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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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Intelligent task processing using mobile edge computing: processing time optimization Maftah, Sara; El Ghmary, Mohamed; El Bouabidi, Hamid; Amnai, Mohamed; Ouacha, Ali
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.pp143-152

Abstract

The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities, battery resources, and storage availability, it enhances the durability and performance of mobile devices by offloading local intensive computation tasks to edge servers. However, the optimal solution is not always guaranteed by offloading computation, therefore, the offloading decision is a crucial step depending on many parameters that should be taken in consideration. In this paper, we use a simulator to compare a two tier edge orchestrator architecture with the results obtained by implementing a system model that aims to minimize a task’s processing time constrained by time delay and the limited device’s computational resource and usage based on a modified version.
A new system for underwater vehicle balancing control based on weightless neural network and fuzzy logic methods Zarkasi, Ahmad; Satria, Hadipurnawan; Primanita, Anggina; Abdurahman, Abdurahman; Afifah, Nurul; Sutarno, Sutarno
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.pp2870-2882

Abstract

The utilization of humans to be in the water for short time, resulting in limited area underwater that can be explored, so the information obtained is very limited, plus the influence of irregular water movements, changes in waves, and changes in water pressure, indirectly also constitutes obstacle to this problem. One of the best solutions is to develop underwater vessel that can travel either autonomously or by giving control of movement and navigation systems. New system for underwater vehicle balance control through weightless neural network (WNN) and fuzzy logic methods was proposed in this study. The aim was to simplify complicated data source on stability system using WNN algorithm and determine depth level of autonomous underwater vehicle (AUV) through fuzzy logic method. Moreover, speed control of underwater vehicle was determined using fuzzy rule-based design and inference. The tests were conducted by showing convergence performance of system in the form of AUV simulator. The results showed that proposed system could produce real-time motion balance performance, faster execution time, and good level of accuracy. This study was expected to produce real-time motion balance system with better performance, faster execution time, and good level of accuracy which could be subsequently used to design simple, cheap, and efficient hardware prototype.
Partial half fine-tuning for object detection with unmanned aerial vehicles Pebrianto, Wahyu; Mudjirahardjo, Panca; Pramono, Sholeh Hadi
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.pp399-407

Abstract

Deep learning has shown outstanding performance in object detection tasks with unmanned aerial vehicles (UAVs), which involve the fine-tuning technique to improve performance by transferring features from pre-trained models to specific tasks. However, despite the immense popularity of fine-tuning, no works focused on to study of the precise fine-tuning effects of object detection tasks with UAVs. In this research, we conduct an experimental analysis of each existing fine-tuning strategy to answer which is the best procedure for transferring features with fine-tuning techniques. We also proposed a partial half fine-tuning strategy which we divided into two techniques: first half fine-tuning (First half F-T) and final half fine-tuning (Final half F-T). We use the VisDrone dataset for the training and validation process. Here we show that the partial half fine-tuning: Final half F-T can outperform other fine-tuning techniques and are also better than one of the state-of-the-art methods by a difference of 19.7% from the best results of previous studies.
Ensemble of naive Bayes, decision tree, and random forest to predict air quality Resti, Yulia; Eliyati, Ning; Rahmayani, Mau’izatil; Alwine Zayanti, Des; Sri Kresnawati, Endang; Setyo Cahyono, Endro; Yani, Irsyadi
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.pp3039-3051

Abstract

Air quality prediction is an important research issue because air quality can affect many areas of life. This study aims to predict air quality using the ensemble method and compare the results with the prediction results using a single method. The proposed ensemble method is built from three singlesupervised methods: naïve Bayes, decision trees, and random forests. The results show that the ensemble method performs better than the single methods. The ensemble method achieves the highest performance with scores of 99.89% accuracy, 79.6% precision, 79.81% recall, and 79.7% F1-score. The performance comparison between single and ensemble models is expected to provide information on the percentage increase in predictive model performance metrics from the single to ensemble methods.
Impact of adaptive filtering-based component analysis method on steady-state visual evoked potential based brain computer interface systems Krishnappa, Manjula; Anandaraju, Madaveeranahally B.
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.pp92-103

Abstract

The significance of brain computer interface (BCI) systems is immensely high, especially for disabled people and patients with nervous system failure. Therefore, in this study, adaptive filtering-based component analysis (AFCA) model is presented to enhance target box identification efficiency at varied flickering frequencies in a visual stimulation process by efficient acquisition of electroencephalogram (EEG) signals for the application of steady-state visually evoked potential based BCI system. Furthermore, optimization of proposed AFCA model is performed based on the maximized reproducibility of correlated components. A multimedia authoring and management using your eyes and mind (MAMEM) steady-state visual evoked potential (SSVEP) dataset is utilized for efficient training of EEG signals and background entities are eliminated using adaptive filters in a pre-processing stage. Additionally, spatial filtering components are obtained to detect target flickering box based on the obtained quality features. Performance is measured by acquisition of SSVEP signals in terms of reconstruction efficiency, classification accuracy and information transfer rate (ITR) using proposed AFCA model. Mean classification accuracy for all 11 subject is 93.48% and ITR is 308.23 bpm. Further, classification accuracy is relatively higher than various SSVEP classification algorithms.
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.
Multi quadrotors coverage optimization using reinforcement learning with negotiation Bonaventura Wijaya, Glenn; Agustinus Tamba, Tua
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.pp2978-2986

Abstract

This paper proposes an optimization scheme to maximize the area coverage of multiple quadrotor unmanned aerial vehicles that are deployed to monitor an operational area/space. Each quadrotor initially performs a single agent reinforcement learning to determine target points with optimal coverage area. Whenever each quadrotor encounters the others within a predetermined negotiation region that is defined by an inter-agent distance threshold, it will activate a multiagent reinforcement learning with action negotiation algorithm and coordinate its movement policies to maximize the total coverage area and avoids inter-agent coverage overlaps. Results of simulation evaluations are shown to illustrate the performance of the proposed learning-based coverage optimization method.
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.
Scalability and performance of decision tree for cardiovascular disease prediction Admassu Assegie, Tsehay; Kumar Napa, Komal; Thulasi, Thiyagu; Kalyan Kumar, Angati; Thiruvarasu Vasantha Priya, Maran Jeyanthiran; Dhamodaran, Vigneswari
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.pp2540-2545

Abstract

As one of the most common types of disease, cardiovascular disease is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. The decision tree is a robust classifier for predicting the risk of cardiovascular disease and getting insights that would assist in making clinical decisions. However, selecting a better model for cardiovascular disease could be challenging due to scalability issues. Hence, this study examines the scalability and performance of decision trees for cardiovascular disease prediction. The study evaluated the performance of a decision tree for predicting cardiovascular disease. The performance evaluation was carried out by employing a confusion matrix, cross-validation score, model complexity, and training score for varying sizes of training samples. The experiment depicted that, the decision tree model was 88.8% accurate in predicting the presence or absence of cardiovascular disease. Therefore, the implementation of the decision tree is beneficial for the prediction and early detection of heart disease events in patients.

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