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31,377 Article Results

An empirical comparison of clustering approaches for recency, frequency, and monetary customer segmentation

10.11591/ijai.v15.i4.pp3402-3410
Upekkha Lau , Meditya Wasesa
This study evaluates effectiveness of three clustering techniques—k-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN)—applied to the recency-frequency-monetary (RFM) model for customer segmentation in the retail sector. Using sales transaction data from a distributor of computer accessories and printing products. The results show that k-means achieved the best clustering validation scores and effectively identified high-value customers, hierarchical clustering generated less meaningful groupings than k-means, and DBSCAN misclassified key customers as noise. These findings highlight k-means as the most suitable technique for RFM-based segmentation in this retail business context. The study offers practical insights for retail and distribution businesses aiming to adopt data-driven customer strategies and suggests future research to enhance segmentation robustness and refine the RFM framework.
Volume: 15
Issue: 4
Page: 3402-3410
Publish at: 2026-08-01

A transfer learning-based approach for automatic monument detection

10.11591/ijai.v15.i4.pp3326-3341
Santosh Giri , Jebish Purbey , Sunil Adhikari , Paarit Pokharel , Babu R. Dawadi , Bipun Man Pati , Atsushi Ito , Sushant Chalise , Sanjivan Satyal
Architectural heritage connects us to the cultural achievements of past civilizations. Patan Durbar Square in Nepal is home to many such structures, yet identifying them remains a challenge for tourists. This paper presents an automated monument recognition system built on the backbone of convolutional neural networks (CNNs). A dataset of 1832 images of 9 important monuments from Patan was created, and build a detection system with MobileNetV2, a light-weight CNN, to detect monuments in Patan Durbar Square with a near-perfect F1 score of 98.94%. The approach utilizes transfer learning to adapt the model to local architectural styles. A detailed ablation study is performed to determine the optimal network design and augmentation strategies. Class-wise performance is further analyzed to verify robustness against visual occlusion and similarity. Finally, the model is deployed as a mobile application using Flutter and the FastAPI framework. This work demonstrates the viability of lightweight CNNs for real-time cultural heritage preservation.
Volume: 15
Issue: 4
Page: 3326-3341
Publish at: 2026-08-01

Deep learning intrusion detection for software-defined networking using synthetic minority oversampling

10.11591/ijai.v15.i4.pp3703-3711
Prajwalasimha Sindugatta Nagaraja , Navya Rajashekara , Pushpa Bangalore Ramesh , Druva Kumar Siddaraju , Santhosh Kumar Ramachandragowda , Trupti Shripad Tagare
This article proposes an advanced method for network intrusion detection using a combination of recurrent neural networks (RNNs), specifically long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BiLSTM) models, enhanced by synthetic minority oversampling technique (SMOTE) to address class imbalance in datasets like network security laboratory–knowledge discovery in databases (NSL-KDD). The method aims to accurately classify network traffic by learning temporal patterns of both normal and malicious activities. SMOTE is employed to balance the dataset, ensuring that underrepresented attack types receive adequate model attention, thereby improving model robustness. The proposed models (LSTM, GRU, and BiLSTM) are trained and evaluated on the NSL-KDD dataset, with hyperparameter tuning performed through RandomizedSearchCV for optimal performance. The results show a significant improvement in accuracy, precision, recall, and F1-score, with BiLSTM demonstrating the highest performance, achieving near-perfect classification results (99.5% accuracy). This method not only mitigates the issue of class imbalance but also leverages the power of RNNs for sequence modeling, offering a promising solution for effective intrusion detection in modern networks.
Volume: 15
Issue: 4
Page: 3703-3711
Publish at: 2026-08-01

SHAP-enhanced ensemble learning for yield prediction: insights from CY-Bench data on Indian wheat

