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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 123 Documents
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Search results for , issue "Vol 13, No 2: June 2024" : 123 Documents clear
Assessing public satisfaction of public service application using supervised machine learning Zharif Mustaqim, Ilham; Melani Puspasari, Hasna; Tri Utami, Avita; Syalevi, Rahmad; Ruldeviyani, Yova
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.pp1608-1618

Abstract

The COVID-19 pandemic has enormously affected the economic situation worldwide, including in Indonesia resulting in 30 million Indonesian tumbling into penury. The Ministry of Social Affairs initiated a program to distribute social assistance aimed at the poorest households. ‘Aplikasi Cek Bansos’ is a public service application that aims to validate their status towards the social assistance program. Understanding the public sentiment and factors affecting public satisfaction levels is crucial to be performed. The goal of this study is to perform a comparative study of supervised machine learning to learn the sentiment of the public and the dominant variable resulting in public satisfaction. Support vector machine, Naïve Bayes dan K-nearest neighbor (KNN) are performed to seek the highest accuracy. This experiment discovered that the KNN algorithm produced outstanding performance where the accuracy hit 99.21%. Sentiment prediction indicated negative perception as the majority covering 83.81%. Trigrams analysis is performed to learn themes affecting satisfaction levels toward the application. Negative themes are grouped into the following categories: App instability, hope for improvement, navigation issues, and low-quality content. Some recommendations are offered for the Ministry of Social Affairs and developers, to overcome negative feedback and enhance public satisfaction level towards the application.
Knee osteoarthritis automatic detection using U-Net Abdellatif, Ahmed Salama; Rahouma, Kamel Hussien; E. Mansour, Fatma
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.pp2122-2130

Abstract

Knee osteoarthritis or OA is one of the most common diseases that can affect the elderly and overweight people. OA is occurred as the result of wear, tear, and progressive loss of articular cartilage. Kellgren-Lawrence system is a common method of classifying the severity of osteoarthritis depends on knee joint width. According to Kellgren-Lawrence, knee OA is divided into five classes; one class represents a normal knee and the others represent four levels of knee OA. In this work, we aim to automatically detect knee OA according to the Kellgren-Lawrence classification. The proposed system uses the U-Net architecture. The overall system yielded an accuracy of 96.3% during training.
Automatic speech recognition for Indonesian medical dictation in cloud environment Jarin, Asril; Santosa, Agung; Uliniansyah, Mohammad Teduh; Aini, Lyla Ruslana; Nurfadhilah, Elvira; Gunarso, Gunarso
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.pp1762-1772

Abstract

This paper introduces SPWPM, an automatic speech recognition (ASR) system designed specifically for Indonesian medical dictation. The main objective of SPWPM is to assist medical professionals in producing medical reports and diagnosing patients. Deployed within a cloud computing service architecture, SPWPM strives to achieve a minimum speech recognition accuracy of 95%. The ASR model of SPWPM is developed using Kaldi and PyChain technologies—creating a comprehensive training dataset involving collaboration with PT Dua-Empat-Tujuh and Harapan Kita Hospital. Several optimization techniques were applied, including language modeling with smoothing, lexicon generation using the Grapheme-to-Phoneme Converter, and data augmentation. The readiness of this technology to assist hospital users was assessed through two evaluations: the SPWPM architecture test and the SPWPM speech recognition test. The results demonstrate the system's preparedness in accurately transcribing medical dictation, showcasing its potential to enhance medical reporting for healthcare professionals in hospital environments.
The potential of ChatGPT technology in education: advantages, obstacles and future growth Al-Ghonmein, Ali M.; Al-Moghrabi, Khaldun G.
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.pp1206-1213

Abstract

Information and communication technology is becoming increasingly prevalent in our daily lives, with interactive communication modes such as social networks and instant messaging reaching unprecedented popularity. These tools are now widely utilised in various academic and research institutions by both faculty and students for communication and distance learning purposes. Chat generative pre-trained transformer (ChatGPT) and artificial intelligence tools hold the potential to revolutionise the way that students obtain knowledge and support. ChatGPT is a cutting-edge language technology capable of constructing intelligent, coherent texts, making it a valuable tool for writing and communication across different fields, including education. However, universities that incorporate ChatGPT as a teaching tool must address concerns regarding plagiarism and academic integrity. This investigation focuses on the advantages and obstacles of applying ChatGPT technology in the education field and its potential for future development. Findings reveal that through careful consideration of the ethical dilemmas and issues, academic institutions can leverage the maximum potential of ChatGPT to provide a more accessible, successful, and personalised learning experience for learners. The development prospects for ChatGPT appear promising, given its potential to grow and enhance its capabilities through on-going research and innovation.
Activity recognition based on spatio-temporal features with transfer learning Gowda, Seemanthini Krishne; Murthy, Shobha Narasimha; Hiremath, Jayaprada S.; Belur Subramanya, Sowmya Lakshmi; S. Hiremath, Shantala; S. Hiremath, Mrutyunjaya
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.pp2102-2110

Abstract

Human action recognition has emerged as a significant area of study due to it is diverse applications. This research investigates convolutional neural network (CNN) structures to extract spatio-temporal attributes from 2D images. By harnessing the power of pre-trained residual network 50 (ResNet50) and visual geometric group 16 (VGG16) networks through transfer learning, intricate human actions can be discerned more effectively. These networks aid in isolating and merging spatio-temporal features, which are then trained using a support vector machine (SVM) classifier. The refined approach yielded an accuracy of 89.71% on the UCF-101 dataset. Utilizing the UCF YouTube action dataset, activities such as basketball playing and cycling were successfully identified using ResNet50 and VGG16 models. Despite variations in frame dimensions, 3DCNN models demonstrated notable proficiency in video classification. The training phase achieved a remarkable 95.6% accuracy rate. Such advancements in leveraging pre-trained neural networks offer promising prospects for enhancing human activity recognition, especially in areas like personal security and senior care.
The performance analysis of hyper-heuristics algorithms over examination timetabling problems Muklason, Ahmad; Tendio, Yusnardo; Angelita Depari, Helena; Arif Nuriman, Muhammad; Agung Premananda, I Gusti
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.pp2155-2164

Abstract

In general, uncapacitated exam timetabling is conducted manually, which can be time-consuming. Many studies aim to automate and optimize uncapacitated exam timetabling. However, pinpointing the most efficient algorithm is challenging since most studies assert that their algorithms surpass previous ones. To identify the optimal algorithm, this research evaluates the performance of four algorithms: Hill climbing (HC), simulated annealing (SA), great deluge (GD), and tabu search (TS) in addressing the exam timetabling problem. The Kempe chain operator’s influence on optimization solutions is also examined. A simple random method is employed to select the low-level heuristic (LLH). The Carter (Toronto) dataset served as the test material, with each algorithm undergoing 200,000 iterations for comparison. The results indicate that the TS algorithm is superior, providing the best solution in 13 instances. The use of a tabu list enhanced the search process’s efficiency by preventing redundant modifications. The Kempe chain LLH exhibited a tendency towards achieving better solutions.
Verifiable data distribution technique for multiple applicants in a cloud computing ecosystem Karemallaiah, Jayalakshmi; Revaiah, Prabha
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.pp1241-1249

Abstract

Cloud computing is the most exploited research technology in both industry and academia due to wide application and increases in adoption from global organizations. In cloud, computing data storage is one of the primary resources offered through cloud computing, however, an increase in participants raises major security concerns, as the user has no hold over the data. Furthermore, recent research has shown great potential for efficient data sharing with multiple participants. Existing researches suggest complicated and inefficient cloud security architecture. Hence, this research work proposes identifiable data sharing for multiple users (IDSMU) mechanism, which aims to provide security for multiple users in a particular cloud group. A novel signature scheme is used for identifying the participants, further verification of the Novel Signature Scheme is proposed along with a retraction process where the secret keys of the participant and the sender is cross-verified; at last, a module is designed for the elimination of any malicious participants within the group. IDSMU is evaluated on computation count and efficiency is proved by comparing with an existing model considering computation count. IDSMU performs marginal improvisation over the existing model in comparison with the existing model using the novel signature scheme. 
Design of a novel deep network model for spinal cord injury prediction Venkatapathi Raju, P. R. S. S.; Asanambigai, Valayapathy; Babu Mudunuri, Suresh
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.pp2131-2142

Abstract

Degenerative cervical myelopathy must be diagnosed with magnetic resonance imaging (MRI) which predicts spinal cord injury (SCI). The growing volume of medical imaging data can be managed by deep learning models, which provide a preliminary interpretation of images taken in basic care settings. Our main goal was to create a deep-learning approach that could identify SCI using MRI data. This work concentrates on modeling a novel 2D-convolutional neural networks (2D-CNN) approach for predicting SCI. For holdouts, training, and validation, various datasets of patients were created. Two experts assigned labels to the images. The holdout dataset was used to evaluate the performance of our deep convolutional neural network (DCNN) over the image data from the available dataset. The dataset is acquired from the online resource for training and validation purpose. With the available dataset, the anticipated model attains 94% AUC, 0.1 p-value, and 92.2% accuracy. The anticipated model might make cervical spine MRI scan interpretation more accurate and reliable.
Kernel density estimation of Tsalli’s entropy with applications in adaptive system training Chawla, Leena; Kumar, Vijay; Saxena, Arti
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.pp2247-2253

Abstract

Information theoretic learning plays a very important role in adaption learning systems. Many non-parametric entropy estimators have been proposed by the researchers. This work explores kernel density estimation based on Tsallis entropy. Firstly, it has been proved that for linearly independent samples and for equal samples, Tsallis-estimator is consistent for the PDF and minimum respectively. Also, it is investigated that Tsallisestimator is smooth for differentiable, symmetric, and unimodal kernel function. Further, important properties of Tsallis-estimator such as scaling and invariance for both single and joint entropy estimation have been proved. The objective of the work is to understand the mathematics behind the underlying concept.
Detection of chronic kidney disease using binary whale optimization algorithm Sutikno, Sutikno; Kusumaningrum, Retno; Sugiharto, Aris; Arif Wibawa, Helmie
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.pp1511-1518

Abstract

Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.

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