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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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Automated classification of age-related macular degeneration from optical coherence tomography images using deep learning approach Gilakara Muni Nagamani; Theerthagiri Sudhakar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp2011-2021

Abstract

Early detection of macular diseases can prevent vision loss. Manual screening can be unreliable due to the similarity in the pathological presentations of common retinal illnesses like age-related macular degeneration (AMD). Researchers are becoming more interested in the accurate automated computer-based detection of macular diseases. Using healthy optical coherence tomography (OCT) images, the drusens (early stage) and choroidal neovascularization (CNV) (late stage) of AMD are thus classified using a completely different approach in this paper. The new deep learning (DL) model is proposed for multiple OCT image segmentation of ophthalmological diseases using attention-based nested U-Net (ANU-Net). The flower pollination optimization algorithm (FPOA) is used to optimize the hyperparameters of the network. The SqueezeNet-based classification can be made in the pre-processed images. A dataset from the University of California San Diego (UCSD) is used to evaluate the proposed method. 98.7% accuracy, 99.8% specificity, and 99.7% sensitivity are achieved by the proposed method. The proposed method produces better identification results for automated preliminary diagnosis of macular diseases in hospitals and eye clinics due to the positive classification results.
Prototyping of e-fisherman web server to support Indonesian fishermen’s activities Syifaul Fuada; Trio Adiono
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1960-1973

Abstract

This paper presents a webserver for Indonesian fishermen, to support fishing activities. This is one of the sub-systems of e-Nelayan (in English: eFisherman) architecture, which was connected to e-Nelayan Apps; it helps to provide interaction between two users, including the administrators and fishermen. Using hypertext preprocessor (PHP) language, the website was developed to function on an Apache web server, with the adaptation of my structured query language (MySQL) framework for the database. This system was subsequently divided into two parts: (1) the front-end, which is responsible for the accessibility of data collection and (2) the back-end, where administrators update or modify crucial information: price, fishing result, illegal activity report, save our ship! (SOS) potential fish zone, and ship tracking. The administrators are unable to update the real-time weather information for the front-end part. The application was found to record the information obtained from the fishermen through the e-Nelayan apps and meteorology, climatology, and geophysical agency (BMKG in Indonesian). This web system is expected to carry out the following functions: to ensure easier interactions between fishermen and administrators, to enable easy update of information, to promote monitoring and recording of results, and to ensure fishermen’s safety.
COVID-19 digital x-rays forgery classification model using deep learning Eman I. Abd El-Latif; Nour Eldeen Khalifa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1821-1827

Abstract

Nowadays, the internet has become a typical medium for sharing digitalimages through web applications or social media and there was a rise inconcerns about digital image privacy. Image editing software’s have preparedit incredibly simple to make changes to an image's content without leavingany visible evidence for images in general and medical images in particular.In this paper, the COVID-19 digital x-rays forgery classification modelutilizing deep learning will be introduced. The proposed system will be ableto identify and classify image forgery (copy-move and splicing) manipulation.Alexnet, Resnet50, and Googlenet are used in this model for feature extractionand classification, respectively. Images have been tampered with in threeclasses (COVID-19, viral pneumonia, and normal). For the classification of(Forgery or no forgery), the model achieves 0.9472 in testing accuracy. Forthe classification of (Copy-move forgery, splicing forgery, and no forgery),the model achieves 0.8066 in testing accuracy. Moreover, the model achieves0.796 and 0.8382 for 6 classes and 9 classes problems respectively.Performance indicators like Recall, Precision, and F1 Score supported theachieved results and proved that the proposed system is efficient for detectingthe manipulation in images. 
Transmission line impulse response modelling using machine learning techniques Wei Min Lim; Khin Leong How; Chan Hong Goay; Nur Syazreen Ahmad; Patrick Goh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1577-1584

Abstract

Conventional methods of circuit simulation such as full-wave electromagnetic fieldsolvers can be very slow. Machine learning is an emerging technology in modelling, simulation, optimization, and design that present attractive alternatives to the conventional methodologies because they can be trained with a small amount of data, and then used to perform fast circuit predictions within the same design space. In this paper, we present applications of machine learning techniques for the modelling of transmission lines from their impulse reponses. The standard multilayer perceptron (MLP) neural network and the gaussian process (GP) regression techniques are demonstrated, andboth models are successfully implemented to model the impulse responses of transmission lines with great accuracies. We show that the GP outperforms the MLP in terms of prediction accuracies and that the GP is more data efficient than the MLP. This is beneficial considering that each training sample is expensive, making the GP a good candidate for the task, compared to the more popular MLP.
Hybrid software defined network-based deep learning framework for enhancing internet of medical things cybersecurity Rbah, Yahya; Mahfoudi, Mohammed; Balboul, Younes; Chetioui, Kaouthar; Fattah, Mohammed; Mazer, Said; Elbekkali, Moulhime; Bernoussi, Benaissa
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.pp3599-3610

