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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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Two-step convolutional neural network classification of plant disease Lumbantoruan, Rosni; Rajagukguk, Nico; Lubis, Anju Ucok; Claudia, Marwani; Simanjuntak, Humasak
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.pp584-591

Abstract

Indonesia is primarily an agricultural country, with farming being the primary source of income for most of its people. Unfortunately, crop production is vulnerable to plant diseases, which are usually caused by plant pests, resulting in a reduction in both the quantity and quality of the expected harvest. In addition to the large number of classes to predict, detecting and accurately classifying each disease on different plants can be difficult. We believe that limiting the number of classes to identify may improve classification accuracy. Thus, in this research, we propose a new approach, two-step convolutional neural network (CNN), which reduces the number of classes with a two-step classification approach. To begin, we identify the number of classes that can be reduced by categorizing them into different characteristics, namely, plant type classification and plant condition classification. Second, we deal with unbalanced datasets, which can result in poor performance, if overlooked. Finally, we compare the proposed two-step CNN to baseline CNN in terms of efficiency and effectiveness. Extensive experiments show that the two-step CNN outperforms the baselines, CNN and jellyfish-residual network (JF-ResNet), increasing accuracy by 4% and 2% to 99%, respectively. In addition, we also provide a simulation evaluation to ensure that this approach is applicable.
Character N-gram model for toxicity prediction Shehab, Eman; Nayel, Hamada; Taha, Mohamed
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.pp4380-4387

Abstract

Molecular toxicity prediction is a crucial step in the drug discovery process. It has a direct relationship with human health and medical destiny. Accurately assessing a molecule’s toxicity can aid in the weeding out of low-quality compounds early in the drug discovery phase, avoiding depletion later in the drug development process. Computational models have been used automatically for molecular toxicity prediction. In this paper, a machine learning-based model has been proposed. TF/IDF representation scheme has been used for N-gram and integrated with simplified molecular-input line-entry system (SMILES). Multiple machine learning classifiers such as logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), AdaBoost, multi-layer perceptron (MLP), and stochastic gradient descent (SGD) classifiers have been implemented. A wide range of N-gram models have been implemented and trigram reported the best results. RF and SVM achieved 85% and 84% accuracy respectively. Comparable to state-of-the-art models, our results are acceptable as we used minimum available resources.
A new texture descriptor for handwritten document writer identification Lazrak, Said; Sadiq, Abbelalim; Semma, Abdelillah; Hannad, Yaˆacoub
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.pp4594-4607

Abstract

Writer identification is a critical task in the realm of pattern classification, aimed at determining the authorship of a manuscript based on labeled handwriting sam- ples. This area has garnered considerable attention from researchers and has seen significant advancements in the last two decades, propelled by the inte- gration of novel computer vision and machine learning algorithms. Commonly, approaches within this field rely on calculating local texture descriptors of im- ages. In this work, we propose a novel local texture descriptor method, termed multi-points local binary patterns (MP-LBP), which is an enhancement of the traditional local binary patterns (LBP) descriptor. Our approach involves apply- ing the MP-LBP descriptor to patches surrounding Harris key points and aggre- gating the image descriptors into encoded vectors using the vector of locally ag- gregated descriptors (VLAD) encoding method. These vectors are subsequently classified by a ball tree classifier to associate the document with the most plau- sible writer. To assess the efficacy of our descriptor, we conducted evaluations on five publicly accessible handwritten databases: CVL, CERUG-EN, CERUG- CH, BFL, and IAM. The results of these tests provide insights into the perfor- mance of the MP-LBP descriptor in the context of writer identification. 
Sentiment analysis of student’s comments using long short-term memory with multi head attention Bhagat, Bhavana Prasanjeet; Dhande-Dandge, Sheetal S.; Raut, Sandeep R
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.pp4747-4756

