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31,456 Article Results

A hybrid extreme learning machine and sine cosine algorithm model for accurate electricity price forecasting

10.11591/ijece.v15i5.pp4366-4375
Udaiyakumar Sambathkumar , Sangeetha Shanmugam , Kannayeram Ganapathiya Pillai
Electricity demand is continually rising due to the advancement of new technology, the switch to greener energy, and the popularity of electric vehicles over conventional ones. The proliferation of businesses in the generation and distribution sectors has increased competition in the electricity market. Forecasting electricity prices enables consumers to control their monthly electricity bills and consumer-owned distributed generation by knowing the forecasted hourly price. For demand management, generation scheduling, and bidding price quotations, electricity price forecasting is crucial for buyers, generation businesses, and bidders alike. Electricity price data is highly nonlinear and affected by numerous factors because of which EPF models are more complex, highly volatile and slow in convergence. A range of neural network models, training algorithms, and hybrid systems comprising two or more models have been suggested for precise and efficient electricity price forecasting by researchers over the decade. This study involves the development of a hybrid neural network model with two intelligent algorithms sine cosine algorithm (SCA) and extreme learning machine (ELM) to predict electricity price for a particular duration. The newly developed network model is trained and tested with real-time Indian electricity price data from the year 2022. The selected annual price data set is divided into three different sets to explore seasonal variations and all the sets are given as the input to the model for training and testing to obtain the effective price forecasting.
Volume: 15
Issue: 5
Page: 4366-4375
Publish at: 2025-10-01

A hybrid steganography scheme with reduced difference expansion and pixel-value ordering

10.11591/ijai.v14.i5.pp3563-3575
I Kadek Agus Ariesta Putra , Ntivuguruzwa Jean De La Croix , Tohari Ahmad
Steganography embeds secret messages into public media while ensuring the stego content remains visually indistinguishable from the original. The primary challenge lies in maximizing embedding capacity and image quality without introducing noticeable distortions. This research proposes a novel reversible data hiding (RDH) scheme that integrates reduced difference expansion (RDE) with four directional pixel-value ordering (PVO) schemes, horizontal, vertical, diagonal-right, and diagonal-left, to enhance embedding efficiency and visual fidelity. Unlike existing RDH methods that apply RDE with fixed or limited PVO directions, the proposed scheme dynamically selects the optimal PVO orientation based on pixel pair characteristics, effectively improving local prediction accuracy and reducing embedding-induced distortion. Previous studies have largely overlooked this relationship between pixel pair selection and embedding performance. Experimental evaluation on medical images with secret data sizes ranging from 5 kb to 100 kb demonstrates significant gains over recent PVO-based methods. The proposed method increases the average embedding capacity from 0.8315 to 0.9781 bit per pixel (bpp) (a 17.6% improvement) and raises the average peak signal-to-noise ratio (PSNR) from 49.44 to 53.40 dB, reducing distortion by approximately 3.96 dB.
Volume: 14
Issue: 5
Page: 3563-3575
Publish at: 2025-10-01

Breaking barriers: the legacy and voices of women pioneers in accounting education in Peru

10.11591/ijere.v14i5.33792
Jorge M. Chávez-Díaz , Nilda Irma Monterroso-Unuysuncco , Jorge De Velazco-Borda , Laura Aquiño-Perales , Juan Alberto Villagómez-Chinchay , Mélida H. Pecho-Rafael
This study examines the contributions of pioneering women in Peruvian accounting, both in scientific research and university teaching, in a historically male-dominated field. The research aims to highlight their academic impact and professional legacy. A qualitative approach was employed, utilizing the collective biography method and a phenomenological perspective. The study analyzed historical undergraduate and graduate theses from the Universidad Nacional Mayor de San Marcos (UNMSM) and conducted interviews with five distinguished female accounting professors. The findings reveal that these women made significant contributions in key areas such as financial auditing, management accounting, and environmental auditing, demonstrating adaptability and ethical commitment. In the educational sphere, participants emphasized their transformative teaching experiences, dedication to students, and the challenges posed by technology and social changes. The study concludes that these pioneers not only shaped the evolution of accounting education but also left a lasting legacy of academic and social engagement. The research underscores the need for gender equity policies and continuous technological adaptation in accounting education. These findings have practical implications for promoting diversity in academia and ensuring inclusive participation in the professional development of future generations.
Volume: 14
Issue: 5
Page: 3587-3600
Publish at: 2025-10-01