Abstract

The risk of cyber-attacks has increased significantly with the rapid development of the Internet of Medical Things (IoMT). The proliferation of IoMT devices in healthcare facilities has made conventional intrusion detection approaches challenging to employ. Our study proposes a novel hybrid framework leveraging Software Defined Network (SDN) controllers and deep learning techniques, specifically Convolutional Neural Networks (CNN) and Bidirectional Long-Term Memory (Bi-LSTM), to address these challenges. Our framework introduces a unique combination of SDN and deep learning, allowing for dynamic and efficient management of IoMT security. The integration of CNN and Bi-LSTM enables the system to handle diverse data types encountered in IoMT, offering a comprehensive approach to threat detection. Unlike traditional methods, our hybrid solution adapts seamlessly to the evolving threat landscape of healthcare IoT systems. The urgency of our research is affirmed by the critical need to fortify IoMT security amid escalating cyber threats. The conventional methods struggle to cope with the complex nature of IoMT networks, making our exploration of a hybrid SDN-based deep learning framework imperative. With a background in cybersecurity and a dedicated focus on healthcare IoT, we recognize the urgency to develop a solution that not only enhances detection accuracy but also ensures real-time responsiveness in healthcare settings. The proposed method has been validated using the “IoT-Healthcare security” dataset, revealing its efficacy in detecting numerous frequent threats and outperforming current state-of-the-art techniques in terms of high detection accuracy of 99.97% and less than 1.8 (s) in terms of speed efficiency.
Comparison of feature extraction and auto-preprocessing for chili pepper (Capsicum Frutescens) quality classification using machine learning Asian, Jelita; Arianti, Nunik Destria; Ariefin, Ariefin; Muslih, Muhamad
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.pp319-328

Abstract

The low-cost camera for machine vision, such as a webcam, still has a problem with resolution noise. Therefore, it is important to learn strategies to reduce noise from low-cost camera images so that they can be widely used for grading machines in the future. This paper aims to compare three feature extraction methods with auto-preprocessing to classify chili pepper (Capsicum Frutescens) quality using a machine learning algorithm. Three extraction methods were used, including the color feature, oriented FAST and rotated BRIEF (ORB), and the combination color feature and ORB. A total of 525 image data for quality chili pepper were collected using the webcam. The auto-preprocessing strategy to classify chili peppers can improve the performance of machine-learning algorithms for all data generated by the feature extractor. The performance of the chili paper quality classification model with auto-preprocessing of the variable color feature can improve the performance of machine learning algorithms by up to 64.21%. The performance improvement of the classification model using the ORB feature variable and the auto-preprocessing of up to 4.41%. The performance improvement of the classification model using machine learning algorithms is 11.27% when using the combination color feature and ORB feature and auto-preprocessing.
Comparative analysis of explainable artificial intelligence models for predicting lung cancer using diverse datasets Makubhai, Shahin; R Pathak, Ganesh; R Chandre, Pankaj
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.pp1980-1991

Abstract

Lung cancer prediction is crucial for early detection and treatment, and explainable artificial intelligence (XAI) models have gained attention for their interpretability. This study aims to compare various XAI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, preprocessed, and used to train models such as logistic regression (LR), support vector classifier (SVC)-linear, SVC-radial basis function (RBF), decision tree (DT), random forest (RF), adaboost classifier, and XGBoost classifier. Preliminary results indicate that RF achieved the highest accuracy of 98.9% across multiple datasets. Evaluation metrics such as accuracy, precision, recall, and F1 score were utilized, along with interpretability techniques like feature importance rankings and rule extraction methods. The study's findings will aid in identifying effective and interpretable AI models, facilitating early detection and treatment decisions for lung cancer
Fragmented-cuneiform-based convolutional neural network for cuneiform character recognition Prasetiadi, Agi; Saputra, Julian
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.pp554-562

Abstract

Cuneiform has been a widely used writing system in one of the human history phases. Although there are millions of tablets, have been excavated today, only around 100,000 tablets have been read. The difficulty in translating also increased if the tablet has damaged areas resulting in some of its characters become fragmented and hard to read. This paper investigates the possibility of reading fragmented cuneiform characters from Noto Sans Cuneiform font based on convolutional neural network (CNN). The dataset is built on extracted 921 characters from the font. These characters are then intentionally being damaged with specific patterns, resulting set of fragmented characters ready to be trained. The model produced by this training phase then being used to read the unseen fragmented pattern of cuneiform sets. The model also being tested for reading normal characters set. From the simulation, 83.86% accuracy of reading fragmented characters are obtained. Interestingly, 96.42% accuracy is obtained while the model is being tested for reading normal characters.
New disturbance observer-based speed estimator for induction motor Indriawati, Katherin; Pandu Wijaya, Febry; Mufit, Choirul
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.pp3510-3522

Abstract

This paper discusses a novel disturbance observer designed as an estimator to determine the rotor speed of an induction motor. This observer is a solution to obtain a simple structure with a small number of compact observer gains. Furthermore, the adaptation law is no longer required to estimate induction motor speed values. This is a machine model-based computation method that uses a stationary reference frame. The nonlinearity problem is solved using an additional state vector in the observer model, which is known as an extended state observer. This approach easily and systematically determines the observer gain by applying the linear quadratic regulator (LQR) method, thereby avoiding time-consuming trial errors. The proposed observer, which was presented in both continuous and discrete forms, was tested using a sensorless V/f- controlled induction motor. The simulation results show that the proposed observer can accurately estimate all states, namely, the rotor flux and stator current; therefore, the proposed estimator provides the speed and electromagnetic torque for a wide operational range of speeds and load torques. It was also shown that the proposed observer was robust to noise and uncertainty in induction motor parameters.
Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales Arini Parhusip, Hanna; Trihandaru, Suryasatriya; Indrajaya, Denny; Labadin, Jane
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.pp3291-3305

Abstract

You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The small number of data points and the small number of epochs have proven reliable algorithms to implement on store products instead of using QR codes in a digital manner. Five models are examined, i.e., YOLOv8-seg, YOLOv8s-seg, YOLOv8m-seg, YOLOv8l-seg, and YOLOv8x-seg, with a data distribution of 64% for the training dataset, 16% for the validation dataset, and 20% for the testing dataset. The best model, YOLOv8l-seg, was obtained with the highest mean average precision (mAP) box value of 99.372% and a mAPmask value of 99.372% from testing the testing dataset. However, the YOLOv8mseg model can be the best alternative model with a mAPbox value of 99.330% since the number of parameters and the computational speed are the best compared to other models.

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