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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 55 Documents
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Search results for , issue "Vol 12, No 4: December 2023" : 55 Documents clear
Aerial image semantic segmentation based on 3D fits a small dataset of 1D Shouket Abdulrahman Ahmed; Hazry Desa; Abadal-Salam T. Hussain
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp2048-2054

Abstract

Time restrictions and lack of precision demand that the initial technique be abandoned. Even though the remaining datasets had fewer identified classes than initially planned for the study, the labels were more accurate. Because of the need for additional data, a single network cannot categorize all the essential elements in a picture, including bodies of water, roads, trees, buildings, and crops. However, the final network gains some invariance in detecting these classes with environmental changes due to the different geographic positions of roads and buildings discovered in the final datasets, which could be valuable in future navigation research. At the moment, binary classifications of a single class are the only datasets that can be used for the semantic segmentation of aerial images. Even though some pictures have more than one classification, images of roads and buildings were only found in a significant number of samples. Then, the building datasets were pooled to produce a larger dataset and for the constructed models to gain some invariance on image location. Because of the massive disparity in sample size, road datasets needed to be integrated.
Attention gated encoder-decoder for ultrasonic signal denoising Nabil Jai Mansouri; Ghizlane Khaissidi; Gilles Despaux; Mostafa Mrabti; Emmanuel Le Clézio
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1695-1703

Abstract

Ultrasound imaging is one of the most widely used non-destructive testingmethods. The transducer emits pulses that travel through the imaged samplesand are reflected by echo-forming impedance. The resulting ultrasonic signalsusually contain noise. Most of the traditional noise reduction algorithmsrequire high skills and prior knowledge of noise distribution, which has acrucial impact on their performances. As a result, these methods generallyyield a loss of information, significantly influencing the final data and deeplylimiting both sensitivity and resolution of imaging devices in medical andindustrial applications. In the present study, a denoising method based on anattention-gated convolutional autoencoder is proposed to fill this gap. Toevaluate its performance, the suggested protocol is compared to widely usedmethods such as butterworth filtering (BF), discrete wavelet transforms(DWT), principal component analysis (PCA), and convolutional autoencoder(CAE) methods. Results proved that better denoising can be achievedespecially when the original signal-to-noise ratio (SNR) is very low and thesound waves’ traces are distorted by noise. Moreover, the initial SNR wasimproved by up to 30 dB and the resulting Pearson correlation coefficient wasmaintained over 99% even for ultrasonic signals with poor initial SNR.
Machine learning classification analysis model community satisfaction with traditional market facilities as public service Hadi Syahputra; Musli Yanto; Muhammad Reza Putra; Aulia Fitrul Hadi; Selvi Zola Fenia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1744-1754

Abstract

Traditional markets are public service facilities that can be utilized by thecommunity. The market function is used place where sellers and buyers meetin conducting transactions. This study aims to build a machine learningclassification analysis model in measuring community satisfaction withtraditional market facilities. The analytical methods used include Fuzzy.multiple linear regression (MRL), artificial neural network (ANN), anddecision tree (DT). Fuzzy is used to generate a pattern of rules in determiningthe level of satisfaction. MRL serves to measure and test the correlation ofrules that have been formed. The ANN method is used to carry out theclassification analysis process based on learning. In the final stage. DT is usedto describe the decision tree of the analysis process. This study presents theresults of machine learning analysis which is very good in determiningsatisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzylogic which can develop a classification rule pattern of 32 patterns. MRL alsoshows a significant correlation level of 81.1% based on the indicator variables.Overall, the machine learning classification analysis model can provideknowledge to be considered in the management of traditional markets aspublic service facilities.
Insights of 6G and artificial intelligence-based internet-of vehicle towards communication Madhusudhan Golla; Veena Kalludi Narasimhalah; Ajay Betur Puttappa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1521-1533