A memory improved proportionate affine projection algorithm for sparse system identification

10.11591/ijece.v15i5.pp4605-4619
Senthil Murugan Boopalan , Sarojini Raju , Krithiga Sukumaran , Manimegalai Munisamy , Kalphana Ilangovan , Sudha Ramachandran , Janani Munisamy , Bharathiraja Ramamoorthi , Sakthivel Pichaikaran
For cluster sparse system identification, it is known that the cluster sparse improved proportionate affine projection algorithm (CS-IPAPA) outperforms the standard IPAPA. However, since CS-IPAPA does not retain past proportionate factors, its performance can be further improved. In this paper, a modification to CS-IPAPA is proposed by utilizing the past instant proportionate elements based on its projection order. Steady-state performance of the proposed memory cluster sparse improved proportionate affine projection algorithm (MCS-IPAPA) is studied by deriving the condition for mean stability. Different simulation setups show that the proposed algorithm outperforms different versions of IPAPA in terms of convergence rate, normalized misalignment (NM) and tracking, for different types of inputs like colored noise, white noise, and speech signal. By incorporating past proportionate factors, the proposed MCS-IPAPA significantly reduces computational complexity for higher projection orders.
Volume: 15
Issue: 5
Page: 4605-4619
Publish at: 2025-10-01

Optimizing internet of things based gas sensors: deep learning and performance optimization strategies

10.11591/ijece.v15i5.pp4813-4828
Mariam M. Abdellatif , Mehmet Akif Çifçi , Asmaa A. Ibrahim , Hany M. Harb , Abeer S. Desuky
The rapid growth of industrialization and internet of things (IoT) driven advancements in Industry 5.0 necessitates efficient and user-friendly engineering solutions. Gas leakage incidents in coal mines, chemical enterprises, and households pose significant risks to ecosystems and human safety, emphasizing the need for automated and rapid gas-type detection. Traditional detection methods rely on single-source data and focus on isolated spatial or temporal features, limiting accuracy. This paper proposes a multimodal artificial intelligence (AI) fusion technique combining pre-trained convolutional neural networks (CNNs), such as VGG16, with a deep neural network (DNN) model. The particle swarm optimization (PSO) algorithm optimizes CNN hyperparameters, outperforming traditional trial-and-error methods. The system addresses challenges posed by gases being odorless, colorless, and tasteless, which limit conventional human detection methods. By leveraging sensor fusion, the late fusion technique integrates distinct network architectures for unified gas identification. Experimental results demonstrate 95% accuracy using DNN with gas sensor data, 96% with optimized VGG16 using thermal imaging, and 99.5% through multimodal late fusion. This IoT-enhanced solution outperforms single-sensor approaches, offering a robust and reliable gas leakage detection system suitable for industrial and smart city applications.
Volume: 15
Issue: 5
Page: 4813-4828
Publish at: 2025-10-01

A computational study of passive cooling of photovoltaic panels using hybrid material heat sink

10.11591/ijece.v15i5.pp4487-4499
Dang Van Binh , Pham Quang Vu , Pham Manh-Hai
Photovoltaic panels generate electricity from solar energy based on the photovoltaic effect. The conversion efficiency of photovoltaic panels depends on many factors such as solar radiation, wind speed, dust, orientation, tilt angle, and operating temperature. When the operating temperature increases by 1 C, the conversion efficiency of photovoltaic panels decreases by 0.4% - 0.5%. Heat sink is a device used to cool electrical and electronic equipment, including photovoltaic panels. This paper presents calculating the cooling capability of hybrid heat sink made from two materials in steady state using heat transfer theory. Heat sink base is constructed from aluminum and copper layers, with copper layer thickness is 1 and 2 mm. Under different conditions of radiation intensity, wind speed, and tilt angle of photovoltaic panel, results show that heat sink added copper layers of 1 and 2 mm, the operating temperature decreases by about 0.6 K and 1.2 K compared to the aluminum base. Accordingly, the conversion efficiency of photovoltaic panel increased by 0.1% and 0.2%.
Volume: 15
Issue: 5
Page: 4487-4499
Publish at: 2025-10-01

