IAES International Journal of Artificial Intelligence (IJ-AI)
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.
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<![CDATA[Intelligent task processing using mobile edge computing: processing time optimization ]]>
<![CDATA[Maftah, Sara]]>;
<![CDATA[El Ghmary, Mohamed]]>;
<![CDATA[El Bouabidi, Hamid]]>;
<![CDATA[Amnai, Mohamed]]>;
<![CDATA[Ouacha, Ali]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i1.pp143-152
<![CDATA[The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities, battery resources, and storage availability, it enhances the durability and performance of mobile devices by offloading local intensive computation tasks to edge servers. However, the optimal solution is not always guaranteed by offloading computation, therefore, the offloading decision is a crucial step depending on many parameters that should be taken in consideration. In this paper, we use a simulator to compare a two tier edge orchestrator architecture with the results obtained by implementing a system model that aims to minimize a task’s processing time constrained by time delay and the limited device’s computational resource and usage based on a modified version.]]>
<![CDATA[Partial half fine-tuning for object detection with unmanned aerial vehicles ]]>
<![CDATA[Pebrianto, Wahyu]]>;
<![CDATA[Mudjirahardjo, Panca]]>;
<![CDATA[Pramono, Sholeh Hadi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i1.pp399-407
<![CDATA[Deep learning has shown outstanding performance in object detection tasks with unmanned aerial vehicles (UAVs), which involve the fine-tuning technique to improve performance by transferring features from pre-trained models to specific tasks. However, despite the immense popularity of fine-tuning, no works focused on to study of the precise fine-tuning effects of object detection tasks with UAVs. In this research, we conduct an experimental analysis of each existing fine-tuning strategy to answer which is the best procedure for transferring features with fine-tuning techniques. We also proposed a partial half fine-tuning strategy which we divided into two techniques: first half fine-tuning (First half F-T) and final half fine-tuning (Final half F-T). We use the VisDrone dataset for the training and validation process. Here we show that the partial half fine-tuning: Final half F-T can outperform other fine-tuning techniques and are also better than one of the state-of-the-art methods by a difference of 19.7% from the best results of previous studies.]]>
<![CDATA[Impact of adaptive filtering-based component analysis method on steady-state visual evoked potential based brain computer interface systems ]]>
<![CDATA[Krishnappa, Manjula]]>;
<![CDATA[Anandaraju, Madaveeranahally B.]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i1.pp92-103
<![CDATA[The significance of brain computer interface (BCI) systems is immensely high, especially for disabled people and patients with nervous system failure. Therefore, in this study, adaptive filtering-based component analysis (AFCA) model is presented to enhance target box identification efficiency at varied flickering frequencies in a visual stimulation process by efficient acquisition of electroencephalogram (EEG) signals for the application of steady-state visually evoked potential based BCI system. Furthermore, optimization of proposed AFCA model is performed based on the maximized reproducibility of correlated components. A multimedia authoring and management using your eyes and mind (MAMEM) steady-state visual evoked potential (SSVEP) dataset is utilized for efficient training of EEG signals and background entities are eliminated using adaptive filters in a pre-processing stage. Additionally, spatial filtering components are obtained to detect target flickering box based on the obtained quality features. Performance is measured by acquisition of SSVEP signals in terms of reconstruction efficiency, classification accuracy and information transfer rate (ITR) using proposed AFCA model. Mean classification accuracy for all 11 subject is 93.48% and ITR is 308.23 bpm. Further, classification accuracy is relatively higher than various SSVEP classification algorithms.]]>
<![CDATA[Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit ]]>
<![CDATA[Anito, Wondimu Lambamo]]>;
<![CDATA[Srinivasagan, Ramasamy]]>;
<![CDATA[Jifara, Worku]]>;
<![CDATA[Alzahrani, Ali]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i1.pp1050-1062
<![CDATA[Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.]]>
<![CDATA[Optimized robust fuzzy sliding mode control for efficient wastewater treatment: a comprehensive study ]]>
<![CDATA[Kumara, Varuna]]>;
<![CDATA[Ganesan, Ezhilarasan]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i1.pp631-638
<![CDATA[Wastewater treatment plants (WWTPs) are plagued by nonlinearities, uncertainties, and disturbances that degrade control performance and may even lead to severe instability. The WWTP control issue has received a lot of research and development during the last several decades. One well-known way of designing a resilient control system is called sliding mode control (SMC). The SMC's greatest strength lies in its innate resistance to disturbances and uncertainty. Incorporating fuzzy SMC would eliminate the chattering effect, the primary drawback of traditional sliding-mode controller, without sacrificing robustness against parametric uncertainties, modeling errors, and variable dynamic loads. This article discusses the hybridization of fuzzy logic with sliding mode control to provide highly excellent stability and accuracy in a control system. As a means of optimizing the fuzzy SMC, the gradient-free optimization technique known as the Jaya algorithm is investigated. By repeatedly altering a population of individual solutions, this population-based method can deal with both limited and unbounded optimization issues.]]>
<![CDATA[Transfer learning for epilepsy detection using spectrogram images ]]>
<![CDATA[Edderbali, Fatima]]>;
<![CDATA[Harmouchi, Mohammed]]>;
<![CDATA[Essoukaki, Elmaati]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i1.pp1022-1029
<![CDATA[Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.]]>
<![CDATA[Sentence embedding to improve rumour detection performance model ]]>
