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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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Optimizing the long short-term memory algorithm to improve the accuracy of infectious diseases prediction Sediyono, Eko; Wahyuni, Sri Ngudi; Sembiring, Irwan
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.pp2893-2903

Abstract

This study discusses the implementation of the proposed optimizedlong short-term memory (LSTM) to predict the number of infectious disease cases that spread in Central Java, Indonesia. The proposed model is developed by optimizing the output layer, which affects the output value of the cell state. This study used cases of four infectious diseases in Indonesia's Central Java Province, namely COVID-19, dengue, diarrhea, and hepatitis A. This model was compared to basic LSTM and MinMax schaler LSTM improvement to see the difference in the accuracy of each disease. The results showed a significant difference in the average prediction results with real cases between the three models. The main objectives of this study were: modifying the LSTM algorithm to predict the number of infectious disease cases to get a smaller residual value, comparing the results of the optimization accuracy of the LSTM algorithm with the LSTM algorithm in previous studies, and evaluating the use of spatial variables in applying infectious disease prediction models using the LSTM algorithm. The results found that the performance difference between the proposed optimization algorithm and the model in the previous study was obtained. The proposed LSTM optimization algorithm had an accuracy improvement of about 2% over the previous model.
Implementation of deep neural networks learning on unmanned aerial vehicle based remote-sensing Ahmed, Shouket Abdulrahman; Desa, Hazry; T. Hussain, Abadal-Salam; A. Taha, Taha
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.pp941-947

Abstract

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.
Seismic trend analysis: a data mining approach for pattern prediction Andrade Arenas, Laberiano; Yactayo-Arias, Cesar
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.pp2623-2634

Abstract

In the global context, seismic movements represent a constant for the population due to geophysical variability and other factors that make them possible, carrying with them the risk of losing innocent lives. The main purpose of our research is to apply data mining techniques to prevent seismic events of any magnitude to anticipate and mitigate future events. In the development of the research, we applied knowledge discovery database methodology. The clustering analysis results revealed the following: cluster 0 encompassed 45 items, with average magnitude of 0.230, representing 15.5% of the total events. Cluster 1 comprised 56 items with average magnitude of 0.156, equivalent to 19.2% of the total. Cluster 2, the largest, consisted of 94 items with average magnitude of 0.156, constituting 32.3% of the total seismic events. Cluster 3 was composed of 54 items, with average magnitude of 0.155, representing 18.3% of the total. Lastly, cluster 4 included 42 items, with average magnitude of 0.155, representing 14.5% of the total. In conclusion, cluster 3 emerged as the most significant, with 94 events and average magnitude of 0.141, equivalent to 32.3% of the total seismic events. This discovery underscores the need to utilize data mining techniques for earthquake prediction, enabling proactive measures against potential events, which are frequent in various geographic areas.
Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging: a review Kandaya, Shaarmila; Abdullah, Abdul Rahim; Saad, Norhashimah Mohd; Muda, Ahmad Sobri; Ahmad Sabri, Muhammad Izzat
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.pp648-657

Abstract

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.
Kernel density estimation of Tsalli’s entropy with applications in adaptive system training Chawla, Leena; Kumar, Vijay; Saxena, Arti
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.pp2247-2253

Abstract

Information theoretic learning plays a very important role in adaption learning systems. Many non-parametric entropy estimators have been proposed by the researchers. This work explores kernel density estimation based on Tsallis entropy. Firstly, it has been proved that for linearly independent samples and for equal samples, Tsallis-estimator is consistent for the PDF and minimum respectively. Also, it is investigated that Tsallisestimator is smooth for differentiable, symmetric, and unimodal kernel function. Further, important properties of Tsallis-estimator such as scaling and invariance for both single and joint entropy estimation have been proved. The objective of the work is to understand the mathematics behind the underlying concept.
Blockchain and machine learning in education: a literature review Alhabeeb, Sarah; Alrusayni, Norah; Almutiri, Reem; Alhumud, Sarah; Al-Hagery, Mohammed Abdullah
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.pp581-596

