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 123 Documents
 2   3   4   5   6 
Search results for , issue "Vol 13, No 4: December 2024" : 123 Documents clear
Transliteration and translation of Hindi language using integrated domain-based Auto-encoder K, Vathsala M; Lingareddy, Sanjeev C.
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.pp4906-4914

Abstract

The main objective of translation is to translate words' meanings from one language to another; in contrast, transliteration does not translate any contextual meanings between languages. Transliteration, as opposed to translation, just considers the individual letters that make up each word.  In this paper an Integrated deep neural network transliteration and translation model (NNTT) based autoencoder model is developed. The model is segmented into transliteration model and translation model; the transliteration involves the process of converting text from one script to another evaluated on the Dakshina dataset wherein Hindi typically uses a sequence-to-sequence model with an attention mechanism, the translation model is trained to translate text from one language to another. Translation models regularly use a sequence-to-sequence model performed on the WAT (Workshop on Asian Translation) 2021 dataset with an attention mechanism, similar to the one used in the transliteration model for Hindi. The proposed NNTT model merges the in-domain and out-domain frameworks to develop a training framework so that the information is transferred between the domains. The results evaluated show that the proposed model works effectively in comparison with the existing system for the Hindi language.
Design and analysis plant factory with artificial light Boonmee, Chaiyant; Wongsuriya, Wipada; Homjan, Jeerawan; Kiatsookkanatorn, Paiboon; Sritanauthaikorn, Patcharanan; Wannakam, Khanittha; Watjanatepin, Napat
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.pp3974-3986

Abstract

It has been challenging to construct an autonomously controlled plant factory with artificial light (PFAL). It is also useful in engineering and bioscience research and education. The purpose of this research is to design and construct a micro-scale PFAL (µPFAL) with automatic environment control for a university project. Then, analyze the effectiveness of managing temperature, humidity, pH, EC, and CO2 on crop production, as well as the cost, and benefit of µPFAL. The µPFAL is made up of LED lighting, air condition, vertical cultivation, EC-pH regulation, a CO2 supply unit, and environmental control and monitoring. Control was provided via Arduino with PC monitor. For economic evaluation, cost-benefit analysis was used. The results of the control environment in µPFAL were achieved with a deviation of less than 2.5%. An Arduino-based environmental control system with a computer for monitoring was suited for university’s PFAL.Our µPFAL could produce 80.45 g/head fresh weight of green oak lettuce, the lettuce’s yield of 19 kg/m2/y. The payback period of µPFAL is 3.28 years, net present value of 82,543.30 THB, an internal rate of return of 24% and the B/C ratio of 1.22. Future research should include solar energy to assist µPFAL in meeting its sustainable goal.
Enhancing breast cancer diagnosis: a comparative analysis of feature selection techniques Benghazouani, Salsabila; Nouh, Said; Zakrani, Abdelali
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.pp4312-4322

Abstract

Breast cancer is a significant contributor to female mortality, emphasizing the importance of early detection. Predicting breast cancer accurately remains a complex challenge within medical data analysis. Machine learning (ML) algorithms offer valuable assistance in decision-making and diagnosis using medical data. Numerous research studies highlight the effectiveness of ML techniques in improving breast cancer prediction. Feature selection plays a pivotal role in data preprocessing, eliminating irrelevant and redundant features to minimize feature count and improve classification accuracy. This study focuses on optimizing breast cancer diagnostics through feature selection methods, specifically genetic algorithms (GA) and particle swarm optimization (PSO). The research involves a comparative analysis of these methods and the application of a diverse set of ML classification techniques, including logistic regression (LR), support vector machine (SVM), decision tree (DT), and ensemble methods like random forest (RF), AdaBoost, and gradient boosting (GB), using a breast cancer dataset. The models' performance is subsequently evaluated using various performance metrics. The experimental findings illustrate that PSO achieved the highest average accuracy, reaching 99.6% when applied to AdaBoost, while GA attained an accuracy rate of 99.5% when employed with both AdaBoost and RF.
Indonesian news article authorship attribution multilabel multiclass classification using IndoBERT Saputra, Karen Etania; Riccosan, Riccosan
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.pp4688-4694

