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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 111 Documents
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Search results for , issue "Vol 14, No 4: August 2024" : 111 Documents clear
Optimizing glaucoma diagnosis using fundus and optical coherence tomography image fusion based on multi-modal convolutional neural network approach Krishna, Nanditha; Kenchappa, Nagamani
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4005-4017

Abstract

A novel approach that combines segmented fundus images (FIs) and optical coherence tomography image (OCTIs) are presented here, by incorporating deep learning network (DLN) techniques, to address the imperative need for advanced diagnostic algorithms in detecting and classifying glaucoma. By combining these two images, glaucoma diagnoses are made to improve the accuracy with more reliability. Multi modal convolutional neural networks (MMCNNs) are proposed for automatically extracting discriminatory features from both segmented FIs and OCTIs, allowing for comprehensive ocular analysis. A significant improvement in glaucoma diagnosis is achieved through segmentation of both FIs and OCTIs, ensuring robustness generalization to diverse clinical scenarios, DLN models are trained on datasets encompassing a wide range of glaucoma cases. The integrated approach outperforms individual modalities in terms of early detection of glaucoma and accurate classification. This method demonstrates promising potential in early glaucoma detection due to its effectiveness. By combining segmented features from both FIs and OCTIs through MMCNNs, improved efficiency in diagnosing predominant ocular glaucoma disorder is achieved compared to existing methods. Within the scope of this research, GoogLeNet (GN) is applied to independently classify glaucoma (uni-modal) in segmented FIs and OCTIs, providing a basis for comparison with the evaluation of MMCNNs.
Comparison performance study of singly-fed and doubly-fed induction generators-based bond-graph wind turbines model Kadiman, Sugiarto; Yuliani, Oni
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3592-3606

Abstract

This paper consecrates to a comparative performance study of singly-fed and doubly-fed of induction generators thrusted by wind power turbine of similar generation capacity of 2.5 kW, and constant or variably wind speed. The singly-fed induction generator model could be represented using natural reference frame and doubly-fed induction generator model is described using a Park reference frame. Because of several physical domains existing in both induction generators like mechanical and electrical, modeling of generators is difficult, therefore the modeling based on physical methods takes a high credibility under these conditions. Among the procedures is Bond-graph method that models the systems based on law of mass conservation and/or law of energy conservation containing in the systems. Modeling the parts of both singly-fed and double-fed induction generators are based on Bond-graph method. We found that the impact of stator coil winding for on the current is in the form of changes of its value and steady state intervals; increasing the number of stator coil windings may also lead to increased stator current and the longer their steady state interval. Our study demonstrates also that doubly-fed induction generator possesses advantages compared to singly-fed induction generator, namely better current quality output and an adaptation to fluctuating wind speeds. The performance study is done in constant and variably wind speeds using simulated results of 20-Sim software.
Internet behavioral models for improving internet quality of service or user profiling: a systematic literature review Lei, Zhang; Kamal Bashah, Nor Shahniza
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4352-4364

Abstract

Internet behavior models have found applications across diverse domains, notably in internet addiction, customer satisfaction analysis, user purchasing behavior prediction, and optimizing internet of things (IoT) sensor performance. However, a notable gap exists in exploring these models in enhancing internet quality of service (QoS), specifically in campus settings, intricately linked to the nuances of students' online behavior. This study elucidates the strategic utilization of internet behavioral models for augmenting internet QoS and facilitating user behavior analysis. Creating datasets grounded in internet users' access behavior represents a pivotal phase, with explicit, implicit, and mixed methods emerging as the prevailing approaches. In this comprehensive literature review, we systematically scrutinized the methods, techniques, and inherent characteristics of constructing internet behavior models according to a systematic literature review process. The qualitative findings extracted from the systematic review encapsulated 1,046 articles, meticulously classified according to predefined inclusion and exclusion criteria. Subsequently, 35 articles were judiciously selected for in-depth analysis. This study culminated in identifying the most pertinent methodologies and salient features pivotal to construct robust internet behavior model for improving internet QoS and user experience.
Object-based image retrieval and detection for surveillance video Jagtap, Swati; Chopade, Nilkanth B.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4343-4351

