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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 1,808 Documents
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Evaluation of genetic algorithm in network-on-chip based architecture Radha, Doraisamy; Moharir, Minal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1479-1488

Abstract

An increase in the number of cores gives a significant bounce in performance than an improvement in any of the factors or hardware. Many core systems use network-on-chip (NoC) for efficient communications among the cores in the system. However, the problem with NoC-based communication is that it significantly consumes a large amount of power and energy because the number of routers increases with the increase in the number of cores in the system. Power consumed by such components leads to degradation of the performance. The placement of cores in the topology is non-deterministic polynomial-time hardness (NP-Hard) problem. The optimal placement of cores in NoC is essential as it minimizes latency and communication costs. Thus, the NP-Hard problem of placing cores is solved using genetic algorithm (GA) based quadtree topology. The proposed work shows the analysis of GA-based quadtree topology, which outperforms other topologies in most aspects. The performance evaluation of GA-based quadtree topology is based on latency, throughput, power, area, bisection bandwidth, and diameter. Comparing these parameters with other topologies shows the prominence of the quadtree topology. The evaluation is performed in the Booksim simulator, and the experimental results revealed that the proposed GA-based quad tree-based topology is efficient for NoC-based communications.
An improved convolutional recurrent neural network for stock price forecasting Pham, Hoang Vuong; Lam, Hung Phu; Duy, Le Nhat; Pham, The Bao; Trinh, Tan Dat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3381-3394

Abstract

Stock price forecasting is a challenging area of research, particularly due to the complexity and unpredictability of financial markets. The accuracy of prediction models is influenced by various factors, including nonlinearity, seasonality, and economic shocks. Deep learning has demonstrated better forecasts of stock prices than traditional approaches. This study, therefore, proposed a new approach to improve forecasting system based on an end-to-end convolutional recurrent neural network (CRNN) with attention mechanism. Our approach first investigates local stock price features using 1D convolutional neural network, and then employs a bidirectional long short-term memory (Bi-LSTM) network for forecasting. This model stands out by effectively utilizing contextual data and representing the temporal character of data. The Bi-LSTM is helpful for understanding the history and future contextual information since it uncovers both past and future contexts of stock data. Furthermore, integrating attention mechanism within the CRNN represents a significant improvement. This allows our model to handle long input sequences more effectively and capture the inherent stochasticity in stock prices, which is often missed by traditional models. The effectiveness of our approach is investigated using data on 10 stock indexes from Yahoo Finance. The results show that our method outperforms ARIMA, LSTM, and conventional methods. 
Evaluating sentiment analysis and word embedding techniques on Brexit Moudhich, Ihab; Fennan, Abdelhadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp695-702

Abstract

In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit. 
Balanced clustering for student admission school zoning by parameter tuning of constrained k-means Zainuddin, Zahir; Nur Risal, Andi Alviadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2301-2313

Abstract

The Indonesian government issued a regulation through the Ministry of Education and Culture, number 51 of 2018, which contains zoning rules to improve the quality of education in school educational institutions. This research aims to compare the performance of the k-means algorithm with the constrained k-means algorithm to model the zoning of each school area based on the shortest distance parameter between the school location and the domicile of prospective students. The study used data from 2,248 prospective students and 22 public school locations. The results of testing the k-means algorithm in grouping showed the formation of non-circular patterns in the cluster membership with different numbers of centroid cluster members. In contrast, testing the constrained k-means algorithm showed balanced outcomes in cluster membership with a membership value of 103 for each school as the cluster center. The research findings state that the developed constrained k-means algorithm solves the problem of unbalanced data clustering and overlapping issues in the process of new student admissions. In other words, the constrained k-means algorithm can be a reference for the government in making decisions on new student admissions.
Fuzzy logic for the management of vaccination during pandemics: A spread-rate-based approach Kareem, Abdul; Kumara, Varuna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2808-2815

Abstract

Pandemics, such as coronavirus disease COVID-19 are known to cause massive damage to the world's economic growth and their impacts are serious and influence across every aspect of social structure. The most inevitable factor in responding to the disaster of pandemics is the right management in terms of allocating a limited vaccine supply. The focus of this research work is to utilize a fuzzy logic inference system in the allocation of vaccine doses to the regional authorities by a central authority. The objective is obtained by designing a system based on fuzzy logic that considers the spread rate as the input to infer the vaccination rate of the local population. This system makes it possible for sufficient doses of vaccines to be allotted to the prioritized regions where the severity of the spread rate is a concern and vaccines are not held up in regions where the severity of the spread rate is lesser. The designed system is verified using MATLAB software, which shows that this method can ensure an effective and efficient allocation of vaccination in the local regions and aid the fight against the disastrous spread of the disease.
A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease Nastiti, Faulinda Ely; Musa, Shahrulniza; Yafi, Eiad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2036-2048

