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29,939 Article Results

EMG-based hand gesture classification using Myo Armband with feedforward neural network

10.11591/ijeecs.v39.i1.pp159-166
Sofea Anastasia Mohd Said , Norashikin M. Thamrin , Megat Syahirul Amin Megat Ali , Mohamad Fahmi Hussin , Roslina Mohamad
This paper presents the development of an electromyography (EMG)-based hand gesture identification system for remote-controlled applications. Even though the Myo Armband is no longer commercially supported, the research discusses its use in EMG data collecting. Open-source libraries were utilized to capture EMG data from this device to solve this problem. Using the developed data acquisition platform, data was collected from 30 participants who performed three (3) gestures - a fist, an open hand, and a pinch. The energy spectral density (ESD) and power ratio (pRatio) were extracted to describe gesture-specific patterns. A feedforward neural network (FFNN) was implemented for classification, initially configured with 10 hidden neurons and later optimized to 40 neurons to improve the performance. The box plot analysis showed channels CH1, CH4, CH5, and CH7 as the most significant for enhancing classification accuracy. The optimized FFNN achieved 80% and 70% for the training and testing accuracies, respectively. However, the results suggest that implementing a systematic protocol during data acquisition to reduce signal overlap between movements could improve classification accuracy. In conclusion, the study successfully developed an open-source EMG data acquisition platform for MYO Armband and demonstrated acceptable hand gesture recognition using an optimized FFNN.
Volume: 39
Issue: 1
Page: 159-166
Publish at: 2025-07-01

Context dependent bidirectional deep learning and Bayesian gaussian auto-encoder for prediction of kidney disease

10.11591/ijeecs.v39.i1.pp387-398
Jayashree M , Anitha N
Chronic kidney disease (CKD) has emerged as a significant global health issue, leading to millions of premature deaths annually. Early prediction of CKD is crucial for timely diagnosis and preventive measures. While various deep learning (DL) methods have been introduced for CKD prediction, achieving robust quantification results remains challenging. To address this, we propose the context-dependent bi-directional DL and Bayesian gaussian autoencoder (CDBDP-BGA) method for CKD prediction. This approach utilizes clinical parameters and symptoms from a structured dataset. By incorporating context dependence into the bi-directional long short-term memory (Bi-LSTM) model, CDBDP-BGA efficiently redistributes the representation of information, enhancing its modeling capabilities. Feature selection is optimized using a BGA-based algorithm, which employs the Bayesian gaussian function. The SoftMax activation function classifies CKD into five distinct stages based on estimated-glomerular filtration-rate (eGFR), considering both symptoms (texture and numerical features) and clinical parameters (age, sex, and creatinine). Simulation results using two datasets demonstrate that CDBDP-BGA outperforms conventional methods, achieving 97.4% accuracy without eGFR and 98.7% with eGFR.
Volume: 39
Issue: 1
Page: 387-398
Publish at: 2025-07-01

Low-resolution image quality enhancement using enhanced super-resolution convolutional network and super-resolution residual network

10.11591/ijeecs.v39.i1.pp634-643
Mohammad Faisal Riftiarrasyid , Rico Halim , Andien Dwi Novika , Amalia Zahra
This research explores the integration of enhanced super-resolution convolutional network (ESPCN) and super-resolution residual network (SRResNet) to enhance image quality captured by low-resolution (LR) cameras and in internet of things (IoT) devices. Focusing on face mask prediction models, the study achieves a substantial improvement, attaining a peak signal-to-noise ratio (PSNR) of 28.5142 dB and an execution time of 0.34704638 seconds. The integration of super-resolution techniques significantly boosts the visual geometry group-16 (VGG16) model’s performance, elevating classification accuracy from 71.30% to 96.30%. These findings highlight the potential of super-resolution in optimizing image quality for low-performance devices and encourage further exploration across diverse applications in image processing and pattern recognition within IoT and beyond.
Volume: 39
Issue: 1
Page: 634-643
Publish at: 2025-07-01

