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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 51 Documents
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Search results for , issue "Vol 12, No 3: September 2023" : 51 Documents clear
Human activity recognition method using joint deep learning and acceleration signal Maytham N. Meqdad; Abdullah Hasan Hussein; Saif O. Husain; Alyaa Mohammed Jawad; Seifedine Kadry
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1459-1467

Abstract

Many studies have been conducted on human activity recognition (HAR) in the last decade. Accordingly, deep learning algorithms have been given more attention in terms of classification of human daily activities. Deep neural networks (DNNs) compute and extract complex features on voluminous data through some hidden layers that require large memory and powerful graphics processing units (GPUs). So, this study proposes a new joint learning (JL) approach to classify human activities using inertial sensors. To this end, a large complex donor model based on a convolutional neural network (CNN) is used to transfer knowledge to a smaller model based on CNN referred to as the acceptor model. The acceptor model can be deployed on mobile devices and low-power hardware due to decreased computing costs and memory consumption. The wireless sensor data mining (WISDM) dataset is used to test the proposed model. According to the experimental results, the HAR system based on the JL algorithm outperforms than other methods.
Colour image steganography through channel transformation approach Jyoti Neginal; Ruksar Fatima
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1062-1069

Abstract

The interest in information security strategies is expanding due the quick expansion in the use of media content and transmission over the web employments. Accordingly, there is a need of special embedding procedure for steganography. The creator presents an original reversible data hiding (RDH) algorithm for colour pictures that further develops the inserting execution by applying a channel transformation function and a versatile expectation blunder extension prediction error expansion structure. The proposed calculation will bring the first colour picture with no information misfortune from the inserted picture (which is unique in addition to target picture). The evaluation is done here at the zero for the given base place for the histogram that marginally changes the pixel esteems for inserting the information. It can insert more information when contrasted with the majority of the current data hiding calculations. A hypothetical confirmation and various examinations state that the embedding capability of the proposed model which consistently is further noteworthy in comparison with other reversible data hiding techniques. The calculation has been applied to a wide scope of various colour pictures effectively. Some exploratory outcomes are introduced to show the legitimacy of the calculation.
Analysis of machine learning classifiers for predicting diabetes mellitus in the preliminary stage Mohammad Atif; Faisal Anwer; Faisal Talib; Rizwan Alam; Faraz Masood
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1302-1311

Abstract

Diabetes is the most common disease all over the world and it must be detected early to receive proper treatment, which can prevent the condition from becoming more severe. Automated detection plays an essential role in diabetes early diagnosis. Over the last few decades, many complicated machine learning algorithms and data analysis approaches have been applied for diabetes prediction. To determine the best model for early-stage diabetes prediction, ten different machine learning classifiers have been used in this study. These models were evaluated in terms of accuracy, precision, specificity, recall, F1-score, negative predictive value (NPV), false positive rate (FPR), rate of misclassification, and receiver operating characteristics (ROC) curve. The experimental findings indicated that all of the models performed well. Gradient boosting (GB), with 97.2% accuracy, is observed to show the best performance on the early-stage diabetes risk prediction dataset. Random forest (RF) and Adaboost performed similarly to the GB; however, RF and Adaboost's precision was not as good as the GB precision (GB’s).
Lexicon-based sentiment analysis for Kannada-English code-switch text Ramesh Chundi; Vishwanath R. Hulipalled; Jay Bharthish Simha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1500-1507

Abstract

Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic and product. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning, machine learning, or a Lexicon-based approach. Machine learning and deep learning methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with machine learning and deep learning. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better Accuracy of 83.2% and 83% of F1-score. 
Artificial intelligence-based lead propensity prediction Aissam Jadli; Mustapha Hain; Anouar Hasbaoui
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1281-1290

Abstract

Lead propensity prediction is a data-driven method used to define the value of prospects, by assigning points to them based on their engagement with the business's digital channels, based on multiple key attributes correlating to their attraction to the proposed services or items. The resulting score is closely related to the financial worth of each lead and may be revealing its position in the buying cycle. The marketing teams can then focus on generated leads and prioritize the most prominent ones to improve the conversion rates, using the assigned score on the lead scoring step. The authors investigated using a combination of a data-driven approach and Artificial intelligence (AI) techniques for the lead-scoring process. The experimentation shows that the random forest (RF) is the most suitable model for this task with an accuracy score of 93.04% followed by the decision tree (DT) model of 91.47%. In contrast, when considering the training time, DT and logistic regression (LR) needed a shorter time to learn from the dataset while maintaining decent performances. In contrast, these models represent promising alternatives to the RF model especially in the case of a huge volume of transactions and prospects or in a big data context. 
High performance of optimizers in deep learning for cloth patterns detection Irma Amelia Dewi; Mahesa Atmawidya Negara Ekha Salawangi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1407-1418

