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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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Recognition of Indonesian sign language using deep learning: convolutional neural network-based approach Kembuan, Olivia; Haryanto, Haryanto; Triyono, Mochamad Bruri
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.pp5008-5016

Abstract

This study focuses on developing an automatic Indonesian sign language (SIBI) recognition system using a convolutional neural network (CNN). Sign language is essential for communication among deaf and hard-of hearing individuals, and automatic recognition helps improve accessibility and inclusivity. CNNs are chosen for their ability to learn image features automatically, eliminating manual extraction and improving classification accuracy. The SIBI dataset used contains 5,280 images of 26 letters, divided into training and validation sets. In early training, the model achieved low accuracy (3.63% training, 3.33% validation), but after five epochs, it significantly improved to 97.58% for training and 100% for validation.
Intelligent route optimization for internet of vehicles using federated learning: promoting green and sustainable IoT networks Narsimha Reddy, Desidi; Buragadda, Swathi; Ramesh, Janjhyam Venkata Naga; Murthy, Garapati Satyanarayana; Srija, Nallathambi; Kavitha, Sarihaddu
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.pp5049-5057

Abstract

As the internet of vehicles (IoV) continues to evolve, optimizing vehicle routing becomes increasingly important for enhancing traffic efficiency and minimizing environmental impact. This paper introduces an intelligent vehicle route optimization protocol leveraging federated learning (FL) to achieve green and sustainable IoV systems. By distributing the learning process across multiple edge devices, the proposed protocol minimizes the need for centralized data processing, reducing network congestion, and preserving user privacy. The system optimizes vehicle routes based on real time traffic conditions, fuel efficiency, and carbon emissions, and promoting greener transportation practices. Simulations conducted in a dynamic IoV environment demonstrate significant improvements in route efficiency, fuel consumption, and carbon emissions. The results underscore the potential of FL in transforming IoV routing by balancing performance and sustainability, making it a promising solution for the future of connected transportation.
Integrating machine learning and deep learning with landscape metrics for urban heat island prediction Pal, Siddharth; Jhajharia, Kavita
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.pp4828-4837

Abstract

Elevated temperatures in urban areas relative to surrounding rural areas, known as the urban heat island (UHI) effect, constitute a pressing challenge to urban sustainability, public health, and energy efficiency. With a comprehensive global dataset from NASA's Socioeconomic Data and Applications Center (SEDAC) that encompasses land surface temperature (LST) and different urban characteristics, this study investigates the UHI phenomenon. The UHI intensity was predicted using advanced machine learning models, random forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and long short-term memory (LSTM) with attention mechanism. The LSTM with attention achieved top R2:0.9998 (day) and 0.9992 (night). Key landscape metrics include urban area size, population, and location. We analyzed spatial temporal UHI patterns to identify local factors like geometry and vegetation. These findings are critical for urban planners and policy makers to identify targeted mitigation options, including green space expansion, the use of low thermal mass, and urban climate resilience strategies. These results advance predictive modeling, supporting resilient, and sustainable cities.
Enhanced object tracking with artificial bee colony, motion modeling, and deep learning Taglout, Ramdane; Saoud, Bilal
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.pp5344-5354

Abstract

As a fundamental aspect of computer vision, visual object tracking supports a wide array of applications, notably in transport infrastructure and advanced industrial automation. Although correlation filter-based trackers demonstrate robust performance, they face persistent limitations including scale changes, object occlusion, boundary artifacts, and complex background interference. To address these issues, we have introduced an approach that combines artificial bee colony (ABC) optimization, deep neural architectures, and Kalman filtering techniques. Our methodology begins with reliability assessment of the tracking pipeline, proceeding to compute target confidence measures at the predicted position, followed by an adaptive update mechanism. The proposed system leverages ABC optimization for dynamic scale adaptation while employing Kalman filtering to model inter-frame target motion dynamics. Comprehensive evaluation across multiple benchmark datasets demonstrates our method's efficacy, precision, and resilience, achieving enhanced performance relative to existing state-of-the art approaches.
Deep learning-based evaluation for distributed denial of service attacks detection S., Neethu; Aradhya, H. V. Ravish; Reddy Karna, Viswavardhan
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.pp4982-4992

Abstract

Software-defined network (SDN) introduces a programmable and centralized control mechanism for managing network infrastructure, enhancing flexibility and efficiency. However, this architecture is prone to security threats, particularly distributed denial of service (DDoS) attacks that exploit centralized control. This study presents a comparative analysis of several deep learning (DL) models—namely, multilayer perceptron (MLP), artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM)—for detecting DDoS threats within SDN environments. The research incorporates key preprocessing techniques such as feature selection and synthetic minority oversampling technique (SMOTE) to handle class imbalance. The results indicate that sequence-aware models like LSTM and RNN are highly effective in interpreting temporal network behavior, with LSTM achieving the highest performance (accuracy: 91%, precision: 86%, recall: 94%, and F1-score: 90%). These findings underscore the potential of advanced DL methods in fortifying SDN infrastructures against complex cyber threats.
Improved copy-move forgery detection through multilevel clustering Abdelazem, Doaa Gamal; Zayed, Hala H.; Taha, Ahmed
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.pp5279-5289

