Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

30,033 Article Results

Applications of artificial intelligence in analyzing Aquilaria essential oils: a review of current machine learning techniques

10.11591/ijai.v15.i2.pp1087-1096
Noor Aida Syakira Ahmad Sabri , Nur Athirah Syafiqah Noramli , Muhammad Ikhsan Roslan , Nurlaila Ismail , Zakiah Mohd Yusoff , Ali Abd Almisreb , Mohd Nasir Taib
This study explores the application of machine learning (ML) techniques in the classification of agarwood oil, focusing on the use of various algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), random forest (RF), and artificial neural networks (ANN). Since 2013, ML has played a pivotal role in analyzing agarwood oil, particularly by leveraging data from a variety of chemical compounds found in the Aquilaria genus. Through a systematic review and bibliometric analysis using the SCOPUS database, this study compiles and highlights recent works that have successfully employed ML techniques for the quality assessment of agarwood oil. These studies utilize chemical data, such as gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR), for the classification and detection of different oil grades. The review reveals a broad range of ML applications, demonstrating their growing importance in the field of essential oil analysis. By systematically presenting the findings from recent research, this work emphasizes the potential for further exploration of ML in the standardization and improvement of agarwood oil classification techniques.
Volume: 15
Issue: 2
Page: 1087-1096
Publish at: 2026-04-01

Explainable social media disaster image classification using a lightweight attention-based deep learning approach

10.11591/ijai.v15.i2.pp1464-1472
Rashmi Kangokar Taranath , Geeta Chidanandappa Mara
In recent years, the rapid dissemination of social media content during natural and man-made disasters has created a need for automated and accurate disaster image classification systems. This paper proposes lightweight explainable attention-based disaster network (LEAD-Net), a deep learning (DL) model designed for classifying disaster-related images with high accuracy and interpretability. The system integrates an EfficientNet-B0 backbone enhanced with squeeze-and-excitation (SE) attention modules and a lightweight neural architecture search (NAS-lite) strategy for tuning the classifier head and training hyperparameters. The model was evaluated on two benchmark datasets comprehensive disaster dataset (CDD) and damage multimodal dataset (DMD) achieving 96% and 87% accuracy, respectively, outperforming several established convolutional neural network (CNN) baselines. To ensure transparency, gradient-weighted class activation mapping (Grad-CAM) was employed to generate visual explanations of the model’s decisions, confirming its focus on semantically relevant image regions.
Volume: 15
Issue: 2
Page: 1464-1472
Publish at: 2026-04-01

Comparative deep learning study for downy mildew detection in vegetables

10.11591/ijai.v15.i2.pp1719-1732
Supreetha Shivaraj , Manjula Sunkadakatte Haladappa
Several vegetable crops are affected by downy mildew, a major foliar disease resulting in notable reductions in yield. For sustainable agriculture and disease prevention, early and precise detection is crucial. To be able to detect downy mildew in five varied vegetables—bitter gourd, bottle gourd, cauliflower, cucumber (Rashid), and cucumber (Sultana)—this study evaluates three deep learning architectures: VGG19, DenseNet201, and MobileNetV2. This work focuses on imbalanced datasets collected from several sources, in opposition to prior work that depended on balanced laboratory datasets. Accuracy, precision, recall, and F1-score metrics were used to evaluate the models shortly after they were trained using transfer learning, data augmentation, and 5-fold cross-validation. Model focus regions were assessed by using gradient-weighted class activation mapping (Grad-CAM) visualizations, and statistical reliability was assessed based on paired t-tests and Wilcoxon signed-rank tests. By achieving mean accuracies above 98% and statistically significant results (p <0.05) on cucumber datasets, DenseNet201 accomplished superior performance. Despite attaining slightly lower accuracy (89.6–100%), MobileNetV2 offered the smallest model size (12.9 MB) and minimum inference time (85 ms). The proposed approach demonstrated a transparent, generalizable, and computationally efficient deep learning pipeline for precision agriculture’s real-time downy mildew detection.
Volume: 15
Issue: 2
Page: 1719-1732
Publish at: 2026-04-01