10.11591/ijai.v15.i4.pp3411-3420
Soma Gupta , Dayal Kumar Behera , Satarupa Mohanty , Subhra Swetanisha , Ritik Mallik , Namita Panda
Predicting crop yield accurately is essential for providing food security and improving agricultural practices. This study examines the use of ensemble machine learning models combined with Shapley additive explanations (SHAP) feature selection to improve wheat yield prediction in India. The study uses CY-Bench data, incorporating normalized difference vegetation index (NDVI), meteorological data, and soil moisture data for yield prediction. Various ensemble techniques, including voting, stacking, and boosting are evaluated. Boost m1 ensemble model consistently outperforms other models in the prediction. Additionally, the integration of SHAP-based feature selection with the best ensemble model significantly improves the model accuracy and interpretability by identifying the most influential features affecting yield. The results show the effectiveness of ensemble boosting model, in capturing the complex relationships within agricultural data particularly when combined with feature selection. This method improves the transparency and actionability of machine learning models for agronomists, policymakers, and farmers.
Volume: 15
Issue: 4
Page: 3411-3420
Publish at: 2026-08-01

Adaptive binary capsule network for complex image recognition

10.11591/ijai.v15.i4.pp3745-3760
Mavis Serwaa Yeboah , Patrick Kwabena Mensah , Adebayo Felix Adekoya , Mighty Abra Ayidzoe
Glaucoma and cataracts are leading causes of blindness worldwide, emphasizing the need for early detection. Convolutional neural networks (CNNs) have shown promise in detecting ocular diseases, but require extensive training datasets. However, medical datasets are scarce, limited, and imbalanced, prompting the use of time-consuming data augmentation techniques. To address these limitations, a computationally efficient and robust capsule network (CapsNet) model was proposed. The model features a novel adaptive contrast spatial filtering (ACSF) algorithm and incorporates a local binary pattern (LBP) algorithm to enhance robustness. This study achieved good recognition accuracy, with scores of 96.90% on the combined dataset, 96.45% on the cataract-only dataset, and 96.78% on the glaucoma-only dataset. The model's performance is comparable to state-of-the-art models, demonstrating its potential to support ophthalmologists in diagnosing cataracts and glaucoma-related eye issues.
Volume: 15
Issue: 4
Page: 3745-3760
Publish at: 2026-08-01

Deep learning-based prognostic modeling of brain tumors

10.11591/ijai.v15.i4.pp3792-3804
Laiali Almazaydeh , Arar Al Tawil
Headline accuracy is not enough to move a deep-learning brain tumor magnetic resonance imaging (MRI) classifier into the clinic. A model also needs repeated-split validation, trustworthy probabilities, a statistical sanity check, and a small enough footprint for hospital hardware. This study cover all four in one framework. Five convolutional neural network (CNN) backbones (visual geometry group (VGG)-16, residual network (ResNet)-50V2, MobileNetV2, EfficientNetB0, and dense convolutional network (DenseNet)-121) are trained on 4,600 public brain MRI scans (2,513 tumor, 2,087 healthy) under a shared two-phase transfer-learning recipe. The usual single split is replaced by stratified 5-fold cross-validation (CV), with paired McNemar and DeLong tests and 1,000 bootstrap resamples. Calibration is judged by expected calibration error (ECE), Brier, reliability diagrams, and temperature scaling. VGG16 wins mean accuracy (98.72 ± 0.42%); ResNet50V2 has the tightest area under the curve (AUC) (0.9986 ± 0.0006); the two are statistically tied. MobileNetV2 is the best-calibrated model out of the box (ECE = 0.0021) with only 2.59 M parameters, making it the most deployable. A four-level confidence-based risk score turns calibrated probabilities into triage tags. This study also flag a measured 3.23% format-duplicate leakage in the public dataset and presents the framework as a radiologist co-pilot, not a prognostic model.
Volume: 15
Issue: 4
Page: 3792-3804
Publish at: 2026-08-01

