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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
Predictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus type 2 Garcia-Rios, Victor; Marres-Salhuana, Marieta; Sierra-Liñan, Fernando; Cabanillas-Carbonell, Michael
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.pp1713-1726

Abstract

Currently, type 2 diabetes mellitus is one of the world's most prevalent diseases and has claimed millions of people's lives. The present research aims to know the impact of the use of machine learning in the diagnostic process of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of the dis-ease quickly and easily. Different machine learning models were designed and compared, being random forest was the algorithm that generated the model with the best performance (90.43% accuracy), which was integrated into a web platform, working with the PIMA dataset, which was validated by specialists from the Peruvian League for the Fight against Diabetes organization. The result was a decrease of (A) 88.28% in the information collection time, (B) 99.99% in the diagnosis time, (C) 44.42% in the diagnosis cost, and (D) 100% in the level of difficulty, concluding that the application of machine learning can significantly optimize the diagnostic process of type 2 diabetes mellitus.
Finger vein identification system using capsule networks with hyperparameter tuning Vandy Achmad Yulianto; Nazrul Effendy; Agus Arif
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.pp1636-1643

Abstract

Safety and security systems are essential for personnel who need to be protected and valuables. The security and safety system can be supported using a biometric system to identify and verify permitted users or owners. Finger vein is one type of biometric system that has high-level security. The finger vein biometrics system has two primary functions: identification and verification. Safety and security technology development is often followed by hackers' development of science and technology. Therefore, the science and technology of safety and security need to be continuously developed. The paper proposes finger vein identification using capsule networks with hyperparameter tuning. The augmentation, convolution layer parameters, and capsule layers are optimized. The experimental results show that the capsule network with hyperparameter tuning successfully identifies the finger vein images. The system achieves an accuracy of 91.25% using the Shandong University machine learning and applications-homologous multimodal traits (SDUMLA-HMT) dataset.
Global-local attention with triplet loss and label smoothed crossentropy for person re-identification Nha Tran; Toan Nguyen; Minh Nguyen; Khiet Luong; Tai Lam
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.pp1883-1891

Abstract

Person re-identification (Person Re-ID) is a research direction on tracking and identifying people in surveillance camera systems with non-overlapping camera perspectives. Despite much research on this topic, there are still some practical problems that Person Re-ID has not yet solved, in reality, human objects can easily be obscured by obstructions such as other people, trees, luggage, umbrellas, signs, cars, motorbikes. In this paper, we propose a multibranch deep learning network architecture. In which one branch is for the representation of global features and two branches are for the representation of local features. Dividing the input image into small parts and changing the number of parts between the two branches helps the model to represent the features better. In addition, we add an attention module to the ResNet50 backbone that enhances important human characteristics and eliminates irrelevant information. To improve robustness, the model is trained by combining triplet loss and label smoothing cross-entropy loss (LSCE). Experiments are carried out on datasets Market1501, and duke multi-target multi-camera (DukeMTMC) datasets, our method achieved 96.04% rank-1, 88,11% mean average precision (mAP) on the Market1501 dataset, and 88.78% rank-1, 78,6% mAP on the DukeMTMC dataset. This method achieves performance better than some state-of-the-art methods.
Efficient method for finding nearest neighbors in flocking behaviors using k-dimensional trees Marwan Al-Tawil; Moh’d Belal Al-Zoubi; Omar Y. Adwan; Ammar Al-Huneiti; Reem Q. Al Fayez
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.pp1628-1635

Abstract

Flocking is a behavior where a group of objects travel, move or collaborate together. By learning more about flocking behavior, we might be able to apply this knowledge in different contexts such as computer graphics, games, and education. A key steppingstone for understanding flocking behavior is to be able to simulate it. However, simulating behaviors of large numbers of objects is highly compute-intensive task because of the n-squared complexity of nearest neighbor for separating n objects. The work in this paper presents an efficient nearest neighbor method based on the k-dimensional trees (KD trees). To evaluate the proposed approach, we apply it using Unity-3D game engine, together with other conventional nearest neighbor methods. The Unity-3D game simulation engine allows users to utilize interaction design tools for programming and animating flocking behaviors. Results showed that the proposed approach outperform other conventional nearest neighbor approaches. The proposed approach can be used to enhance digital games quality and simulations.
Analysis of clustering and association using data mining technique for elderly health condition dataset Panthong, Rattanawadee; Wongkanthiya, Thawin
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.pp1774-1783

Abstract

Data survey on the elderly health condition in each year aimed to investigate the performance result on the elderly health care and to evaluate the elderly’s health and health promotion. Thus, in analyzing the data, it mainly relied on the mining data technique for the evaluating health condition. This study presented the data analysis by clustering method. Then, the data was taken from each group to find the association rule. The analysis results showed that the elderly’s health condition data could be classified into four different groups; cluster 1 (25%) were male elderly with high blood pressure and smoking cigarette, cluster 2 (25%) were female elderly with no the congenital disease but the result from the eye sight examination, it was found that they were long-sighted, cluster 3 (24%) were female elderly with no the congenital disease but having the insomnia and osteoarthritis and cluster 4 (26%) were female elderly with high blood pressure and diabetes. It also indicated that each group had the rule showing the correlation between the data in each group having the minimum value of confidence at 0.8 and the minimum value of support not less than 0.5.
Utilizing deep learning, feature ranking, and selection strategies to classify diverse information technology ticketing data effectively Mudragada Venkata Subbarao; Kasukurthi Venkatarao; Suresh Chittineni; Subhadra Kompella
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.pp1985-1994

