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Classification of Cihateup duck egg fertility using convolutional neural network EfficientNet-B3

10.11591/ijai.v15.i1.pp798-809
Evi Dewi Sri Mulyani , Teuku Mufizar , Dani Rohpandi , Ayu Djuliani , Egi Rahmatulloh , Rinaldi Satia Aulia Rahmat
Accurate detection of egg fertility is crucial to improve hatching success in duck farming. Conventional candling methods rely heavily on human expertise, making them subjective and error-prone. This study proposes an automated classification system for Cihateup duck egg fertility using candling images and a convolutional neural network (CNN) based on the EfficientNet-B3 architecture. Image enhancement techniques, including contrast limited adaptive histogram equalization (CLAHE), unsharp masking, and adaptive thresholding, were applied to improve image quality and feature visibility. The dataset consisted of fertile and infertile egg images captured at two incubation stages: the first 24 hours and the 8th–15th days. Data were split into training, validation, and testing sets with a ratio of 70:15:15. Experimental results show that image enhancement significantly improves classification performance. Without enhancement, the model achieved an accuracy of 49% with an area under curve (AUC) of 0.4226, indicating poor discrimination capability. With image enhancement, the proposed method achieved accuracies of 77% for the first 24 hours dataset and 80% for the 8th–15th days dataset, with AUC values of 0.9962 and 0.9317, respectively. These results demonstrate that EfficientNet-B3 combined with image enhancement provides an effective and computationally efficient solution for automated fertility detection of Cihateup duck eggs.
Volume: 15
Issue: 1
Page: 798-809
Publish at: 2026-02-01

Portable system for real-time traffic volume and speed estimation using YOLOv10

10.11591/ijai.v15.i1.pp300-309
Ida Bagus Sradha Nanda , Masrono Yugihartiman , Eko Primadi Hendri , I Made Suartika
Accurate traffic data is essential for effective transportation planning and policymaking. However, in many regions, especially those lacking intelligent infrastructure, data collection remains dependent on manual methods that are labor-intensive, time-consuming, and susceptible to human error. While advanced systems such as closed-circuit television (CCTV) and area traffic control systems (ATCS) offer automation, their high cost and infrastructure requirements limit widespread adoption. This study proposes a portable, low-cost, and real-time traffic monitoring system based on the YOLOv10 object detection algorithm. The system operates using only a smartphone-grade camera (1080 p, 60 fps) and a standard laptop, eliminating the need for expensive installations. It detects, classifies, and counts vehicles as they pass through a predefined region of interest (ROI), and also estimates their speed based on time–distance measurements. Field evaluations using five one-hour urban traffic videos showed excellent agreement with manual counts, achieving a mean absolute percentage error (MAPE) of just 0.30%. Speed estimation trials conducted on sample clips also demonstrated consistent and plausible results. These findings highlight the system’s potential as a scalable and accurate alternative for traffic monitoring in infrastructure-limited environments.
Volume: 15
Issue: 1
Page: 300-309
Publish at: 2026-02-01

Predicting university student dropouts in Latin America using machine learning

10.11591/ijai.v15.i1.pp628-641
Laberiano Andrade-Arenas , Inoc Rubio Paucar , Margarita Giraldo Retuerto , Cesar Yactayo-Arias
In the university context, student dropout has become one of the most recurring problems, both in the short and long term. The objective of this research was to develop a predictive model using the random forest (RF) algorithm to identify patterns associated with university dropout. To achieve this, the knowledge discovery in databases (KDD) methodology was applied, which encompasses the stages of selection, preprocessing, transformation, data mining, and interpretation of results. The RF model demonstrated superior performance compared to other evaluated models, achieving an accuracy of 87%, a precision of 86%, a recall of 85%, an F1-score of 85%, and an receiver operating characteristic (ROC) area under the curve (AUC) of 0.91, highlighting its high predictive capability compared to other techniques analyzed. Therefore, the application of the proposed model is recommended in various university institutions in order to identify potential dropout cases at an early stage.
Volume: 15
Issue: 1
Page: 628-641
Publish at: 2026-02-01

Change detection and classification of satellite images using convolutional neural network

