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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 55 Documents
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Search results for , issue "Vol 12, No 4: December 2023" : 55 Documents clear
Comparison between autoregressive integrated moving average and long short term memory models for stock price prediction Pi Rey Low; Eric Sakk
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1828-1835

Abstract

This study compares the forecasting accuracy in stock price prediction of twowidely established models - a more traditional autoregressive integratedmoving average (ARIMA) model and a deep learning network, the long shortterm memory (LSTM) model. They perform exceptionally well in time series data analysis and are applied to ten different stock tickers, comprising exchange-traded funds (ETFs) from different market sectors for the purpose of this study. The parameters in both models were optimised and this process revealed several differences from existing literature with regards to the optimal combination of parameters in both models. Upon comparing their performances, despite being more accurate when making point predictions, the ARIMA was outperformed significantly by LSTMs in terms of long-term predictions. Point predictions made by ARIMA were found to have similar accuracies as the long-run predictions made by LSTMs.
Automated classification of age-related macular degeneration from optical coherence tomography images using deep learning approach Gilakara Muni Nagamani; Theerthagiri Sudhakar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp2011-2021

Abstract

Early detection of macular diseases can prevent vision loss. Manual screening can be unreliable due to the similarity in the pathological presentations of common retinal illnesses like age-related macular degeneration (AMD). Researchers are becoming more interested in the accurate automated computer-based detection of macular diseases. Using healthy optical coherence tomography (OCT) images, the drusens (early stage) and choroidal neovascularization (CNV) (late stage) of AMD are thus classified using a completely different approach in this paper. The new deep learning (DL) model is proposed for multiple OCT image segmentation of ophthalmological diseases using attention-based nested U-Net (ANU-Net). The flower pollination optimization algorithm (FPOA) is used to optimize the hyperparameters of the network. The SqueezeNet-based classification can be made in the pre-processed images. A dataset from the University of California San Diego (UCSD) is used to evaluate the proposed method. 98.7% accuracy, 99.8% specificity, and 99.7% sensitivity are achieved by the proposed method. The proposed method produces better identification results for automated preliminary diagnosis of macular diseases in hospitals and eye clinics due to the positive classification results.
Prototyping of e-fisherman web server to support Indonesian fishermen’s activities Syifaul Fuada; Trio Adiono
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1960-1973

Abstract

This paper presents a webserver for Indonesian fishermen, to support fishing activities. This is one of the sub-systems of e-Nelayan (in English: eFisherman) architecture, which was connected to e-Nelayan Apps; it helps to provide interaction between two users, including the administrators and fishermen. Using hypertext preprocessor (PHP) language, the website was developed to function on an Apache web server, with the adaptation of my structured query language (MySQL) framework for the database. This system was subsequently divided into two parts: (1) the front-end, which is responsible for the accessibility of data collection and (2) the back-end, where administrators update or modify crucial information: price, fishing result, illegal activity report, save our ship! (SOS) potential fish zone, and ship tracking. The administrators are unable to update the real-time weather information for the front-end part. The application was found to record the information obtained from the fishermen through the e-Nelayan apps and meteorology, climatology, and geophysical agency (BMKG in Indonesian). This web system is expected to carry out the following functions: to ensure easier interactions between fishermen and administrators, to enable easy update of information, to promote monitoring and recording of results, and to ensure fishermen’s safety.
COVID-19 digital x-rays forgery classification model using deep learning Eman I. Abd El-Latif; Nour Eldeen Khalifa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1821-1827

Abstract

Nowadays, the internet has become a typical medium for sharing digitalimages through web applications or social media and there was a rise inconcerns about digital image privacy. Image editing software’s have preparedit incredibly simple to make changes to an image's content without leavingany visible evidence for images in general and medical images in particular.In this paper, the COVID-19 digital x-rays forgery classification modelutilizing deep learning will be introduced. The proposed system will be ableto identify and classify image forgery (copy-move and splicing) manipulation.Alexnet, Resnet50, and Googlenet are used in this model for feature extractionand classification, respectively. Images have been tampered with in threeclasses (COVID-19, viral pneumonia, and normal). For the classification of(Forgery or no forgery), the model achieves 0.9472 in testing accuracy. Forthe classification of (Copy-move forgery, splicing forgery, and no forgery),the model achieves 0.8066 in testing accuracy. Moreover, the model achieves0.796 and 0.8382 for 6 classes and 9 classes problems respectively.Performance indicators like Recall, Precision, and F1 Score supported theachieved results and proved that the proposed system is efficient for detectingthe manipulation in images. 
Transmission line impulse response modelling using machine learning techniques Wei Min Lim; Khin Leong How; Chan Hong Goay; Nur Syazreen Ahmad; Patrick Goh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1577-1584

Abstract

Conventional methods of circuit simulation such as full-wave electromagnetic fieldsolvers can be very slow. Machine learning is an emerging technology in modelling, simulation, optimization, and design that present attractive alternatives to the conventional methodologies because they can be trained with a small amount of data, and then used to perform fast circuit predictions within the same design space. In this paper, we present applications of machine learning techniques for the modelling of transmission lines from their impulse reponses. The standard multilayer perceptron (MLP) neural network and the gaussian process (GP) regression techniques are demonstrated, andboth models are successfully implemented to model the impulse responses of transmission lines with great accuracies. We show that the GP outperforms the MLP in terms of prediction accuracies and that the GP is more data efficient than the MLP. This is beneficial considering that each training sample is expensive, making the GP a good candidate for the task, compared to the more popular MLP.

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