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
 113   114   115   116   117 
Optimizing the position of photovoltaic solar tracker panels with artificial intelligence using MATLAB Simulink Linelson, Ricardo; Rinanda Saputri, Fahmy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4003-4018

Abstract

This research aims to apply an artificial intelligence (AI) system to control the position of photovoltaic (PV) panels to maximize the use of solar energy using the solar tracker. The implementation of AI algorithms to achieve optimal panel orientation, considering factors such as sunlight intensity and sun position is also discussed. The simulation results using matrix laboratory (MATLAB) Simulink can be observed on the scope, displaying the position control graph of the solar panel from sunrise to sunset. By employing proportional integral derivative (PID) control, the error is likely to be minimal, ensuring that the panel will continue to follow the sun until it sets at the maximum point of 4:00 PM. After that, the panel can be adjusted back or reset to the initial position at 6:00 AM for the following day. In a full-day simulation, the solar panel will follow the sun's movement from sunset to sunrise. At the basic level, sunrise occurs in the first hour at position 1.0, which is 6:00 AM in the minimum point at the bottom left corner of the curve, and sunset occurs in the afternoon at position 5.25, which is 4:00 PM at the maximum point in the top right corner of the curve.
Optimizing seismic sequence clustering with rapid cube-based spatiotemporal approach Hasana, Silviya; Sari, Wina Permana; Rojali, Rojali; Fitrianah, Devi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp737-748

Abstract

Due to their extensive volume and range of features, seismic data is regarded as highly complex data. Earthquakes that typically composed of foreshocks, mainshocks, and aftershocks, exhibit a unique sensitivity to temporal dimension, a characteristic that differs them from other natural hazards. Foreshocks and aftershocks that emanate from a similar epicenter, often display temporal patterns that contribute significantly to determining a sequence. This study introduces a density cube-based approach to cluster spatiotemporal seismic data. It addresses spatial irregularities observed in earthquake clusters and incorporates temporal aspects, acknowledging that seismic events originating from a similar epicenter could occur in separate time frames. We achieved the highest Silhouette score of 0.935 in daily-based clustering and 0.782 in weekly-based clustering. Notably, our analysis reveals a trend where weekly clustering lambda λ tend to be lower (λ=0.01) than in daily clustering (λ=0.1, λ=0.5), thus emphasizing the significance of temporal granularity where daily clustering requires higher λ to capture rapid fluctuations, while weekly clustering benefits from lower λ to cover broader trends. These findings enhance the understanding of the nuanced interplay of temporal dynamics in seismic sequence analysis.
Optimized triangular observer based adaptive supertwisting sliding mode control for wind turbine system El Bouassi, Sanae; El Afou, Youssef; Chalh, Zakaria; Mellouli, El Mehdi; Haidi, Touria
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4229-4240

Abstract

This paper presents a modified adaptive supertwisting sliding mode controller (AST-SMC) that dynamically adjusts control settings without prior knowledge of uncertainty limits, thereby removing chattering and putting reliability first while maintaining the original benefits of sliding mode control (SMC). First, we model and build the wind turbine system using three different controllers: the AST-SMC, the supertwisting sliding mode controller (ST-SMC), and the first-order sliding mode controller (FOSMC). A second comparison is necessary. Only the rotor speed is available to the control law because of concealed state information, which makes use of the full system state. In order to minimize observing errors over time, an asymptotic observer triangle is used to estimate the unknown rotor acceleration. By improving AST-SMC's control law, particle swarm optimization finds the most effective controller. The stability of AST-SMC over a finite time is shown via the Lyapunov stability theorem. Based on simulation findings, it is proven to be more effective than traditional SMC in wind turbine system control. It excels in settling time, tracking accuracy, energy consumption, and control input smoothness.
Regularized Xception for facial expression recognition with extra training data and step decay learning rate Azrien, Elang Arkanaufa; Hartati, Sri; Frisky, Aufaclav Zatu Kusuma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4703-4710

Abstract

Despite extensive research on facial expression recognition, achieving the highest level of accuracy remains challenging. The objective of this study is to enhance the accuracy of current models by adjusting the structure, the data used, and the training procedure. The incorporation of regularization into the Xception architecture, the augmentation of training data, and the utilization of step decay learning rate together address and surpass current constraints. A substantial improvement in accuracy is demonstrated by the assessment conducted on the facial expression recognition (FER2013) dataset, achieving a remarkable 94.34%. This study introduces potential avenues for enhancing facial expression recognition systems, specifically targeting the requirement for increased accuracy within this domain.
Revolutionizing cancer classification: the snr-ogscc method for improved gene selection and clustering Bouazza, Sara Haddou; Bouazza, Jihad Haddou
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp466-472

Abstract

This study presents the signal-to-noise ratio optimized gene selection and clustering for cancer classification (SNR-OGSCC) methodology, aimed at enhancing classification accuracy while reducing the dimensionality of gene expression data across various cancer types. Implemented on a standard computational setup, the SNR-OGSCC method combines advanced filtering, clustering, and machine learning techniques, demonstrating significant improvements in classification accuracy on seven cancer datasets: leukemia, colon cancer, prostate cancer, lung cancer, lymphoma, central nervous system (CNS) tumors, and ovarian cancer. Notably, our approach achieved perfect accuracies of 100% for leukemia, lung cancer, and ovarian cancer, with high accuracies of 98.4% for colon cancer, 99.1% for prostate cancer, 98.3% for lymphoma, and 99.7% for CNS tumors, while requiring as few as 4–5 genes for effective classification. These findings highlight the efficiency and robustness of the SNR-OGSCC methodology, suggesting its potential to identify meaningful biomarkers and improve personalized cancer treatment strategies. Further validation with larger datasets and biological experiments is essential to confirm its applicability in clinical settings.
Automated diagnosis of brain tumor classification and segmentation of magnetic resonance imaging images B. Muddaraju, Chandrakala; Shrinivasa, Shrinivasa; Narasimhamurthy, Shobha; Sontakke, Vaishali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4833-4842

