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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 1,722 Documents
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Graph based semantic email classification: a novel approach for academic institutions B., Aruna Kumara; T., Madan H.; C., Rashmi; R., Sarvamangala D.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5218-5230

Abstract

Electronic mail classification in educational institutes becomes the fundamental task to manage information efficiently. Due to the globalization and the technological advancement, volume of email users increasing consistently, which in turn increases the volume of digital data exponentially. This necessitates the developing automated email classification systems for the better and organized work. This paper develops a novel graph-based similarity (GBS) approach based on semantic similarity to address these challenges. The method initially selects the most relevant features based on feature weights, later it builds a graph by using Jaccard co efficient method for each category with features as nodes and correlation between the nodes as edges. Later, these graphs are used as templates for each category and classifies each new incoming email into the specific class based on the similarity among the graph templates and a new email. The GBS method was compared with the well-known benchmarked email classifiers and the findings demonstrated that the GBS method outperformed with 98.91% accuracy after fine-tuning of graph parameters and the classifier hyper parameters. Additionally, receiver operating characteristic (ROC) curve analysis was conducted, achieving a highest area under curve (AUC) score 0.989, demonstrating robust classification proficiency across all categories.
Support vector machine performance: simulation and rice phenology application Muradi, Hengki; Saefuddin, Asep; Sumertajaya, I Made; Soleh, Agus Mohamad; Domiri, Dede Dirgahayu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4878-4890

Abstract

In the case of classification, model accuracy is expected to result in correct predictions. This study aims to analyze the performance of two kinds of support vector machine (SVM) methods: the support vector machine one versus one (SVM OvO) method and the generalized multiclass support vector machine (GenSVM) method. This method will compare to the generalized linear model, namely the multinomial logistic regression (MLR) method. Simulations were conducted using SVM OvO and GenSVM methods to get an overview of the parameters affecting both methods' performance. Furthermore, the three classification methods are implemented in the case of modelling the rice phenology and tested for performance. Simulation results show that, however, the SVM OvO and GenSVM machine learning methods are sensitive to the choice of model parameters. The empirical study results show that the SVM OvO and GenSVM methods can produce satisfactory model accuracy and are comparable to the MLR method. The best rice phenology model accuracy was obtained from the SVM OvO model, where 79.20 ± 0.21 overall accuracy and 70.69 ± 0.29 kappa were obtained. This research can be continued by handling samples, especially when class members are a minority, and can also add random effects to the SVM model.
Performance evaluation of pre-trained deep learning model on garbage classification with data augmentation approach Wiguna, I Komang Arya Ganda; Desnanjaya, I Gusti Made Ngurah; Sandika, I Kadek Budi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4971-4981

Abstract

Waste classification is one of the interesting topics for classifications in which data can be very varied and complex. This data diversity is a challenge to develop a model that is able to classify well. The purpose of this study is to analyze the performance of the pre-trained deep learning model using a data augmentation approach. There are three pre-training models used in this study, namely residual networks 50 (ResNet50), visual geometric group with 16 layers (VGG-16), and MobileNetV2. The results showed that the MobileNetV2 model received the highest accuracy value, reaching 84.45% for data without augmentation. With data augmentation there is a decrease of 2.73%. Conversely, VGG-16 shows performance stability with an increase in accuracy with augmentation data, reaching 75.84%. While ResNet50 gets the lowest results compared to both models. The application of data augmentation techniques with the aim of increasing data variations does not always have an impact on increasing the generalization of the model.
Automated legal content management system for multi-country integration Pawar, Hardik; Prakash, Nidhi; Srivastava, Smriti; M., Sneha; Syedabdulkader, Shaik Mohideen; D., Pratiba; S., Sandhya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4511-4519

Abstract

This paper presents an automated legal content management system (CMS) designed for multi-country integration, addressing the complex challenges of legal document migration across more than 180 countries while ensuring regulatory compliance and accessibility standards. The system implements a hierarchical four-level architecture, migrating more than 2,740 legal documents with zero data loss incidents through fault-tolerant processing pipelines. The automated portable document format (PDF) migration component demonstrates exceptional efficiency, processing documents 36 times faster compared to manual approaches, while article migration achieves 230 times faster processing speeds. The integrated artificial intelligence (AI)-powered accessibility enhancement system generates contextually appropriate alt text descriptions, allowing organizations to process 10,000 images annually with savings of $14,990. The complete country migration process, covering both PDF and article processing, executes in 30 seconds compared to 56 minutes for manual processing, representing a 112-fold improvement in performance. System scalability demonstrates linear performance characteristics up to more than 5,000 documents with consistent processing metrics while maintaining compliance across diverse regulatory frameworks. These quantitative improvements establish a new paradigm for automated legal content management, providing a scalable foundation for global enterprises managing multi jurisdictional legal documentation requirements.
Large language models for pattern recognition in text data Kosayakova, Aknur; Ildar, Kurmashev; Spada, Luigi La; Zeeshan, Nida; Bakyt, Makhabbat; Khuralay, Moldamurat; Abdirashev, Omirzak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5311-5332