Enhancing network resilience and energy efficiency in the El Abiodh Sidi Cheikh grid through load flow analysis

10.11591/ijece.v15i5.pp4774-4784
Ali Abderrazak Tadjeddine , Soumia Djelaila , Ridha Ilyas Bendjillali , Sofiane Mohammed Bendelhoum , Abdelyamine Boukhobza , Salih Lachache
This study investigates the performance of the El Abiodh Sidi Cheikh (ESC) electrical grid, focusing on energy efficiency, system stability, and the integration of renewable energy sources. Numerical methods, including the Newton-Raphson (NR), accelerated Newton-Raphson (ANR), and fast decoupled (FD) load flow methods, were employed to evaluate power flow, voltage stability, and active power losses. Key results reveal that the NR method achieves the lowest power loss, with a minimal value of 2.32 MW, while voltage violations at specific nodes, such as buses 4 and 13, emphasize the necessity for voltage regulation. Analysis of the sun trajectory and temperature profiles highlights correlations between climatic conditions and energy demand, aiding renewable energy optimization. Additionally, photovoltaic (PV) measurements demonstrate diurnal variations in energy output, critical for enhancing renewable energy integration. These findings underscore the importance of advanced power flow analysis and strategic planning to ensure network resilience, energy efficiency, and reliability in the ESC region.
Volume: 15
Issue: 5
Page: 4774-4784
Publish at: 2025-10-01

Arithmetic artificial bee colony optimization algorithm with flexible manipulator system

10.11591/ijai.v14.i5.pp3790-3801
Mohd Ruzaini Hashim , Ahmad Fitri Mazlan , Mohammad Osman Tokhi
The artificial bee colony (ABC) algorithm, a well-known swarm intelligence-based metaheuristic inspired by the food foraging behavior of honeybees, has been widely applied to solve complex optimization problems. Despite its effectiveness, the standard ABC algorithm suffers from drawbacks such as slow convergence rates, limited balance between exploration and exploitation, and a tendency to get stuck in local optima, thereby hindering its overall performance. This study introduces an enhanced variant of the ABC algorithm, integrating the exploration strategy of the arithmetic optimization algorithm (AOA) to overcome these limitations. The enhanced algorithm is thoroughly tested on a set of benchmark functions as well as a flexible manipulator system model. Comprehensive statistical analyses are employed to evaluate and compare the performance of the improved algorithm against the original ABC. The results demonstrate that the enhanced ABC algorithm delivers superior performance in both benchmark scenarios and the flexible manipulator application.
Volume: 14
Issue: 5
Page: 3790-3801
Publish at: 2025-10-01

Automated vial defect inspection using Gabor wavelets and k-means clustering

10.11591/ijai.v14.i5.pp4279-4289
Vishwanatha C. R. , V. Asha , Channabasava Channabasava , Sreekanth Rallapalli
This study proposes a machine vision-based defect inspection system for pharmaceutical vials, aiming to ensure the quality and safety of medicinal fluids. The system employs a series of image processing techniques, including denoising, feature extraction using the Gabor wavelet transform, segmentation, clustering with the K-means algorithm, and precise defect identification using the Canny edge operator. Experimental results demonstrate high performance, with recall, precision, accuracy, and F1-score exceeding 98%. Additionally, the proposed method achieves area under the curve-receiver-operating characteristic curve (AUC-ROC) and AUC-precision-recall (PR) values of approximately 98%. The system's average computational time is 355 microseconds, indicating its potential for real-time defect detection. Overall, this approach offers an effective solution for identifying various cosmetic defects such as scratches, bruises, cracks, and black spots, in pharmaceutical vials without the need for vial classification training. 
Volume: 14
Issue: 5
Page: 4279-4289
Publish at: 2025-10-01