Abstract

Recent developments in technology have made it easier to produce digital con- tent, especially textual articles. But, it has a negative impact in the form of a rising public skepticism of digital data due to plagiarism. Indonesia, one of the world’s most populous countries, is not resistant to this problem. To resolve it, the authorship attribution (AA) task must be executed. However, there has been little investigation on AA for Indonesian articles. As a result, this research applies the AA task to an Indonesian digital news articles dataset. Continuing the previous research, dataset modification was carried out to increase data com- plexity by adding a new class, namely the author’s gender, and also by balancing the distribution of data versus labels to minimize potential overfitting, and model hyper-parameter configurations were carried out to enhance the results gained. This research successfully applied the IndoBERT model to the Indonesian AA task, yielding results in the form of precision = 0.92, recall = 0.90, and F1-score = 0.91. These results indicate that the Indonesian AA task has a lot of potential for development since it identifies writing patterns that may benefit the forensic field, detect plagiarism, and analyze Indonesian texts.
Leveraging multimodal deep learning for natural disaster event classification and its damage severity analysis through social media posts Kasturi, Nivedita; Guruputra Totad, Shashikumar; Ghosh, Goldina
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.pp4766-4777

Abstract

Accurate and timely information is essential for coordinating an effective disaster response. Traditional methods have struggled to efficiently categorize disaster events and assess damage severity due to the variety and complexity of data sources. Previous research has focused on specific tasks, such as information gathering or humanitarian assistance, but has not adequately addressed the assessment of disaster damage severity. This paper proposes a hybrid learning model to improve disaster event classification and damage severity identification. The model combines image and text data in a cooperative way, using ResNet50 to extract features from images and a LSTM with attention mechanism to learn sequences from text. This combination allows for a more contextual and informative representation of the input data. Compared to existing approaches, the proposed multimodal approach achieves significantly better results in disaster event classification. Apart from the proposed model also shows promising outcome for damage severity of disaster. These advancements are especially important for real-world applications such as disaster management and response coordination, where accuracy and reliability are essential. The comprehensive methodology and empirical results presented in this paper demonstrate the effectiveness and potential of using hybrid learning models to leverage multimodal data for unique and sophisticated analytical tasks in disaster scenarios
Detecting student attention through electroencephalography signals: a comparative analysis of deep learning models Lim, Eng Lye; Murugesan, Raja Kumar; Balakrishnan, Sumathi
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.pp4608-4618

Abstract

In the landscape of educational technology, understanding and optimizing student attention is important to enhance student’s learning experience. This study explores the potential of using electroencephalography (EEG) signals for discerning students' attention levels during educational tasks. With a cohort of 30 participants, EEG data were meticulously collected and subjected to robust preprocessing techniques, including independent component analysis (ICA) and principal component analysis (PCA). The research then employed different deep learning algorithm such as long short-term memory (LSTM), recurrent neural network (RNN), gated recurrent unit (GRU), multi-layer perceptron (MLP), and convolutional neural network (CNN) classifiers to predict students' attention. The results reveal notable variations in the classifiers' predictive performance. Our finding revealed that the LSTM model emerged as the top performer and achieved 96% of the accuracy. This study not only contributes to the advancement of attention detection in educational technology but also underscores the importance of preprocessing methodologies, such as ICA and PCA, in optimizing the performance of deep learning models for EEG-based applications.
Unified and evolved approach based on neural network and deep learning methods for intrusion detection Boukhalfa, Alaeddine; El Attaoui, Anas; Rhouas, Sara; El Hami, Norelislam
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.pp4071-4079

