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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 121 Documents
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Search results for , issue "Vol 13, No 3: September 2024" : 121 Documents clear
The prediction of thermal sensation in building using support vector machine and extreme gradient boosting Effendy, Nazrul; Abiyu Fadhilah, Muhammad Zhafran; Kraton, Danang Wahyu; Abrar, Haidar Alghazian
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.pp2963-2970

Abstract

The building has great potential for energy savings as one of the locations that humans often occupy. In addition to energy efficiency, humans must consider environmental sustainability and the comfort of the building's occupants. Conditioning of indoor air quality, including those related to thermal comfort, continues to be pursued to be more economical, one of which is to utilize the prediction of occupants' thermal sensations. The prediction results can be utilized to adjust room air conditions more economically. This paper proposes using extreme gradient boosting (XGBoost) and support vector machine (SVM) to predict the thermal sensation in the building. The built environment parameters are preprocessed, and the thermal sensation is predicted by intelligent systems. The ten variables that most influence the level of accuracy of this thermal sensation prediction system are thermal preference vote, indoor operative temperature, Griffith's neutral temperature, indoor globe temperature, mean radiant temperature, Indoor air temperature, predicted mean vote, and outdoor mean temperature. SVM with four features, XGBoost and XGBoost with hyperparameter tuning, achieve an accuracy of 99.45%, 97.81%, and 98.08%, respectively. Regarding computational complexity, training an SVM system with the same number of features requires a shorter time than XGBoost training. The same thing also happened with the test of the SVM system, which required a shorter time compared to the time for the examination of the XGBoost system.
Enhanced you only look once approach for automatic phytoplankton identification Wisnu Ardhi, Ovide Decroly; Retnaningsih Soeprobowati, Tri; Adi, Kusworo; Prakasa, Esa; Rachman, Arief
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.pp3426-3436

Abstract

Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of ACVIT-YOLO compared to the YOLOv8m model, further demonstrated by the decreased giga floating-point operations (GFLOP), decreased parameter count, and compact dimensions, significantly improves the automation of phytoplankton species identification. This suggests that the ACVIT-YOLO model could produce a better prediction system for identifying phytoplankton with similar accuracy to the original YOLOv8m model but with lower computational power and resource usage.
Hyperspectral image classification with spectral-spatial feature integration and ensemble learning N, Bhavatarini; Prashant, Jyothi Aracot
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.pp2591-2602

Abstract

Hyperspectral imaging (HSI) has emerged as a robust remote sensing and medical imaging tool. However, HSI classification remains a challenging problem due to the high-dimensional data and the need for efficient feature selection and enhancement techniques. The proposed work addresses the problem of spatial feature extraction in spectral-spatial HSI classification tasks. This paper introduces an innovative model addressing the intricacies of spatial feature extraction in spectral-spatial HSI classification tasks, employing a fusion of spectral and spatial features through an adaptive kernel-based Gaussian filtering mechanism to elevate the quality of HSI data and augment classification performance. The classification is executed using three distinct classifiers, whose decisions are harmoniously integrated within an ensemble learning framework to optimize outcomes. The effectiveness of the proposed system is meticulously evaluated across three diverse datasets, Indian Pine, Pavia, and Salinas. This study also compares the model's efficiency against the existing similar work presented in the literature. The results show that the proposed work outperforms existing methods with constantly showing 99% accuracy and kappa score for each dataset, demonstrating its potential applications in various domains such as remote sensing and medical imaging.
Securing high-value electronic equipment: an internet of things driven approach for camera security Nordin, Siti Aminah; Yusoff, Zakiah Mohd; Hanif Faisal, Muhammad; Kamarudin, Khairul
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.pp2763-2772

Abstract

This article addresses pressing challenge of securing high-value electronic equipment, notably cameras, which face dual threats of damage in high humidity conditions and theft due to their significant market value. To confront these issues, the study introduces innovative internet of things (IoT)-driven approach aimed at strengthening conventional storage box security. Central to this approach is integration of IoT technologies, such as Arduino and ESP32, to develop advanced safety storage box. This enhanced system features essential hardware components, including buzzer, password-protected keypad, radio frequency identification reader, and DHT11 sensor for humidity monitoring. Additionally, mobile alarm system is incorporated to promptly alert owners of any detected movement vibrations, thereby augmenting security measures. By leveraging these components, proposed methodology seeks to mitigate risks associated with camera theft and fungal contamination, thereby advancing electronic device security. The expected outcome is marked enhancement in protection of high-value electronic equipment, particularly cameras, through continuous real-time monitoring and proactive security measures. This research underscore’s critical role of IoT technologies in fortifying security measures for valuable electronic assets, contributing significantly to ongoing discourse on innovative strategies in field. Through its comprehensive approach, this study aims to offer practical solutions to mitigate security risks and safeguard electronic equipment against potential threats, thereby addressing critical need in realm of electronic device security.
Review of early and accurate detection of Parkinson’s disease Mathew Biju, Soly; Al-Khatib, Obada; Chuan Lim, Hock; Malt, Suzanne; Zahid Sheikh, Hashir
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.pp3172-3187