10.11591/ijai.v15.i1.pp329-337
Raghavendra Srinivasaiah , Santosh Kumar Jankatti , Manjunath Ramanna Lamani , Niranjana Shravanabelagola Jinachandra
Satellite and airborne imagery, collectively known as earth observation imagery, are images of the earth collected from spaceborne or airborne platforms such as satellites and aircraft. Over the last 100 years, with the fast development of aviation, space exploration, and imaging technologies, the coming together of these technologies has been inevitable. Earth observation imagery has many applications in regional planning, geology, reconnaissance, fishing, meteorology, oceanography, agriculture, biodiversity conservation, forestry, landscape, intelligence, cartography, education, and warfare. With the rise in the number of these airborne and spaceborne imaging platforms being deployed by government and private entities alike, the capability to sift through and analyze vast amounts of data generated by these platforms is the need of the hour. With the exponential improvement in the computational capabilities of computers over the last half a century, analysts are exceedingly moving towards the practice of artificial intelligence, machine learning (ML), and computer vision solutions to automate a large part of the processes employed in analyzing earth observation imagery. This work recommends a workflow to perceive and classify changes in earth observation imagery of a given area by utilizing the vast flexibility that convolutional neural networks (CNN) provide.
Volume: 15
Issue: 1
Page: 329-337
Publish at: 2026-02-01

Comparison of image enhancement methods for pratima theft detection using artificial intelligence

10.11591/ijai.v15.i1.pp213-228
Made Sudarma , Ni Wayan Sri Ariyani , I Putu Agus Eka Darma Udayana , Ida Bagus Gde Pranatayana , Lie Jasa
The theft of pratima in Balinese temples threatens the spiritual and cultural balance of the community. These sacred objects, regarded as manifestations of God in Hinduism, hold profound religious significance, and their loss represents both material and spiritual desecration. To address this issue, this study investigates a security system that leverages image enhancement for low-light detection. Four techniques—contrast limited adaptive histogram equalization (CLAHE), adaptive histogram equalization (AHE), histogram equalization (HE), and gamma correction—were evaluated to improve image quality. CLAHE yielded the lowest mean squared error (MSE) of 21.16 and the highest peak signal-to-noise ratio (PSNR) of 38.13 dB. For object detection, VGG-19 and AlexNet were assessed. The best configuration, VGG-19 with HE, reached 83.33% accuracy and 93.75% recall, and achieved a receiver operating characteristic area under the curve (ROC AUC) of 0.90±0.02 across five runs. Thresholds derived from the ROC analysis were selected using the Youden J statistic to balance sensitivity and specificity. The approach outperformed lightweight and classical baselines in AUC, indicating superior discrimination under low illumination. These findings show that superior image quality does not always align with higher detection accuracy, and they highlight the importance of pairing effective enhancement with robust detectors for temple security. The study contributes practical insights for preserving Balinese cultural and spiritual heritage by strengthening efforts to protect pratima against theft.
Volume: 15
Issue: 1
Page: 213-228
Publish at: 2026-02-01

Efficient data streaming in dynamic vehicular networks: a hybrid controller for seamless connectivity

10.11591/ijai.v15.i1.pp229-236
Prathibha Thimmappa , Mayuri Kundu
The demand for highly efficient data transmission is being increasingly demanded for dynamic vehicular networks, especially in the case of internet-of-vehicle (IoV). The current data transmission methods are known to encounter inefficiencies in terms of unreliable routing and restricted scalability. Evolving studies have found artificial intelligence (AI) based schemes more suitable to address these issues; however, there are no significant innovations towards developing a potential framework that can not only increase data transmission performance but also minimize the analytical overheads of AI. Hence, this paper presents a novel baseline framework by introducing an optimized controller structure at anchor points with the inclusion of novel ideologies of orientation degree and selection of mediating node. The proposed model witnesses 32.3 dB of signal quality, 857 kbps throughput, 81 ms delay, and 171 ms of response time, exhibiting much better performance in contrast to the frequently used data transmission method. The proposed model contributes to a solid foundation for any futuristic AI model for efficient and reliable data transmission in IoV.
Volume: 15
Issue: 1
Page: 229-236
Publish at: 2026-02-01

