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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 hybrid framework for wild animal classification using fine-tuned DenseNet121 and machine learning classifiers Vijayendrakumar, Dhanushree; Kempegowda, Balakrishna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2083-2095

Abstract

Over the past few decades, wildlife monitoring has become an active research area. Various topics like animal-vehicle collision, human-animal conflict, animal poaching, population demography, and tracking of animal behaviour come under wildlife monitoring. Different methods have been used for wild animal monitoring, out of which machine learning (ML) and deep learning (DL) are widely used for automatic detection and classification of species from digital images. Both ML and DL have their advantages and disadvantages. A hybrid model has been proposed by integrating the advantage of DL and ML to detect and classify animals automatically. Publicly available datasets of five wild animals are used to train the model, and for testing the model, a dataset is created by capturing the images with the help of a camera and mobile device in different locations and under various environmental conditions. Two approaches are proposed using a hybrid model: a pre-trained dense convolution neural network 121 (DenseNet121) model is used in the first approach, and a finetuned DenseNet121 model is used in the second approach for feature extraction. Extracted features from the pre-trained and finetuned DenseNet121 model are fed into ML classifiers such as extreme gradient boosting (XGBoost), random forest (RF), and naïve Bayes (NB) for classification. When the performance was analysed, the second approach performed better than the first.
A new deep steganographic technique for hiding several secret images in one cover Htiti, Mohamed; El Ouaazizi, Aziza; Akharraz, Ismail; Ahaitouf, Abdelaziz
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2210-2219

Abstract

Deep learning has been integrated with image steganography to enhance steganographic security by automatically acquiring the ability to hide information. The issue with current models is that if the cover image is accessible, it is possible to expose the hidden information by simply calculating the differences between the cover image and the steganographic image. This paper introduces a novel image steganography model that utilizes convolutional neural network (CNN) to enhance the dissimulation and extraction capabilities. Specifically, we propose a model that hides two images in a single cover image. Before being hidden within the cover image, a random pixel image is generated and combined with the real secret image. Experimental results show that our proposed method is more effective and relevant.
Robust two-stage object detection using YOLOv5 for enhancing tomato leaf disease detection Suryawati, Endang; Auliyah Hasanah, Syifa; Sandra Yuwana, Raden; Abdel Kadar, Jimmy; Ferdinandus Pardede, Hilman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2246-2257

Abstract

Deep learning facilitates human activities across various sectors, including agriculture. Early disease detection in plants, such as tomato plant that are susceptible to diseases, is critical because it helps farmers reduce losses and control the disease spread more effectively. However, the ability of machine to recognize diseased leaf objects is also influenced by the quality of data. Data collected directly from the field typically yields lower accuracy due to challenges faced in machine interpretation. To address this challenge, we propose a two-stage detection architecture for identifying infected tomato plant classes, leveraging YOLOv5 to detect objects within the images obtained from the field. We use Inception-V3 for classifying objects into known classes. Additionally, we employ a combination of two dataset: PlantDocs which represent field data, and PlantVillage dataset which serves as a cleaner dataset. Our experimental results indicate that the use of YOLOv5 in handling data under actual field conditions can enhance model performance, although the accuracy value is moderate (62.50 %).
Lung cancer patients survival prediction using outlier detection and optimized XGBoost Yotsawat, Wirot; Suebpeng, Peetiphart; Purisangkaha, Saroch; Poonsawad, Akarapon; Phodong, Kanyalag
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2146-2157

Abstract

This research aims to improve the prediction’s model for survival time of lung cancer patients by using outlier detection, hyper-parameter optimization, and machine learning technique. The research compares the performance of several methods including multilayer perceptron (MLP), decision tree (DT), linear regression (LR), Bagging, XGBoost, and random forest (RF). The dataset used for the experiment is obtained from the surveillance, epidemiology, and end result (SEER) cancer database, which contains diagnoses data from 2004 to 2015. The total number of records used is 196,031 with 22 features. 10-fold cross-validation is used for training and testing sets. The evaluation metrics are root mean square error (RMSE), mean squared error (MSE), R-squared (R2), and mean absolute error (MAE). The results show that the lung cancer patient survival prediction model using the optimized XGBoost (O-XGBoost) model performs the best with an RMSE of 13.74 and outperforms the baseline-XGBoost model as well as other models. This research will be useful for developing a clinical decision support system for the care of lung cancer patients. Physicians can use the developed model to assess the patient’s chance of survival in order to plan more effective treatment.
Trend analysis of machine learning techniques for traffic control based on bibliometrics Luthfiyah, Hilda; Syamsuddin Hasrito, Eko; Widodo, Tri; Hidayat, Sofwan; Adam Qowiy, Okghi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2402-2411

Abstract

Machine learning in traffic control for intelligent transportation systems (ML-ITSTC) aims to enhance user coordination and safety within transportation networks, ultimately improving overall traffic system performance. ML-ITSTC is achieved by leveraging data to execute machine learning algorithms in intelligent transportation management and optimizing traffic flow to prevent or reduce congestion. This paper conducts bibliometric analysis to explain the research status, development trajectory, and challenges of ML-ITSTC, drawing insights from literature in the Scopus database literature covering 2013 to November 2023. The bibliometric analysis of ML-ITSTC includes: performance analysis, science mapping analysis, and citation analysis. The evaluation of ML algorithm trends over the 10-year span indicates that traffic prediction (TP), neural networks, and deep learning are frequently used keywords. Further, an examination of keywords used over the entire period and in 2023 (up to November) shows that reinforcement learning (RL) is the latest popular approach for traffic control in transportation. The results provide a comprehensive view of the opportunities and challenges in ML-ITSTC, covering data, models, and applications, offering researchers insights into the current and future directions of ML-ITSTC research.
ApDeC: a rule generator for alzheimer's disease prediction Maju, Sonam Vayaliparambil; Oliver Siryapushpam, Gnana Prakasi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1772-1780