Abstract

With technological advancement worldwide, the video surveillance market is growing drastically in a versatile field. Monitoring, browsing, and retrieving a specific object in a long video becomes difficult due to the enormous amount of data produced by the surveillance camera. With limitations on human resources and browsing time, there is a need for a new video analytics model to handle more complex tasks, such as object detection and query retrieval. The current approach involves techniques like unsupervised segmentation, multiscale segmentation, and feature-based descriptions. However, these methods often encounter extensive space and time consumption challenges. A solution has been developed for retrieving targeted objects from surveillance videos via user queries, employing a graphical interface for input. Extracting relevant frames based on user-entered text queries is enabled through using YOLOv8 for object detection. Users interact through a graphical user interface deployed on a Jetson Xavier Development board. The system's outcome is a time-efficient and highly accurate automated model for object detection and query retrieval, eliminating human errors associated with manually locating objects in videos upon user queries.
Efficient rectifier with wide input power range for 5G applications Yamna, Mounira Ben; Dakhli, Nabil; Sakli, Hedi; Aoun, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3809-3819

Abstract

This article presents three efficient rectifiers for radio frequency energy harvest-ing (RFEH) systems operating at the fifth generation (5G) band (3.5 GHz). Eachrectifier operates at various input power levels (high, low, and across a widepower range). The high and low-power rectifiers feature a single serial topologyusing HSMS-2860 and SMS-7630 Schottky diodes, respectively, along with mi-crostrip lines to implement the input and output filters and the impedance match-ing network. At an radio frequency (RF) power level of 15 dBm, the high-powerrectifier harvests 67.4% to direct current (DC) power with a 300Ωload resistorand an output voltage of 2.5V. The low-power rectifier achieves its maximumpower conversion efficiency (PCE) at -2 dBm, reaching 45% efficiency with a1200Ωload. The rectifier with a extended input power range comprises twobranches of subrectifiers functioning at both high and low power levels. De-pending on the power level, the considered subrectifier harvests radio frequencypower into DC power, while the other subrectifier is deactivated. Across a powerspan of 32.5 dB (ranging from -13 to 19.5 dBm), the rectifier maintains an effi-ciency above 30%. The proposed rectifiers are efficient and suitable for imple-mentation in 5G-enabled RFEH systems.
Insights of machine learning-based threat identification schemes in advanced network system Narasimhamurthy, Thanuja; Hosahalli Swamy, Gunavathi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4664-4674

Abstract

An advanced network system (ANS) is characterized by extensive communication features that can support a sophisticated collaborative network structure. This is essential to hosting various forms of upcoming modernized and innovative applications. Security is one of the rising concerns associated with ANS deployment. It is also noted that machine learning is one of the preferred cost-effective ways to optimize the security strength and address various ongoing security problems in ANS; however, it is still unknown about its overall effectivity scale. Hence, this paper contributes to a systematic review of existing variants of machine learning approaches to deal with threat identification in ANS. As ANS is a generalized form, this discussion considers the impact of existing machine learning approaches on its practical use cases. The paper also contributes towards critical gap analysis and highlights the study's potential learning outcome.
Design and analysis of a metamaterial based biosensor to determine blood glucose concentration Aminuzzaman, Mir Md.; Hossam-E-Haider, Md
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4088-4096

Abstract

In this paper, a biosensor utilizing metamaterials is designed and simulated to detect blood glucose concentration. The proposed sensor comprised of a microstrip patch antenna designed on a Rogers RT5880 substrate. A circular-shaped complementary split ring resonator (CSRR) cell is integrated onto the patch of the antenna which acts as the sensing region. The sensor is analyzed in order to ascertain the blood glucose concentration ranging from 50-300 mg/dL in a human finger model. The sensing parameter is amplitude of reflection coefficient, which exhibits variation in response to alterations in the dielectric characteristics of the sample being tested. The Cole-Cole relaxation model is employed to predict the dielectric properties of different finger tissues. An analysis of the characteristics of the CSRR was conducted to illustrate its significance in the realm of glucose detection. The glucose level is determined through the utilization of a linear regression model that describes the relationship between the reflection coefficient of the sensor and glucose level. The sensor demonstrates an impressive sensitivity of 1.792 dB per (mgdL-1) and has the ability of determining glucose levels with a good accuracy, as verified by the application of Clarke error grid. This sensor exhibits enhanced performance compared to some other recent glucose sensors.
Machine learning-based clothing recommendation system for women: case study of Lady's confecciones Maestre-Matos, Leydis; Manjarres-Rivera, Manuel; Robles-Algarín, Carlos; Navarro-Meneses, Jose
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4616-4626

