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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 120 Documents
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Search results for , issue "Vol 13, No 1: March 2024" : 120 Documents clear
Analyzing the behavior of different classification algorithms in diabetes prediction Mahmood, Israa Nafea; Abdullah, Hasanen S.
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.pp201-206

Abstract

Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the curve (AUC), accuracy, receiver operating characteristic (ROC) curve, f-measure, and recall. Experimental results show that random forest is better than any other classifier in predicting diabetes with a 90.75% accuracy rate.
Comparison of feature extraction and auto-preprocessing for chili pepper (Capsicum Frutescens) quality classification using machine learning Asian, Jelita; Arianti, Nunik Destria; Ariefin, Ariefin; Muslih, Muhamad
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.pp319-328

Abstract

The low-cost camera for machine vision, such as a webcam, still has a problem with resolution noise. Therefore, it is important to learn strategies to reduce noise from low-cost camera images so that they can be widely used for grading machines in the future. This paper aims to compare three feature extraction methods with auto-preprocessing to classify chili pepper (Capsicum Frutescens) quality using a machine learning algorithm. Three extraction methods were used, including the color feature, oriented FAST and rotated BRIEF (ORB), and the combination color feature and ORB. A total of 525 image data for quality chili pepper were collected using the webcam. The auto-preprocessing strategy to classify chili peppers can improve the performance of machine-learning algorithms for all data generated by the feature extractor. The performance of the chili paper quality classification model with auto-preprocessing of the variable color feature can improve the performance of machine learning algorithms by up to 64.21%. The performance improvement of the classification model using the ORB feature variable and the auto-preprocessing of up to 4.41%. The performance improvement of the classification model using machine learning algorithms is 11.27% when using the combination color feature and ORB feature and auto-preprocessing.
Fragmented-cuneiform-based convolutional neural network for cuneiform character recognition Prasetiadi, Agi; Saputra, Julian
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.pp554-562

Abstract

Cuneiform has been a widely used writing system in one of the human history phases. Although there are millions of tablets, have been excavated today, only around 100,000 tablets have been read. The difficulty in translating also increased if the tablet has damaged areas resulting in some of its characters become fragmented and hard to read. This paper investigates the possibility of reading fragmented cuneiform characters from Noto Sans Cuneiform font based on convolutional neural network (CNN). The dataset is built on extracted 921 characters from the font. These characters are then intentionally being damaged with specific patterns, resulting set of fragmented characters ready to be trained. The model produced by this training phase then being used to read the unseen fragmented pattern of cuneiform sets. The model also being tested for reading normal characters set. From the simulation, 83.86% accuracy of reading fragmented characters are obtained. Interestingly, 96.42% accuracy is obtained while the model is being tested for reading normal characters.
Thai COVID-19 patient clustering for monitoring and prevention: data mining techniques Pansayta, Sawitree; Chansanam, Wirapong
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.pp256-265

Abstract

This research aims to optimize emerging infectious disease monitoring techniques in Thailand, which will be extremely valuable to the government, doctors, police, and others involved in understanding the seriousness of the spread of novel coronavirus to improve government policies, decisions, medical facilities, treatment. The data mining techniques included cluster analysis using K-means clustering. The infection data were obtained from the open data of the digital government development agency, Thailand. The dataset consisted of 1,893,941 cumulative cases from January 2020 to October 2021 of the outbreak. The results from clustering consisted of 8 groups. Clustering results determined the three largest, three medium-sized, and the two most minor numbers of infected people, respectively. These clusters represent their activities, namely touching an infected person and checking themselves. The components of emerging diseases in Thailand are closely related to waves, gender, age, nationality, career, behavioral risk, and region. The province of onset was mainly in Bangkok and its vicinity or central Thailand, as well as industrial areas. Adult workers aged 19 to 27 years and 43 to 54 years or over were seeds of new infection sources.
CRNN model for text detection and classification from natural scenes Prakash, Puneeth; Yeliyur Hanumanthaiah, Sharath Kumar; Bannur Mayigowda, Somashekhar
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.pp839-849

