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31,456 Article Results

Two RC model and parameter estimation of lithium-ion battery

10.11591/ijeecs.v37.i2.pp730-739
Girisha Joshi , Lakshmi Narayana Valluru , Amol Prakash Khade
Electric vehicles are the trend of this decade. Frequent high-power requirements of electric vehicles, make the batteries discharge at higher C rate. Discharging at a higher C rate will lead to higher heat production leading to destruction and explosion of the battery. To optimize the charging and discharging C rates considering both safety and performance factors, battery management system (BMS) is used as an eternal component of power source. To estimate the state of charge (SOC), which is an essential component of BMS, accurate battery modelling is required. Two RC model is one of the most used lithium-ion battery model, due to its simplicity and accuracy. The equivalent circuit parameters, resistances and capacitance do change with SOC and temperatures. This paper focuses on estimation of equivalent circuit parameters for a wide range of temperatures and SOCs ranging from -20 degree celsius to +25 degree celsius and 100 to 0 respectively. We have developed two RC model for Panasonic 18650PF and estimated the parameters of the model using hybrid pulse power characterization (HPPC) data. MATLAB based parameter estimator is used in determining the equivalent circuit parameters.
Volume: 37
Issue: 2
Page: 730-739
Publish at: 2025-02-01

Analysis of LLC resonant converter performance with PIDD2 controller for electric vehicle application

10.11591/ijeecs.v37.i2.pp749-757
Sathya K. , K. P. Guruswamy
The key uses of the latest developments is electric vehicles (EV’s). As a result, several researchers were drawn to EV’s control to propose appropriate controllers and predicted that control engineers face a challenge when it comes to regulating the LLC resonant converter output voltage. In this regard, the study proposes a PID Type modified controller for regulation of voltage across output in LLC resonant converter. The design and control procedure of this modified proportional integral derivative double derivative (PIDD2) is explained along with EDF modeling in LLC resonant converter. This work proposes to use two controllers to drive the voltage output of a resonant converter LLC to constantly track the desired value. Proportional integral derivative controller (PID) is the first, while the PIDD2 method is the foundation of the second. Every controller has undergone simulation testing and the results are compared based on how the evaluated controllers respond dynamically in accordance with settling time, rising time and overshoot.
Volume: 37
Issue: 2
Page: 749-757
Publish at: 2025-02-01

An improved reptile search algorithm-based machine learning for sentiment analysis

10.11591/ijece.v15i1.pp755-766
Nitesh Sureja , Nandini M. Chaudhari , Jalpa Bhatt , Tushar Desai , Vruti Parikh , Sonia Panesar , Heli Sureja , Jahnavi Kharva
The rapid growth of mobile technologies has transformed social media, making it crucial for expressing emotions and thoughts. When making significant decisions, businesses and governments can benefit from understanding public opinion. This information makes sentiment analysis vital for understanding public sentiment polarity. This study develops a hyper tuned deep learning model with swarm intelligence and many approaches for sentiment analysis. convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), CNN-LSTM, BERT-LSTM, and BERT-CNN are the six deep learning models of the sentiment analysis using deep learning with reinforced learning based on reptile search algorithm (SA-DLRLRSA) model. The reptile search algorithm, an enhanced swarm intelligence algorithm (SIA), optimizes deep learning model hyper parameters. Word2Vec word embedding is used to convert textual input sequences to representative embedding spaces. Pre-trained Word2Vec embedding is also used to address issue of unbalanced datasets. Experimental results demonstrate that the SA-DLRLRSA model works best with accuracies of 93.1%, 94.7%, 96.8%, 96.3%, 97.2%, and 98.3% utilizing CNN, LSTM, BERT, CNN-LSTM, BERT-CNN, and BERT-LSTM.
Volume: 15
Issue: 1
Page: 755-766
Publish at: 2025-02-01

Breast cancer prediction using genetic algorithm and sand cat swarm optimization algorithm