Abstract

Copy move forgery detection (CMFD) based on keypoints remains a widely used technique; however, it often struggles to effectively identify small and smoothly tampered regions within images. This paper introduces a CMFD method that enhances detection accuracy by integrating a double-matching process with advanced region localization techniques. Delaunay triangles formed by accelerated KAZE (AKAZE) and scale-invariant feature transform (SIFT) features are matched in the double-matching process to identify suspicious regions. To ensure sufficient keypoint pairs, the set of matching triangles is iteratively expanded to include neighboring triangles, covering the entire tampered area. Subsequently, a second matching with a looser threshold is performed on the vertices. In the region localization process, the multilevel density-based spatial clustering of applications with noise (DBSCAN) effectively handles scenarios involving multiple copied regions with varying sizes. Using the standard MICC-F600 and COVERAGE datasets, experiments demonstrate that the proposed CMFD method is robust and achieves better performance than state-of-the-art baselines. 
Semantic search-enhanced healthcare chatbot for hospital information management system using vector database and transformer models Guslinar Perdana, Erda; Nugraha, Arya Adhi
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.pp4600-4613

Abstract

Healthcare chatbots are increasingly used to assist hospital staff, yet most existing systems rely on rule-based or generic machine learning (ML) approaches that lack the ability to comprehend natural language queries, while proprietary deep learning systems often incur high licensing costs. This work addresses this gap by proposing a cost-effective and scalable semantic vector retrieval solution for user intent recognition in a hospital information management system (HIMS) helpdesk chatbot. The MPNet based transformer model is employed to convert user inquiries and predefined intents into feature vectors, enabling highly accurate natural language understanding through cosine similarity retrieval within a dedicated vector database. The proposed vector search method was validated via an ablation study, achieving an accuracy of 0.70 for intent recognition, which demonstrates a significant performance gain of 28.0 percentage points over a traditional keyword-based search baseline. Usability testing across developer and doctor groups yielded an average score of 7.78 on a 10-point Likert scale. This study concludes that integrating semantic vector retrieval with a vector database is highly effective for recognizing specialized clinical intents, offering a more accurate solution that significantly reduces the manual helpdesk workload and enhances 24-hour assistance in healthcare.
Classifying classical music’s therapeutic effects using deep learning: a review Angelin, Angelin; Sanjaya, Samuel Ady; Kristiyanti, Dinar Ajeng
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.pp4933-4942

Abstract

Mental health issues are the leading cause of global disability, increasing the need for treatment options. While there is much research on the emotional recognition of music in general, there is a gap in studies that directly connect musical features with their therapeutic effects using deep learning. This systematic literature review explores the use of deep learning in classifying the therapeutic effects of classical music for mental health. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, a total of 15 papers were reviewed. This review synthesized studies on the role of musical elements that affect mental states. Different feature extraction methods, including mel-frequency cepstral coefficients (MFCCs), spectral contrast, and chroma features, are discussed for their roles in classifying these therapeutic effects. This review also looks at deep learning algorithms like convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) network, and combined models to assess their effectiveness. Common evaluation methods are also assessed to measure the performance of these models in audio classification. This review highlights the potential of combining deep learning with classical music for mental health support, and to future possibilities for applying these methods in the real world.
Classifier model for lecturer evaluation by students using speech emotion recognition and deep learning approaches Diah Rosita, Yesy; Andi Saputra, Wahyu
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.pp5157-5171

Abstract

Lecturers play a crucial role in higher education, with their teaching behavior directly impacting learning and teaching quality. Lecturer evaluation by students (LES) is a common method for assessing lecturer performance, though it often relies on subjective perceptions. As a more objective alternative, speech emotion recognition (SER) uses speech technology to analyze emotions in the speech of lecturers during classes. This study proposes using deep learning-based SER, including convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM), to evaluate teaching quality by analyzing displayed emotions. Removing silence from audio signals is crucial for enhancing feature analysis, such as energy, zero-crossing rate (ZCR), and mel-frequency cepstral coefficients (MFCC). This method removes inactive segments, emphasizing significant segments, and improving accuracy in detecting voice and emotions. Results show that the 1D CNN model with Bi-LSTM, using MFCC with 13 coefficients, energy, and ZCR, performs excellently in emotion detection, achieving a validation accuracy of over 0.851 with an accuracy gap of 0.002. This small gap indicates good generalization and reduces the risk of overfitting, making teaching evaluations more objective and valuable for improving practices.
Deep learning-aided polar-low density parity check decoding for enhanced telemedicine ECG transmission reliability Nagesh, Sushma; Basavaraju, Santhosh Kumar Kenkere; Ramaiah, Dakshayani Mandikeri; Lingappa, Triveni Chitralingappa; Bahaddur, Indira; Kolli, Venkateswara Rao
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.pp5058-5068

Abstract

Telemedicine has emerged as a crucial solution for remote patient monitoring and diagnosis, yet ensuring the reliable transmission of medical data, particularly electrocardiogram (ECG) signals, remains a significant challenge. This work proposes a novel approach that integrates deep learning with a polar-low density parity check (LDPC) decoder to enhance the accuracy, robustness, and efficiency of ECG signal transmission within telemedicine systems. The study aims to evaluate the effectiveness of this integration in improving error correction and decoding performance, validate its efficacy under diverse signal to noise ratios (SNRs) and code rates, and assess its potential impact on remote healthcare delivery. Experimental results confirm that the deep learning-empowered polar-LDPC decoder achieves superior error correction and decoding efficiency compared to conventional methods, ensuring higher fidelity in ECG reconstruction. This advancement presents a promising pathway toward more reliable, precise, and efficient telemedicine systems, thereby enabling improved patient care, especially in remote and underserved regions. The proposed method also opens opportunities for integrating intelligent decision-support tools. Such integration could further enhance real-time diagnostics and broaden telemedicine’s scope.

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