Abstract

Artificial intelligence (AI) paved the way and helping hand for the medical practitioners in various aspects and early disease prediction is one among many. Interdisciplinary research studies on the early prediction of diseases are often analyzed based on the accuracy of the prediction model. But how early these diseases can be predicted will not be answered in many of the research studies unless they have a time series data. This work proposes a machine learning model, ApDeC which solves the above-mentioned problem by generating association rules for the early disease prediction of Alzheimer patients. The ApDeC model calculates the probability of occurrence of eleven Alzheimer disease prediction risk factors and identifies the combination of diseases that can lead to Alzheimer disease. The association rules will be generated by considering the observed combination of risk factors. The research introduces an innovative approach that helps in the early prediction of Alzheimer disease from the risk factors/symptoms. The results show the strong correlation of diabetes and blood pressure with Alzheimer disease.
GradeZen: automated grading ecosystem using deep learning for educational assessments Elangovan, Murugavalli; Kaleeswaran, Rajeswari; Mohankumar, Kathirvel; Rangachari, Shreya; Manivannan, Ubasini; Pandidurai, Rishapa; Bellarmin Joel, Maria; Murugesan, Preethi Meenatchi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1809-1819

Abstract

This study introduces a groundbreaking software solution poised to revolutionize grading procedures in higher education through advanced artificial intelligence and machine learning techniques. Leveraging cutting-edge technologies such as YOLOv8 for real-time object detection, transformer-based optical character recognition (TrOCR), and Mixtral 8x7B transformer models for complex data analysis, the software automates the grading process. By significantly reducing the time and effort required for manual grading, it aims to streamline educational practices while ensuring consistency and scalability. The study provides a comprehensive analysis of use cases, identifies key issues in current grading methods, and elucidates the rationale driving its development. This innovative approach holds immense promise for transforming educational practices, fostering student success through efficient and artificial intelligence assisted automated assessment methodologies.
Artificial intelligence-blockchain synergy ensures Indonesia’s compliance with European Union’s Deforestation-free regulation Iriyani, Silfi; Raflesia, Sarifah Putri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1763-1771

Abstract

This paper introduces a new model that incorporates blockchain and artificial intelligence (AI) in creating traceability on agricultural supply chains to meet European Union's (EU's) regulation on deforestation-free products. This model stands for the system that would be applied for monitoring origins and routes with regard to verifying the status of products being free from deforestation. Particularly, this addressed the European Union's Deforestation-free Regulation products (EUDR)-related issues in Indonesia focused on smallholders and their linkage to traceability tools. The proposed conceptual model demonstrates how blockchain technology combined with AI in agricultural supply chains enhances transparency and reliability in the line of improving environmental sustainability as well as boosting consumers' confidence. Integration of blockchain and AI increases agricultural supply chain transparency, traceability, and reliability whereby smart contracts can execute automatically such as releasing payments once certain conditions are met.
Identification of potential depression in social media posts Munawar, Munawar; Yulhendri, Yulhendri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2096-2103

Abstract

The widespread use of social media to convey emotions (including depression) can be used to identify suspected depression in social media posts by examining the language that they have used on social media. This study aims to develop a system for detecting suspected depression in social media posts using sentiment analysis. This study collected data from X (Twitter) for three months using the keywords depression, mental health, and mental disorders. 1,502 data were generated due to the cleaning process of the 5,000 data collected. The findings of employing the validated by psychologist valence aware dictionary and sentiment reasoner (VADER) and Indonesian sentiment (InSet) lexicons demonstrate that VADER is more accurate (95.1%) than Inset (76.9%). The results of modeling with random forest, naive Bayes, and support vector machine (SVM) showed that random forest had the highest accuracy (83.3%), followed by naive Bayes (80.5%) and SVM (80.4%). Predicting social media data using lexicons and machine learning has limits that can be addressed by validation from clinical psychology. The frequency, timing, and idiom of posts on social media can reveal signs of depression. Depression seems to be best described by words like melancholy, stress, sadness, worthlessness, and depression.
Hyper-parameters optimized deep feature concatenated network for pediatric pneumonia detection Shyni Hillary, Mary; Ekambaram, Chitra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2220-2228

Abstract

Pneumonia, an infection that fills the alveoli of the lung region with pus causes a high rate of chronic illness and fatality amongst children across the globe. The most utilized imaging modality for pediatric pneumonia identification is chest X-rays, whose features are not always readily visible to the naked eye, making it challenging for radiologists to make precise predictions and save lives. Knowing how essential it is to have an early and distinct diagnosis of pneumonia, speeding up or automating the detection process is highly sensible. This article provides a smart, automated system that operates on chest X-ray images and can be successfully utilized for spotting pneumonia. The deep feature concatenation method used by this detection system intends to combine the outcomes of three effective pre-trained models to confirm the reliability of the suggested approach. To obtain its optimal performance, the hyper-parameters are demonstrated using a trial-and-error approach that surpasses existing models with 99.68% accuracy for the early diagnosis of pneumonia. A real-time data sample test is conducted on the proposed pneumonia detection model to evaluate its robustness.

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