Unsupervised voice activity detection based on the envelope's fractal dimension

10.11591/ijai.v15.i4.pp3805-3817
Nesrine Abajaddi , Youssef Elfahm , Laila Elmaazouzi , Ilham Mounir , Badia Mounir , Abdelmajid Farchi
Currently, voice activity detection (VAD) is utilized in many fields, including forensics, healthcare, and medicine, to detect vocal anomalies, as well as in telecommunications and mobile telephony. Due to its importance and the difficulty of distinguishing between speech and nonspeech segments, especially in noisy environments (low signal-to-noise ratio (SNR)), this area remains under continuous development. Most existing VAD algorithms require predefined thresholds or training data, which reduces their compatibility. This study proposes an unsupervised VAD system that utilizes the Katz algorithm to calculate the fractal dimension of envelopes obtained through a single frequency filtering (SFF) approach. This method allows for high temporal and frequency resolution. The proposed VAD algorithm does not require any training data and is suitable for various types of noise and SNRs. To evaluate the effectiveness of the proposed method, two different databases are used: the Texas Instruments Massachusetts Institute of Technology (TIMIT) database and the King Saud University (KSU) Arabic speech database. The experimental results reveal an average detection accuracy of 96.86%, demonstrating its considerable value in various applications.
Volume: 15
Issue: 4
Page: 3805-3817
Publish at: 2026-08-01

A bibliometric analysis of artificial intelligence applications in geospatial and geographic intelligence

10.11591/ijai.v15.i4.pp3036-3052
Chatarina Muryani , Singgih Prihadi , Gentur Adi Tjahjono , Albertus Erico Jerry Krisna Nugroho , Fitria Dewi Kartika
This study presents a comprehensive bibliometric analysis of the application of artificial intelligence in geospatial research and geographic intelligence. Using the Scopus database and visualization tools such as VOSviewer and Microsoft Excel, this study systematically maps the scientific literature across the five analytical dimensions: publication trends, citation structure, co-citation patterns, bibliographic merging, and keyword co-occurrence. The findings indicate a significant increase in global research interest, especially after 2020, with major contributions from the United States, China, and the United Kingdom. The results also reveal thematic clusters ranging from remote sensing and disaster response to smart city planning and spatial prediction. Through science mapping and performance analysis, the study highlights the intellectual structure and conceptual evolution of the field. The study contributes to academic understanding by identifying research gaps, emerging themes, and collaboration patterns, which can guide future interdisciplinary research in the artificial intelligence and geospatial domain.
Volume: 15
Issue: 4
Page: 3036-3052
Publish at: 2026-08-01

EEB7-UNet: a deep learning framework for automated segmentation of fractured C-spine vertebrae

10.11591/ijai.v15.i4.pp3770-3781
Abhishek Kumar Pandey , Pateel G. P. , Kedarnath Senapati
Accurate identification of vertebral fracture (VF) regions in computed tomography (CT) images is crucial for surgeons prior to treatment planning, but remains challenging due to irregular vertebral boundaries, low contrast, noise, and image unevenness. Recent advancements in deep learning have shown promising results compared to conventional manual diagnosis methods in detecting anomalies and segmenting regions of interest in medical imaging. In this study, a deep learning model, enhanced EfficientNetB7 U-Net (EEB7-UNet), is proposed to segment the fractured cervical vertebrae. It includes custom data augmentation to increase the data size and a hybrid learning rate scheduler strategy technique for faster convergence, which increases the generalizability and robustness of the model. The proposed model achieved an improved dice score index of 95.53% and Jaccard coefficient index of 93.85% on the test dataset. Furthermore, the EEB7-UNet has emerged as a moderate size with 98.6 MB. The approach yields superior performance in terms of dice score index and Jaccard coefficient index compared to the other state of the art convolutional neural network (CNN) used as an encoder in the U-Net. This research also compared the performance of the proposed model with three other studies in similar contexts, reported in the literature.
Volume: 15
Issue: 4
Page: 3770-3781
Publish at: 2026-08-01

Text emotion recognition based on deep learning and attention with whale optimization algorithm

