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Utilization of depth-wise and spatially separable convolutional network fusion for classification of white blood cells

10.11591/ijai.v15.i3.pp2595-2605
Firas Muneam Bachay , Ali Abbas Alzaheiree , Hassenien Ali Hussein , Ahmed Nooruldeen Alsafi , Mohammed Hasan Abdulameer
White blood cells (WBCs) are an essential part of the human immune system, playing a significant role in fighting diseases and infections. Their detection and classification from microscopic blood images is a crucial step in diagnosing various diseases. Looking at cells by hand is still key, but it takes a lot of work and mistakes can happen. So, this study tries to improve how to find and sort WBCs using some cool computer tricks. The study tackling issues like cells being on top of each other, looking different, and not having a ton of data. To achieve this, image enhancement techniques were applied using contrast enhancement algorithm, contrast-limited adaptive histogram equalization (CLAHE), and image segmentation techniques using color isolation are employed, which contributes to more accurate separation of overlapping cells, and enables faster and more efficient diagnosis. To efficiently complete the classification process after the segmentation process, a neural network structure consisting of combining three types of convolutional layers (depthwise, spatially, and convolution) was used. To evaluate the proposed technique, experiments were conducted using an open-source blood cell count and detection (BCCD) dataset from the Kaggle platform, and resulted in achieving a classification accuracy of 99.06% and an F1-score of 99.05%. This highlight of the model’s ability to efficiently deal with the challenges associated with WBC classification.
Volume: 15
Issue: 3
Page: 2595-2605
Publish at: 2026-06-01

Boiled ginger and honey intervention for hyperemesis gravidarum

10.11591/ijphs.v15i2.26954
Nursyahid Siregar , Ega Ersya Urnia , Jasmawati Jasmawati , Irma Mustika Ningsih , Rahmawati Wahyuni , Rosalin Ariefah Putri , Dewi Rinda Astuti , Heni Suryani , Elisa Goretti Sinaga
Hyperemesis gravidarum is excessive vomiting which can cause dehydration, lack of carbohydrate and fat reserves in the body, and Mallary Weiss syndrome due to gastrointestinal bleeding. Boiled ginger and honey were administered as an initial complementary treatment to reduce nausea and vomiting in pregnant women with hyperemesis gravidarum. This study aims to determine the effect of giving ginger and honey decoction on the frequency of hyperemesis gravidarum in pregnant women. This study employed a quasi-experimental design with a one-group pretest-posttest approach. The population was all pregnant women who experienced nausea and vomiting, outpatients, and inpatients at Inche Abdoel Moeis Hospital. The sample consisted of 18 people using consecutive sampling technique. The severity of nausea and vomiting was measured using the pregnancy-unique quantification of emesis and nausea (PUQE) questionnaire. Statistical analysis was performed using the Wilcoxon signed-rank test to compare pretest and posttest scores. The Wilcoxon Test results obtained a significance value of 0.000 < 0.05. There is an effect of giving ginger and honey decoction on the frequency of hyperemesis gravidarum. Pregnant women are advised to use a decoction of ginger and honey, apart from that, other researchers are advised to use a control group in subsequent studies.
Volume: 15
Issue: 2
Page: 437-448
Publish at: 2026-06-01

Determinants of community perception on food technology-based shallot waste management for nutritional security: a cross-sectional study

10.11591/ijphs.v15i2.27077
D. Yan El Rizal Unzilatirrizqi , Riska Arsita Harnawati
Shallot production in Indonesia, particularly in Brebes Regency, generates substantial agricultural by-product waste that poses significant environmental and public health risks due to inadequate community-level management. This cross-sectional quantitative study investigated the influence of government role, infrastructure availability, and public knowledge on perceptions of shallot by-product waste management among 180 respondents selected through proportional random sampling. Data were collected using structured Likert-scale questionnaires and analyzed through multiple linear regression, Spearman's rank correlation, and gap analysis. Gap analysis revealed critical deficiencies in public knowledge (50.6-76.1%) and infrastructure (69.2%), despite universally positive perceptions toward waste management. Multiple linear regression demonstrated that government role (β = 0.342, p < 0.001), infrastructure availability (β = 0.298, p < 0.001), and public knowledge (β = 0.152, p = 0.007) significantly predicted waste management perceptions, collectively explaining 37.8% of the variance (Adj. R² = 0.378). The government's role contributed the largest effective contribution at 18.74%. These findings carry substantial public health implications, as improved waste management can reduce disease vectors, minimize environmental contamination, and enable by-product valorization for nutritional supplementation. The study recommends strengthening government intervention, expanding waste infrastructure, and enhancing community health literacy as integrated policy strategies to optimize agricultural by-product management and safeguard community nutritional well-being in shallot production centers.
Volume: 15
Issue: 2
Page: 525-533
Publish at: 2026-06-01