Deep learning ensembles for lung cancer detection in thoracic CT scans leveraging generative adversarial network technology

10.11591/ijai.v15.i2.pp1605-1612
Bineesh Moozhippurath , Jayapandian Natarajan
Effective treatment of lung cancer depends on early and accurate detection, which continues to be a major cause of cancer-related fatalities globally. Conventional diagnostic techniques are useful, but their efficacy in handling large amounts of thoracic computed tomography (CT) scan data is limited by their time-consuming nature and susceptibility to human error. The research here suggests a new deep learning model that integrates generative adversarial networks (GANs) for data improvement with a sophisticated ensemble approach to classification. GANs are employed to generate realistic synthetic CT images, addressing the challenges of limited datasets. The backbone of the proposed approach is a consensus-guided adaptive blending (CGAB) ensemble model that learns to dynamically combine the predictions of three top-performing convolutional neural networks (CNNs): ResNet-152, DenseNet-169, and EfficientNet-B7. The CGAB model improves prediction accuracy through model contribution weighting based on confidence scores and inter-model consensus, while a conflict-resolving auxiliary decision model is used. The approach was tested using the lung image database consortium and the image database resource initiative (LIDC-IDRI) dataset with a detection rate of 97.35%, surpassing single model and traditional ensemble methods. The current work provides a solid and scalable approach to lung cancer detection with better generalization, increased diagnostic consistency, and applicability for clinical use.
Volume: 15
Issue: 2
Page: 1605-1612
Publish at: 2026-04-01

Drone-assisted deep learning weed detection for sustainable agriculture and environmental resilience

10.11591/ijai.v15.i2.pp1428-1440
Agustan Latif , Handaru Jati , Herman Dwi Surjono , Mani Yusuf
Effective weed detection plays a crucial role in sustainable agriculture, boosting crop productivity and supporting environmental conservation. This study compares three deep learning models—YOLOv5, YOLO-NAS, and mask region-based convolutional neural network (Mask R-CNN)-against traditional methods in terms of accuracy, processing speed, and adaptability in tropical agricultural conditions, with Merauke, Indonesia, as the case study. The results show that YOLO-NAS delivers the highest accuracy at 96% with a processing time of 25 ms per image, making it suitable for high precision applications. YOLOv5 balances strong accuracy (94%) with faster processing at 12 ms per image, establishing it as the most effective for real time scenarios. Mask R-CNN also achieves 94% accuracy and provides advanced segmentation capabilities, but its slower processing speed of 31 ms limits large-scale implementation. Traditional methods perform poorly in comparison, with only 85% accuracy and processing time above 50 ms per image. These findings highlight the transformative potential of artificial intelligence (AI)-based weed detection for precision agriculture, particularly in tropical regions like Merauke. Adoption of models such as YOLOv5 reduces manual labor dependence while advancing efficient, eco-friendly weed management. Future research should expand datasets and explore newer models like YOLOv8, YOLO-NAS, vision transformers (ViTs), and hybrid approaches.
Volume: 15
Issue: 2
Page: 1428-1440
Publish at: 2026-04-01