Abstract

In the emerging field of computer vision, text recognition in natural settings remains a significant challenge due to variables like font, text size, and background complexity. This study introduces a method focusing on the automatic detection and classification of cursive text in multiple languages: English, Hindi, Tamil, and Kannada using a deep convolutional recurrent neural network (CRNN). The architecture combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks for effective spatial and temporal learning. We employed pre-trained CNN models like VGG-16 and ResNet-18 for feature extraction and evaluated their performance. The method outperformed existing techniques, achieving an accuracy of 95.0%, 96.3%, and 96.2% on ICDAR 2015, ICDAR 2017, and a custom dataset (PDT2023), respectively. The findings not only push the boundaries of text detection technology but also offer promising prospects for practical applications.
Design and simulation of remote monitoring of the intelligent automatic control system in the production line Nasser, Amal Ibrahim; Sahrab, Ammar Ali; Kadhim, Hasan M.
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.pp133-142

Abstract

In this research, we will introduce implementation requirements of a remote wireless control and monitoring unit of industrial production lines automatically controlled using programmable logic controller (PLC). PLC is capable of collecting different types of data and converting them into electrical signals that can be controlled by the industrial network using supervisory control and data acquisition (SCADA) systems. SCADA will be installed in the main server inside the control unit. The PLC will be used as a decision maker of the received signals for the industrial lines that comes from a group of detectors (sensors/transducers). The output of the PLC processor will trigger the engines, according to a specific industrial process management program. The processed data could be transferred through wireless or wired method. The wireless approach will be shown in this study, along with two other ways to implement it.
Towards a new approach to maximize tax collection using machine learning algorithms Ourdani, Nabil; Chrayah, Mohamed; Aknin, Noura
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.pp737-746

Abstract

Efficient tax debt collection is a challenge for Moroccan local tax authorities. This article explores the potential of machine learning techniques and novel strategies to enhance efficiency in this process. We present a practical use case demonstrating the application of machine learning for taxpayer segmentation, improving accuracy in identifying high-risk debtors. Using a comprehensive dataset of tax payment behavior, we showcase the effectiveness of machine learning algorithms in segmenting taxpayers based on their likelihood of non-compliance or debt accumulation. We also investigate innovative strategies that integrate behavioral economics principles to enable better targeted interventions. Real-world case studies in local tax debt collection highlight the impact of these strategies. The findings underscore the transformative potential of machine learning techniques and novel strategies in improving the efficiency of local tax debt collection. Accurate identification of high-risk debtors and tailored enforcement actions help maximize revenue while minimizing resource waste. This research contributes to the existing knowledge by providing insights into the implementation of machine learning techniques and novel strategies in tax debt collection. It emphasizes the importance of data-driven approaches and highlights how local tax authorities can drive efficiency and optimize revenue collection by embracing these advancements.
A deep learning-based approach for early detection of disease in sugarcane plants: an explainable artificial intelligence model Pudupet Ethiraj, Rubini; Paranjothi, Kavitha
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.pp974-983

Abstract

In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
Towards a system for real-time prevention of drowsiness-related accidents Khadraoui, Abdelhak; Zemmouri, Elmoukhtar; Taki, Youssef; Douimi, Mohammed
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.pp153-161

Abstract

Traffic accidents always result in great human and material losses. One of the main causes of accidents is the human factor, which usually results from driver’s fatigue or drowsiness. To address this issue, several methods for predicting the driver’s state and behavior have been proposed. Some approaches are based on the measurement of the driver’s behavior such as: head movement, blinking time, mouth expression note, while others are based on physiological measurements to obtain information about the internal state of the driver. Several works used machine learning/deep learning to train models for driver behavior prediction. In this paper, we propose a new deep learning architecture based on residual and feature pyramid networks (FPN) for driver drowsiness detection. The trained model is integrated into a system that aims to prevent drowsinessrelated accidents in real-time. The system can detect drivers’ drowsiness in real time and alert the driver in case of danger. Experiment results on benchmarking datasets shows that our proposed architecture achieves high detection accuracy compared to baseline approaches.
Deep learning-based classification of cattle behavior using accelerometer sensors El Moutaouakil, Khalid; Falih, Noureddine
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.pp524-532

Abstract

The increasing demand for food has led to the adoption of precision livestock, which relies on information and communication technology to promote the best practices in meat production. By automating various aspects of the industry, precision livestock allows for increased productivity, more effective management strategies, and decision-making. The paper proposes a methodology that uses deep learning techniques to automatically classify cattle behavior using accelerometer sensors embedded in collars. The work aims to enhance the efficiency and productivity of the industry by improving the classification of cattle behaviors, which is essential for farmers and barn managers to make informed decisions. We tested three different classification techniques to classify rumination, movement, resting, feeding, salting and other cattle behaviors and we achieved promising results that can contribute to a better understanding and management of cattle behavior in the livestock industry.

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