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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 1,722 Documents
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A comparative analysis of optical character recognition models for extracting and classifying texts in natural scenes Prakash, Puneeth; Yeliyur Hanumanthaiah, Sharath Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1290-1301

Abstract

This research introduces prior-guided dynamic tunable network (PDTNet), an efficient model designed to improve the detection and recognition of text in complex environments. PDTNet’s architecture combines advanced preprocessing techniques and deep learning methods to enhance accuracy and reliability. The study comprehensively evaluates various optical character recognition (OCR) models, demonstrating PDTNet’s superior performance in terms of adaptability, accuracy, and reliability across different environmental conditions. The results emphasize the need for a context-aware approach in selecting OCR models for specific applications. This research advocates for the development of hybrid OCR systems that leverages multiple models, aiming to arrive at a higher accuracy and adaptability in practical applications. With a precision of 85%, the proposed model showed an improved performance of 1.7% over existing state of the arts model. These findings contribute valuable insights into addressing the technical challenges of text extraction and optimizing OCR model selection for real-world scenarios.
A proposed approach for plagiarism detection in Myanmar Unicode text Thurain Moe, Sun; Mar Soe, Khin; Than Nwe, Than
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1616-1624

Abstract

Around the world, with technology that improves over time, almost everyone can access the internet easily and quickly. With the increase in the use of the internet, the plagiarism of information that is easily available on the internet has also increased. Such plagiarism seriously undermines originality and ethical principles. In order to prevent these incidents, there is plagiarism detection software for many countries and languages, but there is no plagiarism detection software for the Myanmar language yet. In an attempt to fill that gap, this study proposed a deep learning model with Rabin-Karp hash code and Word2vec model and built a plagiarism detection system. Our deep learning model was trained by randomly obtaining information from Myanmar Wikipedia. According to the experiments, our proposed model can effectively detect plagiarism of educational content and information from Myanmar Wikipedia. Moreover, it is possible to distinguish plagiarized texts by rearranging words or substituting words with some synonyms. This study contributes to a broader understanding of the complexities of plagiarism in the Myanmar academic area and highlights the importance of measures to effectively prevent plagiarism. It maintains the credibility of education and promotes a culture that values originality and intellectual integrity.
Optimizing the gallstone detection process with feature selection statistical analysis algorithm Yanto, Musli; Yuhandri, Yuhandri; Tajuddin, Muhammad; Septiana, Vina Tri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1183-1191

Abstract

Early detection is one form of early anticipation in treating gallstone disease patients using medical images. However, the problem that exists is that there are still many shortcomings in medical images, such as noise in the image that causes the detection process to not run optimally. Based on this, this study aims to carry out the process of detecting gallstone objects in magnetic resonance cholangiopancreatography (MRCP) images by optimizing the performance of extraction techniques for feature selection. Optimization of extraction techniques in feature selection is carried out using the performance of the feature selection statistics analysis (FSSA) algorithm. The performance of the FSSA algorithm can provide improvements in the feature selection process by excelling in the performance of classification methods such as k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN), and the Pearson correlation (PC) method. Based on the tests that have been carried out, the performance of the FSSA algorithm in the detection process provides an accuracy level of 95.69%, a sensitivity of 89.65%, and a specificity of 98.43%. Overall, this study can contribute to the development of extraction and provide a significant technical impact on optimizing the gallstone detection process.
Exploring DenseNet architectures with particle swarm optimization: efficient tomato leaf disease detection Lestari, Cynthia Ayu Dwi; Anam, Syaiful; Sa’adah, Umu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1377-1385