Image feature extraction for road surface damage classification

10.11591/ijai.v15.i2.pp1578-1592
Octaviani Hutapea , Sarifuddin Madenda , Hustinawaty Hustinawaty , Iffatul Mardhiyah
Road surface deterioration poses a critical risk to driving safety and comfort, necessitating timely and accurate detection to support effective maintenance. Manual inspection methods are often inefficient, underscoring the need for automated approaches based on computer vision. This study investigates the integration of feature extraction techniques histogram of oriented gradients (HOG) and local binary pattern (LBP) with convolutional neural network (CNN) architectures ResNet50 and InceptionV3 for the classification of road damage. A dataset of 1,580 images was categorized into five damage types: alligator crack, longitudinal crack, other crack, patching, and potholes. Experimental results indicate that HOG–ResNet50 achieved 79% accuracy, while LBP–InceptionV3 yielded the best performance at 97%. The contributions of this study are threefold: i) an automated framework is proposed that combines texture-based features with deep learning for road damage detection, ii) the LBP–InceptionV3 combination is shown to provide superior accuracy compared to conventional pairings, and iii) the approach offers a scalable and reliable alternative to manual inspection methods, supporting more efficient road maintenance planning.
Volume: 15
Issue: 2
Page: 1578-1592
Publish at: 2026-04-01

A hybrid model for enhanced aspect-based sentiment analysis using large language models

10.11591/ijai.v15.i2.pp1825-1838
Mohammed Ziaulla , Arun Biradar
Aspect-based sentiment analysis (ABSA) is a crucial task within natural language processing (NLP), enabling fine-grained opinion mining by identifying sentiments associated with specific aspects of a product or service. While transformer-based models like bidirectional encoder representations from transformers (BERT) have improved sentiment classification, they still struggle with limited contextual adaptability, especially in customer reviews containing complex expressions. Most existing approaches rely heavily on benchmark datasets such as semantic evaluation (SemEval) and multi-aspect multi-sentiment (MAMS), which do not fully capture the diversity of real-world review scenarios. Hence, this research addresses these limitations by proposing a novel hybrid model, called as hybrid-BERT (H-BERT), that integrates span-aware BERT (SpanBERT) with bidirectional long short-term memory (BiLSTM), conditional random field (CRF), and large language models (LLMs). The objective is to enhance aspect extraction and sentiment classification performance using both annotated and synthetic data. The methodology includes preprocessing, hybrid model training, and evaluation using the SemEval 2014 dataset. Experimental results show that H-BERT achieved 90.58% accuracy and 90.56% F-score in the laptop domain and 91.21% accuracy with a 92.03% F-score in the restaurant domain. These results outperform existing models, confirming H-BERT’s robustness and effectiveness. In conclusion, H-BERT improves sentiment understanding in customer reviews.
Volume: 15
Issue: 2
Page: 1825-1838
Publish at: 2026-04-01

Genetic algorithm-based chicken manure weight prediction system development

10.11591/ijai.v15.i2.pp1247-1260
Rida Hudaya , Septriandi Wirayoga , Moechammad Sarosa , Muhammad Yusuf , Armanda Dwi Prayugo
This research presents design and implementation of internet of things (IoT) based monitoring and predictive system for evaluating chicken manure weight and environmental conditions in poultry housing. The proposed system integrates MQ-137 sensor for ammonia detection, DHT22 sensor for temperature and humidity measurement, and load cell modules for manure weight monitoring. All sensor data are transmitted in real time to cloud platform, enabling continuous environmental assessment. A 30-day experimental study was conducted using two controlled chicken drum models, each containing 15 broiler chickens and provided with different feed types to observe variations in manure production and air quality. Sensor calibration results indicate high accuracy, with average error of 0.31% for ammonia readings and 0.10% for manure weight measurement. Experimental findings show that feed type A generates lower manure weight, reduced ammonia concentration, and more stable temperature conditions compared to feed type B, suggesting improved feed efficiency and better overall chicken health. A genetic algorithm (GA) was employed to optimize regression model predicting manure weight using ammonia concentration and temperature as input features. The GA-optimized model achieved strong predictive performance, with root mean square error (RMSE) of 0.358 g and coefficient of determination (R2) value of 0.992. The results demonstrate that proposed system provides reliable, scalable, and data-driven solution for smart poultry monitoring and early health detection.
Volume: 15
Issue: 2
Page: 1247-1260
Publish at: 2026-04-01

Adaptive control of ball and beam system using SNA-PID combined with recurrent fuzzy neural network identifier

