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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 123 Documents
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Search results for , issue "Vol 13, No 4: December 2024" : 123 Documents clear
Mortality prediction of COVID-19 patients using supervised machine learning Khuluq, Husnul; Astagiri Yusuf, Prasandhya; Aryani Perwitasari, Dyah; Nguyen, Thang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4472-4479

Abstract

Hospitalized patients with COVID-19 are at higher risk of mortality. Machine learning (ML) algorithms have been proposed as a possible strategy for predicting mortality rates among patients hospitalized with COVID-19. This study analyzed various ML algorithms and identified the best model to predict COVID-19 mortality based on demographic, clinical, and laboratory data collected at registration. Data from 4,314 eligible patients (3,384 survivors and 930 who died) was collected from the register of three hospitals in Yogyakarta province, Indonesia, based on the confirmed predictors. Next, ML algorithms were utilized to predict mortality. Finally, the confusion matrix was used to evaluate how effective the models performed. The best five predictors from 26 features were myocardial infarction, SpO2, neutrophil, D dimer, and creatinine. The results indicate that the random forest algorithm showed better performance than other ML algorithms in terms of accuracy, sensitivity, precision, specificity, and area under the curve (AUC), achieving values of 84.15%, 84.0%, 84.1%, 83.9%, and 90.02%, respectively. Implementing ML techniques can accurately predict the mortality rate associated with COVID-19. Therefore, this predictive model can help clinicians and hospitals predict COVID patients with a greater risk of death and effectively target more appropriate treatments.
Proactive cervical cancer risk assessment using data-driven analytics Sreelatha, Sreelatha; Shivashetty, Vrinda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4301-4311

Abstract

This study introduces a sophisticated predictive model integrating clinical and lifestyle data addressing the critical public health challenge of cervical cancer, particularly in regions lacking routine screenings. Leveraging data driven analytics, the proposed model undergoes comprehensive preprocessing, including exploratory data analysis, missing value imputation, and feature extraction. Feature selection is carried out using the XGBoost classifier to ensure model efficacy. Data normalization and class balance via oversampling techniques are applied, with model validation conducted through stratified cross-validation. The optimized feature vector is then employed to train a LightGBM model. Utilizing a retrospective dataset of 858 patients from the Hospital Universitario de Caracas, Venezuela, comprising demographic, lifestyle, and medical history data, the LightGBM model achieves an impressive accuracy of 98%, outperforming similar existing approaches. The study outcome demonstrates the effectiveness of the proposed data modelling framework and feature selection, along with the choice of LightGBM as a suitable classifier. The proposed predictive framework can efficiently aid healthcare professionals in prioritizing high-risk patients for further evaluation and intervention.
ConciseCarNet: convolutional neural network for parking space classification Ramli, Marwan; Rahman, Sayuti; Bayu Syah, Rahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4158-4168

Abstract

The car is a mode of transportation that brings numerous benefits to the community. As a result, the growth of vehicles is increasing, which has a negative impact. Some of the negative impacts include noise, air pollution, traffic congestion, and the need for parking spaces. Drivers that drive around looking for parking places increase the negative impact as well as boredom and even worry for the driver. Therefore, the driver needs this information on the availability of parking spaces. A convolutional neural network (CNN) using a camera is one of the best methods that can be used to solve this problem. We built a more efficient CNN architecture for classifying parking spaces, which was named ConciseCarNet. ConciseCarNet uses 33 and 11 convolution filters, which cause fewer parameters than previous architectures. ConciseCarNet has two branches, each with a different branch structure. This branch is designed to generate additional feature variations, which will help improve the accuracy. Based on testing, the accuracy of ConciseCarNet2x outperforms the accuracy of mAlexnet, Carnet, EfficientParkingNet, and you look once (YOLO)+MobilNet architectures, which is 99.37%. ConciseCarNet has fewer parameters, file sizes, and floating point operations (FLOPs) compared to other architectures.

Page 13 of 13 | Total Record : 123

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