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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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Network intrusion detection in big datasets using Spark environment and incremental learning Elmoutaoukkil, Abdelwahed; Hamlich, Mohamed; Khatib, Amine; Chriss, Marouane
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.pp4414-4421

Abstract

Internet of things (IoT) systems have experienced significant growth in data traffic, resulting in security and real-time processing issues. Intrusion detection systems (IDS) are currently an indispensable tool for self-protection against various attacks. However, IoT systems face serious challenges due to the functional diversity of attacks, resulting in detection methods with machine learning (ML) and limited static models generated by the linear discriminant analysis (LDA) algorithm. The process entails adjusting the model parameters in real time as new data arrives. This paper proposes a new method of an IDS based on the LDA algorithm with the incremental model. The model framework is trained and tested on the IoT intrusion dataset (UNSW-NB15) using the streaming linear discriminant analysis (SLDA) ML algorithm. Our approach increased model accuracy after each training, resulting in continuous model improvement. The comparison reveals that our dynamic model becomes more accurate after each batch and can detect new types of attacks.
User sentiment dynamics in social media: a comparative analysis of X and Threads Khairunnas, Rezki; Pagua, Jeri Apriansyah; Fitriya, Ghina; Ruldeviyani, Yova
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp447-456

Abstract

This research examines the dynamics of user sentiment and its correlation with the usage factors of applications in the context of the competition between X (formerly Twitter) and Threads, a social media application under the umbrella of Meta. Through sentiment analysis of user reviews on the Google Play Store and App Store, the study aims to identify the key factors contributing to a significant decline in user engagement with Threads and the return of users to X. The method employed in this research is the support vector machine (SVM) for sentiment classification of reviews. The study then correlates the classified sentiments with application usage factors: usability, features, design, and support. The research findings indicate user sentiment influences user engagement, especially in features and design. The research concludes with insights regarding implications for application developers and suggests directions for future research.
Implementation of global navigation satellite system software-defined radio baseband processing algorithms in system on chip Devi Kh, Chetna; Panduranga Rao, Malode Vishwanatha
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.pp3869-3878

Abstract

The global navigation satellite system (GNSS) is an international navigation system that determines users' locations globally using a constellation of satellites. Conventional hardware-based receivers often face challenges related to cost-effectiveness and lack of reconfigurability. To address these issues, GNSS software receivers have emerged, executing baseband processing methods on host computers. However, host PC-based GNSS software receivers encounter obstacles during real-time signal acquisition, such as computational complexity and data loss. This research paper introduces a real-time system on chip (SoC)-based GNSS software receiver to mitigate these concerns. The receiver utilizes the USRP N210 radio frequency (RF) front end to acquire GNSS signals in real-time. Baseband processing algorithms are executed using the Zynq 7000 SoC board, with modifications applied to the acquisition module. The effectiveness of the SoC-based GNSS receiver is evaluated under both stationary and dynamic conditions. Experimental outcomes indicate that the receiver provides precise user localization and facilitates prototype development. This methodology not only overcomes the limitations of conventional hardware-based receivers but also leverages the benefits of SoC architecture to process GNSS signals in a flexible and efficient manner.
Intelligent cervical cancer detection: empowering healthcare with machine learning algorithms Yadav, Uma; D. Bondre, Vipin; Bondre, Shweta V.; Thakre, Bhakti; Agrawal, Poorva; Thakur, Shruti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp298-306

Abstract

Cervical cancer remains a significant global health issue, particularly in underdeveloped nations, where it contributes to high mortality rates. Early detection is critical for improving treatment outcomes and survival rates. This study employs machine learning (ML) algorithms to predict cervical cancer risk using a dataset from the University of California at Irvine (UCI), which includes demographic and clinical attributes such as age, sexual history, smoking habits, and medical history. After applying data preprocessing techniques, several classification algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree, adaptive boosting (AdaBoost), and artificial neural networks (ANN), were trained and evaluated. The models were assessed using classification metrics such as precision, recall, and F1 score. Among the models, the ANN demonstrated the highest accuracy, achieving a score of 0.95. In addition, correlation analysis revealed significant relationships between various risk factors, providing insights into cervical cancer mechanisms and potential preventive measures. The study highlights the potential of ML in improving cervical cancer detection and patient outcomes, suggesting that advanced ML techniques can be valuable tools in healthcare research and clinical applications.
3D visualization diagnostics for lung cancer detection M. Mahmoud, Rana; Elgendy, Mostafa; Taha, Mohamed
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.pp4630-4641