10.11591/ijai.v15.i4.pp3692-3702
Tabarek Alwan Tuib , Mohammad-Reza Feizi-Derakhshi , Yaqdhan Mahmood Hussein , Fahad Taha Al-Dhief
Text emotion recognition (TER) represents one of the crucial tasks in natural language processing (NLP), and it is highly important for many applications. To improve the performance of TER, advanced deep learning (DL) model GRU-BiGRU-CNN-Att is presented in this research. It is a combination of gated recurrent units (GRU), bidirectional gated recurrent units (BiGRU) with a convolutional neural network (CNN), and an attention mechanism. Using a GRU, BiGRU, and CNN as part of a deep feature extraction model, combined with an attention mechanism that gives important information different weights, the method that is proposed in the present work enhances the quality of the word vectors and leads to increasing sentiment analysis judgment accuracy. The whale optimization algorithm (WOA), which selects the most informative features for further enhancing the model, is used lastly for the optimization of the feature selection process. After such optimization, those chosen features are trained on a multi-layer perceptron (MLP), successfully combining machine learning (ML) and DL approaches for improving TER. A varied corpus of tweets, sentences, and dialogues had been used in the present work for the assessment of the performance of the suggested model. The proposed method achieves 83.76% accuracy in emotion recognition.
Volume: 15
Issue: 4
Page: 3692-3702
Publish at: 2026-08-01

Detection of illegal drug activities in Indonesia via social media X

10.11591/ijai.v15.i4.pp3376-3388
Adi Hanif Sedar , Lukman Yudokusumo , Indra Budi , Amanah Ramadiah , Aris Budi Santoso , Prabu Kresna Putra
In today's digital world, social media platforms such as X (formerly Twitter) are widely used for communication but also for illegal activities, such as selling illicit drugs. This study examines how posts from X related to illegal drug sales in Indonesia can be analyzed using machine learning techniques, with a focus on incorporating features related to personally identifiable information (PII) and web URLs. The main questions are how to use classification methods to detect these posts and which drugs are most often mentioned. A total of 11,777 posts from X were collected using web scraping. After cleaning and processing the data, five machine learning models were used. The support vector machine (SVM) model showed the best results with a weighted F1-score of 97.08%. The findings show that abortion pills are the most frequently mentioned drugs, followed by sexual enhancement and anesthetic drugs. This study also used visual tools to show the most common hashtags, posts distribution over time, and types of drugs discussed. This study underlines the importance of monitoring social media to protect public health and highlights how incorporating PII features can improve the detection of illegal activities. Future research should look at posts in other languages and from other platforms, considering PII features for enhanced analysis.
Volume: 15
Issue: 4
Page: 3376-3388
Publish at: 2026-08-01

Analysis of normalization technique on multi objective preference analysis method

10.11591/ijai.v15.i4.pp3827-3835
Fristi Riandari , Gabriel Ardi Hutagalung , Ferry Fachrizal
Normalization is a critical step in multi-criteria decision analysis (MCDA) because it influences ranking consistency and decision reliability. This study evaluates the effects of four normalization techniques linear max, linear max-min, linear sum, and semi-linear vector, within the multi objective preference analysis (MOPA) framework using a tourism development case involving 18 alternatives and 8 decision criteria with both cost and benefit attributes. The techniques were compared based on ranking behavior, discriminative capability, and robustness using statistical and non-parametric validation. The results show that linear max-min normalization provides the strongest discriminative performance and the most significant statistical results, while semi-linear vector demonstrates high ranking stability and balanced sensitivity. In contrast, linear sum and linear max exhibit lower discriminative capability under the evaluated conditions. Kendall's tau and robustness analyses further confirm that normalization choice significantly affects ranking consistency and decision reliability. These findings provide practical guidance for selecting appropriate normalization techniques and support the development of more reliable MCDA-based decision-making models for complex applications, including sustainable tourism planning.
Volume: 15
Issue: 4
Page: 3827-3835
Publish at: 2026-08-01

