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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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Python scikit-fuzzy: developing a fuzzy expert system for diabetes diagnosis Rosli Razak, Tajul; Zia Ul-Saufie, Ahmad; Yusoff, Mohamad Hanis; Hafiz Ismail, Mohammad; Mohd Fauzi, Shukor Sanim; Mohd Zaki, Nurul Ain
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.pp1398-1407

Abstract

Nowadays, improvements in diabetes detection that provide patients with vital information are needed. This is due to the fact that Diabetes mellitus has generated a worldwide epidemic that costs society and people. Also, patients tend to misread symptoms, and clinicians who collect insufficient data may produce erroneous outcomes. Therefore, this study aims to demonstrate that a programme that integrates expert advice such as decisions, recommendations, or solutions is an excellent method for reducing the incidence of diabetes. Specifically, this study intends to implement a fuzzy expert system that can detect and report the early stages of diabetes as a viable approach. Furthermore, since this programme is available to everyone, people may easily self-diagnose themselves if they have a blood glucose monitoring device. However, developing the fuzzy expert system for real-world situations, such as diabetes patients, using any programming tools is not straightforward. Therefore, this study will provide a comprehensive approach to constructing a fuzzy expert system using the popular programming language Python.
User interface design of context-input-process-product evaluation application based on weighted product Divayana, Dewa Gede Hendra; Adiarta, Agus; Santiyadnya, Nyoman; Suyasa, P. Wayan Arta; Andayani, Made Susi Lissia; Wiradika, I Nyoman Indhi; Wiguna, I Kadek Arta
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.pp1388-1397

Abstract

This study aimed to show the user interface design form of the context-input-process-product (CIPP) evaluation application based on weighted product as a measuring tool for the effectiveness level of blended learning in health colleges. This research approach was development research. The development model used was Borg and Gall. It focused on the design stage, initial trials, and revisions. The initial test of the user interface design involved 32 respondents. The tool for conducting it was in the form of a questionnaire, which contains 16 questions. The research was at the health colleges in Buleleng Regency. The data analysis technique of the initial test results was quantitative descriptive. It compared the percentage level of user interface design quality from the weighted product-based CIPP evaluation application with a quality standard which referred to a five scale. The results of this study indicated that the quality of the user interface design was relatively good. The research result’s impact on educational evaluation was new knowledge for pedagogic evaluators in maximizing the development of digital-based evaluation tools by integrating the decision support system method (weighted product) with the educational evaluation model (CIPP model).
Transparent precision: Explainable AI empowered breast cancer recommendations for personalized treatment Lokare, Reena R; Wadmare, Jyoti; Patil, Sunita; Wadmare, Ganesh; Patil, Darshan
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.pp2694-2702

Abstract

Breast cancer stands as a prevalent global concern, prompting extensive research into its origins and personalized treatment through Artificial Intelligence (AI)-driven precision medicine. However, AI's black box nature hinders result acceptance. This study delves into Explainable AI (XAI) integration for breast cancer precision medicine recommendations. Transparent AI models, fuelled by patient data, enable personalized treatment recommendations. Techniques like feature analysis and decision trees enhance transparency, fostering trust between medical practitioners and patients. This harmonizes AI's potential with the imperative for clear medical decisions, propelling breast cancer care within the precision medicine era. This research work is dedicated to leveraging clinical and genomic data from samples of metastatic breast cancer. The primary aim is to develop a machine learning (ML) model capable of predicting optimal treatment approaches, including but not limited to hormonal therapy, chemotherapy, and anti-HER2 therapy. The objective is to enhance treatment selection by harnessing advanced computational techniques and comprehensive data analysis. A decision tree model developed here for the prediction of suitable personalized treatment for breast cancer patients achieves 99.87% overall prediction accuracy. Thus, the use of XAI in healthcare will build trust in doctors as well as patients.
Performance analysis of optimization algorithms for convolutional neural network-based handwritten digit recognition Albayati, Abdulhakeem Qusay; Altaie, Sarmad A. Jameel; Al-Obaydy, Wasseem N. Ibrahem; Alkhalid, Farah Flayyeh
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.pp563-571

Abstract

Handwritten digit recognition has been widely researched by the recognition society during the last decades. Deep convolutional neural networks (CNN) have been exploited to propose efficient handwritten digit recognition approaches. However, the CNN model may need an optimization algorithm to achieve satisfactory performance. In this work, a performance evaluation of seven optimization methods applied in a straightforward CNN architecture is presented. The inspected algorithms are stochastic gradient descent (SGD), adaptive gradient (AdaGrad), adaptive delta (AdaDelta), adaptive moment estimation (ADAM), maximum adaptive moment estimation (AdaMax), nesterov-accelerated adaptive moment estimation (Nadam), and root mean square propagation (RMSprop). Experiments have been carried out on two standard digit datasets, namely Modified National Institute of Standards and Technology (MNIST) and Extended MNIST (EMNIST). The results have shown the superior performance of RMSprop and Adam algorithms over the peer methods, respectively.
Feature selection techniques for microarray dataset: a review Nagaraja, Avinash; Sinha, Sitesh Kumar; Mallaiah, Shivamurthaiah
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.pp2395-2402

Abstract

Automatic speech recognition (ASR) approach is dependent on optimal for many researchers working on feature selection (FS) techniques, finding an appropriate feature from the microarray dataset has turned into a bottleneck. Researchers often create FS approaches and algorithms with the goal of improving accuracy in microarray datasets. The main goal of this study is to present a variety of contemporary FS techniques, such as filter, wrapper, and embedded methods proposed for microarray datasets to work on multi-class classification problems and different approaches to enhance the performance of learning algorithms, to address the imbalance issue in the data set, and to support research efforts on microarray dataset. This study is based on critical review questions (CRQ) constructed using feature election methods described in the review methodology and applied to a microarray dataset. We discussed the analysed findings and future prospects of FS strategies for multi-class classification issues using microarray datasets, as well as prospective ways to speed up computing environment.
Comparing emotion classification: machine learning algorithms and hybrid model with support vector machines Hamid Zghair, Ghufran; Shaheed Al-Azzawi, Dheyaa
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.pp3671-3685

