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Machine learning-based solar power prediction for major Indian metro cities

10.11591/ijai.v15.i2.pp1362-1370
Komal Kumar Napa , Rajkumar Govindarajan , J. Senthil Murugan , Billa Manindhar
The growing reliance on renewable energy has intensified the need for accurate solar power forecasting to support efficient grid operation and energy planning. However, reliable prediction remains challenging due to the strong dependence of solar power output on dynamic meteorological conditions. This study proposes a data-driven machine learning (ML) framework for high-precision solar power prediction across several major Indian metro cities. Using hourly weather and power generation data for the year 2023, a random forest regressor was developed to model complex non linear relationships between environmental variables and solar energy output. The proposed model achieved exceptional predictive performance, with an R² score of 0.9999 and a mean absolute error (MAE) of 0.15 kW, significantly outperforming conventional regression approaches. Feature contribution analysis revealed solar radiation as the dominant factor influencing power generation, while cloud cover and elevated temperatures exhibited negative effects. The key contribution of this work lies in demonstrating the robustness and generalizability of ensemble learning for urban-scale solar forecasting under diverse climatic conditions. The findings provide actionable insights for policymakers, grid operators, and energy planners to optimize solar integration and resource management.
Volume: 15
Issue: 2
Page: 1362-1370
Publish at: 2026-04-01

Sentiment-aware user-item recommendation combining weighted XGBoost and optimized similarity metrics

10.11591/ijai.v15.i2.pp1851-1862
Snehal Bhogan , Vijay S. Rajpurohit , Sanjeev S. Sannakki
User-item recommendation systems play a vital role in enhancing personalized digital experiences across e-commerce and social media platforms. Traditional recommendation approaches, such as collaborative filtering (CF) and content-based filtering (CBF), often suffer from challenges like data sparsity, cold-start issues, and limited contextual understanding. Sentiment-aware recommendation systems have emerged as a promising solution by incorporating emotional insights extracted from user reviews, thereby improving recommendation accuracy and personalization. This study proposes a novel sentiment-aware user-item recommendation system (SAUIRS) framework that integrates optimized term frequency inverse document frequency (O-TF-IDF), parameterized bidirectional encoder representations from transformers (P-BERT), weighted extreme gradient boosting (WXGBoost), and an optimized similarity metrics model. The optimized TF-IDF enhances feature selection, reducing dimensionality while preserving relevant textual information. P-BERT, a fine-tuned BERT model, improves sentiment classification accuracy by leveraging deep contextual embeddings. WXGBoost further refined sentiment predictions, addressing class imbalance and enhancing model robustness. The extracted sentiment information is incorporated into an optimized similarity metrics model to improve recommendation precision by aligning user preferences with sentiment-driven insights. Extensive experiments conducted on Amazon benchmark datasets demonstrate the superior performance in terms of accuracy, root mean square error (RMSE), and mean absolute error (MAE) of the proposed framework compared to state-of-the-art recommendation models.
Volume: 15
Issue: 2
Page: 1851-1862
Publish at: 2026-04-01

Accurate stroke area classification using extreme gradient boosting with multi-feature extraction

10.11591/ijai.v15.i2.pp1390-1401
Kavikondala Praveen Kumar Rao , Maha Lakshmi Bondla , Bommaraju Srinivasa Rao , Ambidi Naveena , K. V. Balaramakrishna , Srinivasarao Goda
Stroke, one of the most common neurological disorders leading to long-term disability and mortality, requires accurate detection of affected brain regions for timely treatment planning. However, conventional deep learning models face challenges in achieving precise segmentation and robust classification due to noisy inputs, weak feature representation, and poor generalization. To address these gaps, this study introduces a hybrid framework that integrates the ConvNeXt architecture for stroke region segmentation with XGBoost based classification, strengthened through three complementary feature extraction methods: local binary patterns (LBP), adaptive threshold directional binary gradient matrix (AT-DBGM), and wavelet packet transform (WPT). These methods capture textural, directional, and multi resolution features, which are concatenated into a stacked vector and classified using XGBoost. Preprocessing steps, including normalization and resizing, ensure improved input consistency. Experimental evaluations on benchmark stroke imaging datasets show that the proposed framework achieves 98.56% Dice similarity coefficient (DSC), 12.96 mm Hausdorff distance (HD), 99.12% accuracy, 98.69% sensitivity, 99.06% specificity, 98.98% precision, and 98.85% F1-score.
Volume: 15
Issue: 2
Page: 1390-1401
Publish at: 2026-04-01