10.11591/ijeecs.v37.i2.pp849-858
Velu Sangeetha , Maniyambadi Vaneeta , Arjuna Mamatha , Muniya Shoba , Sugatur Ramamurthy Deepa , Velusamy Sujatha , Shanmugam Sujatha
Breast cancer is the second leading type of cancer, which is mainly found in women and which increases the death rate among women. Early detection and diagnosis of breast cancer can reduce its occurrence and the death rate. Unfortunately, even if cancer treatment is initiated quickly after diagnosis, cancer may relapse because cancer cells may continue to exist in the body, which is also a major problem faced by women who fear facing the same treatment twice. So, detecting cancer at its early stage and predicting the recurrence of it is a major issue in the medical field that needs to be solved. Machine learning (ML) algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and voting classifier (VC) are used for breast cancer prediction. Due to high-dimension data, the predicted results using Machine learning algorithms will increase the errors and decrease the accuracy. So, bioinspired algorithms such as the genetic algorithm (GA) and sand cat swarm optimization (SCSO) are used to reduce the data dimension. Convolutional neural network (CNN) is used for feature extraction from the image dataset. CNN algorithms are used for feature selection, which selects the important features for classification and prediction by applying 10 cross-validation methods. The proposed model using bioinspired optimization algorithms outcomes will yield high accuracy and the best solution.
Volume: 37
Issue: 2
Page: 849-858
Publish at: 2025-02-01

Accurate prediction of chronic diseases using deep learning algorithms

10.11591/ijai.v14.i1.pp570-583
Ronald S. Cordova , Rolou Lyn R. Maata , Malik Jawarneh , Marwan I. Alshar'e , Oliver C. Agustin
In this paper, the researchers studied the effects of different activation functions in hidden layers and how they impact the overfitting or underfitting of the model in the multiclass prediction of chronic diseases. This paper also evaluated the effects of varying the number of layers, and hyperparameters and its impact on the accuracy of the model and its generalization capabilities. It was found that exponential linear unit (ELU) does not have a significant advantage over rectified linear unit (ReLU) when used as an activation function in the hidden layer. Additionally, the performance of softmax function, when used in the output layer, is the same as a classic sigmoid output activation function. In terms of the ability of the model to generalize, the researchers achieved a classification accuracy of 100% when the trained model was used to predict unseen data. Through this research, the researchers should be able to assist medical professionals and practitioners in Oman in the validation and diagnosis of chronic diseases in clinics and hospitals.
Volume: 14
Issue: 1
Page: 570-583
Publish at: 2025-02-01

High body temperature detection solution through touchless machine for health monitoring

10.11591/ijai.v14.i1.pp166-171
Siddharth Swami , Lalit Mohan Joshi , Mohammed Ismail Iqbal , Meera Sharma , Amar Jeet Rawat , Sameer Dev Sharma , Rajesh Singh
The demand for reliable health monitoring systems has surged in today's health-conscious society. Body temperature monitoring is crucial for preserving health and preventing infectious disease outbreaks. In this study an Arduino uno hardware board with a touchless temperature sensor is proposed to detect elevated body temperature, indicating fever and early signs of illness. The system prioritizes real-time health surveillance, accessibility, and usability, blending seamlessly with normal life. Arduino's versatility allows the system to function covertly, uphold privacy and autonomy, and foster wellbeing. The goal is to highlight the system's ability to function covertly, uphold privacy and autonomy, and foster wellbeing. This technology exemplifies the synergy between personal wellness and contemporary technologies, offering a useful and adaptable fever detection solution for various contexts, including homes and public areas.
Volume: 14
Issue: 1
Page: 166-171
Publish at: 2025-02-01

Integration of web scraping, fine-tuning, and data enrichment in a continuous monitoring context via large language model operations