Abstract

Currently, network security has become a major concern for all entities around the world. Attackers employ various methods to disrupt services, which requires new methods to stop them all in one way. Moreover, these intrusions can evolve and overcome security measures and devices, which pushes to use new evolving methods able to accompany the evolution of these threats, to block them. In our paper, we propose a new approach for intrusion detection, founded on neural network (NN) and deep learning (DL) methods. This approach is planned to not only identify threats, but also to develop a long-term memory of them, in order to detect new ones resembling these memorized attacks, and simultaneously, to provide a single way to stop all kinds of intrusions. To test our model, we have chosen the most recently employed methods in literature, NN and DL algorithms: feedforward neural network (FNN), convolutional neural network (CNN), and long short-term memory (LSTM), then we have applied them on network security layer-knowledge discovery in databases (NSL KDD) intrusions dataset. The results of experiments were impressive for all the algorithms, with maximum performances noted by LSTM, which affirms the efficacy of our proposed method for intrusion detection.
Sentiment-electroencephalogram fusion for efficient product review prediction using correlation-based deep learning neural network Sharma, Rahul Kumar; Dagur, Arvind
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.pp4675-4687

Abstract

Various techniques have been proposed and implemented in previous work for sentiment analysis prediction. However, achieving satisfactory quality of description and fault prediction remains a challenging task. To overcome these limitations, this study proposes an efficient prediction technique that utilizes sentiment analysis of product reviews and electroencephalogram (EEG) signals using correlation-based deep learning neural network (CDNN). The study employs two types of datasets: EEG signals and Amazon product reviews. During the pre-processing phase, EEG signals undergo normalization, while Amazon product reviews undergo tokenization, stop word removal, and weighting factor application to convert unstructured data into a structured format. Subsequently, the pre-processed EEG signals and reviews are analyzed to extract features like emotion, demographic information, personality traits, and sentiment. These features are then employed in sentiment analysis via an entropy-based deep-learning neural network. The proposed CDNN utilizes the grasshopper optimization algorithm (EGOA) to optimize hyperparameters for each layer. Comparative performance assessment against established methods like convolutional neural network (CNN), long short-term memory (LSTM), multiclass support vector machine (M-SVM), and bidirectional encoder representations from transformers (BERT) is conducted, and the results are evaluated. Experimental result reveal that the proposed system outperforms traditional approaches.
The use of augmented reality in assessing and training children with attention deficit hyperactivity disorder Joseph, Jesla; M., Vinay
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.pp4045-4053

Abstract

Attention deficit hyperactivity disorder (ADHD) is a serious issue that must be addressed in the modern world. Treatment for ADHD is challenging because it is costly, has adverse effects, might not be successful, and is not considered an emergency. The reason that ADHD is hard to manage is because it causes people-especially children-to make impulsive decisions that hinder their ability to succeed in school, the workplace, and other areas of life. As an alternative approach, neurofeedback therapy or play therapy, which relies on real-time feedback of an individual's brainwave activity typically collected through electroencephalogram (EEG), has demonstrated promising outcomes in the treatment of mental disorders and enhancing cognitive capabilities. On the other hand, prolonged exposure to repetitive feedback might result in lower engagement since people may become disinterested in the process and find it difficult to continue participating. An extensive assessment on the use of augmented reality (AR) in the context of pediatric ADHD has been carried out, with an emphasis on the benefits of creating games specifically for kids with ADHD. By using AR technology in a group of children, the goal of this study was to investigate the basic characteristics of AR systems that aid in the identification and treatment of ADHD in children.
A fuzzy logic-genetic algorithm for full truckload transportation problem EL Bouyahyiouy, Karim; EL Hariz, Zahira; Bellabdaoui, Adil
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.pp4195-4205

Abstract

This work addresses a full truckload commodity selection and multiple depot vehicle routing problem with time windows (FTSMDVRPTW). The goal of the problem is to design a set of selective truck routes that maximize overall profit subject to time window constraints. Each truck route is an arrangement of full truckload transportation commodities that begins at a departure point and ends at an arrival point. It is unnecessary to serve all commodities; only those that provide a higher profit are chosen. We introduce a meta-heuristic based on a combination of fuzzy logic controller (FLC) and genetic algorithm (GA) to solve the FTSMDVRPTW, where the crossover and mutation rates are adjusted during the GA’s evolutionary process using an FLC. We demonstrate the effectiveness and efficiency of the proposed FLC+GA through experimental results on randomly generated instances for the considered problem.

Page 4 of 13 | Total Record : 123

 2   3   4   5   6 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 15, No 1: February 2026 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