Enhanced framework for detecting Vietnamese hate and offensive spans

10.11591/ijai.v15.i1.pp962-971
Dinh-Hong Vu , Tuong Le
The rise of hate and offensive content on social media platforms, such as Facebook and Twitter, has emerged as an escalating concern, especially in Vietnam. Consequently, detecting hate and offensive spans in Vietnamese text is an essential area of research. This study introduces ViHateOff, an advanced framework that combines a hated speech dictionary (HSD) automatically constructed from the Vietnamese hate and offensive spans (ViHOS) dataset with the pre-trained language models for Vietnamese (PhoBERT)-large language model to enhance the detection of offensive expressions. The framework functions through two primary modules. First, it constructs an HSD from the ViHOS dataset, which serves as a reference for identifying hate and offensive language in Vietnamese text. Second, the framework integrates the PhoBERT-large language model with HSD, enhancing the detection of harmful words in the input text. Experimental results demonstrate that the proposed framework significantly outperforms existing state-of-the-art (SOTA), achieving an F1-score of 0.8693 on the all spans subset and 0.8709 on the multiple-spans subset representing relative improvements of over 10% compared to the strongest baseline.
Volume: 15
Issue: 1
Page: 962-971
Publish at: 2026-02-01

Pneumonia classification from chest X-rays using significant feature selection and machine learning

10.11591/ijai.v15.i1.pp592-603
Yugandhar Chodagam , Manjunatha Hiremath
The chest X-ray images of normal lungs differ only subtly from those of lungs with pneumonia, making image-based diagnosis highly challenging. To address this issue, we developed a machine learning (ML)-based, lightweight, end-to-end Python package that processes chest X-ray images, implements robust feature selection methods, and classifies the images using various algorithms. While many studies have focused on improving classification accuracy using newer methods, few have addressed the interpretability of the extracted features or the growing computational demands of complex models. We used four publicly available datasets and extracted first-order, textural, and transform-based radiomic features to test our package. Features were selected using the Shapley additive explanations (SHAP) combined with recursive feature elimination (RFE) and stability selection algorithms. Our final solution contains a method that extracts a finite set of features identified by stability selection and feeds them as inputs into classical ML algorithms. Our model achieved 98% accuracy on the primary dataset, and 97%±1, 96%±2, and 94%±2% accuracy on the other three datasets. Our approach is fast, self-contained, and requires only an ideal set of features, making it suitable for resource-constrained clinical environments.
Volume: 15
Issue: 1
Page: 592-603
Publish at: 2026-02-01

Artificial intelligence framework for multi-stage lung disease detection with audio signals

10.11591/ijai.v15.i1.pp106-115
Bandreddi Venkata Seshukumari , Jyothirmayi Tayi , Rajeshkhanna Bhuthkuri , Bhavani Madireddy , Jhansi Yellapu , Bodapati Venkata Rajanna , Nitalaksheswara Rao Kolukula , Siva Sairam Prasad Kodali , Jayasree Pinajala , James Stephen Meka , Chilakala Rami Reddy
Automated diagnostic systems are increasingly pivotal in advancing the accuracy and efficiency of medical diagnostics. Due to abnormal changes in human life and pollution, lung disease and cancer cases increasing in huge number. Identification and prediction of lung diseases may help to increase the human life span. This study introduces a robust framework for automatic lung disease detection using respiratory sound signals. The methodology brings together a series of activities like preprocessing, feature extraction, selection, and classification to improve diagnostic accuracy. The adaptive empirical stockwell-transform (AEST) is used to enhance the quality of the signal, whereby extracting and refining features, mainly Mel-frequency cepstral coefficients (MFCC), and Mel-spectrograms, are used. The scalable convolutional geyser network (SCGN) helps to mitigate challenges posed by imbalanced datasets, redundant features, and overfitting, ensuring reliable classification of the features. The model is validated when using the International Conference on Biomedical and Health Informatics (ICBHI) dataset, which validates the performance indicators of the model (F1-score 0.94, accuracy 0.95, precision 0.93, recall 0.94). This is shown superior performance compared to other existing models and demonstrates the framework's ability to diagnose a serviceable and reliable medical diagnosis; which indicates the strengths of combining advances in signal processing and scalable deep learning (DL) in healthcare applications.
Volume: 15
Issue: 1
Page: 106-115
Publish at: 2026-02-01