Deep learning for early detection of cardiovascular diseases via auscultation sound classification

10.11591/ijai.v15.i2.pp1746-1761
Shreyas Kasture , Sudhanshu Maurya , Amit Kumar Sharma , Santhosh Chitraju Gopal Varma , Kashish Mirza , Firdous Sadaf Mohammad Ismail
Heart diseases are one of the most prominent causes of death globally, which requires immediate and accurate diagnosis. The auscultation methods used in conventional medical practice, where the doctor listens to the sounds produced by the body without intervention is very ineffective because of the limitations in the actual skills and perception of the doctor. The main goal of this project will be designing a mobile-based system for the early detection of cardiovascular disease (CVD) by utilizing deep learning for auscultation sound classification. The approach involves the use of deep learning structures to classify cardiac sounds into normal and abnormal patterns on its own. Wavelet transformations, time-frequency representations, and Mel frequency cepstral coefficients (MFCC) have been used in feature extraction. The ResNet152V2 model showed high classification performance with area under the receiver operating characteristic curve (AUROC) of 0.9797 and 0.9636 on two datasets. Contrary to that, data augmentation, hyperparameter optimization, attention mechanisms, as well as input-output residual connections, led to better functionality and interpretability. This research seeks to overcome the limitations of traditional stethoscope use through the incorporation of sophisticated algorithms and the availability of mobile technology that could result in early diagnosis and prevention of CVDs, especially in underprivileged areas.
Volume: 15
Issue: 2
Page: 1746-1761
Publish at: 2026-04-01

An intelligent and explainable IoT-Edge-Cloud architecture for real-time water quality monitoring

10.11591/ijai.v15.i2.pp1109-1120
Sara Bouziane , Badraddine Aghoutane , Aniss Moumen , Anas El Ouali , Ali Essahlaoui , Abdellah El Hmaidi
Continuous and reliable monitoring of water quality is critical for early detection of environmental deterioration, yet conventional monitoring approaches are often slow and lack timely data availability. This study proposes an intelligent and explainable internet of things (IoT)–Edge–Cloud architecture to monitor water quality in real time, using IoT sensing, edge based artificial intelligence (Edge AI), cloud-stream processing, and explainable artificial intelligence (XAI). The system calculates the water quality index (WQI) directly at the edge and predicts its evolution using a stacking ensemble model trained on physicochemical measurements taken from the Moulouya River Basin in Morocco. An explainability module based on Shapley additive explanations (SHAP) values gives a clearer image of the contribution of various parameters to WQI predictions, providing transparency of the features, which builds trust in the model’s output. The proposed architecture was implemented as an end-to-end prototype and validated using a simulation-based experimental that mimicked realistic sensor dynamics and connectivity interruptions. The experimental results show strong predictive performance (R² =0.945), stable system operations, and reliable interpretability highlighting the potential of the proposed approach for scalable, intelligent, and transparent environmental monitoring.
Volume: 15
Issue: 2
Page: 1109-1120
Publish at: 2026-04-01

Fetal organ detection using feature enhancement with attention and residual block

10.11591/ijai.v15.i2.pp1593-1604
Nuswil Bernolian , Siti Nurmaini , Ade Iriani Sapitri , Annisa Darmawahyuni , Muhammad Naufal Rachmatullah , Bambang Tutuko , Firdaus Firdaus
The rapid advancements in fetal ultrasonography have significantly enhanced prenatal diagnosis in recent years. Deep learning (DL) architectures have further streamlined the process of organ detection, improved diagnostic accuracy, and reduced observer dependency. This study proposes a computer-aided DL approach for fetal organ segmentation using the you only look once (YOLO) algorithm, a state-of-the-art method for object detection and image segmentation. This study identified and classified 15 fetal organs, including the umbilical vein, stomach, abdomen, brain (trans-cerebellum, trans-thalamic, and trans-ventricular regions), femur, head, thorax (chest cavity), heart (circumference, left atrium, left ventricle, right atrium, right ventricle), and aorta. We compared the performance of YOLOv7, YOLOv8, YOLOv9, and YOLOv11 architectures. The results showed that YOLOv9 outperformed YOLOv7, YOLOv8, and YOLOv11 achieving mAP50 and mAP95 scores of 91.90% and 94.50%, respectively. This performance surpasses previous studies that focused on classifying only a limited number of fetal organs.
Volume: 15
Issue: 2
Page: 1593-1604
Publish at: 2026-04-01

