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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
Congestion and throughput optimization protocol for providing better quality of service and experience VijayKumar, Sathya; Thyagaraj, Shiva Prakash
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.pp2364-2373

Abstract

Multimedia traffic in internet of things (IoT) applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the real-time streaming protocol (RTSP), real-time transport protocol (RTP), and real-time control protocol (RTCP) to improve the throughput and quality of service (QoS) of the IoT applications. Hence, in this congestion and throughput optimization protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the big packet protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing big-packet-protocol (BBP) model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.
Deep learning for audio signal-based tempo classification scenarios Muljono, Muljono; Nurtantio Andono, Pulung; Ayu Wulandari, Sari; Al Azies, Harun; Naufal, Muhammad
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.pp1687-1701

Abstract

This article explains how to determine the tempo of the kendhang, an Indonesian traditional melodic instrument. This research presents novelty as technological research related to gamelan instruments, which has rarely been achieved thus far, through the introduction of kendhang tempo types through the sounds produced, with the hope of creating an automatic system that can recognize the kendhang tempo during a gamelan performance. The testing in this work will categorize the tempo of kendhang into three categories: slow, medium, and fast, utilizing one of the two scenario models proposed, mel frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) in the first scenario, and mel spectrogram and CNN in the second. Kendhang's original audio data, which was captured in real time and later enhanced, makes up the data set. The model 1 scenario, which entails feature extraction using MFCC and classification using the CNN classification approach, is the best scenario in this research, based on the experimental results. When compared to the other suggested modeling scenarios, model 1 has a level of 97%, an average accuracy, and a gain value of 96.67%, making it a solid assistant in terms of kendhang's good tempo recognition accuracy.
Brain magnetic resonance imaging image classification for Alzheimer's disease and its hardware acceleration A. Sujathakumari, Bettadapura; Patil Kulkarni, Sudarshan; Hallikeri, Vikas
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.pp1272-1281

Abstract

Alzheimer's is a progressive neurodegenerative disorder and is considered the sixth leading cause of death after cancer and heart attack. Early detection and diagnosis provide individuals to go through a wider variety of clinical trials and get multiple medical benefits. Research on the application of deep learning and machine learning to the early detection of Alzheimer's disease has recently gained considerable attention. In this paper, we propose a deep learning classification framework to classify the individual with different progression stages of Alzheimer's disease such as mild cognitive impairment (MCI) and cognitive normal (CN). The dataset from Alzheimer’s disease neuroimaging initiative (ADNI) is considered in this paper which is a multisite having collection of Neuroimaging data for researchers. Structural magnetic resonance imaging (MRI) images are considered from the ADNI data set and feature extraction is done using a 2D discrete wavelet transform. 97% of data reduction is achieved during data pre-processing. The algorithm is trained and validated. The algorithm is accelerated in Nvidia Tx2 graphics processing unit (GPU) to get the better throughput. The result shows our algorithm outperforms the other deep learning algorithms with 91.56% accuracy. 
Comparative analysis of fuzzy multi-criteria decision-making methods for quality of service-based web service selection Aazagreyir, Paul; Appiahene, Peter; Appiah, Obed; Boateng, Samuel
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.pp1408-1419

Abstract

This research aims to compare and analyze the effectiveness of four popular fuzzy multi-criteria decision-making methods (FMCDMMs) for quality of service (QoS)-based web service selection. These methods are fuzzy DEMATEL (FD), fuzzy TOPSIS (FT), fuzzy VIKOR (FV), and fuzzy PROMETHEE (FP), including three ranking versions of FV. We assess the ranking similarities among these methods using Spearman's relationship figure. We describe the algorithms of these six FMCDMs in the methods section. In a case study, we collected primary data from five experts who rated nine QoS factors of nine web services. We used modified online software for analysis. The results showed that S6 ranked first in all FMCDMs, except for FD and FP, where it was ranked 2nd and 8th, respectively. The highest association coefficient (Rs) was found between FT and FV ranking in S techniques (0.983), FV ranking in S and FV ranking in Q (0.883), and FT and FV ranking Q (0.833) when comparing the similarity measure of the FMCDMMs. This analysis helps decision-makers and researchers choose the most suitable methods for integrated FMCDMs studies and real-world problem-solving.
Face mask classification using convolutional neural networks with facial image regions and super resolution Wattanakitrungroj, Niwan; Wettayaprasit, Wiphada; Rujirapong, Peemakarn; Tongman, Sasiporn
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.pp2423-2432

Abstract

Face mask classification is relevant to public health and safety, so an approach for face mask classification using multi-task cascaded convolutional networks (MTCNN) for face detection on image data, ResNet152 architecture for feature extraction, and super-resolution method, blind super-resolution generative adversarial networks (BSRGAN), for enhanced image quality was proposed. The classification model was trained by a fully connected layer of neural networks. The goal is to classify each facial image into three classes: the image with a mask, without a mask, or with an incorrectly worn mask. The performance of each classification model on two real-world datasets was evaluated by Accuracy, Precision, Recall, and F1 score for different sets of input patterns which were features extracted from the facial image regions including their combinations. Using multiple image regions, i.e. face, nose, and mouth, as resources for preparing input features showed the improved classification performance compared to using single image regions. In addition, the super-resolution technique applied to medium or large-sized images can improve the performance of the face mask classification model. Our findings may further guide the development for greater effective models and techniques on face mask classification contributing to practical scenarios.
Comparison of WASPAS and VIKOR methods to determine non-cash food assistance recipients Ramadiani, Ramadiani; Luthfi Fahrozi, Muhammad; Labib Jundillah, Muhammad; Azainil, Azainil
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.pp1430-1442