10.11591/ijece.v15i1.pp1027-1037
Anas Bodor , Meriem Hnida , Najima Daoudi
This paper presents and discusses a framework that leverages large-scale language models (LLMs) for data enrichment and continuous monitoring emphasizing its essential role in optimizing the performance of deployed models. It introduces a comprehensive large language model operations (LLMOps) methodology based on continuous monitoring and continuous improvement of the data, the primary determinant of the model, in order to optimize the prediction of a given phenomenon. To this end, first we examine the use of real-time web scraping using tools such as Kafka and Spark Streaming for data acquisition and processing. In addition, we explore the integration of LLMOps for complete lifecycle management of machine learning models. Focusing on continuous monitoring and improvement, we highlight the importance of this approach for ensuring optimal performance of deployed models based on data and machine learning (ML) model monitoring. We also illustrate this methodology through a case study based on real data from several real estate listing sites, demonstrating how MLflow can be integrated into an LLMOps pipeline to guarantee complete development traceability, proactive detection of performance degradations and effective model lifecycle management.
Volume: 15
Issue: 1
Page: 1027-1037
Publish at: 2025-02-01

Road feature extraction from LANDSAT-8 operational land imager images using simplified U-Net model

10.11591/ijece.v15i1.pp328-336
Sama Lenin Kumar Reddy , Chandu Venkateswara Rao , Pullakura Rajesh Kumar
Automatic road feature extraction from the remote sensing (RS) imagery has a significant role in various applications such as urban planning, transportation management, and environmental monitoring. In this paper, propose a method based on the U-Net model to extract the road features from the LANDSAT-8 operational land imager (OLI) images. This method aims to extract road features in OLI images that appear as curvilinear features and roads with widths greater than 25 meters, which are mostly covered within a single pixel of the OLI resolution of multi-spectral images. The U-Net architecture is well-known for its effectiveness in image segmentation tasks. However, to optimize the complexity in the U-Net model, simplified the architecture while retaining its key components and principles. The proposed model by decreasing the convolution layers and the parameters which are involved to optimize the model called as simplified U-Net model. To train this model, the label images were generated for LANDSAT-8 OLI images, by using the saturation based adaptive thresholding and morphology (SATM) method. This reduces the efforts to draw the labels in the vector format labels and convert to raster images. The model is able to effectively generate weights, which are able to extract the road features. This model weights applied on the OLI images which covers the urban and rural areas of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper.
Volume: 15
Issue: 1
Page: 328-336
Publish at: 2025-02-01

Imitation of the human upper limb by convolutional neural networks

10.11591/ijai.v14.i1.pp193-203
Paula Useche Murillo , Robinson Jimenez Moreno , Javier Eduardo Martinez Baquero
The paper outlines the development of an algorithm focused on imitating movements of a human arm and replicating strokes generated by the user's hand within a working environment. The algorithm was crafted to discern the position of either the user's left or right arm, tracking each section (fingers, wrist, elbow, and shoulder) through a detection and tracking system. These movements are then replicated onto a virtual arm, simulating the actions of a cutting tool, generating strokes as it moves. Convolutional neural networks (CNNs) were employed to detect and classify each arm section, while geometric analysis determined the rotation angles of each joint, facilitating the virtual robot's motion. The stroke replication program achieved an 84.2% accuracy in stroke execution, gauged by the closure of the polygon, distance between initial and final drawing points, and generated noise, which was under 10%, with a 99% probability of drawing a closed polygon. A Fast region-based convolutional neural network (Fast R-CNN) network detected each arm section with 60.2% accuracy, producing detection boxes with precision ranging from 17% to 59%. Any recognition shortcomings were addressed through mathematical estimation of missing points and noise filters, resulting in a 90.4% imitation rate of human upper limb movement.
Volume: 14
Issue: 1
Page: 193-203
Publish at: 2025-02-01