Abstract

There is a growing influence around the use of technology in education, with many solutions already being implemented and many others being explored. Using various forms of technology to assist the educational process has increased dramatically in the previous decade in education systems in many respects. Both machine learning and the blockchain have had a significant impact on education. The purpose of this study is to conduct a literature review on the application of machine learning and blockchain technology in educational institutions. Additionally, this study examines the potential applications, benefits, and challenges those educational institutions may face as a result of using machine learning and blockchain technologies. Using machine learning and blockchain in educational systems will have a positive impact on the entire educational process and student achievement. Researchers, academics, and practitioners will benefit from this study to focus on a wider range of educational applications and solve the related issues of machine learning and blockchain technology in the education sector.
Detection of chronic kidney disease using binary whale optimization algorithm Sutikno, Sutikno; Kusumaningrum, Retno; Sugiharto, Aris; Arif Wibawa, Helmie
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.pp1511-1518

Abstract

Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.
An ensemble features aware machine learning model for detection and staging of dyslexia Mulakaluri, Sailaja; Gowdra Shivappa, Girisha
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.pp3147-3156

Abstract

Dyslexia is a specific learning disorder (SLD) which may affect young child's cognitive skills, text comprehension, reading-writing and also problemsolving abilities. To diagnose and identify dyslexia, the testing scale tool has been proposed using artificial intelligence technique. The proposed tool allows the student who is suspected to have dyslexia to take up quiz and perform certain task based on the type of learning impairments. After completion of the test, resultant data is provided as input to the proposed ensemble feature aware machine-learning (EFAM) XGBoost (XGB) model. Based on the student assessment score and time taken by children, the EFAMXGB algorithm predicts dyslexia. The proposed EFAM-XGB is used to develop an integrated and user-friendly tool that is highly accurate in identifying reading disorders even with presence of realistic imbalanced dataset and suggest the most appropriate instructional activities to parents and teachers. The EFAM-XGB-based dyslexia detection method achieves very good accuracy of 98.7% for dyslexia dataset; thus, attain better performance in comparison with existing machine learning (ML)-based methodologies.
Automatic detection of broiler’s feeding and aggressive behavior using you only look once algorithm Wahjuni, Sri; Wulandari, Wulandari; Eknanda, Rafael Tektano Grandiawan; Susanto, Iman Rahayu Hidayati; Akbar, Auriza Rahmad
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.pp104-114

Abstract

The high market demand for broiler chickens requires that chicken farmers improve their production performance. Production cost and poultry welfare are important competitiveness aspects in the poultry industry. To optimize these aspects, chicken behavior such as feeding and aggression needs to be observed continuously. However, this is not practically done entirely by humans. Implementation of precision live stock farming with deep learning can provide continuous, real-time and automated decisions. In this study, the you only look once version 4 (YOLOv4) architecture is used to detect feeding and aggressive chicken behavior. The data used includes 1,045 feeding bounding boxes and 753 aggressive bounding boxes. The model training is performed using the k-fold cross validation method. The best mean average precision (mAP) values obtained were 99.98% for eating behavior and 99.4% for aggressive behavior.
Fault-tolerant control of a permanent magnet synchronous motor based on hybrid control strategies using a multi-variable filter Bahiddine, Miloud; Belhamra, Ali
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.pp2111-2121

Abstract

This paper describes direct torque control (DTC) of a permanent magnet synchronous machine (PMSM) powered by a two-level voltage inverter whose switching of switches is based on the space vector modulation (SVM). To enhance the robustness of control in the presence of faults, we have enhanced DTC with SVM by incorporating a multivariable filter to address fault current effects. In this modification, traditional proportionalintegral controllers are substituted with sliding mode controllers. This approach results in a revised structure known as DTC-SVM with sliding mode control, presenting a new fault-tolerant control diagram. A comparative study between these different control strategies is carried out. The simulation results show clearly the interest of a multi-variable filter association with the DTC-SVM-sliding mode control2 (SMC2) in degraded mode. The outcomes achieved provide substantial evidence supporting the resilience and efficacy of this approach, especially when confronted with the influence of the faults current.

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