cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Imam Much Ibnu Subroto
Contact Email
imam@unissula.ac.id
Phone
-
Journal Mail Official
ijai@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 1,722 Documents
 135   136   137   138   139 
Sign language emotion and alphabet recognition with hand gestures using convolution neural network Patil, Varsha K.; Pawar, Vijaya R.; Patil, Aditya; Bairagi, Vinayak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp954-962

Abstract

American sign language (ASL) is a special means of interaction for hard-of-hearing individuals and has precise conventional rules. Since the general public does not know these sign language protocols, there is a need to have an efficient automatic sign-emotion recognition system. The objective of this paper is, to develop a framework that recognizes standard hand gestures. The gesture represents emotions and alphabet. This paper covers the methodology, results and performance factors, for experimentations. This experimentation of ASL-based alphabet and emotion recognition is novel as till now many efforts of alphabets categorization are done but this is the new direction of research where emotions, such as together’, ‘happy’, ‘peace, ’sad’, ‘confused’, and ‘love’ are captured and automatically classified with hand signs. We mention our approach to increase ‘accuracy’, wherein we capture images and regions of interest (ROI). In this article, a specifically designed convolution neural network (CNN), is used to identify emotions from hand gestures and the addition of ROI enhances accuracy. The captured hand gesture dataset of the size of 94,000 images. “peace” sign emotion has the highest recognition rate (‘98.95%’). Alphabet’s “P” and “Q” sign ASL alphabets have the maximum recognition rate of signs. In all, very impressive accuracy of “92%” and above is detected. The limits of the experimentation are as mentioned i) there is no repeatability of accuracy for the same hand gesture; ii) The distance and angle of hand gestures with camera are crucial factors for an experiment; and iii) the alphabet recognition system is not working for the alphabets “J” and “Z”.
Hindi spoken digit analysis for native and non-native speakers Bhagath, Parabattina; Shanmukha, Malempati; Das, Pradip K.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1561-1567

Abstract

Automated speech recognition (ASR) is the process of using an algorithm orautomated system to recognize and translate spoken words of a specific language. ASR has various applications in fields such as mobile speech recognition, the internet of things and human-machine interaction. Researchers have been working on issues related to ASR for more than 60 years. One of the many use cases of ASR is designing applications such as digit recognition that aid differently-abled individuals, children and elderly people. However, there is a lack of spoken language data in under-developed and low-resourced languages, which presents difficulties. Although this is not a pivotal issue for highly established languages like English, it has a significant impact on less commonly spoken languages. In this paper, we discuss the development of a Hindi-spoken dataset and benchmark spoken digit models using convolutional neural networks (CNNs). The dataset includes both native and non-native Hindi speakers. The models built using CNN exhibit 88.44%, 95.15%, and 89.41% for non-native, native, and combined speakers respectively.
Hybrid methods to identify ovarian cancer from imbalanced high-dimensional microarray data Sapitri, Ni Kadek Emik; Sa'adah, Umu; Shofianah, Nur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1173-1182

Abstract

Scientists have used microarray data to identify healthy people and patients with various types of cancer, including ovarian cancer. Ovarian cancer is the most dangerous of all types of cancer that attacks the female reproductive organ. The right combination of methods is needed to identify ovarian cancer from microarray data because that type of data is high-dimensional and imbalanced. This research aims to propose two hybrid methods which are a combination of infinite feature selection (IFS) as features selector with classification and regression tree (CART) as a classifier. IFS can work with two separate scenarios, namely supervised infinite feature selection (SIFS) and unsupervised infinite feature selection (UIFS). This research also compares the performance of the two hybrid methods proposed (SIFS-CART and UIFS-CART) with CART without IFS. The data used is OVA_ovary that has 10937 columns and 1545 rows. The results shows that SIFS-CART achieves maximum performance using 1000 features and UIFS-CART 5000 features. CART without IFS uses all 10935 features. The balanced accuracy results show SIFS-CART can outperform CART without IFS and UIFS-CART. Using less features to get highest balanced accuracy results, SIFS is more effective in performing feature selection on the OVA_ovary dataset compared to UIFS.
Enhancing sepsis detection using feed-forward neural networks with hyperparameter tuning techniques N., Smitha; R., Tanuja; S. H., Manjula
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1252-1259