<![CDATA[Anggrainingsih, Rini]]>;
<![CDATA[Wihidayat, Endar Suprih]]>;
<![CDATA[Widoyono, Bambang]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i1.pp115-121
<![CDATA[Recently, most individuals have preferred accessing the most recent news via social media platforms like Twitter as their primary source of information. Moreover, Twitter enables users to post and distribute tweets quickly and unsupervised. As a result, Twitter has become a popular platform for disseminating false information, such as rumours. These rumours were then propagated as accurate and influenced public opinion and decision-making. The issue will arise when a decision or policy with substantial consequences is made based on rumours. To avoid the negative impacts of rumours, several researchers have attempted to detect them automatically as early as feasible. Previous studies employed supervised learning methods to identify Twitter rumours and relied on feature extraction algorithms to extract tweet content and context elements. However, manually extracting features is time-consuming and labour-intensive. To encode each tweet's sentence as a vector based on its contextual meaning, we proposed utilising Bidirectional Encoder Representation of Transformer (BERT) as a sentence embedding. We then used these vectors to train some classifier models to detect rumours. Finally, we compared the performance of BERT-based models to feature engineering-based models. We discovered that the suggested BERT-based model improved all parameters by around 10% compared to the feature engineering-based classification model.]]>
<![CDATA[Under-sampling technique for imbalanced data using minimum sum of euclidean distance in principal component subset ]]>
<![CDATA[Kasemtaweechok, Chatchai]]>;
<![CDATA[Suwannik, Worasait]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i1.pp305-318
<![CDATA[Imbalanced datasets are characterized by a substantially smaller number of data points in the minority class compared to the majority class. This imbalance often leads to poor predictive performance of classification models when applied in real-world scenarios. There are three main approaches to handle imbalanced data: over-sampling, under-sampling, and hybrid approach. The over-sampling methods duplicate or synthesize data in the minority class. On the other hand, the under-sampling methods remove majority class data. Hybrid methods combine the noise-removing benefits of under-sampling the majority class with the synthetic minority class creation process of over-sampling. In this research, we applied principal component (PC) analysis, which is normally used for dimensionality reduction, to reduce the amount of majority class data. The proposed method was compared with eight state-of-the-art under-sampling methods across three different classification models: support vector machine, random forest, and AdaBoost. In the experiment, conducted on 35 datasets, the proposed method had higher average values for sensitivity, G-mean, the Matthews correlation coefficient (MCC), and receiver operating characteristic curve (ROC curve) compared to the other under-sampling methods. ]]>
<![CDATA[Implementation of deep neural networks learning on unmanned aerial vehicle based remote-sensing ]]>
<![CDATA[Ahmed, Shouket Abdulrahman]]>;
<![CDATA[Desa, Hazry]]>;
<![CDATA[T. Hussain, Abadal-Salam]]>;
<![CDATA[A. Taha, Taha]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i1.pp941-947
<![CDATA[Due to efficient and adaptable data collecting, unmanned aerial vehicle (UAV) has been a popular topic in computer vision (CV) and remote sensing (RS) in recent years. Inspiring by the recent success of deep learning (DL), several enhanced object identification and tracking methods have been broadly applied to a variety of UAV-related applications, including environmental monitoring, precision agriculture, and traffic management. In this research, we present efficient neural network (ENet), a unique deep neural network architecture designed exclusively for jobs demanding low latency operation. ENet is up to quicker, takes fewer floating-point operations per second (FLOPs), has fewer parameters, and offers accuracy comparable to or superior to that of previous models. We have tested it on the street and cityscapes reports on comparisons with current state-of-the-art approaches and the tradeoffs between a network's processing speed and accuracy. We give measurements of the proposed architecture's performance on embedded devices and offer software enhancements that might make ENet even quicker.]]>
<![CDATA[Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging: a review ]]>
<![CDATA[Kandaya, Shaarmila]]>;
<![CDATA[Abdullah, Abdul Rahim]]>;
<![CDATA[Saad, Norhashimah Mohd]]>;
<![CDATA[Muda, Ahmad Sobri]]>;
<![CDATA[Ahmad Sabri, Muhammad Izzat]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i1.pp648-657
<![CDATA[Stroke is a leading cause of disability and death worldwide. Early diagnosis and treatment are crucial in reducing the risk of stroke-related complications. Brain magnetic resonance imaging (MRI) is a common diagnostic tool used for stroke evaluation. However, manual interpretation of MRI images can be time-consuming and subjective. Machine learning (ML) algorithms have shown promise in automating and improving stroke diagnosis accuracy. This article focuses on classification and segmentation techniques used to detect early stroke diagnosis using brain magnetic imaging. The diagnosis, treatment, and prognosis of complications and patient outcomes in a number of neurological diseases are currently made possible by ML through pattern recognition algorithms. However, the use of MRI is limited because of MRI plays an important role in diagnosing lumbar disc disease. However, the use of MRI is limited due to its high cost and significant operational and processing time. More importantly, MRI is contraindicated in some patients who are claustrophobic or have pacemakers due to the potential for damage. Recent studies have shown that treatment within six hours of a stroke can save a patient's life. Unfortunately, Malaysia is facing a shortage of neuroradiologists, hampering efforts to treat its growing number of stroke patients.]]>
