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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 81 Documents
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Search results for , issue "Vol 14, No 1: February 2025" : 81 Documents clear
Reliable backdoor attack detection for various size of backdoor triggers Rah, Yeongrok; Cho, Youngho
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.pp650-657

Abstract

Backdoor attack techniques have evolved toward compromising the integrity of deep learning (DL) models. To defend against backdoor attacks, neural cleanse (NC) has been proposed as a promising backdoor attack detection method. NC detects the existence of a backdoor trigger by inserting perturbation into a benign image and then capturing the abnormality of inserted perturbation. However, NC has a significant limitation such that it fails to detect a backdoor trigger when its size exceeds a certain threshold that can be measured in anomaly index (AI). To overcome such limitation, in this paper, we propose a reliable backdoor attack detection method that successfully detects backdoor attacks regardless of the backdoor trigger size. Specifically, our proposed method inserts perturbation to backdoor images to induce them to be classified into different labels and measures the abnormality of perturbation. Thus, we assume that the amount of perturbation required to reclassify the label of backdoor images to the ground-truth label will be abnormally small compared to them for other labels. By implementing and conducting comparative experiments, we confirmed that our idea is valid, and our proposed method outperforms an existing backdoor detection method (NC) by 30%p on average in terms of backdoor detection accuracy (BDA).
Improving lithium-ion battery reliability through neural network remaining useful life prediction Zraibi, Brahim; Mansouri, Mohamed
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.pp83-91

Abstract

The reliable performance of lithium-ion batteries is crucial for the safe and efficient operation of electrical systems, particularly in electric vehicles. To mitigate the risk of battery failure due to degradation, accurate forecasting of the remaining useful life (RUL) is imperative. In this study, we propose employing various recurrent neural network (RNN) methods, including RNN, gated recurrent unit (GRU), and long short-term memory (LSTM), to enhance RUL prediction accuracy for lithium-ion batteries. Our approach aims to provide reliable, accurate, and simple estimates of remaining battery life, facilitating effective management of electric vehicle power systems and minimizing the risk of failure. Performance evaluation metrics such as mean absolute error (MAE), R-squared (R²), mean absolute percentage error (MAPE), and root mean squared error (RMSE) are utilized to assess prediction accuracy. Experimental validation conducted using the NASA lithium-ion battery dataset demonstrates the superiority of LSTM in reducing prediction error and enhancing RUL prediction performance compared to alternative approaches. These findings underscore the potential of neural network methodologies in advancing battery management practices and ensuring the longevity and reliability of lithium-ion battery systems.
Machine learning for mental health: predicting transitions from addiction to illness Alkhazraji, Ali; Alsafi, Fatima; Dbouk, Mohamed; Ibrahim, Zein Al Abidin; Sbeity, Ihab
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.pp385-396

Abstract

The increasing prevalence of infection-causing diseases due to environmental factors and lifestyle choices has strained the healthcare system, necessitating advanced techniques to save lives. Disease prediction plays a crucial role in identifying individuals at risk, enabling early treatment, and benefiting governments and health insurance providers. The collaboration between biomedicine and data science, particularly artificial intelligence and machine learning, has led to significant advancements in this field. However, researchers face challenges related to data availability and quality. Clinical and hospital data, crucial for accurate predictions, are often confidential and not freely accessible. Moreover, healthcare data is predominantly unstructured, requiring extensive cleaning, preprocessing, and labeling. This study aims to predict the likelihood of patients transitioning to mental illness by monitoring addiction conditions and constructing treatment protocols, with the goal of modifying these protocols accordingly. We focus on predicting such transformations to illuminate the underlying factors behind shifts in mental health. To achieve this objective, data from an Iraqi hospital has been collected and analyzed yielding promising results. 
Optimizing seismic sequence clustering with rapid cube-based spatiotemporal approach Hasana, Silviya; Sari, Wina Permana; Rojali, Rojali; Fitrianah, Devi
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.pp737-748