Detect and envision of pandemic disease exposure using CNN

10.11591/ijeecs.v37.i2.pp948-958
Ch. Rupa , Kanakam Siva Rama Prasad , Aremanda Lakshmi Rajeswari , Elika Sambasiva Rao , Mohammad Sirajuddin
COVID-19 has emerged as a pandemic, affecting millions globally with its high transmission rate, especially in colder climates. The virus's multiple mutations have made it progressively harder to detect and manage. Despite widespread awareness of preventive measures such as masks and sanitizers, early detection remains critical. Traditional methods like blood tests are time-consuming, and existing studies utilizing fuzzy K-means clustering, principal component analysis (PCA), stochastic discriminant analysis (SDA), decision trees (DT), and support vector machines (SVM) have faced limitations, including small datasets, insufficient accuracy, inadequate medical data, weak methodologies, and failure to consider primary symptoms. This work proposes a deep learning (DL) convolutional neural network (CNN) architecture utilizing CT scan images of the lungs for the rapid and accurate identification of COVID-19 infections. The approach leverages the Visual Geometry Group 16 (VGG16) model to extract significant features, such as size and color differences, from computed tomography (CT) scan images, facilitating a swift and precise diagnosis. The VGG16 model, implemented using the Keras library on top of TensorFlow, processes the preprocessed images through neural network layers to classify the images as COVID-19 positive or negative. The proposed model demonstrates a high accuracy rate of 94.12%, indicating that this method is both efficient and reliable for detecting COVID-19, offering a significant improvement over conventional diagnostic techniques and existing studies.
Volume: 37
Issue: 2
Page: 948-958
Publish at: 2025-02-01

Tomato plant disease prediction system with a new framework SSMAN using advanced deep learning techniques

10.11591/ijece.v15i1.pp940-948
Saravanan Madderi Sivalingam , Lakshmi Devi Badabagni
Agriculture plays a pivotal role in India's economy, and the timely detection of plant infections is essential to safeguard crops and prevent further spread of diseases. The conventional approach involves manual inspection of plant leaves to identify the specific type of disease, a task typically carried out by farmers or plant pathologists. In previous studies, you only look once (YOLO) and faster region-based convolutional neural network (R-CNN), machine learning algorithms were applied to datasets for detecting objects on tomato leaves which includes a total of images 2403 and got accuracies of 86 and 82 percent. In this paper, a deep convolutional neural network (DCNN) model proposed with a new framework separate, shift, and merge based AlexNet50 algorithm (SSMAN) is used to predict the disease at an earlier stage with higher accuracy. Among various pre-trained deep models, AlexNet emerges as the top performer, achieving the highest accuracy in disease classification. SSMAN can address anomalies in images by employing a class decomposition approach to scrutinize class boundaries. AlexNet exhibits a notable accuracy of 98.30% in successfully identifying tomato leaf diseases from images, with pre-trained new framework, superior to the original AlexNet architecture as well as traditional classification methods with other algorithms.
Volume: 15
Issue: 1
Page: 940-948
Publish at: 2025-02-01

Performance analysis of 10 machine learning models in lung cancer prediction

10.11591/ijeecs.v37.i2.pp1352-1364
Joselyn Zapata-Paulini , Michael Cabanillas-Carbonell
Lung cancer is one of the diseases with the highest incidence and mortality in the world. Machine learning (ML) models can play an important role in the early detection of this disease. This study aims to identify the ML algorithm that has the best performance in predicting lung cancer. The algorithms that were contrasted were logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), gaussian Naive Bayes (GNB), multinomial Naive Bayes (MNB), support vector classifier (SVC), random forest (RF), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and gradient boosting (GB). The dataset used was provided by Kaggle, with a total of 309 records and 16 attributes. The study was developed in several phases, such as the description of the ML models and the analysis of the dataset. In addition, the contrast of the models was performed under the metrics of specificity, sensitivity, F1 count, accuracy, and precision. The results showed that the SVC, RF, MLP, and GB models obtained the best performance metrics, achieving 98% accuracy, 98% precision, and 98% sensitivity.
Volume: 37
Issue: 2
Page: 1352-1364
Publish at: 2025-02-01