Abstract

The critical challenge of tomato leaf disease demands effective solutions surpassing manual detection limitations, ensuring rapid intervention, optimal crop health, and maximizing yield for farmers. DenseNet, a convolutional neural network (CNN) architecture, is lauded for its adept handling of gradient flow issues by extensive interlayer connectivity. Its application holds significant promise in tackling the intricate task of identifying tomato leaf diseases. This research introduces an innovative methodology employing particle swarm optimization (PSO) to fine-tune the DenseNet architecture and hyperparameter. The proposed approach efficiently converges on optimal configurations encompassing parameters, such as the number of layers in dense blocks, growth rates, dropout rates, activation functions, and optimizers tailored for DenseNet. The DenseNet-PSO model achieves remarkable accuracy and precision in classifying various tomato leaf diseases, outperforming alternative architectures in total parameters, computational efficiency, and overall performance compared with six other architecture models. These outcomes elucidate DenseNet-PSO's efficacy in tomato leaf disease detection and demonstrate.
Enhancing fall detection and classification using Jarratt‐butterfly optimization algorithm with deep learning Durga Bhavani, Kakirala; Ukrit, Melkias Ferni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1461-1470

Abstract

Falls pose significant risk to the health and safety of individuals, specifically for vulnerable populations as the elderly and those with specific medical conditions. The repercussions of falls can be severe, leading to injuries, loss of independence, and increased healthcare costs. Consequently, the development of effective fall detection systems is crucial for providing timely assistance and enhancing the overall well-being of affected individuals. Recent advancements in deep learning (DL) have opened new avenues for automating fall detection through the analysis of sensor data and video footage. DL algorithms are especially well-suited for this task because they can automatically learn complex features and patterns from raw data, eliminating the need for extensive manual feature engineering. This article introduces a novel approach to fall detection and classification, termed the fall detection and classification using Jarratt‐butterfly optimization algorithm with deep learning (FDC-JBOADL) algorithm. The FDC-JBOADL technique employs a median filtering (MF) method to mitigate noise and utilizes the EfficientNet model for robust feature extraction, capturing both motion patterns and appearance characteristics of individuals. Furthermore, the classification of fall events is achieved through a long short-term memory (LSTM) classifier, with hyperparameter optimization facilitated by Jarratt‐butterfly optimization algorithm (JBOA). Through a comprehensive series of experiments, the efficacy of FDC-JBOADL technique is validated, demonstrating superior performance compared to existing methodologies in the domain of fall detection.
Enhancing fire detection capabilities: Leveraging you only look once for swift and accurate prediction Nugroho, Agung; Agastya, I Made Artha; Kusrini, Kusrini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1326-1334

Abstract

Detecting fires is crucial to prevent potentially catastrophic outcomes. Traditional fire detection methods, relying on electronic, chemical, or mechanical sensors, often suffer from time delays in activation due to threshold parameters. An emerging alternative utilizes artificial intelligence, particularly image-based fire detection, using convolutional neural networks (CNNs). You only look once (YOLO) is a state-of-the-art object detection framework prized for speed and real-time capabilities. In our research, we conducted multiple training experiments employing various deep neural network (DNN) architectures as feature extractors for object detection within the YOLOv5 framework. These architectures included MobileNetV3, ResNet, and CSP-Darknet53. Among these configurations, YOLOv5 with CSP-Darknet53 (scale s) emerged as the most accurate, boasting mAP@50 of 0.88 and an impressive FPS of 73, with training model size of 14.50 MB. Furthermore, we integrated the selected model with the streamlit package to create a user-friendly web application interface for fire detection testing. The resulting model demonstrates remarkable efficiency, detecting fires within 0.01 seconds. This research represents a significant advancement in fire detection technology, offering both rapid detection and enhanced accuracy, with potential applications in various settings, from indoor facilities to outdoor environments.
Predicting tourist arrivals to a tropical island using artificial intelligence Suharto, Bambang; Edi Suharno, Novianto; Sinatriya Marjianto, Rachman; Firdaus, Aji Akbar; Suprapto, Sena Sukmananda; Andria Kusuma, Vicky; Amalia Sinulingga, Rizky
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1022-1030

