cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Imam Much Ibnu Subroto
Contact Email
imam@unissula.ac.id
Phone
-
Journal Mail Official
ijai@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 46   47   48   49   50 
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.
A deep learning-based multimodal biometric system using score fusion Chahreddine Medjahed; Abdellatif Rahmoun; Christophe Charrier; Freha Mezzoudj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp65-80

Abstract

Recent trends in artificial intelligence tools-based biometrics have overwhelming attention to security matters. The hybrid approaches are motivated by the fact that they combine mutual strengths and they overcome their limitations. Such approaches are being applied to the fields of biomedical engineering. A biometric system uses behavioural or physiological characteristics to identify an individual. The fusion of two or more of these biometric unique characteristics contributes to improving the security and overcomes the drawbacks of unimodal biometric-based security systems. This work proposes efficent multimodal biometric systems based on matching score concatenation fusion of face, left and right palm prints. Multimodal biometric identification systems using convolutional neural networks (CNN) and k-nearest neighbors (KNN) are proposed and trained to recognize and identify individuals using multi-modal biometrics scores. Some popular biometrics benchmarks such as FEI face dataset and IITD palm print database are used as raw data to train the biometric systems to design a strong and secure verification/identification system. Experiments are performed on noisy datasets to evaluate the performance of the proposed model in extreme scenarios. Computer simulation results show that the CNN and KNN multi-modal biometric system outperforms most of the most popular up to date biometric verification techniques.
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.
An efficient machine learning-based COVID-19 identification utilizing chest X-ray images Mahmoud Masadeh; Ayah Masadeh; Omar Alshorman; Falak H Khasawneh; Mahmoud Ali Masadeh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp356-366

Abstract

There is no well-known vaccine for coronavirus disease (COVID-19) with 100% efficiency. COVID-19 patients suffer from a lung infection, where lung-related problems can be effectively diagnosed with image techniques. The golden test for COVID-19 diagnosis is the RT-PCR test, which is costly, time-consuming and unavailable for various countries. Thus, machine learning-based tools are a viable solution. Here, we used a labelled chest X-ray of three categories, then performed data cleaning and augmentation to use the data in deep learning-based convolutional neural network (CNN) models. We compared the performance of different models that we gradually built and analyzed their accuracy. For that, we used 2905 chest X-ray scan samples. We were able to develop a model with the best accuracy of 97.44% for identifying COVID-19 using X-ray images. Thus, in this paper, we attested the feasibility of efficiently applying machine learning (ML) based models for medical image classification.
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.
Bi-directional long short term memory using recurrent neural network for biological entity recognition Rashmi Siddalingappa; Kanagaraj Sekar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp89-101

Abstract

Biomedical named entity recognition (NER) aims at identifying medical entities from unstructured data. A quintessential task in the supervision of biological databases is handling biomedical terms such as cancer type, DeoxyriboNucleic and RiboNucleic Acid, gene and protein name, and others. However, due to the massive size of online medical repositories, data processing becomes a challenge for a gazetteer without proper annotation. The traditional NER systems depend on feature engineering that is tedious and time-consuming. The research study presents a new model for Bio-NER using recurrent neural network. Unlike existing approaches, the proposed method uses bidirectional traversing with GloVe vector modelling performed at character and word levels. Bio-NER is performed in three stages; firstly, the relevant medical entities in electronic medical records from PubMed were extracted using the skip-gram model. Secondly, a vector representation for each word is created through the 1-hot method. Thirdly, the weights of the recurrent neural network (RNN) layers are adjusted using backward propagation. Finally, the long-short-term memory cells store the previously encountered medical entity to tackle context-dependency. The accuracy and F-score are calculated for each medical entity type. The MacroR, MacroP, and MacroF are equal to 0.86, 0.88, and 0.87. The overall accuracy achieved was 94%.
State of charge estimation of lithium-ion batteries using adaptive neuro fuzzy inference system Imane Chaoufi; Othmane Abdelkhalek; Brahim Gasbaoui
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.pp473-484

Abstract

A battery’s state of charge (SOC) is used to assess its residual capacity. It is a very important parameter for the control of the electric vehicle (EV). The objective of this paper is to estimate the SOC of a lithium-ion battery (LIB) using an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) because SOC of a battery must be estimated from measurable battery parameters such as current, voltage or temperature. Two intelligent SOC estimation methods are compared according to their suitability and accuracy. ANN estimation is more precise and perfectly represents the experimental data.
Fake news detection using naïve Bayes and long short term memory algorithms Sarra Senhadji; Rania Azad San Ahmed
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.pp746-752

Abstract

Information and communication technologies have revolutionized the numerical world by offering the freedom to publish and share all types of information. Unfortunately, not all information circulated on the internet is accurate, which can have serious consequences, including misleading readers. Detecting false news is a complicated task to overcome. Massive studies focus on using machine and deep learning techniques in an attempt to classify the news as authentic or not. The goal of this research is an attempt to glance and evaluate how naïve bayes (NB) and long short-term memory (LSTM) classifiers can be used to positively identify fake news. The outcomes of this experiment reveal that LSTM achieves an accuracy of 92 percent over naive bayes. Moreover, the findings of the proposed approach’s results outperform the related work results.
Intrusion prevention system using convolutional neural network for wireless sensor network Pankaj Ramchandra Chandre; Parikshit Mahalle; Gitanjali Shinde
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.pp504-515

Abstract

Now-a-days, there is exponential growth in the field of wireless sensor network. In wireless sensor networks (WSN’s), most of communication happen through wireless media hence probability of attacks increases drastically. With the help of intrusion prevention system, we can classify user activities into two categories, normal and suspicious activity. There is need to design effective intrusion prevention system by exploring deep learning for WSN. This research aims to deal with proposing algorithms and techniques for intrusion prevention system using deep packet inspection based on deep learning. In this, we have proposed deep learning model using convolutional neural network. The proposed model includes two steps, intrusion detection and intrusion prevention. The proposed model learns useful feature representations from large amount of labeled data and then classifies them. In this work, convolutional neural network is used to prevent intrusion for WSN. To evaluate and check the effectiveness of the proposed system, the wireless sensor network dataset (WSNDS) dataset is used and the tests are performed. The test results show that proposed system has an accuracy of 97% and works better than existing system. The proposed work can be used as future benchmark for the deep learning and intrusion prevention research communities.

Page 48 of 181 | Total Record : 1808

 46   47   48   49   50 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 15, No 1: February 2026 Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue