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28,451 Article Results

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

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

Secure data transmission towards mitigating potentially unknown threats in wireless sensor network

10.11591/ijeecs.v39.i1.pp523-530
Chaya Puttaswamy , Nandini Prasad Kanakapura Shivaprasad
Wireless sensor network (WSN) is known for its wider range of applications towards sensing physical attributes over human-inaccessible regions. With consistently rising concerns of security threats, WSN is the pivotal topic of network security. A literature review showcases the shortcomings of conventional data transmission schemes in WSN. This manuscript introduces an innovative approach to mitigating the potentially vulnerable and unknown threats. The implemented model promotes a group-based communication followed by a newly introduced threat onlooker node capable of identifying the malicious request of a newly designed adversary module. The scheme also hybridizes symmetric and asymmetric encryption at the end to cipher the aggregated data. The validation of the model is carried out considering standard scores of simulation parameters related to system variables. Further, the scheme has been compared with frequently adopted real-world encryption algorithms. Scripted in MATLAB, the model is assessed to confirm 35% of increased residual energy, 57% of better threat detection, 27% of enhanced throughput, and 68% of reduced processing time in contrast to existing secure data transmission schemes.
Volume: 39
Issue: 1
Page: 523-530
Publish at: 2025-07-01

Non-contact breathing rate monitoring using infrared thermography and machine learning

10.11591/ijeecs.v39.i1.pp669-680
Anadya Ghina Salsabila , Rachmad Setiawan , Nada Fitrieyatul Hikmah , Zain Budi Syulthoni
Monitoring vital physiological parameters such as breathing rate (BR) is crucial for assessing patient health. However, current contact-based measurement methods often cause discomfort, particularly in infants or burn patients. This study aims to develop a non-contact system for monitoring BR using infrared thermography (IRT). This approach permits to detects and tracks the nose from thermal video, extracts temperature variations into a breathing signal, and processes this signal to estimate BR. The estimated BR is then classified into three health categories (bradypnea/normal/tachypnea) using k-nearest neighbors (k-NN). To evaluate system accuracy and robustness, experiments were conducted under three conditions: (i) stationary breathing, (ii) breathing with head movements, and (iii) specific breathing patterns. Results demonstrated high consistency with contact-based photoplethysmography (PPG) measurements, achieving complement of the absolute normalized difference (CAND) index values of 94.57%, 93.71%, and 96.06% across the three conditions and mean absolute BR errors of 1.045 bpm, 1.259 bpm, and 0.607 bpm. The k-NN classifier demonstrated high performance with training, validation, and testing accuracies of 100%, 100%, and 99.2%, respectively. Sensitivity, specificity, precision, and F-measure results confirm system reliability for non-contact BR monitoring in clinical and practical settings.
Volume: 39
Issue: 1
Page: 669-680
Publish at: 2025-07-01

Using ResNet architecture with MRI for classification of brain images

10.11591/ijeecs.v39.i1.pp148-158
Subramanian Dhanalakshmi , Subramanian Arulselvi
A strong classification model that can correctly detect abnormalities and neurological disorders in brain images is the main goal. The focus of this research is on improving the accuracy of MRI brain image categorization using residual networks (ResNet) methods. Improving the model's capacity to extract complex characteristics from MRI images and achieving more accurate classification results is the aim of using ResNet architectures. By conducting extensive experiments and validating our results, our project aims to attain top-notch performance in brain image classification tasks. The goal is to help improve medical diagnosis and treatment planning. A secondary goal of the research is to determine if deep learning approaches have any use in radiology, with the hope that this will lead to better medical image analysis pipelines. The main objective is to make it easier to identify neurological problems early on, which will enhance patient outcomes and allow for more calculated treatment decisions. Results proved that the proposed ResNet system achieves 98.8% overall accuracy with 98.6% sensitivity and 99% specificity.
Volume: 39
Issue: 1
Page: 148-158
Publish at: 2025-07-01

Enhancing urban cyclist safety through integrated smart backpack system

10.11591/ijeecs.v39.i1.pp118-130
Sergio Gómez , Daniel Mejía , Fredy Martínez
The increasing adoption of bicycles as a sustainable mode of urban transportation has underscored the urgent need for enhanced safety measures for cyclists. This paper presents the development and implementation of an integrated smart backpack system designed to improve the safety and visibility of urban cyclists. The system leverages advanced technologies, including the ESP32 microcontroller, GPS modules, proximity sensors, and LED lighting, to create a semiautomatic solution that adapts to environmental conditions and cyclist behavior in real-time. Extensive testing under various conditions, including low visibility and adverse weather, demonstrated the system’s reliability in enhancing cyclist visibility and reducing accident risks. The smart backpack also features a userfriendly mobile application, providing real-time data on speed, distance, and location, which further contributes to rider safety. The results indicate significant potential for this technology to be widely adopted, offering a practical and effective solution to the growing safety concerns of urban cyclists. This work not only advances the field of wearable safety technologies but also sets the foundation for future innovations in smart transportation systems, contributing to safer and more sustainable urban mobility
Volume: 39
Issue: 1
Page: 118-130
Publish at: 2025-07-01

Web-Based Attacks Detection Using Deep Learning Techniques: A Comprehensive Review

10.11591/ijeecs.v39.i1.pp466-484
Lujain Nasser Alghofaili , Dina M. Ibrahim
Web applications are utilized extensively by a broad user base, and the services provided by these applications assist enterprises in enhancing the quality of their service operations as well as increasing their revenue or resources. To gain control of web servers, attackers will frequently attempt to modify the web requests that are sent by users from web applications. Attacks that are based on the web can be detected to help avoid the manipulation of web applications. In addition, a variety of research has offered many methods, one of which is artificial intelligence (AI), which is the method that has been utilized the most frequently to identify web-based attacks recently. When it comes to the protection of web applications, anomaly detection techniques used by intrusion prevention systems are preferred.  Deep learning, often known as DL, is going to be covered in this paper as anomaly-based web attack detection methods and machine learning techniques. With the purpose of organizing our selected techniques into a comprehensive framework that encourages future studies, we first explained the most concepts that related to web-based attacks detection, then we moved on to discuss the most prevalent web risks and may provide inherent difficulties for keeping web applications safe.  We classify previous studies on detecting web attacks into two categories: deep learning and machine learning. Lastly, we go over the features of the previously utilized datasets in summary form.
Volume: 39
Issue: 1
Page: 466-484
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

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

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

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

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

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

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

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
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