Enhanced solar panels fault detection approach using lightweight YOLO

10.11591/ijai.v14.i5.pp3554-3562
Naima El Yanboiy , Mohamed Khala , Ismail Elabbassi , Nourddine Elhajrat , Omar Eloutassi , Youssef El Hassouani , Choukri Messaoudi
Artificial intelligence (AI)-driven fault detection improves the reliability of solar energy by reducing the chances of system failures. However, existing single-stage object detection methods excel in accuracy but demand high computational resources, preventing seamless integration into embedded systems. This paper introduces a lightweight approach using YOLOv5, which incorporates a multi-backbone design, specifically tailored for accurate fault detection in solar cells. It evaluates YOLOv5 and TinyYOLOv5. The findings emphasize the effectiveness of YOLOv5l with Ghost backbone, particularly notable for its precision rates of 96% for faulty and 93% for non-faulty instances. Additionally, it showcases commendable mean average precision (mAP) scores, achieving 78% at an intersection over union (IoU) threshold of 0.5 and 72% at an IoU of 0.95. Additionally, YOLOv5_Ghost emerges as the optimal selection, showcasing competitive precision, processing speed of 42.1 giga floating point operations per second (GFLOPS), and remarkable efficiency with 2.4 million parameters. This evaluation underscores the effectiveness of YOLOv5 models, thereby leading to advanced solar energy technology significantly.
Volume: 14
Issue: 5
Page: 3554-3562
Publish at: 2025-10-01

Detecting autism with Vietnamese child facial images using deep learning

10.11591/ijece.v15i5.pp4762-4773
Tran Van Thanh , Lam Thanh Hien , Do Nang Khoa , Le Anh Tu , Ha Manh Toan , Do Nang Toan
Deep learning techniques created a significant increase in intelligent systems, especially in the medical field. Among mental problems, autism is a dangerous neurodevelopmental disorder and it needs to be diagnosed early because of the malleability of child brain development. In our study, we focused on autism detection by using the Vietnamese facial child image and studied the role of international data and Vietnamese data when applying deep learning approach to diagnose autism. To do that, we proposed different strategies based on our hypothesis about factors of the transfer learning and training set types. To conduct the experiment, we prepared a Vietnamese facial child image set from several kindergartens in Ho Chi Minh City, Vietnam and we applied different deep architectures such as ResNet, DenseNet, and AlexNet in the autism classification experiment with both Vietnamese and international facial child images. We analyzed important factors from the experiment results with area under the curve (AUC), accuracy, sensitivity, and specificity, including applying transfer learning and the appearance of Vietnamese data in the training set. Besides, we also discussed the difference of international and Vietnamese data domains. The exposure of data distribution differences in the proposed strategies also highlights the importance of collecting facial data of Vietnamese children.
Volume: 15
Issue: 5
Page: 4762-4773
Publish at: 2025-10-01

Stock market liquidity: hybrid deep learning approaches for prediction

10.11591/ijai.v14.i5.pp3624-3633
Mariam Ait Al , Said Achchab , Younes Lahrichi
Predicting stock market liquidity especially in emerging or frontier financial markets, such as the Casablanca stock exchange (CSE), presents significant challenges given the relative narrowness and volatility of these markets. In this paper, we conduct a comprehensive study to address the predictions accuracy gaps between five main deep learning models: convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and two hybrid architectures, CNN-LSTM and CNN-BiLSTM. The proposed methodology focused on training and testing these models on historical data from the CSE, with precision on capturing both spatial and temporal market dynamics. The models were fine-tuned using key hyperparameters and validated on 20% of the dataset to ensure reliable results. The evaluation of performance was conducted using error metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The study demonstrates that the hybrid CNN-biLSTM model consistently outperformed all standalone and other hybrid models in predictive accuracy. This underscores the considerable promise of hybrid deep learning architectures for addressing the unique challenges of predicting stock market liquidity in volatile and emerging financial markets.
Volume: 14
Issue: 5
Page: 3624-3633
Publish at: 2025-10-01