Abstract

Parkinson’s disease (PD) is a central sensory system-based progressive illness with no cure. The origin of this illness is unknown. According to various research, it has been found that it is caused due to genetics or environmental factors. It is usually found in older people. However, there is no accurate treatment for this disease. So, the patient must be monitored periodically. It usually starts with deterioration in speech performance. The major problem with this disease is that it’s very costly to treat. The paper aims to report details of numerous aspects of detection of PD published in recent years based on the focus and benefits of the study, the methodology being used, accuracy of the system, and future research suggested for the study. A systematic study was done based on a search of the literature. A total of 50 articles were discovered.
Barcode-less Fruits Classification Using Deep Learning Abdel-raouf, Amal; Sheta, Alaa; Baareh, AbdelKarim; Rausch, Peter
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.pp3211-3217

Abstract

Barcode-less fruit recognition technology has revolutionized the checkout process by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcode to fruits require using adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advanced automation, enhanced accuracy, and increased efficiency. These benefits translate into improved productivity, cost reduction, and superior quality control. This study introduces a Convolutional Neural Network (CNN) designed explicitly for automatic fruit recognition, even in challenging real-world scenarios. The proposed method assists fruit sellers in accurately identifying and distinguishing between different types of fruit that may exhibit similarities. A dataset that includes 44,406 images of different fruit types is used to train and test our technique. Employing a CNN, the developed model achieves an impressive classification accuracy of 97.4% during the training phase and 88.6% during the testing phase respectively, showcasing its effectiveness in precise fruit recognition.
New insight in cervical cancer diagnosis using convolution neural network architecture Khozaimi, Ach; Firdaus Mahmudy, Wayan
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.pp3092-3100

Abstract

The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), root mean square propagation (RMSprop), Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis.
Software defined network-based controller system in intelligent transportation system Kirthima, Arjunan Mari; Krishnaraju, Pushpa Sothenahalli
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.pp2645-2655

Abstract

Intelligent transportation system (ITS) is meant for redefining the conventional transport by incorporating various analytical features that not only offers safety but also enriches traffic data quality extensively. Review of existing literature shows that there is a significant gap towards utilizing vehicular adhoc network (VANET) for optimal performance in ITS environment. Therefore, this paper contributes towards a simplified and yet intelligent controller system harnessing potential of software defined network (SDN) towards effective directional management of complex transportation system. The novelty of this model is dual-fold. The first novelty is about the usage of locally and globally processed traffic information for undertaking decision towards clearing the waiting vehicles in observation point in specific route segment. The second novelty is associated with relaying of distinct traffic clearance signal to the distinct vehicles unlike any of teh existing transportation management scheme.
TourMapQA: using deep learning to develop a vietnam map-based tourism question answering system Pham, Vuong Ba; Nguyen, Phuc Chi-Hong; Phung, Bao The; Phan, Truong H. V.
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.pp3203-3210

Abstract

A question answering system is an important task in information retrieval. In recent years, this system has been interested in research and achieved outstanding results. In general, the output of the question answering is text. However, few studies have used a map as an answer to the question answering in Vietnam tourism. This paper introduces a question answering system integrating long short-term memory (LSTM) on the Vietnam map. Specifically, our model received an input question about any road in Vietnam. Then, the model used LSTM to indicate the coordinate of that road and called the Dijkstra algorithm to find the shortest path from the current location to the input road. Next, from the coordinate of the input road, we leveraged the LSTM model to identify sightseeing places that were on the shortest path. Finally, our system showed all the sightseeing places on the Vietnam map. Technically, the experimental results showed that our model’s performance was improved than previous models such as recurrent neural network, recurrent neural network with embedding, bidirectional recurrent neural network, and encoder-decoder recurrent neural network. Practically in terms, we applied our method to build a real application and compared it with Google Maps, and Bing Map.
Hybrid embedded and filter feature selection methods in big-dimension mammary cancer and prostatic cancer data Md Noh, Siti Sarah; Ibrahim, Nurain; M. Mansor, Mahayaudin; Md Ghani, Nor Azura; Yusoff, Marina
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.pp3101-3110

Abstract

The feature selection method enhances machine learning performance by enhancing learning precision. Determining the optimal feature selection method for a given machine learning task involving big-dimension data is crucial. Therefore, the purpose of this study is to make a comparison of feature selection methods highlighting several filters (information gain, chi-square, ReliefF) and embedded (Lasso, Ridge) hybrid with logistic regression (LR). A sample size of n=100, 75 is chosen randomly, and the reduction features d=50, 22, and 10 are applied. The procedure for feature reduction makes use of the entire sample sizes. Each sample size's results are compared, including tests with no feature selection process. The results indicate that LR+ReliefF is the best method for mammary cancer data, whereas LR+IG is the best for prostatic cancer data, making the filter more suitable than embedded for big-dimension data. This study revealed that the sample's features and size influence the most effective method for selecting features from big-dimension data. Therefore, it provides insight into the most effective methods for particular features and sample sizes in high-dimensional data.

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