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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 40 Documents
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Search results for , issue "Vol 11, No 2: June 2022" : 40 Documents clear
Ensemble machine learning algorithm optimization of bankruptcy prediction of bank Bambang Siswoyo; Zuraida Abal Abas; Ahmad Naim Che Pee; Rita Komalasari; Nano Suyatna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp679-686

Abstract

The ensemble consists of a single set of individually trained models, the predictions of which are combined when classifying new cases, in building a good classification model requires the diversity of a single model. The algorithm, logistic regression, support vector machine, random forest, and neural network are single models as alternative sources of diversity information. Previous research has shown that ensembles are more accurate than single models. Single model and modified ensemble bagging model are some of the techniques we will study in this paper. We experimented with the banking industry’s financial ratios. The results of his observations are: First, an ensemble is always more accurate than a single model. Second, we observe that modified ensemble bagging models show improved classification model performance on balanced datasets, as they can adjust behavior and make them more suitable for relatively small datasets. The accuracy rate is 97% in the bagging ensemble learning model, an increase in the accuracy level of up to 16% compared to other models that use unbalanced datasets.
The multimodal parameter enhancement of electroencephalogram signal for music application Zarith Liyana Zahari; Mahfuzah Mustafa; Rafiuddin Abdubrani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp414-422

Abstract

Blinding of modality has been influenced decision of multimodal in several circumstances. Sometimes, certain electroencephalogram (EEG) signal is omitted to achieve the highest accuracy of performance. Therefore, the aim for this paper is to enhance the multimodal parameters of EEG signals based on music applications. The structure of multimodal is evaluated with performance measure to ensure the implementation of parameter value is valid to apply in the multimodal equation. The modalities’ parameters proposed in this multimodal are weighted stress condition, signal features extraction, and music class. The weighted stress condition was obtained from stress classes. The EEG signal produces signal features extracted from the frequency domain and time-frequency domain via techniques such as power spectrum density (PSD), short-time Fourier transform (STFT), and continuous wavelet transform (CWT). Power value is evaluated in PSD. The energy distribution is derived from STFT and CWT techniques. Two types of music were used in this experiment. The multimodal fusion is tested using a six-performance measurement method. The purposed multimodal parameter shows the highest accuracy is 97.68%. The sensitivity of this study presents over 95% and the high value for specificity is 89.5%. The area under the curve (AUC) value is 1 and the F1 score is 0.986. The informedness values range from 0.793 to 0.812 found in this paper.
An intelligent demand forecasting model using a hybrid of metaheuristic optimization and deep learning algorithm for predicting concrete block production Huthaifa AL-Khazraji; Ahmed R. Nasser; Sohaib Khlil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp649-657

Abstract

Demand forecasting aims to optimize the production planning of industrial companies by ensuring that the production planning meets the future demand. Demand forecasting utilizes historical data as an input to predict future trends of the demand. In this paper, a new approach for developing an intelligent demand forecasting model using a hybrid of metaheuristic optimization and deep learning algorithm is presented. Firefly algorithmbased gated recurrent units (FA-GRU) is used to tackle the production forecasting problem. The proposed model has been evaluated and compared with the standard gated recurrent unit (GRU) and standard long short-term memory model (LSTM) using historical data of 36 months of concrete block manufacturing at dler company in Iraq. The prediction accuracy of the three models is evaluated using the root mean square error (RMSE), the mean absolute percentage error (MAPE) and the statistical coefficient of determination (R2 ) indicators. The outcomes of the study show that the proposed FA-GRU gives better forecasting results compared to the standard GRU and standard LSTM.
Redesigning U-Net with dense connection and attention module for satellite based cloud detection Aarti Kumthekar; Gudheti Ramachandra Reddy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp699-708

Abstract

In this paper, we present an upgraded U-Net technique for satellite-based cloud detection, with additional features, such as, more relevant spatial information, improvement in gradient propagation, feature reuse and controlling the network parameters using growth rate by adding dense connections. Furthermore, incorporation of attention module helps to learn strong inter-spatial and inter-channel relationships of feature maps by adding a few trainable parameters to the network. The two attention blocks namely position attention module (PAM) and channel attention module (CAM) focus on important parts of the image by neglecting the redundant information. The experimental results prove that the put forward technique with dense and attention modules could detect cloud with an accuracy of 95.69%.
A real-time data association of internet of things based for expert weather station system Indrabayu Indrabayu; Intan Sari Areni; Anugrayani Bustamin; Rizka Irianty
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp432-439

Abstract

The wind carries moisture into an atmosphere and hot or cold air into a climate, affecting weather patterns. Knowing where the wind is coming from gives essential insight into what kind of temperatures are to be expected. However, the wind is affected by spatial and temporal variabilities, thus making it difficult to predict. This study focuses on finding data associations from the weather station installed at Hasanuddin University Campus based on internet of things (IoT) using Raspberry Pi as a gateway that associated all the meteorological data from sensors. The generation of association rules compares the Apriori and FP-growth algorithms to determine relations among itemsets. The results show that high humidity and warm temperature tend to associate with a westerly wind and occur at night. In contrast, conditions with less humid and moderate temperatures tend to have southerly and southeasterly wind.
Prediction analysis of the happiness ranking of countries based on macro level factors Dini Oktarina Dwi Handayani; Muharman Lubis; Arif Ridho Lubis
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp666-678