Abstract

This research leverages artificial intelligence (AI) techniques to develop a predictive model for forecasting tourist arrivals in East Java Province, Indonesia, using a comprehensive dataset encompassing historical tourism statistics from 2018 to 2020, seasonal trends, promotional campaigns, and various economic and social variables. The study evaluates three AI methodologies: artificial neural network (ANN), extreme learning machine (ELM), and Jordan recurrent neural network (JRNN), each known for their distinct strengths in processing complex data and adapting to changing trends. The comparative analysis reveals that the JRNN model outperforms others with the highest precision, achieving an average prediction deviation of just 2.98% from actual data, effectively capturing temporal and seasonal trends. The ANN follows closely with a deviation of 3.31%, showing strong capabilities in handling complex, nonlinear relationships. In contrast, the ELM, though fastest in training, exhibits a larger deviation of 10.51%, indicating a trade-off between speed and accuracy. These results highlight the potential of AI to significantly enhance the accuracy and operational efficiency of tourism forecasts, offering robust tools for stakeholders to engage in informed strategic planning and resource allocation in dynamic market conditions.
Primary phase Alzheimer's disease detection using ensemble learning model Dasarwar, Priya; Yadav, Uma; Chavhan, Nekita
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1531-1539

Abstract

Alzheimer's disease (AD) is a noteworthy problem for public health. Older people are most impacted by this neurological disease. It leads to memory loss and various cognitive impairments, eventually hindering communication. As a result, research on early AD detection has intensified in recent years. In current research work, we propose an ensemble learning strategy to identify AD by classifying brain images into two groups: AD brain and normal brain. Researchers have recently explored various machine learning (ML) and deep learning techniques to improve early disease detection. Patients with AD can recover from it more successfully and with less damage if they receive early diagnosis and therapy. This research presents an ensemble learning model to predict AD using decision trees (DT), logistic regression (LR), support vector machines (SVM), and convolutional neural networks (CNN). The open access series of imaging studies (OASIS) dataset is used for model training, and performance is measured in terms of various kinds of outcome namely accuracy, precision, recall, and F1 score. Our results demonstrated that, for the AD dataset, the CNN achieved the maximum validation accuracy of 90.32%. Thus, by accurately detecting the condition, ensemble algorithms can potentially significantly reduce the annual mortality rates associated with AD.
Optimizing potato crop productivity: a meteorological analysis and machine learning approach Hoque, Md. Jiabul; Islam, Md. Saiful; Al Noman, Abdullah; Hoque, Md. Abrarul; Chowdhury, Irfan A.; Saifuddin, Mohammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1116-1129

Abstract

Motivated by the critical need to enhance potato production in Bangladesh, particularly in the face of a changing climate, this study investigates the significant impact of weather on potato yield. This research employs various statistical and machine-learning approaches to identify key weather factors influencing potato crops. We utilize ANOVA F regression and random forest (RF) with feature importance analysis to pinpoint crucial monthly weather variables. Additionally, a correlation study employing Pearson's and Spearman's coefficients alongside p-values is conducted to determine the relationships between weather conditions and crop yield. Seaborn's bivariate kernel density estimation is then used to visualize ideal weather conditions for optimal harvests. Furthermore, to predict future yields, the study implements thoroughly trained and validated machine learning models including k-nearest neighbors (KNN), RF, and support vector regressor (SVR). Our analysis reveals that the RF model emerges as the most reliable predictor, achieving a high correlation coefficient (R²=0.9990), and minimal error values (mean absolute percentage error (MAPE)=0.70, mean absolute error (MAE)=0.0803, and root mean square error (RMSE)=0.1114). These findings provide valuable insights to guide informed agricultural decisions and climate-related strategies, particularly for resource-limited countries like Bangladesh.
Two-dimensional Klein-Gordon and Sine-Gordon numerical solutions based on deep neural network Nouna, Soumaya; Nouna, Assia; Mansouri, Mohamed; Tammouch, Ilyas; Achchab, Boujamaa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1548-1560

Abstract

Due to the well-known dimensionality curse, developing effective numerical techniques to resolve partial differential equations proved a complex problem. We propose a deep learning technique for solving these problems. Feedforward neural networks (FNNs) use to approximate a partial differential equation with more robust and weaker boundaries and initial conditions. The framework called PyDEns could handle calculation fields that are not regular. Numerical exper- iments on two-dimensional Sine-Gordon and Klein-Gordon systems show the provided frameworks to be sufficiently accurate.

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