Optimized convolution neural network with ant colony algorithm for accurate plant disease detection

10.11591/ijai.v14.i5.pp3724-3733
Shweta V. Bondre , Uma Yadav , Vipin D. Bondre , Poorva Agrawal
In India, agriculture is the primary source of income for half the people. Even in situations of fast population growth, agriculture supplies nourishment for all people. To provide food for the entire population, it is advised to detect plant diseases at an early stage. Plant leaf diseases are recognized using images of the affected leaves. Deep learning (DL) research seems to offer several opportunities for increased accuracy. Ant colony optimization with convolution-neural-network (ACO-CNN), a new deep learning technique for identifying and categorizing diseases, is presented in this article. Ant colony optimization (ACO) was used to examine the efficacy of disease diagnostics in plant leaves. The convolution neural network (CNN) classifier is used to remove texture, color, and leaf arrangement geometry from the input images. The ACO-CNN model outperformed the support vector machine (SVM) and CNN models in terms of precision, recall, and accuracy. CNN's rate is 81.6% as compared to SVM's 80% accuracy level. In the “ACO-CNN” approach, the F1-score, recall, and precision have higher rates as compared to other models, and the “F1-score” has the highest rate compared with other models since the ACO-CNN model has an accuracy rate of 91.00%.
Volume: 14
Issue: 5
Page: 3724-3733
Publish at: 2025-10-01

Enhanced classification of aromatic herbs using EfficientNet and transfer learning

10.11591/ijai.v14.i5.pp4123-4136
Samira Nascimento Antunes , Madalena De Oliveira Barbosa Divino , Luana Dos Santos Cordeiro , Fernanda Pereira Leite Aguiar , Marcelo Tsuguio Okano
Herbs have long been used for culinary and medicinal purposes, as well as in religious rituals, due to their essential oils and aromatic properties. However, distinguishing between aromatic and medicinal herbs based on visual characteristics alone can be challenging. With recent advances in computer vision, plant identification from images has seen significant growth, offering promising applications in several domains. This article aims to evaluate the classification of aromatic herbs using the EfficientNet convolutional neural network (CNN) technique with transfer learning. The methodology used is experimental research, systematically manipulating variables to observe their effects on the object of study. The researcher plays an active role in this process, rather than being a passive observer. Based on the results and the literature review, it is evident that the objective of this research was achieved, as despite the opportunities for improvement in training to achieve accuracy above 0.8, it was possible to evaluate the classification of aromatic herbs using EfficientNet CNN through the transfer learning technique.
Volume: 14
Issue: 5
Page: 4123-4136
Publish at: 2025-10-01

Multi-class stock market forecasting with deep learning models: an explainable artificial intelligence

10.11591/ijai.v14.i5.pp4342-4352
Chhaya Patel , Ashwin Raiyani
In this research, we investigated the influence of different deep learning techniques on time series stock market data, especially for all Nifty50 companies in the Indian stock market. Our proposed method of stock market prediction focused on multi-class classification with explainable artificial intelligence (XAI). Our proposed model incorporates convolutional neural network (CNN) for operational feature extraction and long short-term memory (LSTM) to capture time-based dependencies. Predicted value is classified with multiclass classes-very bullish, bullish, neutral, bearish, very bearish signals for all Nifty50 stocks. The model integrates essential technical indicators to find patterns from basic price data. XAI techniques are also used to find feature contributions to model prediction. It improves the clarity of the model’s administrative procedure by figuring out how technical indicators influence stock estimates. The outcomes highlight the model’s ability to generate actionable trading signals, reinforced by performance progress metrics, contributing to more well-informed and planned venture decisions. The proposed model reveals greater performance, reaching an average accuracy of 96%, beating LightGBM at 89%, random forest at 85%, and support vector machine at 60%.
Volume: 14
Issue: 5
Page: 4342-4352
Publish at: 2025-10-01
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