Evaluation of machine learning approach in modelling and forecasting real gross domestic product growth: a comparative study

10.11591/ijece.v16i3.pp1339-1349
Moiz Qureshi , Muhammad Ismail , Nawaz Ahmad , Ibrar Hussain , Abbas Ali Ghoto , Jolita Vveinhardt
This study aims to provide an efficient and accurate machine-learning approach for modelling and forecasting the real gross domestic production (GDP) in the context of Pakistan. The study forecasts Pakistan's GDP growth rate using different forecasting models, such as naïve, seasonal naïve (SNaive), smoothing, and k-nearest neighbors (k-NN). Machine learning algorithms provide additional advice for data-driven decision-making. According to the findings, the k-NN-based forecasting gives minimum mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) compared to the other three models. Economic policymakers can use accurate models to measure significant economic activity and formulate plans. The results indicate that the model produced accurate projections of future GDP levels for Pakistan.
Volume: 16
Issue: 3
Page: 1339-1349
Publish at: 2026-06-01

Migrant worker mental health in Southeast Asia: a bibliometric analysis of public health research (2005–2025)

10.11591/ijphs.v15i2.27120
Suamuang Ruangrit
Southeast Asia hosts an estimated 10-14 million intraregional labor migrants who face disproportionate mental health burdens. No bibliometric study has systematically mapped the intellectual landscape or knowledge gaps of migrant worker mental health research specific to this region. A bibliometric analysis was conducted on peer-reviewed literature indexed in Web of Science and Scopus (January 2005-December 2025), following the BIBLIO checklist. VOSviewer and Bibliometrix (R v4.4) were used for keyword co-occurrence network mapping and publication trend analysis, respectively. A total of 487 eligible records were identified. Annual output grew from fewer than 10 publications per year before 2010 to over 60 per year from 2021 onward. Thailand dominated as the primary study setting (38.6%) and top-producing country (31.4%). Five thematic clusters emerged: i) depression and anxiety screening, ii) occupational stressors, iii) acculturation and social support, iv) healthcare access barriers, and v) social determinants of health. Symptom-prevalence research declined from 68% to 31% while equity-focused research grew from <1% to 14% over two decades. The field has grown substantially but remains geographically concentrated. Equity-focused, structurally informed research agendas are urgently needed to reduce mental health disparities among the region's most vulnerable workers.
Volume: 15
Issue: 2
Page: 360-369
Publish at: 2026-06-01

Fine-tuning convolutional neural network for artificial intelligence generated image detection enhancement

10.11591/ijai.v15.i3.pp2238-2246
Steven Vincent Hendrawan , Moeljono Widjaja , Alethea Suryadibrata
The relationship between art and technology has changed how people engage with creativity, leading to the industrialization of the field. Various digital media have been utilized in the endeavour of art creation, such as artificial intelligence (AI) generation for images. The utilization of AI-generated art has yielded negative reactions due to its exploitative nature on pre-existing artworks without the creator’s consent, which raises plagiarism concerns. This research utilized convolutional neural network (CNN) to help detect such images to reduce public concerns on the abuse of AI images. The algorithm is proposed to detect such images as it involves spatial convolution within two-dimensional spaces, matching the nature of images. The model was developed from pre-existing architectures, namely EfficientNetB1 and Xception, which was pre-trained on ImageNet classification task with the modification of inclusion or exclusion of dropout in the top layer. After assessing the models, removing top layer dropout from EfficientNetB1 model improved it to reach the F1-score of 97.66% compared to 97.44% in the base model and Xception with a dropout layer yields lower F1-score of 95.56% compared to 97.07% in the base model.
Volume: 15
Issue: 3
Page: 2238-2246
Publish at: 2026-06-01

Deep hybrid models for bitcoin forecasting: EMD, CEEMDAN,and LSTM in comparison

10.11591/ijai.v15.i3.pp2797-2810
Ayoub Aarabi , Maryem Ait Moulay , Issam Bouganssa , Abdelali Lasfar
In this study, an artificial neural network (ANN) was developed to forecast Bitcoin prices using one of the most successful deep learning architectures for time series analysis: long short-term memory (LSTM) networks. This model was enhanced with a signal processing layer that reduces the impact of the instrument’s high volatility on prediction accuracy by applying two signal decomposition techniques: empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). This study is motivated by the major fluctuations in Bitcoin prices, which make precise forecasting difficult but crucial for experts and investors. This findings demonstrate that forecasting performance improves when decomposition techniques are used. In particular, compared to the conventional LSTM and EMD-LSTM models, the CEEMDAN-LSTM model achieved the highest accuracy, with a mean absolute error (MAE) of 167.837 and a root mean square error (RMSE) of 255.673, outperforming both EMD-LSTM (MAE =168.785, RMSE =256.042) and the standard LSTM (MAE =169.516, RMSE=256.225). The combination of CEEMDAN and LSTM results in a more reliable model that can accurately capture short-term fluctuations in Bitcoin prices.
Volume: 15
Issue: 3
Page: 2797-2810
Publish at: 2026-06-01

AMAC-LW: Adaptive medium access control for long range wide area network with energy-aware routing

10.11591/ijece.v16i3.pp1626-1644
Sowmya M. , S. Meenakshi Sundaram , Pandiyanathan Murugesan , Santhosh Kumar K. S. , Tejaswini R. Murgod
To enhance the performance of long range wide area network (LoRaWAN), a routing algorithm and a novel medium access control (MAC) layer protocol are required. In addition to addressing scalability and security issues, the protocol seeks to improve communication efficiency, dependability, and power consumption. It presents a dynamic routing method that reduces energy consumption by utilizing machine learning processes, adaptive routing tactics, and route optimization approaches. Simulations in a range of deployment situations are used to assess the suggested solutions. These results imply that the suggested protocol and routing scheme have the potential to greatly enhance the sustainability, energy efficiency, and performance of LoRaWAN-based Internet of Things networks. The effectiveness of the proposed solutions is evaluated through extensive simulations across diverse deployment scenarios. The results demonstrate that the proposed MAC protocol achieves a throughput of 350 bps, outperforming conventional protocols that typically reach only 220 bps. Latency is reduced to 50 ms from 85 ms, energy consumption is decreased to 2.5 joules from 4.5 joules, and the packet delivery ratio (PDR) is improved to 95%, compared to 75% in existing approaches. These findings highlight the potential of the proposed protocol and routing scheme to significantly enhance the performance, energy efficiency, and sustainability of LoRaWAN-based IoT networks.
Volume: 16
Issue: 3
Page: 1626-1644
Publish at: 2026-06-01

Identification of areas of influence using a thematic modeling approach and belief function theory

10.11591/ijai.v15.i3.pp2838-2848
Fatima-Zahrae Sifi , Wafae Sabbar , Amal El Mzabi
Social networks have become essential platforms for the dissemination of information and the exchange of ideas. They are transforming the way influencers interact and exert influence over their peers. On platforms such as Twitter, Facebook and Instagram, influence is expressed through social interactions such as retweets, likes, mentions, comments and shares. These activities play a key role in amplifying messages and shaping opinions. Studying the practices of influencers allows for a more precise identification of the domains in which their impact is particularly significant. However, this task is complex. It requires rigorous methods capable of integrating various forms of social engagement while managing the uncertainties associated with heterogeneous data. In this context, we propose a method to identify the domain of influence of a social media influencer. This approach combines thematic modeling Latent Dirichlet Allocation (LDA) with Belief Function Theory (BFT) to analyze social interactions and dominant topics of interest. By incorporating indicators such as retweets, likes and mentions, the method provides a robust framework for evaluating the influencer's impact across different domains. It thus offers precise tools for researchers, practitioners and decision-makers aiming to better understand these complex dynamics.
Volume: 15
Issue: 3
Page: 2838-2848
Publish at: 2026-06-01