Abstract

This paper investigates the use of feed-forward neural networks for sepsis detection, emphasizing class imbalance mitigation and hyperparameter optimization. Leveraging random oversampling, synthetic minority over-sampling technique (SMOTE), and random sampling techniques, we address class imbalance, significantly improving feed-forward neural network performance. The resulting model achieves an impressive 83% accuracy on the test set, with notable enhancements in precision, recall, and F1-score for the positive class. Hyperparameter tuning using RandomizedSearchCV identifies optimal parameters, including an alpha value of 0.01 and the logistic activation function, leading to a remarkable 57.5% test accuracy. GridSearchCV also contributes to model refinement, albeit with a slightly lower test accuracy of 51.5%. These findings underscore the importance of robust hyperparameter tuning methods in optimizing feed-forward neural network models for imbalanced datasets, particularly in sepsis detection. The insights gained hold promise for the development of more accurate diagnostic tools, ultimately improving patient outcomes in clinical practice.
Deep learning-based techniques for video enhancement, compression and restoration Lhiadi, Redouane; Jaddar, Abdessamad; Kaaouachi, Abdelali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1518-1530

Abstract

Video processing is essential in entertainment, surveillance, and communication. This research presents a strong framework that improves video clarity and decreases bitrate via advanced restoration and compression methods. The suggested framework merges various deep learning models such as super-resolution, deblurring, denoising, and frame interpolation, in addition to a competent compression model. Video frames are first compressed using the libx265 codec in order to reduce bitrate and storage needs. After compression, restoration techniques deal with issues like noise, blur, and loss of detail. The video restoration transformer (VRT) uses deep learning to greatly enhance video quality by reducing compression artifacts. The frame resolution is improved by the super-resolution model, motion blur is fixed by the deblurring model, and noise is reduced by the denoising model, resulting in clearer frames. Frame interpolation creates additional frames between existing frames to create a smoother video viewing experience. Experimental findings show that this system successfully improves video quality and decreases artifacts, providing better perceptual quality and fidelity. The real-time processing capabilities of the technology make it well-suited for use in video streaming, surveillance, and digital cinema.
Enhancing convolutional neural network based model for cheating at online examinations detection Ouahabi, Sara; Aboudihaj, Rihab; Sael, Nawal; El Guemmat, Kamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp843-852

Abstract

In the last few years, e-learning has revolutioning education, giving students access to diverse and adaptable on-line resources, but it has also face a major challenge: cheating on online exams. Students now use variant cheating methods include consulting unauthorized documents, communicating with others during the exam, searching for information on the internet. Combating these cheating practices has become crucial to preserving the integrity of academic assessments. In this context, artificial intelligence (AI) has emerged as an essential tool for mitigating this fraudulent behavior. Equipped with advanced machine learning capabilities, AI can examine a wide range of data to detect student suspicious behavior. This study develops an approach based on a convolutional neural network (CNN) model designed to detect cheating by analyzing candidates' head movements during online exams. By exploiting the FEI dataset, this model achieves an interesting accuracy of 97.28%. In addition, we compare this model to the well-known transfer learning models used in the literature namely, ResNet50, VGG16, DenseNet21, MobileNetV2, and EfficientNetB0 demonstrating the out performance of our approach in detecting cheating during online exams.
New family of error-correcting codes based on genetic algorithms Bellfkih, El Mehdi; Nouh, Said; Chemseddine Idrissi, Imrane; Louartiti, Khalid; Mouline, Jamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1077-1086

Abstract

This paper introduces a novel error-correcting code (ECC) construction and decoding approach utilizing genetic algorithms (GAs). Classical ECCs often struggle with efficiency in correcting multiple errors due to time-consuming matrix-based encoding and decoding processes. Our GA-based method optimizes generator vectors to maximize the minimum distance between codewords, enhancing error correction capabilities. Specifically, we construct a new family of ECCs with code length 31, dimension 12, and minimum distance 7, reducing complexity from O(kn) to O(k(n−k)) by encoding message blocks with vectors instead of matrices. In the decoding phase, the GA effectively corrects errors in received codewords. Experimental results show that at a signal-to-noise ratio (SNR) of 7.7 dB, our method achieves a bit error rate (BER) of 10−5 after only 9 generations of the GA. These results demonstrate improved error correction and decoding performance compared to traditional methods. This study contributes an innovative approach using GAs for error correction, offering simpler encoding and robust performance in coding schemes.
Hybrid object detection and distance measurement for precision agriculture: integrating YOLOv8 with rice field sidewalk detection algorithm Tungkasthan, Anucha; Jongsawat, Nipat; Crisnapati, Padma Nyoman; Thwe, Yamin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1507-1517