Abstract

In deep learning, optimization methods are an essential role. Optimizers are used to change weights and learn rates to reduce or minimize losses in a neural network. Nowadays, deep learning is widely used, especially in object detection tasks. In this case, cloth patterns are considered for object detection and assist visually impaired people. The visually impaired person had many limitations when doing their activity, not least when choosing clothes; it would be difficult without guidance or tools like Braille labels. In this research, a system was researched to detect 11 different cloth patterns (Argyle, Batik, Camouflage, Gingham, Dotted, Floral, Leopard, Solid, Striped, Zebra, and Zigzag) using RetinaNet with ResNet-152 architecture. To achieve the best model performance, compare 6 optimizers, such as Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Moment Estimation, Adaptive Delta, Adaptive Norm, and Adaptive Gradient was conducted. Each optimizer has trained with three different learning rates (1E-3, 1E-4, and 1E-5). A model with Adamax optimizer and learning rate 1E-4 was achieved with the highest accuracy with mAP (mean Average Precision) 91.28% during the training process. Based on the testing result this model was achieved precision 93.01%, recall 92.91%, F1-Score 92.79%, and accuracy 92.91%. 
The evaluation of convolutional neural network and genetic algorithm performance based on the number of hyperparameters for English handwritten recognition Muhammad Munsarif; Edi Noersasongko; Pulung Nurtantio Andono; Moch Arief Soeleman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1250-1259

Abstract

Convolutional neural network (CNN) has been widely applied to image recognition, especially handwritten English recognition. CNN's performance is good if the hyperparameter values are correct. However, the determination of precise hyperparameters is not a trivial task. This task is made more difficult when combined with a larger number of hyperparameters resulting in a high dimensionality of the search space. Usually, hyperparameter optimization uses a finite number. Previous studies have shown that a large number of hyperparameters can result in optimal CNN performance. However, the studies only apply to text mining datasets. This study offers two novelties. First, it applied 20 hyperparameters and their ranges to handwritten English. Second, this paper conducted seven experiments based on different hyperparameters and the number of hyperparameters. This paper also compares the existing methods, namely random and grid search. The experiment resulted in the proposed model being superior to the existing methods. EX3 is better than other experiments and a larger number of hyperparameters and layer-specific hyperparameter values are unimportant.
Image preprocessing and hyperparameter optimization on pretrained model MobileNetV2 in white blood cell image classification Parmonangan R. Togatorop; Yohanssen Pratama; Astri Monica Sianturi; Mega Sari Pasaribu; Pebri Sangmajadi Sinaga
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1210-1223

Abstract

White blood cells play a role in maintaining the immune system which consists of several types such as neutrophils, lymphocytes, monocytes, eosinophils and basophils. MobileNetV2 is one of the pretrained convolutional neural network (CNN) models that provides excellent advantages and performance in classifying images. In this research was conducted to find out how to apply optimization hyperparameters and the impact of image processing on white blood cell image classification using MobileNetV2, so that it is expected to find a combination of preprocessing and combination of hyperparameter values that can produce the highest accuracy value. To maximize the classification process, before classifying the image, several stages of image preprocessing are carried out, namely cropping, grayscale, resizing and augmentation. Hyperparameter tuning was carried out for an experiment to improve model performance. The three main parameters used in hyperparameter tuning are learning rate, batch size, and number of epochs. Performance optimization model performance will be measured using accuracy, sensitivity, specificity and using a confusion matrix. Based on the experimental results in this study, it shows that the best learning rate value is 0.00001, the best batch size value is 32, and the best epoch value is 250.
Background subtraction challenges in motion detection using Gaussian mixture model: a survey Nor Afiqah Mohd Aris; Siti Suhana Jamaian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1007-1018

Abstract

Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-performing technique due to various challenges. The degree of difficulty for this challenge is primarily determined by how the object to be detected is defined. It could be influenced by the changes in the object posture or deformations. In this context, we describe and bring together the most significant challenges faced by the background subtraction techniques based on GMM for dealing with a crucial background situation. Therefore, the findings of this study can be used to identify the most appropriate GMM version based on the crucial background situation.
Earthquake prediction technique: a comparative study Abbas H. Hassin Alasadi; Kadhim Mahdi Hashim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1026-1032

Abstract

Earthquakes are one of the most dangerous natural disasters facing humans because of their occurrence without warning and their impact on their lives and property. In addition, predicting seismic movement is one of the main research topics in seismic disaster prevention. In geological studies, scientists can predict and know the locations of earthquakes in the long term. Therefore, about 80% of the major global earthquakes lie along the Pacific Ring belt, known as the Ring of Fire. Machine learning methods have also been used for short-term earthquake prediction, and studies have applied the random forest method to determine the factors that precede earthquakes. The machine learning method was based on various decision trees, each of which predicted the time to the nearest oscillation. The third group of scientists used the hybrid prediction method, which combines machine learning and geological studies. This research deals with a review of most of the geological studies and machine learning techniques applied to earthquake data sets, which showed a total lack of prediction of potential earthquakes through one approach, so studies designed by geologists were combined with machine learning.

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