scholarly journals Prediksi Nilai Tukar Dollar (USD) ke Rupiah (IDR) menggunakan Artificial Neural Network

2019 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
Ahmad Saparudin ◽  
Tiya Maulidina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exchange Rates ◽  
Business Strategy ◽  
Training Dataset ◽  
Python Programming Language ◽  
The Future ◽  
Business Forecasting ◽  
Artificial Neural ◽  
Python Programming

Prediction (forecasting) is the activity of predicting events in the future. In terms of business forecasting has many uses, especially for the leadership of the company one of them i.e. to define its business strategy in the future. In this research, carried out the predictions of exchange rates dollar (USD) to Indonesian rupiah (IDR) on 11/03/2019 - 15/03/2019 using artificial neural networks (ANN) with a training dataset from 01/01/2018 - 08/03/2019. Establishment of ANN in the study formed in the Python programming language. Based on the research conducted, a decrease in the price of the exchange rate of USD to IDR on 11/03/2019 – 15/03/2019.

scholarly journals Identificación de figuras geométricas en un sistema de visión basándose en el entrenamiento de una red neuronal artificial convolucional utilizando Python

2019 ◽  
pp. 7-16
Author(s):  
Martha Isabel Aguilera-Hernández ◽  
Jorge Alan Velasco-Marín ◽  
Manuel Ortiz-Salazar ◽  
Jose Luis Ortiz-Simón
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Industry 4.0 ◽  
Vision System ◽  
Support Point ◽  
Process Systems ◽  
Geometric Figures ◽  
Different Types ◽  
Artificial Neural ◽  
Python Programming

The image processing projects through vision systems, are a great didactic support point in the mechatronics career, since they have wide application in the industry in the process lines primarily to perform assembly, inspection, selection and component placement. One of the methods used is to apply artificial neural networks for the identification of images and a factor to analyze is the evaluation of the learning capacities of these networks in the identification of geometric figures. In this article, the training of a convolutional artificial neural network using Python is presented. This type of work is focused on joining projects based on industry 4.0 that may contain link options with process systems based on these technologies. In this work, a vision system based on python programming was made and has its contribution in the libraries that were designed and can be linked to different types of applications within a manufacturing process.

Neural Network Based Prediction Model for Job Applicants

2019 ◽  
Vol 16 (9) ◽  
pp. 3867-3873
Author(s):  
Sourav Thakial ◽  
Bhavna Arora
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Predictive Analytics ◽  
Fuzzy Neural Networks ◽  
Job Applicants ◽  
Support Vector ◽  
The Future ◽  
Artificial Neural

Predictive analytics, a division of the advanced analytics that uses various techniques like machine learning, data mining and so on, to predict the future events. Predictive analytics is summarized with the data collection, modelling, statistics and deployment. It can be used to predict the future possibilities in different areas like business, healthcare, telecom, finance. An effective technique for prediction is Artificial Neural Network. The model accuracy for prediction can be enhanced using neural networks. The model can also be used easily for prediction of output parameters because of its ability to solve the complex computation which are difficult to be solved by other techniques. In this paper, a brief review of Artificial Neural Network used for prediction analysis is presented with various techniques like Multi-Layer Perceptron, T-S Fuzzy Neural Networks, Support Vector Machine, Radial Basis Function Network, Levenberg-Marquardt Algorithm and Back Propagation and their applications are also presented. This paper also presents the neural network-based prediction model for job applicants which is used to predict the jobs of various applicants based on certain parameter ratings.

scholarly journals Well-placement optimisation using sequential artificial neural networks

2017 ◽  
Vol 36 (3) ◽  
pp. 433-449 ◽  
Author(s):  
Ilsik Jang ◽  
Seeun Oh ◽  
Yumi Kim ◽  
Changhyup Park ◽  
Hyunjeong Kang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Global Solution ◽  
Search Space ◽  
Superior Performance ◽  
Training Dataset ◽  
Well Placement ◽  
Artificial Neural

In this study, a new algorithm is proposed by employing artificial neural networks in a sequential manner, termed the sequential artificial neural network, to obtain a global solution for optimizing the drilling location of oil or gas reservoirs. The developed sequential artificial neural network is used to successively narrow the search space to efficiently obtain the global solution. When training each artificial neural network, pre-defined amount of data within the new search space are added to the training dataset to improve the estimation performance. When the size of the search space meets a stopping criterion, reservoir simulations are performed for data in the search space, and a global solution is determined among the simulation results. The proposed method was applied to optimise a horizontal well placement in a coalbed methane reservoir. The results show a superior performance in optimisation while significantly reducing the number of simulations compared to the particle-swarm optimisation algorithm.

scholarly journals APPLICATION OF NEURAL NETWORKS TO DETERMINE THE COORDINATES OF THE SEAT OF FIRE BY MULTIPOINT ELECTRO-OPTICAL SYSTEM

2021 ◽  
pp. 248-251
Author(s):  
Д.Ф. Пирова ◽  
Б.Э. Забержинский ◽  
А.Г. Золин
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
Network Models ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Python Programming Language ◽  
Python Programming

Статья посвящена исследованию методов проектирования интеллектуальных информационных систем и применение моделей искусственных нейронных сетей для диагностического прогнозирования развития пневмонии посредством анализа рентгеновских снимков. В этой работе основное внимание уделяется классификации пневмонии и туберкулеза - двух основных заболеваний грудной клетки - на основе рентгеновских снимков грудной клетки. Данное исследование проводилось при помощи открытой нейросетевой библиотеки Keras и языка программирования Python. Система дает пользователю заключение о том, болен он или нет, тем самым помогая врачам и медицинскому персоналу принять быстрое и информированное решение о наличии заболевания. Разработанная модель, может определить, является ли рентгеновский снимок нормальным или имеет отклонения, которые могут быть пневмонией с точностью 94,87%. Полученные результаты указывают на высокую эффективность применения нейронных сетей при диагностировании пневмонии по рентгеновским снимкам. This paper is devoted to the study of methods of designing intellectual information systems and neural network models application on diagnostic prediction of pneumonia development by X-ray images analysis. This article focuses on the classification of pneumonia and tuberculosis - the two main chest diseases - based on chest x-rays. This study was carried out using the Keras open neural network library and the Python programming language. System returns user a conclusion whether the patient is ill or not helping medical staff to make a quick and informed decision about the presence of the disease. The developed model can determine is the X-ray image normal or has anomalies that can be pneumonia with accuracy up to 94.87%. The results obtained indicate the high performance of the applying neural networks in the diagnosis of pneumonia by X-ray images.

scholarly journals The concept of a software and analytical complex of the educational process based on ontology and artificial neural networks

2021 ◽  
Vol 11 (3) ◽  
pp. 339-350
Author(s):  
V.V. Antonov ◽  
◽  
G.G. Kulikov ◽  
L.A. Kromina ◽  
L.E. Rodionova ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Professional Education ◽  
Intelligent System ◽  
Educational Process ◽  
Training Dataset ◽  
Network Node ◽  
Ontological Approach ◽  
Artificial Neural

Effective management of the learning process of additional professional education programs at the university is condi-tioned by providing unique needs of students as requested by employers in the real sector of the economy in accord-ance with the selected competencies and areas of training. At the same time, when solving a number of tasks, the algo-rithm of which is unknown, there are more and more actively developed and implemented systems using artificial neu-ral networks, which allow classifying and analyzing data for making managerial decisions. Based on such widespread use of artificial neural networks, there is an increasing need for systematization of data to improve the performance of software analytical complex processing, storage, search and analysis of data, for the implementation of training pro-grams at all stages of the life cycle, taking into account uncertainty. The developed software-analytical complex is pre-sented on the example of a model of an intelligent system used to control and analyze the acquired competencies of students, built on the basis of an ontological approach, a model of continuous quality improvement, which makes it possible to determine the interaction of business processes, their sequence and performance benchmarks. To imple-ment this theory, a neural network node scheme of a software analytic complex capable of data-driven learning has been developed. The presented scheme of a neural network node assumes the use of a supervised learning algorithm when a training dataset arrives at the input.

scholarly journals Mathematical Problems of Artificial Intelligence and Artificial Neural Networks

2021 ◽  
pp. 6-14
Author(s):  
В. Б. Бетелин ◽  
В. А. Галкин
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Training Dataset ◽  
Interpolation Theory ◽  
The Best Approximation ◽  
Mathematical Problems ◽  
Artificial Neural

Предложен общий топологический подход для анализа искусственных нейронных сетей на основе симплициальных комплексов и свойств аппроксимации непрерывных отображений их симплициальными приближениями. Выявлены существенные для этого класса задач явления вычислительной неустойчивости, связанной с общими проблемами некорректных задач в гильбертовом пространстве и методами их регуляризации, типичными для обработки Big Data. Сформулированы критерии точности и применимости моделей искусственных нейронных сетей, рассмотрены примеры их реализации на основе теории интерполяции функций. Развитие идей П.Л.Чебышёва о наилучшем приближении служит отправной точкой для широкого класса математических исследований по оптимизации обучающих наборов для построения ИНС. We propose a general topological approach to the analysis of artificial neural networks using simplicial complexes and the approximation of continuous mappings with simplicial ones. The essential properties of numerical instability in such problems were identified. It is associated with ill-posed problems in Hilbert space and regularization methods typically applied to Big Data processing. We formulated the criteria of artificial neural network accuracy and applicability and included some implementation examples based on the interpolation theory. Advancing P.L. Chebyshev’s ideas about the best approximation may be an entry point to various mathematical research on artificial neural network training dataset optimization.  

scholarly journals Neural Network Programming in Python

2019 ◽  
Vol 8 (6S4) ◽  
pp. 373-377
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Programming Language ◽  
Function Approximation ◽  
Learning Rule ◽  
Network Programming ◽  
Short Introduction ◽  
Python Programming Language ◽  
Python Programming ◽  
Backpropagation Learning

In this paper a basic introduction to neural networks is made. An emphasis is given on a two layer perceptron used extensively for function approximation. The backpropagation learning rule is than briefly introduced. A short introduction into Python programming language is made and a program for the perceptron design is written and discussed in some detail. The “neurolab” library is used for this purpose.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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