10.11591/ijai.v15.i2.pp1202-1210
Minh-Thanh Le , Chi-Ngon Nguyen
The ball and beam system is a nonlinear and inherently unstable single input, multiple-output (SIMO) system, which poses significant challenges for control design. Intelligent control algorithms are often applied to autonomously control complex systems when there are changes in parameters or the control environment. Therefore, in this paper, we research and develop two methods: proportional integral derivative (PID) and single neuron adaptive (SNA)-PID-recurrent fuzzy neural network identifier (RFNNI) to control the ball and beam system. Simulation results on MATLAB/Simulink show that the SNA-PID-RFNNI controller provides a more stable output signal than the traditional PID controller, with minimal overshoot and a settling time of about 15 seconds. Next, we will conduct real-time experiments on the object using the proposed algorithm through the MEGA2560 control board with an ultrasonic positioning mechanism.
Volume: 15
Issue: 2
Page: 1202-1210
Publish at: 2026-04-01

Blockchain-enabled framework using diversity mutation with siberian tiger optimization for offloading in fog computing

10.11591/ijai.v15.i2.pp1371-1380
Srikanta Murthy Rajini , Reginald Shilpa
Fog computing has developed as a promising framework to support latency sensitive internet of things (IoT) applications for mobile devices operating in dynamic environments. During the offloading process, malicious activities interrupt the existing methods, which increases the execution time. Therefore, this research proposes a diversity mutation with siberian tiger optimization (DM-STO) for computation offloading in blockchain based fog computing. The blockchain is used to secure offload and attain quality of service (QoS) mobile users with less energy consumption and execution time. The DM-STO can balance workloads among local devices and fog servers. The diversity mutation operation improves the exploration ability to dynamic network conditions, leading to efficient computational offloading in fog computing. The execution time, service cost and energy consumption are evaluated to calculate the performance of the proposed DM-STO with varying numbers of IoT requests such as 50, 100, 200, and 300. For 50 IoT requests with a fixed fog server of 10, the DM-STO achieves an execution time of 18 s, a service cost of 10$ and energy consumption of 5 mJ compared to the BAT algorithm.
Volume: 15
Issue: 2
Page: 1371-1380
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

Energy management in smart grids using internet of things and price-based demand response with a hybrid EVO-PDACNN approach

10.11591/ijece.v16i2.pp699-716
Manju Jayakumar Raghvin , Manjula R. Bharamagoudra , Ritesh Dash
Network control systems for energy distribution play an essential role when renewable energy sources (RES) expand and the smart grid (SG) infrastructure increases. A new approach to energy management (EM) in SGs combines energy valley optimizer (EVO) with pyramidal dilation attention convolutional neural network (PDACNN) to achieve its objectives. Through EVO-PDACNN, the system performs accurate energy consumption forecasting with PDACNN, while the EVO algorithm supports systematic scheduling capabilities. Due to its use, this method reduces the peak-to-average ratio (PAR) by 22% also the cost of electricity (COE) by 12%. This method performs better than the wind-driven bacterial forging algorithm (WBFA), genetic algorithm (GA), particle swarm optimization (PSO), modified elephant herd optimization algorithm (MEHOA), and ant colony optimization (ACO) because it has a new prediction ability and quick response. EVO-PDACNN establishes better performance through lower root mean square error (RMSE), together with mean squared error (MSE) and mean absolute error (MAE), which indicates enhanced cost efficiency and resource management capabilities for SGs. The method strengthens both energy forecasting and operational scheduling operations while effectively dealing with changes in supply and demand, which helps build resilient power systems.
Volume: 16
Issue: 2
Page: 699-716
Publish at: 2026-04-01

Design and simulation of an electric vehicle charger with integrated interleaved boost converter and phase-shifted full-bridge converter using MATLAB/Simulink