Abstract

Non-cash food assistance or bantuan pangan non-tunai (BPNT) is a government program of the Republic of Indonesia by distributes food assistance in non-cash to beneficiary families. The process of distributing BPNT still needs to be done with the data and criteria set, because the existing BPNT distribution is considered not right on target. We need a method that can help provide an objective decision. One method that can be used in making decisions is the weighted aggregated sum product assessment (WASPAS) and Vlsekriterijumsko Koompromisno Rangiranje (VIKOR) methods. The results of the calculations from the two methods will then be chosen which is the best, by conducting sensitivity tests and accuracy tests. This study uses 100 sample data and 16 criteria. The sensitivity test results are 9.780678997% for the WASPAS method and -0.0759182% for the VIKOR method, while the results of the accuracy test show that both methods have the same level of accuracy, which is 80%. Based on the comparison of the sensitivity test and accuracy test of the two methods, the WASPAS method is considered more accurate in determining the recipients of the BPNT program because the WASPAS method has a higher sensitivity test value than the VIKOR method.
Bangla song genre recognition using artificial neural network Akter, Mariam; Sultana, Nishat; Haider Noori, Sheak Rashed; Hasan, Md Zahid
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.pp2413-2422

Abstract

Music has a control over human moods and it can make someone calm or excited. It allows us to feel all emotions we experience. Nowadays, people are often attached with their phones and computers listening to music on Spotify, SoundCloud, or any other internet platform. Music information retrieval plays an important role for music recommendation according to lyrics, pitch, pattern of choices, and genre. In this study, we have tried to recognize the music genre for a better music recommendation system. We have collected an amount of 1820 Bangla songs from six different genres including Adhunik, rock, hip hop, Nazrul, Rabindra, and folk music. We have started with some traditional machine learning algorithms having k-nearest neighbor, logistic regression, random forest, support vector machine, and decision tree but ended up with a deep learning algorithm named artificial neural network with an accuracy of 78% for recognizing music genres from six different genres. All mentioned algorithms are experimented with transformed mel-spectrograms and mean chroma frequency values of that raw amplitude data. But we found that music tempo having beats per minute value with two previous features present better accuracy.
Artificial intelligence for choosing an agile method Merzouk, Soukaina; Bouhsissin, Soukaina; Hamim, Touria; Sael, Nawal; Marzak, Abdelaziz
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.pp1557-1566

Abstract

Agile methods are widely known in different companies, including information technology (IT) companies. They appeared intending to solve the problems of traditional methods while proposing an iterative and incremental cycle. These methods consist of four values and the twelve principles agreed upon in 2001 in a Manifesto. However, each method holds singularities from which it is difficult to choose one to adopt in different project cases. The selection of the method to adopt positively or negatively affects the final product following the criteria of the project and the personnel. Project experts must research and compare methods manually to make a choice, a thing that drains time, which is a key factor in project realization. Currently, there is no intelligent system or model that allows choosing the agile method to adopt for such a project. For this purpose, artificial intelligence (AI) techniques will be used to develop a Chatbot that allows reaching the aim. This Chatbot will be developed based on a decision tree model that will be proposed from an experimental study.
Strategies for improving the quality of community detection based on modularity optimization Setiadi, Tedy; Yaakub, Mohd Ridzwan; Abu Bakar, Azuraliza
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.pp1794-1804

Abstract

Community detection is a field of interest in social networks. Many new methods have emerged for community detection solution, however the modularity optimization method is the most prominent. Community detection based on modularity optimization (CDMO) has fundamental problems in the form of solution degeneration and resolution limits. From the two problems, the resolution limit is more concerned because it affects the resulting community's quality. During the last decade, many studies have attempted to address the problems, but so far they have been carried out partially, no one has thoroughly discussed efforts to improve the quality of CDMO. In this paper, we aim to investigate works in handling resolution limit and improving the quality of CDMO, along with their strengths and limitations. We derive six categories of strategies to improve the quality of CDMO, namely developing multi-resolution modularity, creating local modularity, creating modularity density, creating new metrics as an alternative to modularity, creating new quality metrics as a substitute for modularity, involving node attributes in determining community detection, and extending the single objective function into a multi-objective function. These strategies can be used as a guide in developing community detection methods. By considering network size, network type, and community distribution, we can choose the appropriate strategy in improving the quality of community detection.
Sentiment analysis of student feedback using attention-based RNN and transformer embedding Zyout, Imad; Zyout, Mo’ath
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.pp2173-2184

Abstract

Sentiment analysis systems aim to assess people’s opinions across various domains by collecting and categorizing feedback and reviews. In our study, researchers put forward a sentiment analysis system that leverages three distinct embedding techniques: automatic, global vectors (GloVe) for word representation, and bidirectional encoder representations from transformers (BERT). This system features an attention layer, with the best model chosen through rigorous comparisons. In developing the sentiment analysis model, we employed a hybrid dataset comprising students’ feedback and comments. This dataset comprises 3,820 comments, including 2,773 from formal evaluations and 1,047 generated by ChatGPT and prompting engineering. Our main motivation for integrating generative AI was to balance both positive and negative comments. We also explored recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM), with and without pre-trained GloVe embedding. These techniques produced F-scores ranging from 67% to 69%. On the other hand, the sentiment model based on BERT, particularly its KERAS implementation, achieved higher F-scores ranging from 83% to 87%. The Bi-LSTM architecture outperformed other models and the inclusion of an attention layer further enhanced the performance, resulting in F-scores of 89% and 88% from the Bi-LSTM-BERT sentiment models, respectively.

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