Abstract

The significance of internet-of-vehicle (IoV) is spontaneously increasing with exponentially rising demands towards transportation system and road safety. At present, there are various number of scientific approaches which is meant for leveraging the communication performance in IoV, but yet the problem still exists over multiple attributes e.g. resource management, privacy, security, and service offloading. Such problems are anticipated to be solved by 6G services that offers better communication capabilities compared to its prior version of 5G. At the same time, the quality of communication system can be enhanced by artificial intelligence (AI), which is capable of solving complex real-world problems. Therefore, this manuscript offers an insight towards strength and weakness of existing 6G based study model as well as AI-based solution in order to contribute towards highlighting an essential research gap that could directly offer better insight towards future planning towards improving communication in IoV.
Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural network with convolutional neural network Azhar Muneer Abood; Ahmed Raoof Nasser; Huthaifa Al-Khazraji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1704-1712

Abstract

Predictive maintenance (PdM) is a cost-cutting method that involves avoiding breakdowns and production losses. Deep learning (DL) algorithms can be used for defect prediction and diagnostics due to the huge amount of data generated by the integration of analog and digital systems in manufacturing operations. To improve the predictive maintenance strategy, this study uses a hybrid of the convolutional neural network (CNN) and conditional generative adversarial neural network (CGAN) model. The proposed CNN-CGAN algorithm improves forecast accuracy while substantially reducing model complexity. A comparison with standalone CGAN utilizing a public dataset is performed to evaluate the proposed model. The results show that the proposed CNN-CGAN model outperforms the conditional GAN (CGAN) in terms of prediction accuracy. The average F-Score is increased from 97.625% for the CGAN to 100% for the CNN-CGAN.
Using machine learning to improve a telco self-service mobile application in Indonesia Jwalita Galuh Garini; Achmad Nizar Hidayanto; Agri Fina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1947-1959

Abstract

The use of mobile applications extends to the telecommunication sector, mainly due to COVID-19. Failure to provide it can cause dissatisfaction and result in the removal of the mobile application. Moreover, this leads to lost service opportunities, so paying attention to the mobile application's quality is essential. There has yet to be a study on measuring the service quality of a self-service mobile application in the telecommunication sector using online customer reviews. This study uses sentiment analysis and topic modeling to determine the service quality of a self-service mobile application in the telecommunication sector from reviews on Google Play Store and Apple App Store. This study uses myIndiHome as a case study. The total data obtained from both platforms are 20,452 reviews. Sentiment analysis was performed using Naïve Bayes, support vector machine, and logistic regression, while topic modeling was performed using latent dirichlet allocation. The results show that logistic regression performs better than support vector machine and Naïve Bayes. Meanwhile, topic modeling shows that the positive review data has three topics, including application features, products/services, and application interfaces. Moreover, the negative review data has five topics, including application availability, application feature reliability, application processing speed, bugs, and application reliability.
Eligibility of village fund direct cash assistance recipients using artificial neural network Dwi Marisa Midyanti; Syamsul Bahri; Suhardi Suhardi; Hafizhah Insani Midyanti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1611-1618

Abstract

Bantuan Langsung Tunai Dana Desa (BLT-DD), or known as Village Fund Direct Cash Assistance is assistance from the Indonesian government which causes problems and conflicts in the community when the assistance is not on target. The classification algorithm is proven to use in determining BLT-DD recipients. In this study, the radial basis function (RBF) and elman recurrent neural network (ERNN) models compare to classify the eligibility of BLTDD recipients. In the experiment, the optimal performance of the RBF and ERNN compare in determining the eligibility of BLT-DD recipients. Also, it’s compared with the classification algorithm that implements the same data, namely BLT-DD data for Kubu Raya District. The experimental results show the effectiveness of the RBF model in recognizing test data, while the ERNN model is effective in identifying test data. The RBF and ERNN models can achieve the same total accuracy of 98.10%.
Low-rate distributed denial of service attacks detection in software defined network-enabled internet of things using machine learning combined with feature importance Muhammad Abizar; Muhammad Ferry Septian Ihzanor Syahputra; Ahmad Rizky Habibullah; Christian Sri Kusuma Aditya; Fauzi Dwi Setiawan Sumadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1974-1984