Abstract

Large language models (LLMs) are widely deployed in settings where both reliability and efficiency matter. We present a calibrated, seed‑robust empirical comparison of an encoder fine‑tuned model (bidirectional encoder representations from transformers (BERT)‑base) and a decoder in‑context model (generative pre-trained transformer (GPT)‑2 small) across Stanford question answering dataset v2.0 (SQuAD v2.0) and general language understanding evaluation (GLUE)-multi-genre natural language inference (MNLI), Stanford sentiment treebank 2 (SST‑2). Beyond accuracy, we assess reliability (expected calibration error with reliability diagrams and confidence–coverage analysis) and efficiency (latency, memory, throughput) under matched conditions and three fixed seeds. BERT‑base yields higher accuracy and lower calibration error, while GPT‑2 narrows gaps under few‑shot prompting but remains more sensitive to prompt design and context length. Efficiency benchmarks show that decoder‑only prompting incurs near‑linear latency/memory growth with k‑shot exemplars, whereas fine‑tuned encoders maintain stable per‑example cost. These findings offer practical guidance on when to prefer fine‑tuning versus prompting and demonstrate that reliability must be evaluated alongside accuracy for risk‑aware deployment.
Enhancing cross-site scripting attack detection by using FastText as word embeddings and long-short term memory Mashuri, Muhammad Alkhairi; Surantha, Nico
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4923-4932

Abstract

Cross-site scripting (XSS) is one of the dangerous cyber-attacks and the number of attacks continues to increase. This study takes a new approach to detect attacks by utilizing FastText as word embedding, and long-short term memory (LSTM), which aims to improve the performance of deep learning. This method is proposed to capture the broader meaning and context of the data used, leading to better feature extraction and model performance. This study not only improves the detection of XSS attacks, but also highlights the potential for better text processing techniques. The results obtained showing this method achieves higher results than other methods, with an accuracy of 99.89%.
Explainable artificial intelligence with anchors method for breast cancer treatment recommendation Lokare, Reena; Rathod, Mansing; More, Jyoti Sunil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4494-4501

Abstract

In the search of precision medicine for breast cancer, the integration of artificial intelligence (AI) offers unprecedented opportunities to improve diagnosis, prognosis, and treatment strategies. This paper discovers the prospective of explainable artificial intelligence (XAI) to demystify the black-box landscape of AI, fostering both transparency and trust. We introduce an XAI-based approach, anchored by the anchors explanation method, to provide interpretable predictions for breast cancer treatment. Our results demonstrate that while anchors improve the interpretability of model predictions, the precision and coverage of these explanations vary, highlighting the challenges of achieving high-fidelity explanations in complex clinical scenarios. Our findings underscore the importance of balancing the trade-off between model complexity and explainability. They advocate for the iterative development of AI systems with iterative feedback loops from clinicians to align the model's logic with clinical reasoning. We propose a framework for the clinical deployment of XAI in breast cancer. Ultimately, XAI, equipped with techniques like Anchors, holds the promise of enhancing precision medicine by making AI-assisted decisions more transparent and trustworthy, empowering clinicians and enabling patients to engage in informed discussions about their treatment options. However, anchors lag in the accuracy of rules and remains a challenge to the AI developers.
Combining convolutional operators in unsupervised networks for kidney abnormalities Suksukont, Aekkarat; Prommakhot, Anuruk; Srinonchat, Jakkree
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4541-4551

Abstract

Deep learning plays a pivotal role in advancing the diagnosis of renal dysfunction, achieving performance levels comparable to those of medical experts. However, disease domain variations and model differences can impact learning quality. To address renal dysfunction, we propose dual stream convolutional (DSC) and dual-input convolutional (DIC) for unsupervised learning. The proposed network is designed to process multi scale data and employs parallel data aggregation to enhance learning capabilities, improving the reliability of the experimental results. DSC achieved training losses of 0.0069, 0.0056, 0.0042, and 0.0048 for normal, cyst, stone, and tumor datasets, respectively, while DIC achieved losses of 0.0066, 0.0063, 0.0044, and 0.0058 for the same categories. The experimental results demonstrate that our proposed models outperform state of-the-art approaches, making them well-suited for broad application in clinical research studies.
The contribution of artificial intelligence in people with autism: a systematic literature review Moza-Villalobos, Anderson; Cabanillas-Carbonell, Michael
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4442-4453

Abstract

Autism is a disorder that poses significant challenges in various areas such as health, education, social interaction, and how the world perceives them. The implementation of artificial intelligence in daily life and different fields offers an innovative approach to addressing these challenges, facilitating early detection, support in learning, and social interaction for individuals with this condition. The systematic literature review focuses on studying 50 out of 144 articles obtained from various databases such as EBSCO Host, IEEE Xplore, ScienceDirect, Scopus, ProQuest, and Web of Science. These articles were systematically organized using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, providing information about machine learning as the most utilized discipline, the types of infrastructure it relies on, and the countries that are at the forefront of this topic. This review will serve as a reference for stakeholders regarding the advancements and contributions of artificial intelligence for individuals with autism.
Classification system of banana types and ripeness levels based on convolutional neural network Jambola, Lucia; Darlis, Arsyad Ramadhan; Malaha, Windi; Aryanta, Dwi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4891-4901

Abstract

Recently, the availability of bananas in supermarkets has been relatively abundant. However, most buyers experience problems categorizing the type and level of ripeness of bananas, so the level of purchases of this fruit decreases. This study implements a system that can automatically classify bananas based on type and level of ripeness so that buyers can choose them based on their needs. In this study, the proposed system could classify the types and degrees of banana ripeness using a Convolutional Neural Network (CNN) where the system was implemented in real-time using the hardware of the Jetson Nano as a processing unit and a camera system as a sensor. The methodology adopted in this research involves implementing CNN architectures, i.e., ResNet-18 and ResNet-50, under various conditions. The training phase comprises 60 epochs, while testing considers illumination parameters from LED lights with power of 6 watts, 12 watts, and 22 watts under distances ranging from 10 to 100 cm. The results show that the system could classify the type and level of ripeness of bananas in real-time with an accuracy of 93% that is achieved using the 22-watt power for all type and ripeness levels. 

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