VisionEyeNet: a customized deep learning framework for early diagnosis of keratitis and uveitis

10.11591/ijai.v15.i3.pp2709-2722
Somashekhar Bannur Mayigowda , Raghavendra Kodandarama , Sudhamani Mallaiah , Manjunath Naganna , Jamuna Jamuna , Kiran Kumar B. S.
Keratitis and uveitis are increasingly prevalent ocular disorders, often linked to delayed detection and limited specialist access, particularly in rural healthcare settings. These diseases can lead to severe visual impairment or irreversible blindness if not identified at an early stage. Traditional diagnostic approaches are manual, time-consuming, and prone to human error, making them challenging for large-scale screening. To address these limitations, this study presents VisionEyeNet, a framework for automatic classification of keratitis and uveitis. VisionEyeNet integrates MobileNetV2 and DenseNet121 within a fusion architecture, along with image enhancement methods such as adaptive gamma correction and specular reflection suppression. The model was trained and evaluated on a curated dataset of 1,860 slit-lamp images (960 uveitis and 900 keratitis) using a patient-wise split (71.5% training, 8.4% validation, and 20% testing). On the independent test set, it achieved 98.0% accuracy (95% CI: 97.1–98.8%) with balanced performance across classes. Inference analysis showed an average processing time of 51±2 ms per image, supporting real-time use. These results indicate that VisionEyeNet has strong potential as a clinically useful decision-support tool, particularly in resource-limited settings.
Volume: 15
Issue: 3
Page: 2709-2722
Publish at: 2026-06-01

Pneumothorax detection using a learning focal point architecture

10.11591/ijai.v15.i3.pp2041-2052
Salah-Eddine Mansour , Bouabid Qabliyane , Abdelhak Sakhi , Zakaria Khoudi , Mohamed Baslam
Automatic image segmentation and feature analysis play a crucial role in improving the accuracy and efficiency of disease diagnosis and treatment within modern medical practice. This study propose the use of the learning focal point (LFP) architecture, which is based on the LFP algorithm, to perform effective segmentation of medical images by dividing each image in the dataset into multiple meaningful zones. This zonal segmentation strategy enables the precise extraction of critical regions of interest that are most relevant for pathological analysis. The proposed approach is specifically applied to the detection of common pneumothorax in lung imaging, a condition that requires timely and accurate diagnosis. By concentrating on essential lung zones, the LFP architecture enhances the reliability and robustness of pneumothorax identification. The results demonstrate that this method has the potential to significantly assist clinicians by providing more accurate diagnostic support and facilitating earlier medical intervention, ultimately improving patient outcomes.
Volume: 15
Issue: 3
Page: 2041-2052
Publish at: 2026-06-01

Flashover of a polluted high voltage insulator under electric field distribution

10.11591/ijece.v16i3.pp1097-1106
Zainab Abdullah , Izham Zainal Abidin , Miszaina Osman , Nurulazmi Abd. Rahman , Muhammad Shafiq
This study investigates the effect of surface pollution on a single-unit 11 kV glass suspension insulator using two-dimensional (2D) axisymmetric simulations in COMSOL Multiphysics. The developed model incorporates the electrical properties of glass, cement, steel electrodes, surrounding air, and a uniform pollution layer, with an applied AC voltage of 11 kV under quasi-static conditions. Simulation results demonstrate pronounced electric field intensification in the polluted configuration, particularly at the air–glass–cap triple junction region, where localized electrical stress is significantly higher compared to the clean condition. While the clean insulator operates within IEC 60383 recommended limits, the polluted model exhibits elevated peak electric field magnitudes, indicating increased flashover vulnerability. The findings highlight the strong influence of surface contamination, material permittivity, and geometric configuration on electric field distribution along the creepage path. This study establishes a reliable and computationally efficient predictive framework for optimizing insulator design, improving maintenance strategies, and enhancing the long-term reliability of high-voltage transmission systems, especially in pollution-prone environments.
Volume: 16
Issue: 3
Page: 1097-1106
Publish at: 2026-06-01

Sub-X-band reconfigurable antenna network with graphene slots

10.11591/ijece.v16i3.pp1249-1260
Hassna Agoumi , Seddik Bri , Youssef El Amraoui , Adil Saadi
This paper presents the design and analysis of a graphene-slotted hexagonal microstrip patch antenna and its extension to a compact 4×4 planar array operating in the sub-X-band. The objective of this work is to demonstrate that graphene-based electrical reconfigurability can be extended from a single antenna element to an array configuration while improving radiation performance. The proposed antenna integrates graphene slots etched into the radiating patch, where reconfigurability is achieved by electrically tuning the graphene conductivity through an external gate voltage Vg. The single antenna operates around 9.4 GHz with an impedance bandwidth of 400 MHz and a peak gain of 6 dB. The design is then extended to a 4×4 array with an inter-element spacing of approximately 1.2 wavelengths. The array operates in the 9–10 GHz range, provides a bandwidth of 380 MHz, and achieves a maximum gain of 13.08 dB. The results confirm that graphene-enabled reconfigurability can be preserved at the array level without increasing structural complexity.
Volume: 16
Issue: 3
Page: 1249-1260
Publish at: 2026-06-01

GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection

10.11591/ijece.v16i3.pp1307-1318
Saiful Islam , Md. Nasim Akhtar , M. Mahadi Hassan , A. N. M. Rezaul Karim , Israt Binteh Habib
Early and accurate identification of rice leaf diseases is essential for sustainable crop management; however, many existing convolutional neural networks (CNNs) based solutions struggle with class imbalance and limited robustness when applied to real-field data. In this work, a generative adversarial network (GAN) augmented vision transformer (ViT) framework is introduced to overcome these limitations. A deep size representative samples for underrepresented disease categories, resulting in a more balanced training dataset and achieving a Fréchet inception distance (FID) score of 18.6. The balanced dataset is then used to train a vision transformer model that leverages self-attention to capture global contextual features of rice leaf images. Experimental evaluation across ten disease classes shows that the proposed approach attains an overall classification accuracy of 96.5%, exceeding the performance of several established CNN architectures. Additionally, the model demonstrates strong generalization capability on an external field dataset, achieving 94.8% accuracy. To validate real-world applicability, the trained model is deployed on a Jetson Nano edge device, where it delivers efficient inference performance suitable for practical agricultural applications. The findings indicate that combining GAN-based data augmentation with transformer-based learning provides a reliable and scalable solution for rice leaf disease detection.
Volume: 16
Issue: 3
Page: 1307-1318
Publish at: 2026-06-01

A risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control

10.11591/ijece.v16i3.pp1531-1542
Joni Fat , Parwadi Moengin , Pudji Astuti , Sally Cahyati
Algorithmic trading systems operate in highly dynamic and uncertain environments where learning-based decision agents must balance adaptability with strict risk control. Reinforcement learning (RL) methods provide adaptive policy optimization but often suffer from unstable exploration and limited interpretability in financial markets. This study proposes a risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control for algorithmic trading. The framework integrates a compact SARSA-based reinforcement learning environment with a Sugeno-type fuzzy inference system (FIS) that converts reinforcement signals into interpretable trading decisions. Exploration follows a decaying ε-greedy policy with a drawdown-triggered reset mechanism to maintain bounded risk exposure during learning. The system was implemented as a MetaTrader 5 Expert Advisor and evaluated on the GBPUSD currency pair using historical market data. Experimental results show that the hybrid framework improves trading performance compared with a rule-based baseline. During a six-month out-of-sample evaluation, the system achieved a net profit of 90 USD and a profit factor of 1.35, compared with 10 USD and 1.02 for the baseline. Extended one-year testing confirmed stable profitability and controlled drawdown behavior. The results demonstrate that integrating reinforcement learning, fuzzy decision mapping, and explicit risk constraints provides a practical approach for developing adaptive trading agents.
Volume: 16
Issue: 3
Page: 1531-1542
Publish at: 2026-06-01
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