Abstract

This paper presents a clothing recommendation system for women based on their body type, aiming to facilitate the purchasing process on the online sales channel of the company Lady's Confecciones located in the city of Santa Marta, Colombia. For this process, a user interface was designed to function in two ways: using a prediction model that takes as inputs a photograph of the user and their height, and a manual mode that receives the measurements of bust, hip and waist. The prediction model implemented the OpenCV library and the skinned multi-person linear (SMPL) model to process images and predict body shape and pose. Five body types were considered: triangle, apple, rectangle, hourglass and inverted triangle, differentiated by bust, waist and hip measurements, according to the conditions provided by the company. The system was able to predict the body measurements of the female participants with a maximum Pearson correlation coefficient of 0.97. For predicting body type, the best results were obtained for the rectangle body shape, with an accuracy of 92.31%.
Hybrid convolutional neural network-long short-term memory combined model for arrhythmia classification Badiger, Raghavendra; Manickam, Prabhakar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4204-4213

Abstract

The automated examination of electrocardiogram (ECG) signals holds significant importance within the medical field for managing various critical cardiac conditions. Identifying cardiomyopathy and arrhythmias is presently recognized as a challenging endeavor. While machine learning techniques have garnered substantial attention for categorizing these patterns, a predominant focus has been on the classification of arrhythmias. However, existing studies have overlooked instances where arrhythmia leads to cardiomyopathy, a specific cardiac disease scenario. In our research, we introduce an innovative method aimed at distinguishing between cardiomyopathy and cardiomyopathy accompanied by arrhythmia by employing a convolutional neural network (CNN-based) model. This novel approach fills the gap in existing literature by addressing the critical need to classify cases where arrhythmia induces cardiomyopathy, thereby presenting a potential advancement in accurately identifying and managing complex cardiac conditions. The proposed model uses convolution-based CNN model for feature extraction and combines these features with temporal features. Further, a CNN combined long short-term memory (CNN-LSTM) model is presented for classification where CNN models help to obtain the spatial information and LSTM helps to retain the temporal information resulting in improved classification accuracy. the experimental analysis is carried out into two phases where we have classified the rhythms and arrhythmias.
Prediction of novel malware using hybrid convolution neural network and long short-term memory approach Pachhala, Nagababu; Jothilakshmi, Subbaiyan; Battula, Bhanu Prakash
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4508-4517

Abstract

The rapid evolution of network communication technologies has led to the emergence of new forms of malware and cybercrimes, posing significant threats to user safety, network infrastructure integrity, and data privacy. Despite efforts to develop advanced algorithms for detecting malicious activity, constructing models that are both accurate and reliable remains a challenge, especially in handling vast and dynamically shifting data patterns. The prevalent bag-of-words (BOW) method, while widely used, falls short in capturing crucial spatial and sequence information vital for detecting malware patterns. To address this challenge, the work presented in this paper proposes hybrid convolution neural network-long short-term memory network (CNN-LSTM) combination models, leveraging CNN's spatial information extraction and LSTM's temporal modeling capabilities. Focused on predicting the infiltration of malicious software into personal computers, the proposed hybrid CNN-LSTM model considers factors such as location, firmware version, operating system, and anti-virus software. The proposed models undergo training and evaluation using Microsoft's malware dataset, demonstrating superior performance compared to traditional CNN and LSTM models. The CNN-LSTM model achieves an impressive accuracy of 95% on the Microsoft malware dataset, highlighting its effectiveness in malware detection.

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