The three methods used in this research are Multiple Linear Regression, Artificial Neural Network, and Deep Learning. In this study, the data used is Local Revenue data from 2010 to 2020. The research was conducted using RapidMiner software and the CRISP-DM framework. The tests carried out showed an RMSE value of 97 billion when using the Multiple Linear Regression method and R2 of 0,942, the ANN method shows an RMSE value of 135 billion and R2 of 0.911, and the Deep Learning method shows the RMSE value of 104 billion and R2 of 0.846. This study shows that for the prediction of Local Government Revenue, the Multiple Linear Regression method is better than the ANN or Deep Learning method. Keywords— Local Government Revenue, Multiple Linear Regression, Artificial Neural Network, Deep Learning, Coefficient of Determination


Author(s):  
Wilhen Huaman Hinostroza ◽  
◽  
Brian Meneses Claudio ◽  
Alexi Delgado

One of the big problems in the city of Cerro de Pasco is the air pollution caused by the mining activity that occurs in this area, this activity generates a total of 3737 metric tons per year of particulate matter, which are thrown into the environment in an alarming way, thus reaching that this particulate matter can lodge inside the organism of the inhabitants permanently, causing in them diseases in the respiratory system, thus affecting the most vulnerable population, producing in them infections in the respiratory tract that can even carry those to death. Nowadays the teams that carry out the monitoring of air quality are restricted only to the academic and governmental sphere, so much so that the population does not know the degree of air pollution. For this reason, it is proposed to carry out a system to measure the level of air pollution taking as main data, the measurement of particulate matter and the emission of carbon monoxide, the data that can be recovered from these measurements to be able to compare them with the parameters dictated by the Ministry of the Environment. As a result, an autonomous system was obtained, with which the level of particulate matter, possible toxic gases, and the measurement of the level of carbon monoxide can be measured, all these pollutants that could be in the environment, all these measurements are constant, thus leading the population to become aware of the level of quality of the environment where they live. Keywords- Environmental pollution, articulated matter, carbon monoxide, toxic gases, mining activity, air quality


Author(s):  
Jaya Gupta ◽  
◽  
Sunil Pathak ◽  
Gireesh Kumar

Image classification is critical and significant research problems in computer vision applications such as facial expression classification, satellite image classification, and plant classification based on images. Here in the paper, the image classification model is applied for identifying the display of daunting pictures on the internet. The proposed model uses Convolution neural network to identify these images and filter them through different blocks of the network, so that it can be classified accurately. The model will work as an extension to the web browser and will work on all websites when activated. The extension will be blurring the images and deactivating the links on web pages. This means that it will scan the entire web page and find all the daunting images present on that page. Then we will blur those images before they are loaded and the children could see them. Keywords— Activation Function, CNN, Images Classification , Optimizers, VGG-19


Author(s):  
Akselrod Roman ◽  
◽  
Shpakov Andrii ◽  
Ryzhakova Galyna ◽  
Tetyana Honcharenko ◽  
...  

This study is devoted to the problem of digital transformation in the construction industry. To solve the proposed emerging approach for integrating information flows based on Building information modeling (BIM). This research describes the advanced methodology for creating a unified information model in construction, which combines architectural, design, engineering, cost and depreciation models of a construction object, raises a number of problems of reorganizing business processes at enterprises. The novelty of the research lies in the model of adapted common data environment based on life cycle of a construction project by stages of implementation and emergence of information. The proposed architecture is a platform for intelligent parametric modeling as intelligent CAD, which allows linking all elements of the model "Digital Enterprise".The result of the study is a conceptual mechanism for improving the efficiency of enterprise management processes based on the architectural approach in terms of digitalization of construction. Emerging technology in the study is the architecture of the intelligent decision support system in construction based on 5D BIMtechnology and Digital Enterprise. The main advantage of the proposed approach is the possibility of multiple reuse of information by all participants in the development and implementation of an investment and construction project without changes and distortions of data about the construction object. Keywords — Building Information Modeling, BIM, Digital Enterprise, Common Data Environment, life cycle of construction project, digitalization of construction


Author(s):  
Adoum Traoré Ndama ◽  
◽  
Elysée Obame Ndong ◽  
Yves Constant Mombo Boussougou ◽  
Grace Jourdain Tsoumou ◽  
...  

Medium-voltage motors dedicated to the applications of traction operate in an environment with strong multi-physics constraints. Electrical insulation of these engines is a complex multi-layered impregnated system which requires a given number of steps during the manufacturing process. In the present study, we theoretically investigated the potential manufacturing insulation defects of traction motors in low frequency domain. The aim is to assess the theoretical ability of dielectric spectroscopy method for the detection of these defects and the extension of the method to others insulation systems. The theoretical study is based on numerical modelling and simulation achieved by using Comsol Multiphysics software. In our numerical modelling the properties of the main dielectric elementary materials are frequency–dependent. The identification of each potential defect is carried out by comparing its equivalent capacitance and dissipation loss spectra with the characteristics of insulation without defect. As the results, all artificial defects are identifiable with a specific relative deviation. The detection of all the defects analysed will need a measuring device with resolution of 0.4%. Keywords—AC electric motors, Capacitance, dielectric, dissipation factor, composite insulation, numerical modelling.


Author(s):  
Elizalde L. Piol ◽  
◽  
Luisito Lolong Lacatan ◽  
Jaime P. Pulumbarit

The use of Linear Regression in predicting enrolment has been shown to be beneficial, although it varies with various datasets and attributes; varying weights of the correlation of the attributes can be discarded if they do not impact the prediction. Data collecting had grown since prior investigations, resulting in a more complicated dataset with many varieties. As a result of the data being created by multiple clerks, cleaning and combining proved tough; nonetheless, the fundamental parameters remain intact. Different algorithms were examined but Linear Regression obtained the highest accuracy with a 12.398 percentage for the absolute error and a root mean squared of 26.936 to create a tangible model to anticipate the enrolment of Region IVA CALABARZON in the Philippines. This demonstrates that it was 2.067 percentage points more than the prior research.


Author(s):  
Irina Alekseevna Vorobeva ◽  
◽  
Alexander Vladimirovich Panov ◽  
Alexander Arkadyevich Safronov ◽  
Alexey Ivanovich Sazonov

The idea of cloud computing is not a new one, it has been developed and discussed for many years. Cloud computing is a model which allows to get access to the network upon request from the set of adjustable computing services, such as infrastructure, applications and storages. Cloud services and data storage products allow their users to store and share any type of document and file from any device connected to Internet. There are several types of cloud services, which can be subdivided into: SaaS (Software as a Service), PaaS (Platform as a Service), IaaS (Infrastructure as a Service). Besides, there are several deployment models, such as public, residential, hybrid or community cloud. Cloud computing models are based on modern process paradigm, which offers new alternatives to the companies of various ranges for implementation of innovative business models. With the help of these new business models small companies will be able to use cloud computing platforms and to increase gradually their computation capacities and data storage capacities depending on the requirements in real time mode, which creates a unique opportunity for market competition. Keywords— cloud computing, IaaS, OpenStack, PaaS, SaaS.


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