Abstract

Happiness is an essential universal human goal in their life that can improve the quality of life. Since the introduction of positive psychology, the primary consideration has been pointed out to the study of the role from certain factors in predicting the happiness, especially the advancement of technology that allows computer-mediated to be part of human interaction. It provides a multidimensional approach and indirect influence to the human expression and communication. The project investigates what it takes to build a happy country by analysing on the relationship between the happiness ranking of countries and their macro level factors. The World Happiness Report 2019 is used coupled with Python programming for visualizing and extracting information from the dataset to better understand the bigger picture.
Toward a multitask aspect-based sentiment analysis model using deep learning Trang Uyen Tran; Ha Thanh Thi Hoang; Phuong Hoai Dang; Michel Riveill
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp516-524

Abstract

Sentiment analysis or opinion mining is used to understand the community’s opinions on a particular product. This is a system of selection and classification of opinions on sentences or documents. At a more detailed level, aspect-based sentiment analysis makes an effort to extract and categorize sentiments on aspects of entities in opinion text. In this paper, we propose a novel supervised learning approach using deep learning techniques for a multitasking aspect-based opinion mining system that supports four main subtasks: extract opinion target, classify aspect, classify entity (category) and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of the entity. We have used a part-of-speech (POS) layer to define the words’ morphological features integrated with GloVe word embedding in the previous layer and fed to the convolutional neural network_bidirectional long-short term memory (CNN_BiLSTM) stacked construction to improve the model’s accuracy in the opinion classification process and related tasks. Our multitasking aspect-based sentiment analysis experiments on the dataset of SemEval 2016 showed that our proposed models have obtained and categorized core tasks mentioned above simultaneously and attained considerably better accurateness than the advanced researches.
Error detection and comparison of gesture control technologies Aditya Prasad Mahapatra; Bishweashwar Sukla; Harikrishnan K. M.; Debani Prasad Mishra; Surender Reddy Salkuti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp709-716

Abstract

This paper showcases the study and observation on the error occurrence in gesture control technologies. An arduino-based gesture control environment has been developed by using the arduino board to use motion gestures to control the contents on the screen. This environment is made using position sensitive diodes as sensing devices, arduino as a micro-controller and Python to execute commands in the system. It is performed on 2 different software applications namely Google Chrome and VideoLAN Client Media Player. In Google Chrome gestures are used to traverse between tabs and also move up and down within a web page, whereas in VideoLAN Client Media Player gestures are used to control the volume and speed. Through this, the difference between two technologies i.e., infrared and ultrasonic are worked and compared. Various data visualization cues are prepared to better understand the error and the factors causing it. Thorough investigation of factors affecting the error has been done using our observation. The future of this technology and its limitations have been also discussed.
Evaluation of efficiency of hedging strategies with option portfolios for buyers of the currency US dollar/Colombian peso Manuela Gutierrez-Salazar; Miguel Jiménez-Gómez; Natalia Acevedo-Prins
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp572-581

Abstract

This paper evaluates the efficiency to mitigate the exchange rate risk of nine hedging strategies with financial options. Strategies to hedging the purchase of US dollar Colombian peso (USDCOP) by importers in Colombia were raised. In this way, the traditional strategy with call options and eight strategies with investment portfolios were evaluated. These portfolios of options for hedge are offered by financial entities in Colombia. These nine hedged scenarios were compared with the unhedged scenario that corresponds to the foreign exchange risk exposure of importers. The USDCOP currencies were modeled with a mean reversion with jumps models, option premiums were valued with the black-scholes method and the best hedging strategy was determined through a Monte Carlo simulation. According to the results obtained, the nine hedging strategies manage to mitigate risk, but the most efficient was the option portfolio called collar.
Decision support for predicting revenue target determination with comparison of double moving average and double exponential smoothing Dyna Marisa Khairina; Yulius Daniel; Putut Pamilih Widagdo; Septya Maharani; Shabrina Shabrina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp440-447

Abstract

The success of the company requires careful planning. Perusahaan Daerah Air Minum (PDAM) is a drinking water facility management company that plays an important role in supporting the smooth development of the region with the influence of revenue targets. Prediction of revenue targets is deemed necessary for accurate and effective decision making. Predictions are made by comparing the double moving average (DMA) and double exponential smoothing (DES) methods which refer to the actual data from the previous five (5) years. Measuring forecasting accuracy using mean absolute percentage error (MAPE) and assessing accuracy analysis results using tracking signal. Prediction test uses five (5) order values on DMA and five (5) alpha values on DES. Based on the test, it shows that the DMA has the advantage of a smaller MAPE value <10 with very good criteria and the results of the analysis of the pattern graph on the tracking signal that do not exceed the upper control limit (UCL) and the lower control limit (LCL). It is concluded that the DMA method is more recommended as a reference approach to support decisions to determine PDAM revenue targets and as a basis for planning and policy making to predict future revenue targets.

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