Artificial intelligence in writing: unveiling a research landscape

10.11591/ijai.v15.i1.pp66-75
Wan Rusydiah Salehudin , Zilal Saari , Hafiza Abas
This study examines the expanding research landscape of artificial intelligence (AI) in writing, a field that continues to reshape the way ideas are produced, refined, and communicated. While AI has been widely examined in education and technology, limited research has mapped its thematic evolution and ethical dimensions in writing. To address this gap, 1,596 publications indexed in Scopus between 2021 and 2024 were analyzed using bibliometric mapping tools such as Scopus analyzer and VOSviewer. The analysis covers publication patterns, collaboration networks, and keyword relationships to trace the intellectual structure of the field. The results indicate a sharp increase in scholarly output over the past four years, supported by contributions from multiple disciplines, including computer science, social sciences, and education. Several thematic clusters were identified, centering on AI-assisted creative writing, authorship ethics, educational use, and cross-sector innovation. Despite these advances, ethical frameworks and responsible AI applications in writing remain underexplored. This paper offers a comprehensive overview of current trends and presents a foundation for future research on how AI can be integrated into writing practices responsibly and in ways that uphold human creativity and academic integrity.
Volume: 15
Issue: 1
Page: 66-75
Publish at: 2026-02-01

Gradient descent optimization based weighted federated learning for privacy-preserving framework

10.11591/ijai.v15.i1.pp878-887
Gururaj Prakash Murthy , Chandrashekhar Pomu Chavan
Federated learning (FL) is a disseminated machine learning (ML) paradigm that gained significant consideration in modern days, particularly in a domain of the internet of things (IoT). FL saves communication bandwidth when compared to centralized ML processes by eliminating the need to transmit raw client data to a central server, thereby enhancing data privacy. Nevertheless, participant privacy is still compromised through inference attacks and similar threats. Additionally, a data excellence provided through clients can differs significantly, and excessive inclusion of low-quality data during training may degrade the overall performance of the global model. Hence, this research introduces a gradient descent optimization assisted weighted federated learning (GDO-WFL) method for privacy preservation. The proposed GDO-WFL approach is significantly efficient as it strengthens privacy preservation through reducing exposure to inference attacks and optimises gradient updates for secure learning. Through weighting client contributions based on data quality, an undesirable effect of low-quality data can be minimised, helping to maintain a strength as well as accuracy of the global model. The experimental results illustrate a proposed GDO-WFL approach maintains an overall accuracy of 99.3 and 91.5% on MNIST and CIFAR-10 datasets as compared to the existing method of FedlabX method.
Volume: 15
Issue: 1
Page: 878-887
Publish at: 2026-02-01

Deep learning-based cervical cancer detection via colposcopy images integrated into an Android mobile application

10.11591/ijai.v15.i1.pp338-349
Retno Supriyanti , Arsil Kultura Anzil , Yogi Ramadhani , Suroso Suroso , Wahyu Widanarto , Muhammad Alqaaf , Kartika Dwi Hapsari , Futiat Diana Kartika
Cervical cancer is a form of cancer that develops in the cells of the cervix, the lower part of the uterus that connects the uterus to the vagina. Early detection is essential for improving the chances of recovery from cervical cancer. One method for early detection is colposcopy image analysis, a medical procedure that examines the cervix and captures images for evaluation. These images were analyzed to observe color changes after the visual inspection with acetic acid (VIA) process. However, this analysis requires experienced and specially trained medical personnel. To address this challenge, a system that can automatically classify cervical cancer images is needed. Therefore, researchers proposed designing and developing an Android mobile application to enable early detection of cervical cancer using the convolutional neural network (CNN) algorithm. The CNN model was tested using test data to evaluate its performance. The optimized CNN model utilizing the ResNet50 architecture achieved 86% test accuracy, 85% precision, and 87% recall. The test results indicate that the model's accuracy is consistent before and after its implementation on the mobile application, confirming the effectiveness of both the model and its implementation as diagnostic tools.
Volume: 15
Issue: 1
Page: 338-349
Publish at: 2026-02-01

Real-time object detection to classify export quality of mangosteen using variants of you only look once version 8