Random forest method for predicting discharge current waveform and mode of dielectric barrier discharges

10.11591/ijeecs.v39.i1.pp101-109
Laiadi Abdelhamid , Chentouf Abdellah , Ezziyyani Mostafa
This study addresses the classification of Homogeneous and Filamentary discharge modes in dielectric barrier discharge (DBD) systems and predicts the Homogeneous current waveform using machine learning (ML). The motivation stems from the need for accurate modelling in non-thermal plasma systems. The problem tackled is distinguishing between these two modes and predicting the current waveform for Homogeneous discharge. A random forest classification algorithm is applied, using experimental features such as applied voltage, frequency, gas gap, dielectric material, and gas type. An exponential model is proposed for the discharge current, with Gaussian regression transforming the model’s parameters. The classification results are evaluated through a confusion matrix, showcasing 80% accuracy in distinguishing discharge modes. The regression analysis reveals strong Pearson correlation coefficients between predicted and experimental waveforms. In conclusion, the results demonstrate the efficacy of ML techniques in enhancing DBD system modelling, though improvements can be made by expanding the dataset and refining feature selection for better classification and prediction performance.
Volume: 39
Issue: 1
Page: 101-109
Publish at: 2025-07-01

BFT water color classification in tilapia aquaculture using computer vision

10.11591/ijeecs.v39.i1.pp497-508
Bondan Suwandi , Sakinah Puspa Anggraeni , Toto Bachtiar Palokoto , Budi Sulistya , Wisnu Sujatmiko , Reza Septiawan , Nashrullah Taufik , Arief Rufiyanto , Arif Rahmat Ardiansyah
Biofloc technology (BFT) is one of the most promising aquaculture cultivation methods in the modern aquaculture era because of its high efficiency level, especially in water and fodder use. Usually, the general condition of the biofloc can be known from the color of the water. By utilizing the vision sensor, BFT color identification can be done automatically, which helps cultivators find out their BFT system’s condition. In this research, a classification was made for the watercolor of the BFT Tilapia system based on the microbial community color index (MCCI) value and the initial cultivation conditions where algae and nitrifying bacteria had not developed significantly. The color classifications of the bioflocs are clear, green, browngreen, green-brown, and deep-brown. Clear color is the new classification to indicate BFT water conditions in the initial cultivation phase. Further, two computer vision algorithm methods are introduced to classify the color of BFT system water. The first method combines the B/W algorithm and MCCI calculations, while the second algorithm uses the Manhattan distance algorithm approach. From the experiments that have been carried out, both computer vision algorithms methods for classifying biofloc colors have shown promising results.
Volume: 39
Issue: 1
Page: 497-508
Publish at: 2025-07-01

Enhanced performance and efficiency of robotic autonomous procedures through path planning algorithm

10.11591/ijeecs.v39.i1.pp214-224
Raman Latha , Saravanan Sriram , Balu Bharathi , John Bennilo Fernandes , Ayalapogu Ratna Raju , Kannan Boopathy , Subbiah Murugan
To optimize surgical routes for better patient outcomes and more efficient operations, we want to test how well these algorithms work. Finding the best algorithms for different types of surgeries and seeing how they affect things like time spent in surgery, precision, and patient safety is the goal of this exhaustive study. By shedding light on the effectiveness of route planning algorithms, this work aspires to aid in the development of autonomous robotic surgery. To find out how well various algorithms work in actual surgical settings; this study compares them. The results of this work have the potential to enhance robotic surgery efficiency and improve surgical outcomes by informing the creation of more efficient route planning algorithms. The overarching goal of this study is to provide evidence that autonomous robotic surgery can benefit from using sophisticated route planning algorithms, which might lead to more accurate, faster, and safer procedures. The surgical patient dataset exhibits a wide variety of medical variables, including ages 38–62, weight 65–85 kg, height 160–180 cm, blood pressure 110–140/90 mm Hg, heart rate 70–85 bpm, hemoglobin 12–14 g/DL, and body mass index (BMI) 25.4–29.4.
Volume: 39
Issue: 1
Page: 214-224
Publish at: 2025-07-01