Zoneout regularization-gated recurrent unit algorithm on NIDS with class imbalance handling

10.11591/ijai.v15.i2.pp1505-1512
Mala Kariyappa , Manjunath Hanumanthappa Rangappa , Venugopal Dasappa , Gururaja Hebbur Satyanarayana , Girish Keshava Rao , Gousia Thahniyath
Network intrusion detection system (NIDS) is primarily utilized tool to identify malicious threats on the network. It plays an essential role in safeguarding against an increasing variety of attacks and ensures enhanced security for the network. The existing model struggled to handle the imbalance of class issues during the process of classification due to their biased nature, which reduced the performance of the algorithm. In this paper, the zoneout regularization–gated recurrent unit (ZR-GRU) algorithm is developed to detect and classify intrusions in the network. Incorporating the ZR into GRU reduces overfitting by preventing the model from becoming overly dependent on specific features. It provides good generalization by maintaining diversity in learned representation. Synthetic minority oversampling technique (SMOTE) and Near Miss methods are utilized to balance the samples in the dataset, which helps to increase the performance of a classifier in NIDS. The ZR-GRU technique attained 99.91% accuracy on UNSW-NB15, 99.92% accuracy on CIC-IDS2018, and 99.14% accuracy on CIC-DDoS2019 when comparing with a convolutional neural network bidirectional long short-term memory (CNN-BiLSTM).
Volume: 15
Issue: 2
Page: 1505-1512
Publish at: 2026-04-01

Enhanced VGG-19 model for rice plant disease detection and classification

10.11591/ijai.v15.i2.pp1691-1700
Aye Thida Win , Khin Mar Soe , Myint Myint Lwin
Rice is the main staple food and rice farming plays a crucial role in the agriculture sector of Myanmar. It is also an essential pillar in generating foreign income. However, rice diseases seriously reduced the rice production and quality. Early detection of rice diseases is one of the effective ways to reduce the disease spreading and increase yields. Most Myanmar farmers detect rice diseases based on visual judgment and their experience, which leads to delay in taking efficient action. To overcome this challenge, we intend to propose an enhanced rice plant disease classification model that contributes as artificial intelligence (AI) in Myanmar agriculture sector. The proposed model enhances original visual geometry group 19 (VGG-19) by integrating the algorithms: mixture of Gaussians 2 (MOG2), GrabCut, and relevance estimation with linear feature (RELIEF) for classification. It was trained on 6,326 rice plant images of Kaggle and Eastern Shan State and validated using 5-fold nested cross-validation. The training and testing of proposed model are followed as 80:20. The proposed model experimental result is (98.3%) and lowest standard deviation (0.004) across seven classes than the original VGG-19, MobileNet, Efficient Net, and RestNet50 respectively. Future work will expand dataset diversity, enhance early-stage disease prediction, and support mobile diagnostics for real-world agricultural application.
Volume: 15
Issue: 2
Page: 1691-1700
Publish at: 2026-04-01

Hybrid recommender for computer aided design software

10.11591/ijai.v15.i2.pp1931-1946
Younes Zidani , Younes Zahrou , Salah Nissabouri , Moulay El Houssine Ech-Chhibat , Khalifa Mansouri
Choosing the right computer-aided design (CAD) software is a complex task due to the wide variety of available options. Using user opinions and reviews may not be sufficient, which highlighting the need for a decision support system. In this paper, we develop and evaluate a hybrid recommendation program (HRP) for CAD software written in the Python programming language, combining collaborative filtering (CF) and content-based filtering (CBF) using k-nearest neighbors (KNN). CF uses user ratings to identify similar users, while CBF compares software characteristics to find similar options. In our hybrid approach, we integrate both filtering techniques with KNN to generate personalized recommendations. It will improve the relevance of software options, help users make choices (students, educators, and professionals), and encourage the adoption of tools most appropriate for every profile. We used the analytic hierarchy process (AHP) method to choose the criteria for our recommendation program. We tested the HRP on a simulated CAD dataset and found that it made recommendations much more accurately than using CF and CBF separately. Evaluation metrics like precision (0.81), recall (0.95), and F1-score (0.87) show that this hybrid approach works, making it a more reliable tool for helping people choose CAD software.
Volume: 15
Issue: 2
Page: 1931-1946
Publish at: 2026-04-01