Abstract

One of the main challenges in developing the internet of things (IoT) is the existence of availability problems originated from the low-rate distributed denial of service attacks (LRDDoS). The complexity of IoT makes the LRDDoS hard to detect because the attack flow is performed similarly to the regular traffic. Integration of software defined IoT (SDN-Enabled IoT) is considered an alternative solution for overcoming the specified problem through a single detection point using machine learning approaches. The controller has a resource limitation for implementing the classification process. Therefore, this paper extends the usage of Feature Importance to reduce the data complexity during the model generation process and choose an appropriate feature for generating an efficient classification model. The research results show that the Gaussian Naïve Bayes (GNB) produced the most effective outcome. GNB performed better than the other algorithms because the feature reduction only selected the independent feature, which had no relation to the other features.
Comparative analysis of machine learning algorithms on myoelectric signal from intact and transradial amputated limbs Dhirgaam A. Kadhim; Mitha N. Raheema; Jabbar S. Hussein
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1735-1743

Abstract

Control strategies of smart hand prosthesis-based myoelectric signals inrecent years don't provide the patients with the sensation of biologicalcontrol of prostheses hand fingers. Therefore, in current workhyperparameters optimization in machine learning algorithm and handgesture recognition techniques were applied to the myoelectric signal-basedon residual muscles contraction of the amputees corresponding to intactforearm limb movement to improve their biological control. In this paper,myoelectric signals are extracted using the MYO armband to recognize tengestures from ten volunteers (healthy and transradial amputation) on theforearm, thereafter the noise of myoelectric signals using a notch filter (NF)is removed. The proposed classification system involved two machinelearning algorithms: (1) the decision tree (DT), tri-layered neural network(TLNN), k-nearest-neighbor (KNN), support vector machine (SVM) andensemble boosted tree (EBT) classifiers. (2) the optimized machine learningclassifiers, i.e., OKNN, OSVM, OEBT with optical diffraction tomography(ODT) and ommatidia detecting algorithm (ODA). The experimental resultsof classifiers comparison pointed out an algorithm that outperformed withhigh accuracy is OEBT closely followed by OKNN achieves an accuracy of97.8% and 97.1% for intact forearm limb, while for transradial amputationwith an accuracy of 91.9% and 91.4%, respectively.
Toddler monitoring system in vehicle using single shot detector mobilenet and single shot detector-inception on Jetson Nano Kok Jia Quan; Zamani Md Sani; Tarmizi Bin Ahmad Izzuddin; Azizul Azizan; Hadhrami Abd Ghani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1534-1542

Abstract

Road vehicles are today’s primary form of transportation; the safety of children passengers must take precedence. Numerous reports of toddler death in road vehicles, include heatstroke and accidents caused by negligent parents. In this research, we report a system developed to monitor and detect a toddler's presence in a vehicle and to classify the toddler's seatbelt status. The objective of the toddler monitoring system is to monitor the child's conditions to ensure the toddler's safety. The device senses the toddler's seatbelt status and warns the driver if the child is left in the car after the vehicle is powered off. The vision-based monitoring system employs deep learning algorithms to recognize infants and seatbelts, in the interior vehicle environment. Due to its superior performance, the Nvidia Jetson Nano was selected as the computational unit. Deep learning algorithms such as faster region-based convolutional neural network (R-CNN), single shot detector (SSD)- MobileNet, and single shot detector (SSD)-Inception was utilized and compared for detection and classification. From the results, the object detection algorithms using Jetson Nano achieved 80 FPS, with up to 82.98% accuracy, making it feasible for online and real-time in-vehicle monitoring with low power requirements

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