An innovative image encryption scheme integrating chaotic maps, DNA encoding and cellular automata

10.11591/ijeecs.v39.i1.pp710-719
Gaverchand Kukaram , Venkatesan Ramasamy , Yasmin Abdul
In the current digital era, securing image transmission is crucial to ensure data integrity, prevent tampering, and preserve confidentiality as images traverse unsecured channels. This paper presents an innovative encryption scheme that synergistically combines a two-dimensional (2-D) logistic map, deoxyribonucleic acid (DNA) encoding, and 1-D cellular automata (CA) rules to significantly bolster encryption robustness. The proposed model initiates with the generation of a key image via the 2-D logistic map, yielding intricate chaotic sequences that fortify the encryption mechanism. DNA cryptography is employed to amplify randomness through diffusion properties, providing robust defense against various cryptographic attacks. The integration of 1-D CA rules further intensifies encryption complexity by iteratively processing DNA-encoded sequences. Experimental results substantiate that the proposed encryption scheme demonstrates exceptional endurance against a vast spectrum of attacks, affirming its superior security.
Volume: 39
Issue: 1
Page: 710-719
Publish at: 2025-07-01

Virtual learning environment on satisfaction and academic performance of students in institutions of higher learning

10.11591/ijeecs.v39.i1.pp258-271
Odunayo Dauda Olanloye , Peter Adebayo Idowu , Abidemi Emmanuel Adeniyi , Afolake Afusat Badmus , Oluwasegun Julius Aroba
As a result of the COVID-19 outbreak in 2020, education institutions across the world had to come to a functional standstill since they had to protect their students from viral exposures thereby affecting academic activities. However, several institutions had to adopt online virtual learning environments (VLE) using basic information and communication technology tools to provide platforms for teaching and learning thereby mitigating the effects of the pandemic on the students. This study was focused on the identification of the various types of VLE tools that were adopted alongside the impact that these tools had on learning satisfaction and the academic performance of students of higher learning in Nigeria. This study adopted a purposive simple random selection of undergraduate students of the department of computer science who had adopted the use of VLE to learn during the period of the pandemic. The results of the study showed that the most popular VLE tools were Zoom, Google Classroom, WhatsApp, Telegram, Coursera, Google Forms and learning management systems (LMS) while the least popular VLE tools were Microsoft Teams, Moodle/Edmondo, and Google Meet. The results showed that the students agreed to their behavioral intention to use VLE, the impact of VLE on learning satisfaction, and the impact of VLE on academic performance alongside the existence of a positive correlation among the research variables.
Volume: 39
Issue: 1
Page: 258-271
Publish at: 2025-07-01

RIBATS: RSSI-based adaptive tracking system with ASEKF for indoor WSN

10.11591/ijeecs.v39.i1.pp225-234
Rafina Destiarti Ainul , Hendi Wicaksono Agung
Wireless indoor tracking systems face challenges due to environmental conditions and signal attenuation, affecting location accuracy, crucial in wireless sensor network (WSN) applications. Many tracking techniques rely on specific path loss models proposed by previous researches, but these models are susceptible to changes in environmental conditions, impacting estimation outcomes. In order to solve these problems, this paper propose adaptive tracking system using received signal strength indicator (RSSI) measurement parameter called as RIBATS. Adaptive in this system refers to the reliability of an algorithm for obtaining the accurate location without any path loss modelling at dynamic indoor environments. The enhancement of weighted centroid localization (eWCL) scheme calculates the location estimation only using RSSI data measurement without propagation characterisic determination. However, estimation result from eWCL still have high error at certain area. Hence, by defining a multiplier factor as adaptive scaled to the covariance matrix of EKF can eliminate distortion effects from eWCL called as adaptive scaled extended Kalman filter (ASEKF) algorithm. An effective variance estimation algorithm for adaptive indoor tracking system using eWCL and ASEKF combination achieve 0.82 meters mean square error (MSE) value with 55.67% error reduction. Then, without using multiplier scale factor at EKF algorithm only reduce previous eWCL at 3.78% with 1.78 meters MSE value.
Volume: 39
Issue: 1
Page: 225-234
Publish at: 2025-07-01