Abstract

Due to their extensive volume and range of features, seismic data is regarded as highly complex data. Earthquakes that typically composed of foreshocks, mainshocks, and aftershocks, exhibit a unique sensitivity to temporal dimension, a characteristic that differs them from other natural hazards. Foreshocks and aftershocks that emanate from a similar epicenter, often display temporal patterns that contribute significantly to determining a sequence. This study introduces a density cube-based approach to cluster spatiotemporal seismic data. It addresses spatial irregularities observed in earthquake clusters and incorporates temporal aspects, acknowledging that seismic events originating from a similar epicenter could occur in separate time frames. We achieved the highest Silhouette score of 0.935 in daily-based clustering and 0.782 in weekly-based clustering. Notably, our analysis reveals a trend where weekly clustering lambda λ tend to be lower (λ=0.01) than in daily clustering (λ=0.1, λ=0.5), thus emphasizing the significance of temporal granularity where daily clustering requires higher λ to capture rapid fluctuations, while weekly clustering benefits from lower λ to cover broader trends. These findings enhance the understanding of the nuanced interplay of temporal dynamics in seismic sequence analysis.
Revolutionizing cancer classification: the snr-ogscc method for improved gene selection and clustering Bouazza, Sara Haddou; Bouazza, Jihad Haddou
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.pp466-472

Abstract

This study presents the signal-to-noise ratio optimized gene selection and clustering for cancer classification (SNR-OGSCC) methodology, aimed at enhancing classification accuracy while reducing the dimensionality of gene expression data across various cancer types. Implemented on a standard computational setup, the SNR-OGSCC method combines advanced filtering, clustering, and machine learning techniques, demonstrating significant improvements in classification accuracy on seven cancer datasets: leukemia, colon cancer, prostate cancer, lung cancer, lymphoma, central nervous system (CNS) tumors, and ovarian cancer. Notably, our approach achieved perfect accuracies of 100% for leukemia, lung cancer, and ovarian cancer, with high accuracies of 98.4% for colon cancer, 99.1% for prostate cancer, 98.3% for lymphoma, and 99.7% for CNS tumors, while requiring as few as 4–5 genes for effective classification. These findings highlight the efficiency and robustness of the SNR-OGSCC methodology, suggesting its potential to identify meaningful biomarkers and improve personalized cancer treatment strategies. Further validation with larger datasets and biological experiments is essential to confirm its applicability in clinical settings.
Mobile robot localization using visual odometry in indoor environments with TurtleBot4 Singh, Gurpreet; Goyal, Deepam; Kumar, Vijay
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.pp760-768

Abstract

Accurate localization is crucial for mobile robots to navigate autonomously in indoor environments. This article presents a novel visual odometry (VO) approach for localizing a TurtleBot4 mobile robot in indoor settings using only an onboard red green blue – depth (RGB-D) camera. Motivated by the challenges posed by slippery floors and the limitations of traditional wheel odometry, an attempt has been made to develop a reliable, accurate, and low-cost localization solution. The present method extracts oriented FAST and rotated BRIEF (ORB) features for feature extraction and matching using brute-force matching with Hamming distance. The essential matrix is then computed using the 5-point algorithm and decomposed to recover the relative rotation and translation between poses. The absolute pose is obtained by chaining the incremental motions estimated from VO. Through experimentation and comparison with wheel odometry, the findings demonstrate the effectiveness of our VO system, achieving a positional accuracy with minimal error of 4-5%. The article also compares VO with wheel odometry and shows the advantages of using a visual approach, especially in environments with slippery floors where wheel slippage causes large odometry errors. Overall, this work presents an effective VO system for reliable, accurate, and low-cost localization of TurtleBot4 in indoor environments without relying on external infrastructure.
Artificial intelligence-driven method for the discovery and prevention of distributed denial of service attacks ALDabbas, Ashraf; Baniata, Laith H.; AlSaaidah, Bayan A.; Mustafa, Zaid; Alali, Muath; Rateb, Roqia
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.pp614-628

Abstract

Distributed denial of service (DDoS) attacks has emerged as a prominent cyber threat in contemporary times. By impeding the machine's capacity to give services to legitimate clients, the impacted system performance and buffer size are reduced. Researchers are working to build sophisticated algorithms that can identify and thwart DDoS violations. An effective approach for DDoS attacks has been proposed in this work. This research presents a model as a potential explanation for DDoS assaults. In order to successfully identify this kind of attacks, which may stop or block the urgent and vital transmission of data, we present a distinctive method that integrates a pair of fully connected layers within an amalgamated deep learning (DL) framework with long short-term memory (LSTM) and a max pooling layer. The acquired accuracy reached 99.58%.
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.
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.
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.

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