Abstract

Classroom teaching is a viable and effective approach for enhancing student learning and promoting engagement in the educational process. The opinions of students play a vital role in the evaluation of teachers. This paper presents a comprehensive overview of sentiment analysis techniques based on recent research and subsequently explores machine learning, i.e., ensemble classifiers, deep learning, long short-term memory (LSTM), convolutional neural network (CNN), LSTM with single attention, LSTM with multi-head attention, and feature extraction techniques (TFidfVector and Word2Vec), in the context of sentiment analysis over student opinion datasets, i.e., the Vietnamese student feedback corpus, as well as data collected from a final-year student's comment in 2023. Further, the Vietnamese student feedback corpus is translated to English and pre-processed with the proposed framework, which yields interesting facts about the capabilities and deficiencies of different methods. In this paper, we conducted experiments with ensemble classifiers, LSTM and CNN, LSTM with single attention, and LSTM with multi-head attention. We conclude that LSTM with multi-head attention produces an accuracy result of 95.57%, which outperform as compare to other three baseline methods and earlier studies.
A review of machine learning methods to build predictive models for male reproductive health Adimoelja, Ariawan; Firdaus Mahmudy, Wayan; Kurnianingtyas, Diva
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.pp3739-3749

Abstract

Developing of artificial intelligence (AI) technology in the medical sector, especially in the part of male reproduction and infertility, is growing rapidly. In both supervised learning and unsupervised learning, AI has been tested and applied to medical personnel to treat their patients. Calculations from simple to complex probability and a combination of some different methods have conducted results of accurate and precise. The results can help determine the condition of male infertility. Artificial neural network (ANN) and fuzzy inference system (FIS) are AI techniques applied to male health issues. ANN is adequate for processing large amounts of combined data in a short time. ANN also has a high level of accuracy and excellent adaptive capabilities. Afterwards, FIS can reflect problems using models with easy to understand, flexible, and also competent to model complex linear functions for decision-making. Based on the advantages of ANN and FIS, it is hoped acquiring prediction results of better and more accurate in male health issues.
Hyperparameter optimization of convolutional neural network using particle swarm optimization for emotion recognition Rini, Dian Palupi; Sari, Tri Kurnia; Sari, Winda Kurnia; Yusliani, Novi
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.pp547-560

Abstract

Emotion identification has been widely researched based on facial expressions, voice, and body movements. Several studies on emotion recognition have also been performed using electroencephalography (EEG) signals and the results also show that the technique has a high level of accuracy. EEG signals that detected by standart method using exclusive representations of time and frequency domains presented unefficient results. Some researchers using the convolutional neural network (CNN) method performed EEG signal for emotional recognition and obtained the best results in almost all benchmarks. Although CNN has shown fairly high accuracy, there is still a lot of room for improvement. CNN is sensitive to its hyperparameter value because it has considerable effect on the behavior and efficiency of the CNN architecture. So that the use of optimization algorithms is expected to provide an alternative selection of appropriate hyper parameter values on CNN. Particle swarm optimization (PSO) algorithm is a metaheuristic-based optimization algorithm with many advantages. This PSO algorithm was chosen to optimize the hyperparameter values on CNN. Based on the evaluation results in each model, hybrid CNN-PSO showed better results and achieved the best value in 80:20 split data which is 99.30% accuracy.
Predicting levels of legal case difficulties using machine learning Sari, Ilmiyati; Kosasih, Rifki; Indarti, Dina
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.pp4364-4371

Abstract

Lawyers play a crucial role in the courtroom, assisting clients in their defense. Because of their lack of legal expertise, a person or organization facing legal issues requires professional aid. However, we need to know how much money will be spent on paying lawyers. The level of complexity in a case can be used to determine lawyer costs. Therefore, in this research, we propose employing machine learning methodologies, i.e., random forest classifiers and support vector machines (SVM), to determine the level of legal case difficulties. The novelty of this research is applying a machine learning approach in predicting the level of difficulty of legal cases. The data utilized consists of 990 records, which are divided into training and testing data in a 90:10 ratio. The term frequency-inverse document frequency (TF-IDF) approach was then utilized to perform text preprocessing. The text-preprocessing findings are utilized as input in the classification process. According to the research findings, an accuracy value of 85%, a value of weighted average precision is 88%, and a value of weighted average recall is 85%, for support vector machine. Using random forest, we achieve an accuracy value of 89%, a value of weighted average precision is 85.6%, and a value of weighted average recall is 80%.
Arabic text diacritization using transformers: a comparative study Zubeiri, Iman; Souri, Adnan; El Mohajir, Badr Eddine
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.pp702-711