Abstract

Recently, the use of artificial intelligence techniques has become widespread, having been adopted in brain-computer interfaces (BCIs) with electroencephalograms (EEGs). BCIs allow direct communication between a person's brain and a computer, and have various uses ranging from assistive technology to neuroscientific study. This paper provides an introductory overview of BCIs and EEG. We adopted the use of machine learning (ML) algorithms, including K-nearest neighbors (KNN), logistic regression, decision trees, random forests, and support vector machine (SVM). Additionally, we proposed a hybrid model of deep learning (DL) and ML by combining convolutional neural networks (CNNs) and SVMs. Our achieved 98% accuracy. The goal is to classify EEG signals into three emotional states: happy, normal, and sad. The study aims to achieve a comprehensive understanding of the effectiveness of these algorithms in accurately classifying emotional states based on EEG data. By comparing the performance of traditional ML methods and the proposed hybrid model, we seek to identify the most robust and accurate approach to sentiment classification.
Application of machine learning in chemical engineering: outlook and perspectives Al Sharah, Ashraf; Abu Owida, Hamza; Alnaimat, Feras; Hassan, Mohammad; Abuowaida, Suhaila; Alhaj, Mohammad; Sharadqeh, Ahmad
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.pp619-630

Abstract

Chemical engineers' formulation, development, and stance processes all heavily rely on models. The physical and economic consequences of these decisions can have disastrous effects. Attempts to employ a hybrid form of artificial intelligence for modeling in various disciplines. However, they fell short of expectations. Due to a rise in the amount of data and computational resources during the previous five years. A lot of recent work has gone into developing new data sources, indexes, chemical interface designs, and machine learning algorithms in an effort to facilitate the adoption of these techniques in the research community. However, there are some important downsides to machine learning gains. The most promising uses for machine learning are in time-critical tasks like real-time optimization and planning that require extreme precision and can build on models that can self-learn to recognize patterns, draw conclusions from data, and become more intelligent over time. Due to their limited exposure to computer science and data analysis, the majority of chemical engineers are potentially vulnerable to the development of artificial intelligence. But in the not-too-distant future, chemical engineers' modeling toolbox will include a reliable machine learning component.
Leveraging machine learning techniques for student’s attention detection: a review Lim, Eng Lye; Murugesan, Raja Kumar; Balakrishnan, Sumathi
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.pp1195-1205

Abstract

With the advances of the internet and today's innovation, it has become conceivable to conduct teaching and learning activities remotely through the online platform. Existing research says that student’s attention state and learning result are strongly correlated. However, despite its importance, this can be a challenging task, as students in general taking an online class may be in a variety of different environments and may be multitasking or distracted by other factors. This review paper aims to address these challenges by exploring the opportunities offered by machine learning techniques in attention detection for effective online teaching and learning. By leveraging machine learning algorithms, which can analyze large volumes of data, including eye-tracking, facial expressions, and body movements, we can develop robust models for attention detection in online learning environments. This paper reviews the challenges specific to online learning, such as students' attention deficits and learning styles, and highlights the limitations of current attention detection methods. Furthermore, it provides recommendations to advance attention detection technology, emphasizing the potential of machine learning to enhance attention detection technology for effective online teaching and learning.
Predicting baccalaureate student result to prevent failure: a hybrid model approach Essayad, Abdesslam; Moulay Abdella, Kassimi
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.pp764-774

Abstract

The Moroccan Ministry of National Education has seen substantial modifications over the previous ten years, which have contributed to improving the quality of education. However, there is a discrepancy in the percentage of academic achievement between the regional directorates and educational institutions. Machine learning techniques have become a powerful tool for proactively predicting student admission. The goal of our paper is to build machine learning models using various algorithms to predict the final baccalaureate school year outcomes. We compare regression and classification to find the reasons behind students' failure and to choose an appropriate model for predicting the results. This helps decision-makers make appropriate interventions.
Sensitivity and feature importance of climate factors for predicting fire hotspots using machine learning methods Hasafah Nugrahani, Endar; Nurdiati, Sri; Bukhari, Fahren; Khoirun Najib, Mohamad; Muliawan Sebastian, Denny; Nur Fallahi, Putri Afia
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.pp2212-2225

Abstract

Every year, Indonesia experiences a national crisis due to forest fires because the resulting impacts and losses are enormous. Hotspots as indicators of forest fires capable of quickly monitoring large areas are often predicted using various machine learning methods. However, there is still few research that analyzes the sensitivity and feature importance of each predictor that forms a machine learning prediction model. This study evaluates and compares machine learning methods to predict hotspots in Kalimantan based on local and global climate factors in 2001-2020. Using the most accurate machine learning model, each climate factor used as a predictor is analyzed for its sensitivity and feature importance. Four methods used include random forest, gradient boosting, Bayesian regression, and artificial neural networks. Meanwhile, measures of sensitivity and feature importance used are variance, density, and distributionbased sensitivity indices, as well as permutation and Shapley feature importance. Evaluation of the machine learning model concluded that the Bayesian linear regression model outperformed other models with an RMSE of 750 hotspots and an explained variance score of 68.96% on testing data. Meanwhile, tree-based models show signs of overfitting, including gradient boosting and random forest. Based on the results of sensitivity analysis and feature importance of the Bayesian linear regression model, the number of dry days is the most important feature in predicting fire hotspots in Kalimantan.

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