Research themes and trends in the field of blockchain engineering: a topic modelling analysis

10.11591/ijai.v15.i2.pp1863-1875
Dinara Zhaisanova , Madina Mansurova
This study employed topic modeling to identify key research themes in blockchain engineering and examined how these themes have evolved over time. The dataset of collected abstracts from 3,665 relevant papers of Web of Science (WoS) core collection for the period from 2019 to 2024 was analyzed with latent Dirichlet allocation (LDA) approach. Based on the results of the topic development trends analysis, the topics collectively highlight the evolving landscape of technologies such as blockchain, smart contracts, the internet of things (IoT), and edge computing, focusing on their integration and impact across sectors like finance, healthcare, supply chain management, and energy systems. It offers valuable insights and implications for research related to blockchain engineering. Latent semantic indexing (LSI) provided further understanding by highlighting strong connections between specific topics, such as energy trading, supply chains, and medical applications. A comparison of LDA and LSI topics revealed overlapping themes, which supports the reliability of the topic structure identified by LDA.
Volume: 15
Issue: 2
Page: 1863-1875
Publish at: 2026-04-01

RBC_Frame_Net: a hybrid deep learning framework for detection of red blood cells in malaria diagnostic smear

10.11591/ijai.v15.i2.pp1486-1496
Muhammad Shameem P. , Mathiarasi Balakrishnan
Malaria continues to pose a major global health threat, especially in areas where timely and accurate diagnosis is essential for effective treatment. Conventional diagnostic techniques, such as manually examining Giemsa stained blood smears, are often time-intensive, laborious, and susceptible to human error. To overcome these challenges, this study presents red blood cell frame network (RBC_Frame_Net), a novel deep-learning framework that combines convolutional neural networks (CNNs) with transformer based architectures, augmented by attention mechanisms, for the automated identification of RBCs in malaria smear images. The framework leverages the convolutional block attention modules (CBAM)-UNet model for segmentation, enhancing both spatial and channel features through CBAM and integrates the detection transformer (DETR) to accurately detect and classify RBCs within the diagnostic images. The model achieved outstanding performance with a segmentation intersection over union (IoU) of 0.97, a Dice coefficient of 0.98, and near-perfect detection results (precision: 0.999, recall: 0.998, and mean average precision (mAP): 0.995). When compared to leading models such as YOLOv8, faster region-based convolutional neural network (Faster R-CNN), and EfficientDet-D3, and RBC_Frame_Net demonstrated superior accuracy and robustness. The inclusion of attention mechanisms and a hybrid architecture enhance its adaptability, making it well-suited for deployment in real-world, resource limited environments and positioning it as a valuable asset in automated malaria diagnostics.
Volume: 15
Issue: 2
Page: 1486-1496
Publish at: 2026-04-01

Deep learning for mental health analysis: long short-term memory approach to text-based condition classification

10.11591/ijai.v15.i2.pp1762-1770
Zaqqi Yamani , Dinda Lestarini , Sarifah Putri Raflesia , Purwita Sari , Ghita Athalina
The increasing prevalence of mental health disorders highlights the need for scalable and automated approaches to early detection. This study proposes a deep learning–based text classification framework using a long short-term memory (LSTM) network to identify mental health conditions from user generated textual data. A corpus of 103,488 labeled texts representing anxiety, stress, bipolar disorder, depression, personality disorder, suicidal ideation, and normal states was preprocessed through tokenization, padding, and word embedding. The proposed LSTM model achieved overall accuracy of 87% on test set, with strong class-wise performance reflected by precision, recall, and F1-scores, particularly for anxiety, personality disorder, and normal classes. Comparative error analysis using a confusion matrix revealed challenges in distinguishing depression from suicidal ideation, indicating semantic overlap between these conditions. The results demonstrate that LSTM-based models can effectively capture sequential linguistic patterns relevant to mental health classification. This framework shows potential as a decision-support tool for early screening and digital mental health applications, complementing clinical assessment rather than replacing it.
Volume: 15
Issue: 2
Page: 1762-1770
Publish at: 2026-04-01