Predictive modeling for equity trading using sentiment analysis

10.11591/ijeecs.v39.i1.pp575-584
Chetan Gondaliya , Abhishek Parikh
Warren Buffett’s investment philosophy highlights the importance of generating wealth through available capital, but investors require more advanced tools for informed decision-making. Current research is focused on developing a modeling technique that leverages computer algorithms, including sentiment analysis. This method evaluates public sentiment about companies through social media, aiding investors in identifying promising stocks and safeguarding their wealth against unfavorable market conditions. In India, the banking, real estate, and pharmaceutical sectors are among the most robust and rapidly growing industries; however, deciding to invest in these sectors remains debatable. To address this, the proposed study aims to develop a hybrid prediction model that combines sentiment and technical analysis to uncover short-term trading opportunities. This model utilizes a two-layer ensemble stacking technique, training three distinct machine learning algorithms in the first layer and aggregating their outputs in the second layer. The proposed model significantly outperforms traditional methods in terms of accuracy, enabling investors to make more confident and profitable decisions in the Indian stock market.
Volume: 39
Issue: 1
Page: 575-584
Publish at: 2025-07-01

Phasor measurement unit optimization in smart grids using artificial neural network

10.11591/ijeecs.v39.i1.pp625-633
Ashpana Shiralkar , Suchita Ingle , Haripriya Kulkarni , Poonam Mane , Shashikant Bakre
The wide area measurements systems (WAMS) play a vital role in the operation of smart grids. The phasor measurement units (PMU) or synchrophasors are one of the principle components under WAMS. PMU in a smart grid converts power system signals into phasor from voltage and current which enhances the observability of the power system. A variety of operations is performed by the PMUs such as adaptive relaying, instability prediction, state estimation, improved control, fault and disturbance recording, transmission and generation modeling verification, wide area protection and detection of fault location. The PMUs can improve the performance of grid operations and monitoring. Thus, PMU optimization is very necessary to achieve the desired power system observability. The performance of the PMUs can be optimized using artificial intelligence (AI) technologies. The novice method of monitoring maximum power transfer using PMUs equipped with artificial neural networks has been discussed in this paper. In this paper, a two-bus system model is developed that can be generalized to multiple bus systems. The proposed method is novel, simple, feasible, and cost effective for smart grids.
Volume: 39
Issue: 1
Page: 625-633
Publish at: 2025-07-01

New technic of transfer learning for detecting epilepsy by EfficientNet and DarkNet models

10.11591/ijeecs.v39.i1.pp345-352
Fatima Edderbali , Hamid El Malali , Elmaati Essoukaki , Mohammed Harmouchi
Epileptic seizures are one of the most prevalent brain disorders in the world. Electroencephalography (EEG) signal analysis is used to distinguish between normal and epileptic brain activity. To date, automatic diagnosis remains a highly relevant and significant research topic which can help in this task, especially considering that such diagnosis requires a significant amount of time to be carried out by an expert. As a result, the need for an effective seizure approach capable to classify the normal and epileptic brain signal automatically is crucial. In this perspective, this work proposes a deep neural network approach using transfer learning to classify spectrogram images that have been extracted from EEG signals. Initially, spectrogram images have been extracted and used as input to pre-trained models, and a second refinement is performed on certain feature extraction layers that were previously frozen. The EfficientNet and DarkNet networks are used. To overcome the lack of data, data augmentation was also carried out. The proposed work performed excellently, as assessed by multiple metrics, such as the 0.99 accuracy achieved with EfficientNet combined with a support vector machine (SVM) classifier.
Volume: 39
Issue: 1
Page: 345-352
Publish at: 2025-07-01