Comparison algorithms with optimization for clustering multi-criteria

10.11591/ijai.v15.i4.pp3476-3491
Ida Mulyadi , Titik Khawa Abdul Rahman , Muhammad Faisal
In higher education, forecasting student graduation is crucial for early intervention development, policy formulation, and class planning. This categorizes the factors that affect student graduation according to both academic and non-academic traits. To compare various clustering techniques, such as K-means, fuzzy C-means (FCM), K-medoids, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, Gaussian mixture model (GMM), and deep learning (DL), as well as particle swarm optimization (PSO) and genetic algorithm (GA) optimization for K-means and FCM, this article applies the hybrid elbow + silhouette optimization prior to the clustering process. Two clusters were found in the pre-cluster optimization research results. K-means, FCM, GMM, and DL clustering all demonstrated more distinct centroid separation; K-means and GMM were the most visually stable and comprehensible. Inter-cluster separation is the main goal of post-cluster optimization, and the K-means + PSO method is the best option. The cross-dataset validation findings indicate that the clustering model exhibits moderate consistency on the new dataset, with an adjusted Rand index (ARI) of 0.478 and a normalized mutual information (NMI) of 0.433.
Volume: 15
Issue: 4
Page: 3476-3491
Publish at: 2026-08-01

Performance evaluation of YOLOv11-based vehicle detection and tracking for urban intelligent transportation systems

10.11591/ijai.v15.i4.pp3518-3527
Thang C. Vu , Dung T. Nguyen , Minh T. Nguyen , Long Q. Dinh , Mui D. Nguyen
This paper proposes and evaluates an integrated vehicle detection and tracking framework based on you only look once (YOLO)v11 for intelligent transportation systems (ITS). The combination of the deep simple online and real-time tracking (DeepSORT) algorithm helps maintain vehicle identity across consecutive frames, thereby enhancing the stability of the multi-object tracking system. Additionally, the slicing-aided hyper inference (SAHI) technique is integrated to improve the detection efficiency of small vehicles in remote sensing imagery and urban surveillance video data collected in Thai Nguyen, Vietnam. The system's performance is comprehensively evaluated through several key quantitative indicators, including mean average precision (mAP), multiple objects tracking accuracy (MOTA), and identification F1-score (IDF1), across realistic urban traffic scenarios. The results show that the framework significantly improves detection accuracy, tracking consistency, and small object recognition efficiency in real-world urban traffic scenarios. This paper provides useful insights for selecting appropriate detection and tracking configurations in ITS applications.
Volume: 15
Issue: 4
Page: 3518-3527
Publish at: 2026-08-01

Indonesia Islamic students' belief towards the performance of artificial intelligence: perceptions, impacts, and anticipations

10.11591/ijai.v15.i4.pp3955-3966
Yuni Sugiarti , Ence Oos Mukhamad Anwas , Fitroh Fitroh , Janu Arlinwibowo , Ihsan Maulana Anwas
This research aims to i) determine the belief level of Islamic university students towards AI for learning and ii) what factors related to student confidence in using AI for learning. This study employed a mixed-methods sequential explanatory design. The research subjects were students of the strata 1, Universitas Islam Negeri Syarif Hidayatullah, and students of Universitas Islam Negeri Sunan Kalijaga, totaling 411 students. The research sample was obtained using random techniques. The quantitative data collection method is questionnaire-based, and data in-depth analysis is carried out qualitatively using the focus group discussion (FGD) technique. The quantitative data analysis method was carried out using descriptive statistics, and the qualitative study was carried out thematically. This research found that the majority of students have a high level of confidence in the performance of artificial intelligence (AI) as a tool for them. This level of belief is significantly and positively related to understanding of the AI platform used, lecturer support and guidance, parental support and guidance, and support from fellow students. However, belief in AI cannot be absolute because it is feared that it will result in poor use. AI platform users, especially students, lecturers, researchers, and anyone who uses it, need to recheck and deepen its accuracy and correctness.
Volume: 15
Issue: 4
Page: 3955-3966
Publish at: 2026-08-01
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