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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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Innovative credit card fraud detection: A hybrid model combining artificial neural networks and support vector machines Ndama, Oussama; Bensassi, Ismail; En-naimi, El Mokhtar
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.pp2674-2682

Abstract

In recent years, escalating fraudulent activities have led to significant financial losses across industries, intensifying the critical challenge of fraud detection. This study introduces a novel hybrid model that combines artificial neural networks (ANN) with support vector machines (SVM) to construct a robust additive model for fraud detection. Emphasizing the Synthetic Minority Over-sampling Technique (SMOTE), our investigation addresses the imbalanced nature of fraud versus non-fraud transactions. The clear novelty of our research lies in the seamless integration of these two powerful tools, offering a comprehensive and effective solution to the challenges posed by credit card fraud detection. Furthermore, our study stands out by emphasizing the collaborative synergy between ANN and SVM, particularly through the integration of multiple kernels, which improves the adaptability and accuracy of the proposed hybrid model. We conducted a thorough examination of 284,807 anonymized transactions, placing special emphasis on comparing the hybrid approach's performance and showcasing its superiority over traditional methodologies in the realm of fraud detection.
Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis Bel, Felcia; Selvaraj, Sabeen
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.pp2011-2018

Abstract

Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method  has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers.
Emergency patient forecasting with models based on support vector machines Hernandez, Carlos; Lagos, Dafne; Leal, Paola; Castillo, Jaime
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.pp3129-3140

Abstract

Understanding the dynamic nature of the influx of patients is crucial for efficiently managing supplies, medical personnel, and infrastructure in an emergency room (ER). While overestimation can lead to resource wastage, underestimation can result in shortages and compromised service quality. This study addresses emergency patient forecast by means of implementing support vector machine (SVM) algorithms. Along four phases (analysis, design, development, and validation), more than 50,000 ER records were preprocessed and analyzed. Traditional error metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were utilized alongside monthly consolidated forecasts. To benchmark performance, actual values and forecasts derived from linear regression (LR) models were used. Experiments revealed that LR models had lower errors compared to SVM models. However, monthly consolidated forecasts showed that SVM-based models underestimated less than LR-based models. In conclusion, SVM-based models could help planners to accurately estimate the requirements for supplies and medical personnel during the period under study.
Internet of things in public healthcare organizations: the mediating role of attitude Noor, Bashar Dheyaa; Katheeth, Zainab Dalaf; Noor, Ammar Dheyaa
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.pp57-65

Abstract

Internet of things (IoT) is a promising technology to face the challenges of COVID19 and enhance the capacities of public hospitals. However, few of the literature examined the behavioural intention (BI) of patients to use the IoT wearable health device (IoTWHD). This paper aims to examine the factor that affect the BI toward using the IoTWHD. The study proposes that variables of Technology acceptance model (TAM3) along with unified theory of acceptance and use of technology (UTAUT) can explain the BI. The population is the patients of public hospitals. Convivence sampling was deployed to collect the data using a questionnaire. 161 respondents participated in this study. The finding of Smart Partial Least Square showed that subjective norms (SN) affected the perceived usefulness (PU). Perceived enjoyment (PE) affected the perceived ease of use (PEOU). Further, PU, PEOU and perceived security (PS) affected the BI to use IoTWHD. Attitude mediated the effect of PU and PEOU on BI. More positive word of mouth are needed to enhance the perception of patients about BI to use IoTWHD in public health organizations.
Sophisticated face mask dataset: a novel dataset for effective coronavirus disease surveillance Ul Haque, Sheikh Burhan; Wani, Mohd Hanief
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.pp1030-1037

Abstract

Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
On-device training of artificial intelligence models on microcontrollers Thai, Bao-Toan; Tran, Vy-Khang; Pham, Hai; Nguyen, Chi-Ngon; Nguyen, Van-Khanh
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.pp2829-2839