Abstract

Lung cancer is a leading cause of cancer deaths worldwide with an estimated 2 million new cases and 1·76 million deaths yearly. Early detection can improve survival, and CT scans are a precise imaging technique to diagnose lung cancer. However, analyzing hundreds of 2D CT slices is challenging and can cause false alarms. 3D visualization of lung nodules can aid clinicians in detection and diagnosis. The MobileNet model integrates multi-view and multi-scale nodule features using depthwise separable convolutional layers. These layers split standard convolutions into depthwise and pointwise convolutions to reduce computational cost. Finally, the 3D pulmonary nodular models were created using a ray-casting volume rendering approach. Compared to other state-of-the-art deep neural networks, this factorization enables MobileNet to achieve a much lower computational cost while maintaining a decent degree of accuracy. The proposed approach was tested on an LIDC dataset of 986 nodules. Experiment findings reveal that MobileNet provides exceptional segmentation performance on the LIDC dataset, with an accuracy of 93.3%. The study demonstrates that the MobileNet detects and segments lung nodules somewhat better than other older technologies. As a result, the proposed system proposes an automated 3D lung cancer tumor visualization.
Artificial intelligence and machine learning adoption in the financial sector: a holistic review Sayari, Karima; Jannathl Firdouse, Mohamed Kasim; Al Abri, Fathiya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp19-31

Abstract

The evolution of new technologies has spurred a growing body of literature exploring their application and impact on the financial sector, particularly the integration of artificial intelligence (AI). This paper delves into the rapid adoption of AI and machine learning within the financial sector, highlighting their potential to enhance financial stability and productivity. By reviewing research from 2018 to 2023, the study categorizes AI applications in finance into three main areas: cybersecurity, customer services, and financial management. Furthermore, the research identifies and classifies various threats posed to the integrity and stability of the financial system by AI, along with associated challenges for policy and regulatory frameworks. It also addresses the risks and obstacles inherent in deploying AI within financial markets and banking sectors, offering recommended strategies to mitigate these limitations. Despite the recognized advantages, the comprehensive understanding of AI's benefits and drawbacks remains incomplete due to its evolving nature and varied applications in banking. Clear policies governing AI usage are imperative to safeguard financial consumers and promote a fair and transparent financial market. These guidelines should prioritize human decision-making and foster an unbiased approach to policymaking, ultimately fostering innovation within the industry.
Chinese paper classification based on pre-trained language model and hybrid deep learning method Luo, Xin; Mutalib, Sofianita; Syed Aris, Syaripah Ruzaini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp641-649

Abstract

With the explosive growth in the number of published papers, researchers must filter papers by category to improve retrieval efficiency. The features of data can be learned through complex network structures of deep learning models without the need for manual definition and extraction in advance, resulting in better processing performance for large datasets. In our study, the pre-trained language model bidirectional encoder representations from transformers (BERT) and other deep learning models were applied to paper classification. A large-scale chinese scientific literature dataset was used, including abstracts, keywords, titles, disciplines, and categories from 396 k papers. Currently, there is little in-depth research on the role of titles, abstracts, and keywords in classification and how they are used in combination. To address this issue, we evaluated classification results by employing different title, abstract, and keywords concatenation methods to generate model input data, and compared the effects of a single sentence or sentence pair data input methods. We also adopted an ensemble learning approach to integrate the results of models that processed titles, keywords, and abstracts independently to find the best combination. Finally, we studied the combination of different types of models, such as the combination of BERT and convolutional neural networks (CNN), and measured the performance by accuracy, weighted average precision, weighted average recall, and weighted average F1 score.
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.

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