Hydrophobicity signal analysis for robust SARS-CoV-2 classification

10.11591/ijeecs.v37.i2.pp1294-1305
Mohammad Jamhuri , Mohammad Isa Irawan , Imam Mukhlash , Ni Nyoman Tri Puspaningsih
Rapid and accurate classification of viral pathogens is critical for effective public health interventions. This study introduces a novel approach using convolutional neural networks (CNN) to classify SARS-CoV-2 and non-SARS-CoV-2 viruses via hydrophobicity signal derived from DNA sequences. Conventional machine learning methods grapple with the variability of viral genetic material, requiring fixed-length sequences and extensive preprocessing. The proposed method transforms genetic sequences into image-based representations, enabling CNNs to handle complexity and variability without these constraints. The dataset includes 8,143 DNA sequences from seven coronaviruses, translated into amino acid sequences and evaluated for hydrophobicity. Experimental results demonstrate that the CNN model achieves superior performance, with an accuracy of over 99.84% in the classification task. The model also performs well with extended sequence lengths, showcasing robustness and adaptability. Compared to previous studies, this method offers higher accuracy and computational efficiency, providing a reliable solution for rapid virus detection with potential applications in bioinformatics and clinical settings.
Volume: 37
Issue: 2
Page: 1294-1305
Publish at: 2025-02-01

Enhancing malware detection capabilities using deep learning with advanced hyperparameter tuning

10.11591/ijeecs.v37.i2.pp985-994
Walid El Mouhtadi , Yassine Maleh , Soufyane Mounir
As the threat landscape evolves with sophisticated malware and advanced persistent threats (APTs), the need for effective detection solutions increases. Traditional methods, such as signature-based and heuristic analysis, struggle to keep up with rapidly changing malicious activities. While machine learning offers a promising approach, it often falls short due to the manual extraction and selection of features, leading to time-consuming and error-prone processes. This research introduces a novel malware detection solution leveraging deep learning and focusing on portable executable (PE) file analysis to address these weaknesses. By customizing the hyperparameters of artificial neural networks (ANN), convolutional neural networks (CNN), and recurrent neural networks (RNN), the proposed approach enhances detection capabilities. The primary objective is to overcome the limitations of traditional and machine learning methods by tailoring these deep learning algorithms. The methodology includes a comparative study to demonstrate the advantages of the customized approach over conventional methods. Key findings reveal the proposed solution’s superior performance, accuracy, and adaptability in combating evolving cyber threats. This research contributes to the development of robust and adaptive malware detection solutions.
Volume: 37
Issue: 2
Page: 985-994
Publish at: 2025-02-01

An efficient load-balancing in machine learning-based DC-DC conversion using renewable energy resources

10.11591/ijai.v14.i1.pp307-316
Kavitha Hosakote Shankara , Kavitha Hosakote Shankara , Mallikarjunaswamy Srikantaswamy , Sharmila Nagaraju , Sharmila Nagaraju
This paper introduces the machine learning-based DC-DC conversion algorithm (ML-DC2A), a pioneering machine learning (ML) approach designed to enhance load-balancing in DC-DC conversion systems powered by renewable energy sources. Traditional control strategies, such as pulse-width modulation (PWM), maximum power point tracking (MPPT), and basic voltage and current controls, are foundational yet often fall short in adapting to the rapid fluctuation’s characteristic of renewable energy supply. The ML-DC2A optimizes crucial performance indicators including conversion efficiency, reliability, adaptability to energy supply variability, and response time to changing loads. By leveraging predictive analytics and adaptive algorithms, it dynamically manages the conversion process, offering superior performance over traditional techniques. A notable drawback of conventional methods is their inability to anticipate and adjust to real-time changes in energy availability and demand, leading to inefficiencies and potential system instability. The proposed ML-DC2A addresses these challenges by incorporating a sophisticated ML framework that predicts future energy scenarios and adaptively adjusts system parameters to maintain optimal performance. Initial results highlight the transformative potential of integrating ML into renewable energy conversion systems, promising significantly enhanced efficiency and system resilience, thus marking a significant step forward in sustainable energy management.
Volume: 14
Issue: 1
Page: 307-316
Publish at: 2025-02-01
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