Abstract

Numerous studies are currently training artificial intelligence (AI) models on tiny devices constrained by computing power and memory limitations by implementing model optimization algorithms. The question arises whether implementing traditional AI models directly on small devices like micro-controller units (MCUs) is feasible. In this study, a library has been developed to train and predict the artificial neural network (ANN) model on common MCUs. The evaluation results on the regression problem indicate that, despite the extensive training time, when combined with multitasking programming on multi-core MCUs, the training does not adversely affect the system's execution. This research contributes an additional solution that enables the direct construction of ANN models on MCU systems with limited resources.
Machine learning for text document classification-efficient classification approach Mohammed Ali, Sura I.; Nihad, Marwah; Mohamed Sharaf, Hussien; Farouk, Haitham
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.pp703-710

Abstract

Numerous alternative methods for text classification have been created because of the increase in the amount of online text information available. The cosine similarity classifier is the most extensively utilized simple and efficient approach. It improves text classification performance. It is combined with estimated values provided by conventional classifiers such as Multinomial Naive Bayesian (MNB). Consequently, combining the similarity between a test document and a category with the estimated value for the category enhances the performance of the classifier. This approach provides a text document categorization method that is both efficient and effective. In addition, methods for determining the proper relationship between a set of words in a document and its document categorization is also obtained.
Classification of nutmeg ripeness using artificial intelligence Bil Qisthi, Imam; Siswono, Hartono
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.pp2441-2450

Abstract

Nutmeg seeds can produce a lot of oil if they have optimal maturity. In other words, they have little moisture content. Based on observations made at one of the refineries in Sukabumi, farmers do not pay attention to the maturity level of nutmeg seeds after drying which can cause a decrease in the quality of nutmeg seeds and the quality of the oil produced. This study aims to make it easier for nutmeg farmers to classify the maturity of nutmeg seeds. This study used the convolutional neural network (CNN) method to help with classification problems and several image processing methods. This program will be run through an Android application. When the application containing this CNN model is run, the camera system will turn on, and the program will classify in real-time nutmeg objects into 1 of 3 class labels namely LowQuality, MidQuality, or HighQuality class labels classifying. The results will be displayed on the application screen, the results are displayed in the form of class names and scores. The results of CNN model training accuracy are 97.92%.
Computer aided detection for vertebral deformities diagnosis based on deep learning OUNASSER, Nabila; Rhanoui, Maryem; mikram, Mounia; El Asri, Bouchra
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.pp3414-3425

Abstract

The diagnosis of spinal deformities is one of the most frequent daily clinical routine. X-ray images are used to diagnose several pathologies in order to reduce harmful radiations of the patient. Spinal deformities are diagnosed essentially from vertebral shapes, orientations, and positions, so their detection and segmentation are major steps required for diagnosis. Deep learning could be applied for automatic diagnosis to detect scoliosis and its variants with a favourable performance. In this study, based on 609 spinal anterior-posterior x-ray images obtained from the public SpineWeb, we examine generative ad- versarial network (GAN) based architectures and convolutional neural network (CNN) based architectures models that are capable of automatically detecting anomalies in radiograph and achieve expert-level performances in various fields providing a solid comparative study. Most of the implemented models are apt to automatically distinguish limits between vertebrae so determining their shape with a very good visual performance. The GAN-based architecture estimates the required vertebral landmarks with an accuracy rate of 0.966, signify its capacity for automatic scoliosis assessment in a clinical setting.
An boosting business intelligent to customer lifetime value with robust M-estimation Elveny, Marischa; Y. Syah, Rahmad B.; M. Nasution, Mahyuddin K.
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.pp1632-1639

Abstract

When a business concentrates too much on acquiring new clients rather than retaining old ones, mistakes are sometimes made. Each customer has a different value. Customer lifetime value (CLV) is a metric used to assess longterm customer value. Customer value is a key concern in any commercial endeavor. When there are variations in customer behavior, CLV forecasts the value of total customer income when the data distribution is not normal, and outliers are present. Robust M-estimation, a maximum likelihood type estimator, is used in this study to enhance CLV data. Through the minimization of the regression parameter from the residual value, robust Mestimation eliminates data outliers in customer metric data. With an accuracy of 94.15%, R-square is used to gauge model performance. This research shows that CLV optimization can be used as a marketing and sales strategy by companies.

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