10.11591/ijai.v15.i1.pp116-128
Dian Sa'adillah Maylawati , Mi’raj Fuadi , Kurniawan Yniarto , Yuhendra AP , Rizky Rahmat Nugraha , Akbar Hidayatullah Harahap , Agung Wahana
Mangosteen is one of the leading export commodities from Indonesia. Despite its great economic potential, only about 25% of Indonesian mangosteens meet export standards, mainly due to visual defects such as yellow sap and spots on the skin of the fruit. The process of sorting export worthy mangosteens has been done manually, which tends to be time consuming and inconsistent. Therefore, this study aims to utilize artificial intelligence technology in building a real-time image recognition model to improve the efficiency and accuracy of the export-quality mangosteen sorting process. This study uses you only look once version 8 (YOLOv8) as an image recognition model with YOLOv8 variants, including nano, small, medium, large, and extra large variants. The results of the study using 4,014 primary and 255 secondary data of mangosteen, the highest performance is reached by YOLOv8 medium 82% of accuracy, 0.856 of mean average precision (mAP)50, and 0.616 of mAP50-95. This result is obtained from 70% training, 20% validation, and 10% testing data with epoch stop 85. These results indicate that the model can provide good performance in mangosteen export quality classification. This research contributes to the fields of agricultural technology and artificial intelligence by offering an innovative solution to a practical problem, enhancing efficiency, accuracy, and scalability in export-quality mangosteen sorting.
Volume: 15
Issue: 1
Page: 116-128
Publish at: 2026-02-01

Interpretable artificial intelligence system for personalized cognitive stimulation

10.11591/ijai.v15.i1.pp164-176
Rubén Baena-Navarro , Yulieth Carriazo-Regino , Mario Macea-Anaya
The growing need to preserve cognitive health in aging populations has intensified interest in adaptive digital interventions that provide personalized and interpretable support. This study presents a web-based cognitive stimulation system for older adults integrating a multilayer perceptron (MLP) classifier, expert-derived symbolic rules, and explainable artificial intelligence (XAI) techniques, including Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME). The platform was evaluated through a 24-week intervention involving 150 participants aged 65 years and older, combining baseline cognitive profiling, rule-guided recommendation logic, and neural prediction to support individualized task allocation. Compared with a control group, participants in the intervention arm showed statistically significant improvements in cognitive outcomes (p <0.05), with measurable gains in memory- and attention-related tasks. The explainability component enabled examination of model behavior at the level of individual features through feature attribution analysis and symbolic consistency checks, supporting interpretation beyond aggregate performance metrics. Unlike approaches dependent on high-end extended reality (XR) infrastructures or game centered interaction, the system was implemented to operate under low connectivity conditions and was tested with participants from diverse educational backgrounds. This hybrid configuration provides an interpretable basis for cognitive support initiatives adaptable to community settings contexts.
Volume: 15
Issue: 1
Page: 164-176
Publish at: 2026-02-01

Secure and interoperable electronic health record exchange using blockchain and ECDHE-based access control

10.11591/ijai.v15.i1.pp310-321
Krishna Prasad Narasimha Rao , Chinnaiyan Selvan
Electronic health records (EHRs) act as comprehensive records of health related transactions and essential resources of data in the healthcare sector. However, the integrity and security problems of EHR continue to be inflexible. The architecture of blockchain-enabled EHR addresses the issues of integrity efficiently. This paper developed the decentralized patient centric healthcare data management (PCHDM) system using blockchain enabled EHR architecture for addressing problems with access control, record privacy and data confidentiality. This framework places the patient at the control center, ensuring secure storage of EHR records and obtaining efficient data management through the integration of blockchain and interplanetary file system (IPFS). To prevent access by unauthorized users, the proposed elliptic curve Diffie-Hellman ephemeral (ECDHE) mechanism incorporates smart contract-enabled access control for managing EHR transactions and enforcing access strategies. This architecture incorporates hyperledger fabric endorsement policies (HFRP) to address scalability problems while preserving patient privacy and securing medical data. The developed method secures the EHR data and facilitates the data exchange across heterogeneous healthcare platforms, ensuring standard communication among different EHR systems. The architecture is assessed with parameters of time for block creation, the computational overhead of transaction with encryption key size and EHR upload and download time.
Volume: 15
Issue: 1
Page: 310-321
Publish at: 2026-02-01
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