Abstract

The Arabic language presents challenges for natural language processing (NLP) tasks. One such challenge is diacritization, which involves adding diacritical marks to Arabic text to enhance readability and disambiguation. Diacritics play a crucial role in determining the correct pronunciation, meaning, and grammatical structure of words and sentences. However, Arabic texts are often written without diacritics, making NLP tasks more complex. This study investigates the efficacy of advanced machine learning models in automatic Arabic text diacritization, with a concentrated focus on the Arabic bidirectional encoder representations from transformers (AraBERT) and bidirectional long short-term memory (Bi-LSTM) models. AraBERT, a bidirectional encoder representation from transformers (BERT) derivative, leverages the transformer architecture to exploit contextual subtleties and discern linguistic patterns within a substantial corpus. Our comprehensive evaluation benchmarks the performance of these models, revealing that AraBERT significantly outperforms the Bi-LSTM with a diacritic error rate (DER) of only 0.81% and an accuracy rate of 98.15%, against the Bi-LSTM's DER of 1.02% and accuracy of 93.88%. The study also explores various optimization strategies to amplify model performance, setting a precedent for future research to enhance Arabic diacritization and contribute to the advancement of Arabic NLP.
Using pattern mining to determine fine climatic parameters for maize yield in Benin Gloria Tahi, Souand Peace; Ratheil Houndji, Vinasetan; Gbêmêmali Hounmenou, Castro; Glèlè Kakaï, Romain
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.pp3930-3941

Abstract

This study investigates the relationships between Benin's climate and maize production to develop an association rule algorithm for accurate yield prediction. The datasets utilized extend 26 years (1995 to 2020) and include climate and maize yield data from five districts with synoptic weather stations in two agroclimatic zones (Sudanian and Sudano-Guinean). Climate variables were combined with yield using "year" and "districts" to find the association rules. Several techniques were used to determine the correlation between weather parameters and maize yields: support vector machines, K nearest neighbor, artificial neural networks, decision trees, and recurrent neural networks. The most performed method was the decision tree (R2=0.998, mean squared error (MSE)=0.021, and mean absolute error (MAE)=0.0008). This model is difficult to understand, though the frequent pattern growth technique was then applied to the dataset to facilitate the discovery of the rules. The Sudano-Guinean zone exhibits high maize yields for medium minimum and maximum temperature values, rainfall, evapotranspiration, and humidity. In the Sudanian zone, medium minimum and maximum temperatures and maximum humidity levels are associated with high maize yields. The discovered association rules showed that optimizing maize output might be done dependably and effectively.
Classification of Bharatanatyam postures using tailored features and artificial neural network Bhandage, Venkatesh; Anami, Basavaraj; J, Andrew; Hadimani, Balachandra
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.pp482-491

Abstract

Bharatanatyam is a classical dance form of India that upholds the rich culture of India. This dance is learned under the supervision of Guru, the teacher traditionally called in India. The scarcity of experts resulted in the decline of people practicing this dance. There is a need for leveraging technology in preserving and promoting this traditional dance and propagating it amongst the youth. In this research, it is attempted to develop a methodology for automated classification of Bharatanatyam dance postures. The methodology involves extraction of existing features such as speeded up robust features (SURF) and histogram of oriented gradients (HOG), which are used to train and test an artificial neural network (ANN). The results are corroborated with deep learning architectures such as AlexNet and GoogleNet. The proposed methodology has yielded a classification accuracy of 99.85% as compared with 93.10% and 94.25% of AlexNet and GoogleNet respectively. The proposed method finds applications such as assistance to Bharatanatyam dance teachers, e-learning of dance, and evaluating the correctness of the postures.

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