Advanced generalized integrator based phase lock loop under complex grid condition: a comparative analysis

10.11591/ijeecs.v39.i1.pp23-32
Poonam Tripathy , Banishree Misra , Byamakesh Nayak
Integration of renewable energy systems (RESs) to the grid leads to various power quality issues. A proper control approach for the interfaced inverter is required to mitigate the uncertainties caused in the grid due to the RESs association to maintain the grid stability. The presence of harmonics and DC offset in the input grid voltage of a phase lock loop (PLL) leads to inaccurate phase estimation due to fundamental frequency oscillations. Though many advanced generalized integrator (GI) based PLLs have been developed still there is a need for a robust PLL for synchronization with faster dynamic response, both the harmonics and DC offset rejection ability with precise estimation. This paper proposes some simple yet effective advanced PLLs employing low pass filters (LPFs) in the existing GI based PLLs for faster and accurate phase angle estimation for seamless synchronization under complex grid circumstances. These advanced generalized integrators with LPFs (GI-LPF) based PLLs will provide enhanced and robust synchronization for the grid integrated RESs thereby addressing multiple power quality issues like voltage unbalance, harmonics and DC offsets. The simulation based comparative analysis of the proposed controllers confirm their effective disturbance rejection capability under complex grid conditions by providing advanced and precise response.
Volume: 39
Issue: 1
Page: 23-32
Publish at: 2025-07-01

Smart enterprise architecture framework for developing patent office

10.11591/ijeecs.v39.i1.pp681-690
Yoga Prihastomo , Harjanto Prabowo , Agung Trisetyarso , Haryono Soeparno
Technology and communication’s impact on daily life makes innovation vital for economic growth, highlighting prizing intellectual property (IP) asset protection and management. Patent office, pivotal custodians of legal frameworks and repositories of IP assets, grapples with significant challenges, and backlogs stemming from escalating patent applications and outdated processes. Patent office encounters the challenge of balancing innovation and IP protection because of the convergence of rapid advancements in technologies, for instance, AI, and blockchain. This research employs a design science research methodology to generate a tailored framework addressing these multifaceted challenges. The proposed smart enterprise architecture (SEA) framework offers a strategic, multidimensional approach to modernizing the patent office. It integrates principles from enterprise architecture, information systems management, and IP law, emphasizing efficiency, scalability, and security. The framework leverages the quadruple helix model, fostering collaboration between government, industry, academia, and civil society to enhance stakeholder engagement and innovation ecosystems. Optimizing patent office functions and adapting to IP management’s evolution, the SEA framework integrates technology and organizational goals for a comprehensive approach.
Volume: 39
Issue: 1
Page: 681-690
Publish at: 2025-07-01

Torque ripple minimization and performance enhancement of switched reluctance motor for electric vehicle application

10.11591/ijeecs.v39.i1.pp70-78
Yogesh B. Mandake , Deepak S. Bankar , Amit L. Nehete
Switched reluctance motors (SRMs) are an attractive choice for electric vehicle (EV) applications but suffer from certain limitations, such as high torque ripple and acoustic noise. This paper presents ongoing research and development activity details to enhance the performance of SRMs for EV applications. The poor performance of a conventional SRM which is available in market with a rating of 8/6 poles, 48 V, 500 W, and 2,000 rpm is tested. A motor model of the same rating is developed using ANSYS Maxwell software. Motor performance parameters important for EV applications, such as efficiency, rated torque and torque ripple are compared with the conventional motor. One novel technique to reduce the torque ripple of SRM is discussed along with the results. Torque ripple of developed software model is reduced by 24.52% without a reduction in the efficiency and rated torque of the motor. The performance of the developed SRM software model is better compared to conventional SRMs available in the market. 2D and 3D models of SRM were presented using ANSYS Maxwell software.
Volume: 39
Issue: 1
Page: 70-78
Publish at: 2025-07-01
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