Abstract

In this study, a novel approach geared toward predicting the estimated number of coronavirus disease (COVID-19) cases was developed. Combining long short-term memory (LSTM) neural networks with particle swarm optimization (PSO) along with grey wolf optimization (GWO) employ hybrid optimization algorithm techniques. This investigation utilizes COVID-19 original data from the Ministry of Health of Indonesia, period 2020-2021. The developed LSTM-PSO-GWO hybrid optimization algorithm can improve the performance and accuracy of predicting the spread of the COVID-19 virus in Indonesia. In initiating LSTM initial weights with weaknesses, using the hybrid optimization algorithm helps overcome these problems and improve model performance. The results of this study suggest that the LSTM-PSO-GWO model can be utilized as an effective and reliable predictive tool to gauge the COVID-19 virus’s spread in Indonesia. 
Javanese part-of-speech tagging using cross-lingual transfer learning Enrique, Gabriel; Alfina, Ika; Yulianti, Evi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3498-3509

Abstract

Large datasets that are publicly available for POS tagging do not always exist for some languages. One of those languages is Javanese, a local language in Indonesia, which is considered as a low-resource language. This research aims to examine the effectiveness of cross-lingual transfer learning for Javanese POS tagging by fine-tuning the state-of-the-art Transformer-based models (such as IndoBERT, mBERT, and XLM-RoBERTa) using different kinds of source languages that have a higher resource (such as Indonesian, English, Uyghur, Latin, and Hungarian languages), and then fine-tuning it again using the Javanese language as the target language. We found that the models using cross-lingual transfer learning can increase the accuracy of the models without using cross-lingual transfer learning by 14.3%–15.3% over LSTM-based models, and by 0.21%–3.95% over Transformer-based models. Our results show that the most accurate Javanese POS tagger model is XLM-RoBERTa that is fine-tuned in two stages (the first one using Indonesian language as the source language, and the second one using Javanese language as the target language), capable of achieving an accuracy of 87.65%
Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory Syakti, Agsanshina Raka; Rhamadhan, Syahri; Laziola, Ghora; Pahrizal, Pahrizal; Apdillah, Dony; Ritha, Nola
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2003-2010

Abstract

The sea brings many benefits for society, especially for a maritime country such as Indonesia. The potential in various sectors is limited only by the willingness of a party to invest in it. One such investment is in learning the knowledge and information that can be gathered from the sea, and even predicting its behavior with enough data. Using a residual LSTM algorithm, we will predict the tidal level in eastern Bintan island, a tropical island on the tip of Malay peninsula. The dataset is acquired from two sensor points in eastern Bintan coast from July 2018 to June 2019 for a span of one year, giving a total of 7,961 data points. The residual LSTM model consists of a residual wrapper with two consecutive LSTM layers and one dense layer. The model is also compared with variations of LSTM and RNN models. The result of the residual LSTM model has an MAE value of 0.1495 cm and an RMSE value of 0.3353 cm, compared to the baseline model’s 1.1148 cm and 1.4107 cm respectively. The model also has an RMSE value improvement of 76.23% compared to the base model.
Neobots: an open-source platform for a low-cost neonatal incubator with internet of things approach Aryanto, I Komang Agus Ady; Maneetham, Dechrit; Triandini, Evi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1817-1837

Abstract

A baby incubator implements the internet of things (IoT) with an architectural design combining several scientific fields, such as networks, software, and hardware. Furthermore, this research develops an open-source platform called Neobots, including open-source program code to create a baby incubator. Then an overview of the system includes sending sensor data to the IoT Broker with the message queuing telemetry transport (MQTT) protocol and automatically storing data in the database. The results of the comparison value on each temperature sensor with a temperature sensor at the midpoint with an error of less than 0.7°C. Then testing the fuzzy between the Neobots program and the simulation in MATLAB got an error rate of 0-28.27%. In addition, in less than 10 minutes, the system response can adjust the temperature conditions to a setpoint value of 34°C from 29°C, and the average error value is 0.35°C during 1 hour of the Fuzzy implementation on the incubator. Then transfer data from the incubator to the database in a room without noise and full noise to get results for lost data less than 16.41% and 42.14%, delay rates between 0-6 seconds and 0-7 seconds with testing for 1 hour at every 1 second.
Sampling methods in handling imbalanced data for Indonesia health insurance dataset Kurniadi, Felix Indra; Purwandari, Kartika; Wulandari, Ajeng; Permai, Syarifah Diana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp348-357

Abstract

Health insurance fraud is one of the most frequently occurring fraudulent acts and has become a concern for every insurance. According to data from The Indonesian General Insurance Association or Asosiasi Asuransi Umum Indonesia (AAUI), the private insurance industry suffered losses up to billions rupiah throughout 2018 due to the fraudulent acts commited by the perpetrators. The problem in with the number of frauds in Indonesia is that the current system is highly vulnerable and they is still done manually. The other problem from this detection is imbalance data which often occurs in fraudulent cases. In this research, we used a sampling methods using several machine learning as the baseline. The result shows that the instance hardness thresholding algorithm and extreme gradient boosting gives the best performance for all the case. It shows the method can reduced the bias and can achieve better generalization.

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