Abstract

In today's internet world, information technology (IT) ticketing services are potentially increasing across many corporations. Therefore, the automatic classification of IT tickets becomes a significant challenge. Feature selection becomes most important, particularly in data sets with several variables and features. However, enhance classification's precision and performance by stopping insignificant variables. Through our earlier research, we have categorized the unsupervised ticket dataset. As a result, we have converted the dataset into a supervised dataset. In this article, the classification of different IT tickets on Machine learning algorithms, Feature ranking, and feature selection techniques are used to improve the efficiency of machine learning algorithms. However, compared to the machine learning (ML) algorithms, the convolutional neural network (CNN) algorithm provides a better classification of the token IDs and provide better accuracy.
Improving Indonesian multietnics speaker recognition using pitch shifting data augmentation Kristiawan Nugroho; Isworo Nugroho; De Rosal Igniatus Moses Setiadi; Omar Farooq
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.pp1901-1908

Abstract

Speaker recognition to recognize multiethnic speakers is an interesting research topic. Various studies involving many ethnicities require the right approach to achieve optimal model performance. The deep learning approach has been used in speaker recognition research involving many classes to achieve high accuracy results with promising results. However, multi-class and imbalanced datasets are still obstacles encountered in various studies using the deep learning method which cause overfitting and decreased accuracy. Data augmentation is an approach model used in overcoming the problem of small amounts of data and multiclass problems. This approach can improve the quality of research data according to the method applied. This study proposes a data augmentation method using pitch shifting with a deep neural network called pitch shifting data augmentation deep neural network (PSDA-DNN) to identify multiethnic Indonesian speakers. The results of the research that has been done prove that the PSDA-DNN approach is the best method in multi-ethnic speaker recognition where the accuracy reaches 99.27% and the precision, recall, F1 score is 97.60%.
Performance investigation of two-stage detection techniques using traffic light detection dataset Sunday Adeola Ajagbe; Adekanmi Adeyinka Adegun; Ahmed Babajide Olanrewaju; John Babalola Oladosu; Matthew Olusegun Adigun
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.pp1909-1919

Abstract

Using a camera to monitor an object or a group of objects over time is the process of object detection. It can be used for a variety of things, including security and surveillance, video communication, traffic light detection (TLD), object detection from compressed video in public places. In recent times, object tracking has become a popular topic in computer science particularly, the data science community, thanks to the usage of deep learning (DL) in artificial intelligence (AI). DL which convolutional neural network (CNN) as one of its techniques usually used two-stage detection methods in TLD. Despite all successes recorded in TLD through the use of two-stage detection methods, there is no study that has analyzed these methods in experimental research, studying the strength and witnesses by the researchers. Based on the needs this study analyses the applications of DL techniques in TLD. We implemented object detection for TLD using 5 two-stage detection methods with the traffic light dataset using a Jupyter notebook and the sklearn libraries. We present the achievements of two-stage detection methods in TLD, going by standard performance metrics used, FASTER-CNN was the best in detection accuracy, F1-score, precision and recall with 0.89, 0.93, 0.83 and 0.90 respectively.
New approach to similarity detection by combining technique three-patch local binary patterns (TP-LBP) with support vector machine Ahmed Chater; Hicham Benradi; Abdelali Lasfar
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.pp1644-1653

Abstract

Recognition systems have received a lot of attention because of their varioususes in people's daily lives, for example in robotic intelligence, smart cameras,security surveillance or even criminal identification. Determining thesimilarity of faces by different face variations is based on robust algorithms.The validation of our experiment is done on two sets of data. In this paper, wecompare two facial recognition system techniques according to therecognition rate and the average authentication time: in order to increase theaccuracy rate and decrease the processing time. our approach is based onfeature extraction by two algorithms principal components analysis scaleinvariant feature transform (PCA-SIFT) and speeded up robust features (SURF), then uses the random sample consensus (RANSAC) technique to cancel outliers. Finally, face recognition is established on the basis of proximity determination. The second technique is based on the association of support vector machine (SVM) classifier with the key point recovery technique. the results obtained by the second technique is better for both databases: The recognition rate of the base olivetti research laboratory (ORL) should be 98.125800 and that of the Grimace base 97.2851500. The evaluation according to the time of the second technique does not exceed 300ms on average.
Classification of meat using the convolutional neural network Detty Purnamasari; Koko Bachrudin; Dede Herman Suryana; Robert Robert
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.pp1845-1853

Abstract

Every animal meat has different color and texture, for example, beef has a dark red color with a chewy texture, while pork has a pale red color and smooth fiber. A previous study has classified types of meat using gray level co-ocurrence matrix (GLCM), hue saturation value (HSV), and color intensity. In this research, we created meat classification between beef, pork, and horse meat using a convolutional neural network (CNN) develop in jupyter notebook, using the MobileNetV2 model, and 315 meat images as a dataset divided into 3 groups, 70% image for the training dataset, 20% image for the testing dataset, and 10% image for validation dataset. Before dividing the image into 3 groups, the image is resized to 224×224, and convert the color to grayscale. The model is trained with a training dataset, the epoch of 50, and Adam optimizer, the results show an accuracy of 93.15%.

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