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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 43 Documents
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Search results for , issue "Vol 11, No 4: December 2022" : 43 Documents clear
Proposing a route recommendation algorithm for vehicles based on receiving video Phat Nguyen Huu; Phuong Tong Thi Quynh; Thien Pham Ngoc; Quang Tran Minh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

In this paper, we propose a method to classify traffic status for the route recommendation system based on received videos. The system will determine the number of vehicles in the region of interest (ROI) to determine and calculate the coefficient of variation (CV) based on the videos extracted from cameras at intersections. It then predicts the congested traffic junctions in the city. The data then goes through the routing module and is transmitted to the website to find the best path between the source and destination requested by users. In this system, we use you only look once (YOLOv5) for vehicle detection and the A* algorithm for routing. The results show that the proposed system achieves 91.67% accuracy in detecting traffic status comparing with YOLOv1, deep convolutional neural network (DCNN), convolutional neural network (CNN), and support vector machine (SVM) models as 91.2%, 90.2%, 89.5%, and 85.0%, respectively. 
Selecting goldfish broods use the weighted product and simple additive weighting methods Ramadiani Ramadiani; Surya Adithama; Muhammad Labib Jundillah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Majalaya carp is a freshwater fish that has important economic value and is widespread in Indonesia. Goldfish is the most cultivated fish because it has many advantages both physiologically and genetically. Several factors of assessment in the selection of superior brood stock that can be considered in the cultivation of goldfish cultivators are; ideal body weight, fish movement, physical deformities, scales, and the base of the tail. All of these factors can help in the decision-making process for superior goldfish. This study uses two methods, namely the simple additive weighting (SAW) method and the weighted product (WP) method. Based on the results of research that has been carried out on 20 superior broodfish of Majalaya goldfish, the level of accuracy is obtained by comparing with existing data. The WP method gets an accuracy value of 90% while the SAW method gets an accuracy value of 80%.
On searching the best mode for forex forecasting: bidirectional long short-term memory default mode is not enough Seng Hansun; Farica Perdana Putri; Abdul Q. M. Khaliq; Hugeng Hugeng
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Presently, the Forex market has become the world’s largest financial market with more than US$5 trillion daily volume. Therefore, it attracts many researchers to learn its traded currency pairs characteristics and predict their future values. Here, we propose simple three layers Bidirectional long short-term memory (Bi-LSTM) networks for Forex forecasting with four different merge modes. Moreover, the proposed model is also compared to the conventional long short-term memory (LSTM) networks with the same architecture. Five major Forex currency pairs, namely AUD/USD, EUR/USD, GBP/USD, USD/CHF, and USD/JPY, with more than ten years of historical records are considered in this study. It is revealed from the experimental results that among four available merge modes, the concatenation mode as the default merge mode in Bi-LSTM networks is actually the less preferred mode for Forex forecasting (Root mean square error 0.30685517, mean absolute error 0.27442235, mean absolute percentage error 0.827108%). Moreover, Bi-LSTM average mode gets the highest  score that could achieve 89.579%. Therefore, the proposed three layers Bi-LSTM networks could provide a baseline result for developing a good trading strategy in Forex forecasting.

Page 5 of 5 | Total Record : 43

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