Abstract

This study aims to propose a new approach to semantic segmentation of sidewalk images in rice fields using the YOLOv8 algorithm, with the objective of enhancing agricultural monitoring and analysis. The experimental process involved preparing the development environment, extracting data from JSON, and training the model using YOLOv8. Evaluation reveals consistent and accurate sidewalk detection with a confidence score of 0.9-1.0 across various environmental conditions. Confusion matrix and precision-recall analysis confirmed the robustness and accuracy of the model. These findings validate the effectiveness of the approach in detecting and measuring sidewalks with high precision, potentially improving agricultural monitoring. The novelty of this study lies in the utilization of the RIFIS-D algorithm as an integral part of a hybrid approach with YOLOv8. This hybridization enriches the model with additional capability to detect the distance between the sidewalk and the tractor, addressing specific needs in agricultural applications. This contribution is significant in the advancement of automatic navigation and monitoring technology in agriculture, enabling the implementation of more sophisticated and efficient systems in field operations.
Hybrid model detection and classification of lung cancer Yousuf, Rami; Daraghmi, Eman Yaser
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1496-1506

Abstract

Lung cancer ranks among the most prevalent malignancies worldwide. Early detection is pivotal to improving treatment outcomes for various cancer types. The integration of artificial intelligence (AI) into image processing, coupled with the availability of comprehensive historical lung cancer datasets, provides the chance to create a classification model based on deep learning, thus improving the precision and effectiveness of detecting lung cancer. This not only aids laboratory teams but also contributes to reducing the time to diagnosis and associated costs. Consequently, early detection serves to conserve resources and, more significantly, human lives. This study proposes convolutional neural network (CNN) models and transfer learning-based architectures, including ResNet50, VGG19, DenseNet169, and InceptionV3, for lung cancer classification. An ensemble approach is used to enhance overall cancer detection performance. The proposed ensemble model, composed of five effective models, achieves an F1-score of 97.77% and an accuracy rate of 97.5% on the IQ-OTH/NCCD test dataset. These findings highlight the effectiveness and dependability of our novel model in automating the classification of lung cancer, outperforming prior research efforts, streamlining diagnosis processes, and ultimately contributing to the preservation of patients' lives.
Enhancing financial cybersecurity via advanced machine learning: analysis, comparison Odette Boussi, Grace; Gupta, Himanshu; Hossain, Syed Akhter
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1281-1289

Abstract

The financial sector is a prime target for cyber-attacks due to the sensitive nature of the data it handles. As the frequency and sophistication of cyber threats continue to rise, implementing effective security measures becomes paramount. In this paper we provide a comprehensive comparison of six prominent machine learning techniques utilized in the financial industry for cyber-attack prevention. The study aims to identify the best-performing model and subsequently compares its performance with a proposed model tailored to the specific challenges faced by financial institutions. This paper looks at using advanced machine learning methods to make cybersecurity stronger for financial institutions. The work explores the deployment of cutting-edge machine learning algorithms - logistic regression, random forest, support vector machines (SVM), K-nearest neighbour (KNN), naïve Bayes, extreme gradient boosting (XGBoost), and deep learning technique (Dense Layer) - to fortify the cybersecurity framework within financial institutions. Through a meticulous analysis and comparative study, we explore the efficacy, scalability, and practical implementation aspects of various machine learning algorithms tailored to address cybersecurity concerns. Additionally, we propose a framework for integrating the most effective machine learning models into existing cybersecurity infrastructure, offering insights into bolstering resilience against evolving cyber threats. In our comparison, XGBoost exhibited outstanding performance with an accuracy of 95%.

Page 137 of 173 | Total Record : 1722

 135   136   137   138   139 

Filter by Year

2012 2025


Reset
Filter By Issues
All Issue Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue