International Stock Index Prediction Using Artificial Neural Network (ANN) and Python Programming

Safari, Ashkan; Ghavifekr, Amir Aminzadeh

doi:10.1109/iccia52082.2021.9403580

Cited by 23 publications

(10 citation statements)

References 8 publications

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“…Artificial Neural Network (ANN) merupakan salah satu metode terbaik dalam memprediksi, termasuk memprediksi kebutuhan obat [4]. Cara kerja yang dilakukan dengan mengadopsi jaringan syaraf pada ANN dapat dilakukan dengan beragam model seperti Multilayer Perceptron (MLP), Deep Neural Network (DNN), Generalized Regeression Neural Network (GRNN) atau Monotonic MLP [5], [6]. Namun Neural Network memiliki masalah dalam penentuan jumlah neuron dan hidden layer optimal pada model arsitekturnya [7].…”

Section: Pendahuluanunclassified

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Khairati

Putra²

2022

jsisfotek

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Prediction of the amount of drug use at public health centers is needed to ensure the availability of drugs for patients in service quality management. A good prediction of the amount of medicine needed helps the quality of development planning in the health sector. Scientific developments in the field of Artificial Intelligence (AI) deliver a variety of the best techniques for making predictions. By adopting the workings of neural networks (neurons) in the human brain or Artificial Neural Network (ANN), the Backpropagation Neural Network (BPNN) algorithm is one of the best algorithms in making predictions, including predicting drug use in health services. The problem of this research is how to design the best architectural model such as the number of neurons in the input layer, hidden layer and other parameters so as to produce predictions with optimal accuracy. This study aims to develop an ANN architectural design with the Backpropagation algorithm to predict the need for drug use. The data used is data on drug use reports from 2015 to 2021 at the Andalas Community Health Center (Puskesmas) Padang City. The steps taken to predict are; collect data, pre-process data and perform analysis, design ANN architecture, make predictions. Learning using the backpropagation algorithm through the initial weight initialization process, activation stage, weight training (weight change) and iteration stage. The proportion of the amount of data used for training is 70% data and 30% for testing data. The results of this study indicate that the best ANN architecture is 12-12-1 with an accuracy of predicting the quantity of drug use reaching 97.87% for paracetamol with a Mean Absolute Percentage Error (MAPE) of 2.13%. The prediction results become a reference for the Puskesmas and the Health Office for service planning and development.

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Section: Pendahuluanunclassified

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Khairati

Putra²

2022

jsisfotek

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“…The BPNN model outperformed the other models, according to the research. Another study [ 37 ] used the ANN approach to examine stock price prediction. The RMSE measures were used to evaluate the model’s performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on the Pakistan stock market using a new stock crisis prediction method

Javid

Ghazali²,

Syed³

et al. 2022

PLoS ONE

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A Stock market collapse occurs when stock prices drop by more than 10% across all main indexes. Predicting a stock market crisis is difficult because of the increased volatility in the stock market. Stock price drops can be triggered by a variety of factors, including corporate results, geopolitical tensions, financial crises, and pandemic events. For scholars and investors, predicting a crisis is a difficult endeavor. We developed a model for the prediction of stock crisis using Hybridized Feature Selection (HFS) approach. Firstly, we went for the suggestion of the HFS method for the removal of stock’s unnecessary financial attributes. The Naïve Bayes approach, on the other hand, is used for the classification of strong fundamental stocks. In the third step, Stochastic Relative Strength Index (StochRSI) is employed to identify a stock price bubble. In the fourth step, we identified the stock market crisis point in stock prices through moving average statistics. The fifth is the prediction of stock crises by using deep learning algorithms such as Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Root Mean Square Error (RMSE), Mean Squared Error (MSE) and Mean Absolute Error (MAE) are implemented for assessing the performance of the models. The HFS-based GRU technique outperformed the HFS-based LSTM method to anticipate the stock crisis. To complete the task, the experiments used Pakistan datasets. The researchers can look at additional technical factors to forecast when a crisis would occur in the future. With a new optimizer, the GRU approach may be improved and fine-tuned even more.

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“…Python is an interpreted programming language whose philosophy emphasizes the readability of its code. It is a multi-paradigm programming language, since it partially supports object-oriented and imperative programming [18].…”

Section: Pythonmentioning

confidence: 99%

Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

Huamaní¹,

Cunyarachi²

2021

IJETAE

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Due to the pandemic caused by Covid-19, daily life has changed significantly. For this reason, biosecurity measures have been implemented to prevent the spread of the virus as an effective way to reactivate economic activities. In this sense, the present paper focuses on real-time face detection as a measure of control at the entrance to an entity, thus avoiding the spread of the virus while recognizing the identity of workers despite the use of masks and thus reducing the risk of entry of individuals outside the organization. Therefore, the objective is to contribute to the security of a company through the application of machine learning methodology. The selection of methodology is justified due to the adaptation of the same according to the interests of this project. Consequently, algorithms were used in a progressive manner, obtaining as a result the control system that was intended, since each particularity of the face of the individual was recognized in relation to its corresponding identification. Finally, the results of this article benefit the security of organizations regardless of their field or sector. Keywords— Control, Detection, Facial Recognition, Facial Mask, Face recognition, Machine learning.

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International Stock Index Prediction Using Artificial Neural Network (ANN) and Python Programming

Cited by 23 publications

References 8 publications

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Study on the Pakistan stock market using a new stock crisis prediction method

Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

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