Foreign exchange prediction based on indices and commodities price using convolutional neural network

Rassetiadi, Rian; Suharjito, Suharjito

doi:10.11591/ijeecs.v18.i1.pp494-501

Cited by 5 publications

(5 citation statements)

References 13 publications

(16 reference statements)

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“…In this regard, max pooling is an algorithm used to reduce calculation problems and increase calculation speed [8]. Upon completion of the feature extraction process using filters and max pooling [27,28], the output will be in the form of a dataset that must be pre-processing through re-arrangement to be in the form of one-dimension vector. From the CNN structure, the neurons of the previous layer i.e.…”

Section: Materials and Methods 21 Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Deep image mining for convolution neural network

Jasm

Hamad

Alrawi

2020

IJEECS

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<p>Image mining is the method of searching and discovering valuable information and knowledge from a huge image dataset. Image mining is based on data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining handled with the hidden information extraction, an association of image data and additional pattern which are not clearly visible in the image. Choosing the proper objects or the feature of the image to be suitable for image mining process is the main challenge would face the programmer. The process includes fine out the most efficient routes at a shorter time and saving the users effort. The main objective of this paper is to design and implement the image classification system with a higher performance, where a CIFAR-10 data set is used to train and testing classification models using CNN. A convolutional neural network is trustworthy, and it could lead to high-quality results. The high accuracy of 98% has been obtained using deep convolutional neural network (DCNN).</p>

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Section: Materials and Methods 21 Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Deep image mining for convolution neural network

Jasm

Hamad

Alrawi

2020

IJEECS

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“…Based on the experimental results, deep learning AutoML was found to have the best accuracy rate, which ranges from 81% to 92% across all companies, and accuracy after DNN optimization using PSO varies from 73% to 82% in all companies. Rassetiadi and Suharjito [18] examine the external factors that can influence forecast results, looking for the relationship between the value of indices such as S and P500 (standard and poor's) and the value of commodities such as gold and silver in the process EUR/USD prediction. When comparing the mean squared error (MSE) values, it turned out that the best combination was a combination of the FTSE100 (financial times stock exchange) and natural gas values.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Forex prediction model using multiple regression, simulated annealing, reinforcement learning and technical analysis

Jamali

Chihab

García‐Magariño

et al. 2023

IJ-AI

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Foreign exchange market refers to the market in which currencies from around the world are traded. It allows investors to buy or sell a currency of their choice. Forex interests several categories of stakeholders, such as companies that carry out international contracts, large institutional investors, via the main banks, which carry out transactions on this market for speculative purposes. One of the most important aspects in the Forex market is knowing when to invest by buying, selling, and this through the recorded trend of a currency pair, but given the characteristics of the Forex market namely its chaotic, noisy and not stationary nature, prediction becomes a big challenge for traders when it comes to predicting accuracy. This paper aims to predict the right action to be taken at a certain moment through the development of a model that combines multiple techniques such multiple regression, simulated annealing meta-heuristics, reinforcement learning and technical indicators.

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“…It mainly scans this input image. It then generates k output boxes, all with two scores representing probability for an object's availability [16], [17]. Figure 1 depicts the F-RCNN architecture.…”

Section: Architecture Of F-rcnnmentioning

confidence: 99%

Novel deep learning model for vehicle and pothole detection

Gayathri

Thangavelu

2021

IJEECS

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The most important aspect of automatic driving and traffic surveillance is vehicle detection. In addition, poor road conditions caused by potholes are the cause of traffic accidents and vehicle damage. The proposed work uses deep learning models. The proposed method can detect vehicles and potholes using images. The faster region-based convolutional neural network (CNN) and the inception network V2 model are used to implement the model. The proposed work compares the performance, accuracy numbers, detection time, and advantages and disadvantages of the faster region-based convolution neural network (Faster R-CNN) with single shot detector (SSD) and you only look once (YOLO) algorithms. The proposed method shows good progress than the existing methods such as SSD and YOLO. The measure of performance evaluation is Accuracy. The proposed method shows an improvement of 5% once compared with the previous methods such as SSD and YOLO.

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Foreign exchange prediction based on indices and commodities price using convolutional neural network

Cited by 5 publications

References 13 publications

Deep image mining for convolution neural network

Deep image mining for convolution neural network

Hybrid Forex prediction model using multiple regression, simulated annealing, reinforcement learning and technical analysis

Novel deep learning model for vehicle and pothole detection

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