Novel convolution neural network model for dysgraphia affected handwriting classification

10.11591/ijai.v15.i2.pp1418-1427
Nisha Ameya Vanjari , Prasanna J. Shete
It is estimated that 10% of the population in the world suffers from learning disabilities like dyslexia, dysgraphia, and dyscalculia. Learning disabilities are neurological disorders in which children struggle with reading, writing and mathematical skills. Dysgraphia disorder impacts on writing abilities of students and thus may be a hurdle in their learning and evaluation of subject matter. Hence early detection/prediction of learning disability (LD) in school going children will greatly help in providing necessary accommodations so as to ease their future learning curve. In recent years researchers have used several deep learning algorithms that produce automated and trained models which can be useful in the handwriting classification. To properly capture the distinct handwriting inconsistencies linked to dysgraphia, this study contains experiments that determine how various convolution neural network (CNN) model layers contribute to performance. To address it, this research focused on the improved novel model based on CNN and targeted dysgraphia English handwriting classification with 98% accuracy with 102,691 trainable parameters. The model is trained on both normal and dysgraphia-affected handwriting, increasing its accuracy in identifying individual differences.
Volume: 15
Issue: 2
Page: 1418-1427
Publish at: 2026-04-01

Unified voting-based ensemble learning for rice leaf disease detection using improved pretrained models

10.11591/ijai.v15.i2.pp1646-1663
Govindarajan Subburaman , Mary Vennila Selvadurai
As a staple food for a large portion of the global population, rice is particularly susceptible to leaf diseases that adversely affect its yield and overall quality. This study utilizes four pretrained convolutional neural network (CNN) models to construct a unified voting-based ensemble approach for rice leaf disease classification. The models include VGG16, DenseNet121, InceptionV3, and Xception. The dataset used in this study was collected from Kaggle and further enriched with images obtained from Google sources. It comprises a total of 4,000 images categorized into six classes: bacterial leaf blight, brown spot, leaf blast, leaf scald, narrow brown spot, and healthy leaves. It was split into training (327 images/class), validation (140 images/class), and testing (200 images/class). Images were normalized to [0,1] and augmented through rotation, flipping, shifting, shear, zoom, brightness, and channel adjustments to improve generalization. Individually, the fine-tuned models achieved accuracies of 91.3% (VGG16), 95.6% (DenseNet121), 92.1% (InceptionV3), and 89.8% (Xception). The ensemble leveraged majority voting (93.6%), weighted voting (96.5%), and soft voting (97%), yielding an absolute gain of 1.4% over the best individual model and 4.8% over the average of all models. To our knowledge, this is the first ensemble combining these four architectures with unified voting for identifying diseases in rice leaves, delivering a scalable and computationally efficient solution suitable in advance diagnosis and timely execution in agricultural settings with limited resources.
Volume: 15
Issue: 2
Page: 1646-1663
Publish at: 2026-04-01

IoT-enabled smart nutrition scale using fuzzy logic for dietary assessment and recommendation

10.11591/ijai.v15.i2.pp1194-1201
Wahyu Wijaya Widiyanto , Edy Susanto , Sri Suparti
Childhood malnutrition, particularly stunting, remains a major public health challenge that requires preventive and technology-supported nutritional interventions. This study presents an IoT-enabled smart nutrition scale integrated with fuzzy logic to support real-time dietary assessment and personalized recommendation. The system combines IoT-based sensing, mobile and web applications, and a fuzzy inference engine that evaluates child profiles and food composition data to generate nutritional adequacy scores and tailored dietary guidance. Experimental validation demonstrates high measurement accuracy of the sensing system, achieving a strong linear correlation (R² ≈0.9995). Comparison with expert nutritionist assessments shows strong agreement, supported by low error values (mean absolute error (MAE) =2.96; root mean square error (RMSE) =3.41), and Bland–Altman analysis. Usability evaluation involving community health workers and caregivers yields an excellent system usability scale (SUS) score, indicating strong acceptance for practical deployment. By integrating IoT sensing with fuzzy reasoning, the proposed system shifts nutritional monitoring from retrospective assessment toward proactive dietary intervention. This work highlights the potential of intelligent nutrition technologies to enhance decision-making in community-based stunting prevention programs and provides a scalable foundation for preventive digital health applications.
Volume: 15
Issue: 2
Page: 1194-1201
Publish at: 2026-04-01
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