Abstract

Brain tumors are one of the most prevalent disorders of the central nervous system and are dangerous. For patients to receive the best treatment, early diagnosis is crucial. For radiologists to correctly detect brain tumor images, an automated approach is required. The identification procedure can be time-consuming and prone to mistakes. In this work, the issue of fully automated brain tumor classification and segmentation of magnetic resonance imaging (MRI) including meningioma, glioma, pituitary, and no tumor is taken into consideration. In this study, convolutional neural network (CNN) and mask region-based convolutional neural network (R-CNN) are proposed for classification and segmentation problems respectively. This study employed 3,200 images as a training set and the system achieved an accuracy of 96% for classifying the tumors and 94% accuracy in segmentation of tumors.
Federated inception-multi-head attention models for cyber-attacks detection AL-Halboosi, Imad Tareq; Mohamed Elbagoury, Bassant; Amin El-Regaily, Salsabil; M. El-Horbaty, El-Sayed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4778-4794

Abstract

With the proliferation of internet of things (IoT) devices, ensuring the security of these interconnected systems has become a critical concern. Cyberattacks targeting IoT devices pose significant threats to individuals and organizations due to the generation of vast amounts of data across many connected devices, which traditional centralized methods cannot solve. Federated learning (FL) could be a promising solution to mitigate privacy concerns associated with centralized approaches and address cybersecurity concerns. This paper uses FL and deep learning (DL) approaches to cybersecurity in IoT applications. The goal of cyber security is achieved by forming a federation of acquired and shared models at the head of the various participants. We use inception time and multi-head attention (CNN) algorithm based on FL to detect cyber-attacks and avoid data privacy leaks under two distribution modes, namely IID and Non-IID. In contrast, the FedAvg and FedMA algorithms aggregate local model updates. A global model is produced after several communication rounds between the IoT devices and the model parameter server. Cyber threats are simulated using edge-IIoT datasets. Experiment results show that the federated inception model's best global accuracy was 93, 91%, and 93, 49% using multi-head attention.
Mobile robot localization using visual odometry in indoor environments with TurtleBot4 Singh, Gurpreet; Goyal, Deepam; Kumar, Vijay
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp760-768

Abstract

Accurate localization is crucial for mobile robots to navigate autonomously in indoor environments. This article presents a novel visual odometry (VO) approach for localizing a TurtleBot4 mobile robot in indoor settings using only an onboard red green blue – depth (RGB-D) camera. Motivated by the challenges posed by slippery floors and the limitations of traditional wheel odometry, an attempt has been made to develop a reliable, accurate, and low-cost localization solution. The present method extracts oriented FAST and rotated BRIEF (ORB) features for feature extraction and matching using brute-force matching with Hamming distance. The essential matrix is then computed using the 5-point algorithm and decomposed to recover the relative rotation and translation between poses. The absolute pose is obtained by chaining the incremental motions estimated from VO. Through experimentation and comparison with wheel odometry, the findings demonstrate the effectiveness of our VO system, achieving a positional accuracy with minimal error of 4-5%. The article also compares VO with wheel odometry and shows the advantages of using a visual approach, especially in environments with slippery floors where wheel slippage causes large odometry errors. Overall, this work presents an effective VO system for reliable, accurate, and low-cost localization of TurtleBot4 in indoor environments without relying on external infrastructure.
Anchor selection based deep learning two stage fabric defect localization Pooja, Hattarki; Soma, Shrideva
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4711-4721

Abstract

Localizing and classifying fabric defects is a crucial step in the quality control process used in the production of textiles. Recently, fabric defect classification and detection have made use of deep learning approaches based on anchor selection. But due to in effectiveness in anchor selection, the computational overhead and localization error are higher in these solutions. As a solution to this problem, this work proposes a two-stage improvised anchor selection deep learning technique. In first stage, quaternion fourier transform frequency domain analysis along with super pixel segmentation is done over the fabric image to select probable defect regions. In the second stage deep learning based regression along with super pixel segment comparison is done over the probable defect regions localize and categorize the defect. Due to effectiveness in selection of probable defect regions and categorization of regions, the defect localization accuracy is increased at a comparative low computational overhead in the proposed two stage improvise anchor selection deep learning technique. Testing against the irish longitudinal study on ageing (TILDA) fabric defect detection dataset, the proposed solution is found to provide 1.2% higher fabric defect localization accuracy at a 3% lower computation overhead compared to most recent existing works.
Artificial intelligence-driven method for the discovery and prevention of distributed denial of service attacks ALDabbas, Ashraf; Baniata, Laith H.; AlSaaidah, Bayan A.; Mustafa, Zaid; Alali, Muath; Rateb, Roqia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp614-628

Abstract

Distributed denial of service (DDoS) attacks has emerged as a prominent cyber threat in contemporary times. By impeding the machine's capacity to give services to legitimate clients, the impacted system performance and buffer size are reduced. Researchers are working to build sophisticated algorithms that can identify and thwart DDoS violations. An effective approach for DDoS attacks has been proposed in this work. This research presents a model as a potential explanation for DDoS assaults. In order to successfully identify this kind of attacks, which may stop or block the urgent and vital transmission of data, we present a distinctive method that integrates a pair of fully connected layers within an amalgamated deep learning (DL) framework with long short-term memory (LSTM) and a max pooling layer. The acquired accuracy reached 99.58%.

Page 115 of 181 | Total Record : 1808

 113   114   115   116   117 

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