10.11591/ijece.v16i2.pp687-698
Ahmad Saudi Samosir , Tole Sutikno , Alfin Fitrohul Huda , Luthfiyyatun Mardiyah
This paper presents the design and simulation of a high-efficiency electric vehicle (EV) charger that integrates a two-phase interleaved boost converter (IBC) with a phase-shifted full-bridge (PSFB) converter using MATLAB/Simulink. In contrast to existing studies that treat these converter stages independently, this work introduces a unified AC–DC–DC architecture that simultaneously minimizes input current ripple, improves DC-bus stability, and enables soft-switching operation for reduced switching losses. The values of the inductors and capacitors are derived analytically based on ripple constraints and switching frequency considerations, and example calculations are explicitly provided. Simulation results demonstrate that the proposed charger maintains a stable 600-V DC bus with less than 2% voltage ripple, delivers a controlled charging current of 100 A with 3 A ripple, and achieves an overall efficiency of 95%. These findings indicate that the integrated interleaved–PSFB topology provides superior conversion efficiency and power quality, making it a strong candidate for future EV fast-charging infrastructure.
Volume: 16
Issue: 2
Page: 687-698
Publish at: 2026-04-01

The developing a smart grid control system based on Konnex electrical equipment and internet of things technology

10.11591/ijece.v16i2.pp1020-1029
Tran Duc Chuyen , Mai Van Tao , Hoang Dinh Co
In this research, the authors present a method for developing a smart grid control system based on Konnex (KNX) electrical equipment and internet of things (IoT) technology to control and monitoring electrical energy processes such as: voltage, current, frequency, and power for independent or grid-connected power systems in industry and civil use. The system includes: KNX electrical equipment (KNX-connectivity), IoT control board, Solar panels that produce electricity to supply the system, battery storage devices, converters and controllers, power consumption loads, and many measuring, switching and protection devices for the system. With computer control programming devices, software, and control algorithms, access is possible via website, computer, smartphone, iPhone, and iPad. The goal is to monitor electricity and automatically control the smart building system, which is being used for high-end apartment buildings (luxury housing estate); offices, hotels, and garden villas. The system was researched and tested at the practice workshop for industrial factories and enterprises, bringing high results. The system aims to save energy in the context of increasingly depleted fossil energy, both in Vietnam and around the world.
Volume: 16
Issue: 2
Page: 1020-1029
Publish at: 2026-04-01

FADTESE: A framework for automated deployment and effectiveness evaluation for big data tools

10.11591/ijece.v16i2.pp1051-1062
Mony Ho , Sokroeurn Ang , Sopheaktra Huy , Midhunchakkaravarthy Janarthanan
Manual deployment of big data tools such as Hadoop, Sqoop, and Python is often slow, complex, and error prone because of extensive configuration steps, dependency conflicts, and inconsistent command-line execution. These challenges lead to unreliable installations and variations across systems. This study introduces framework for automated deployment and time, error, satisfaction evaluation (FADTESE), a unified framework that automates the installation of big data tools and evaluates its performance. The framework consists of two integrated components. The first is the automated deployment model, which validates environment readiness using the automation deployment readiness index (ADRI) and achieved a readiness value of 1.0 in this study. The second is the time, error, and satisfaction evaluation model, which quantifies improvements gained from automation and produced a score of 0.5941 through bootstrap resampling with ten thousand samples, indicating moderate effectiveness. The FADTESE script was technically validated across multiple Linux environments, including Ubuntu, Linux Mint, and AWS Ubuntu server systems. The performance evaluation involving eighty IT practitioners was conducted on Ubuntu systems to ensure consistent testing conditions and confirmed substantial gains in installation time, error reduction, and user satisfaction. Combining readiness and effectiveness yields a composite score of 0.5941 or 59.41%. FADTESE provides a reproducible and data driven method that standardizes big data deployment and improves reliability across local and cloud-based Linux environments.
Volume: 16
Issue: 2
Page: 1051-1062
Publish at: 2026-04-01
1 2 3
...
2002 2003
Show 21 of 2003

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration