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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 6,301 Documents
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Effectiveness of filtering methods in enhancing pulmonary carcinoma image quality: a comparative analysis Elavarasu, Moulieswaran; Govindaraju, Kalpana
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp358-365

Abstract

In recent years, information technology has vastly improved. The quality of the image has been degraded by noise, which defeats the purpose of the noisy images. The major purpose of this paper is to find out which filters provide a better outcome while preprocessing medical images using computer tomography scans. The purpose of this paper is to remove noise from any images, whether they are real-time datasets or online datasets. To enhance an image for preprocessing, we have compared various filters; these filters are already available, but the major purpose is to identify the best filter. We compared the different parameters to find the best and finally found that the modified bilateral filtering provided a better result. The noise has been removed by using a bilateral filter, and the image clarity has not changed when using this filter. We have discussed the advantages and drawbacks of each approach. The effectiveness of these filters is compared using the peak signal-to-noise ratio, structural similarity index, contrast-to-noise ratio, and mean square error. The proposed algorithm is tested on 5 sample lung images. The results show that the modified bilateral filter produces better results.
Real-time business intelligence development using machine learning to increase the potential of the dairy goat milk business Primawati, Alusyanti; Sitanggang, Imas Sukaesih; Annisa, Annisa; Astuti, Dewi Apri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5612-5625

Abstract

The development of big data and real-time data warehouse (RTDW) technologies has transformed traditional business intelligence (BI) into real-time business intelligence (RTBI). The RTBI framework is developed in this study by incorporating machine learning-based real-time prediction features. The complexity of layer integration in the RTBI framework is a challenge in building RTBI. The development of RTBI was carried out in business areas that did not have RTBI from the beginning, such as the dairy goat milk business in Probolinggo, East Java. Another main reason is that the dairy goat milk business is a food alternative to cow's milk in Indonesia. The results of this study can contribute to increasing the potential value of the goat milk business. The research method was developed by adapting to the Kimball method and unified modeling language (UML). The real-time prediction feature with the long short-term memory (LSTM) algorithm is the main feature in the RTBI framework developed in the research. The calculation results of real-time predictive analysis latency successfully approached 0 milliseconds (ms), namely 9.35×10-5 ms. The application of RTBI in the dairy goat milk business was successfully built but the real data is very limited, so RTBI is less able to describe the movement of the business.
Predictive maintenance of rotational machinery using deep learning Ali, Mohamed Iyad; Lai, Nai Shyan; Abdulla, Raed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1112-1121

Abstract

This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability.
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.
60 GHz millimeter-wave indoor propagation path loss models for modified indoor environments Qasem, Nidal; Alkhawatrah, Mohammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2737-2752

Abstract

The 60 GHz band has been selected for short-range communication systems to meet consumers’ needs for high data rates. However, this frequency is attenuated by obstacles. This study addresses the limitations of the 60 GHz band by modifying indoor environments with square loop (SL) frequency selective surfaces (FSSs) wallpaper, thereby increasing its utilization. The SL FSS wallpaper response at a 61.5 GHz frequency has been analyzed using both MATLAB and CST Studio Suite software. ‘Wireless InSite’ is also used to demonstrate enhanced wave propagation in a building modified with SL FSSs wallpaper. The demonstration is applied to multiple input multiple output system to verify the effectiveness of FSSs on such systems’ capacity, as well as the effect of the human body on capacity. Simulation results presented here show that modifying a building using SL FSS wallpaper is an attractive scheme for significantly improving the indoor 60 GHz wireless communications band. This paper also presents and compares two large-scale indoor propagation path loss models, the close-in (CI) free space reference distance model and the floating intercept (FI) model. Data obtained from ‘Wireless InSite’ over distances ranging from 4 to 14.31 m is analyzed. Results show that the CI model provides good estimation and exhibits stable behavior over frequencies and distances, with a solid physical basis and less computational complexity when compared to the FI model.
Comparative analysis of deep Siamese models for medical reports text similarity Kurniasari, Dian; Usman, Mustofa; Warsono, Warsono; Lumbanraja, Favorisen Rosyking
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6969-6980

Abstract

Even though medical reports have been digitized, they are generally text data and have not been used optimally. Extracting information from these reports is challenging due to their high volume and unstructured nature. Analyzing the extraction of relevant and high-quality information can be achieved by measuring semantic textual similarity (STS). Consequently, the primary aim of this study is to develop and evaluate the performance of four models: Siamese Manhattan convolution neural network (CNN), Siamese Manhattan long short-term memory (LSTM), Siamese Manhattan hybrid CNN-LSTM, and Siamese Manhattan hybrid LSTM-CNN, in determining STS between sentence pairs in medical reports. Performance comparisons were conducted using Cosine Similarity and word mover's distance (WMD) methods. The results indicate that the Siamese Manhattan hybrid LSTM-CNN model outperforms the other models, with a similarity score of 1 for each sentence pair, signifying identical semantic meaning.
A novel dynamic enterprise architecture model: leveraging MAPE-K loop and case-based reasoning for context awareness Ettahiri, Imane; Doumi, Karim
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1875-1889

Abstract

Nowadays, enterprises are required to take the uncertainty of the environment as decisive factor of success. For this reason, Enterprises should be prepared up-stream to react dynamically to the turbulent context. Considering that enterprise architecture is a tool drawing a blueprint that gives a holistic view of the enterprise, this blueprint should be able to represent this awareness to context and implements the techniques and mechanisms to react in a dynamic manner depending on the triggers of change. In this paper, the proposed model stipulates a “context-awareness” that monitors the internal and external context, and then adapt its reaction in alignment with the prefixed goals. The operationalization of our conception is realized through the monitor-analyze-plan-execute-knowledge (MAPE-K) loop, the case-based reasoning and machine learning techniques organized and orchestrated through a global algorithm of 6 main functions to monitor, compare, analyze, plan, execute and enrich the knowledge base. The results are verified in the light of a case study that demonstrates the applicability of our proposed model.
Detection of fungal diseases of plants from leaf images based on neural network technologies Fedorchenko, Ievgen; Yusof, Mohd Faizal; Oliinyk, Andrii; Chornobuk, Maksym; Khokhlov, Mykola; Alsayaydeh, Jamil Abedalrahim Jamil
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5866-5873

Abstract

The paper addresses the issue of automating the detection of fungal diseases in plants using digital images of their leaves. The spread of diseases among agricultural and horticultural crops causes significant economic losses worldwide, making the development of an effective and affordable solution to this problem highly valuable. Literature analysis suggests the viability of employing a convolutional neural network (CNN) to tackle this issue. The 'Fungus recognition' model was developed based on a custom CNN architecture using the TensorFlow library. The model underwent training and testing on a publicly available dataset. Test results show that 'Fungus recognition' achieves a classification accuracy level of 90%, surpassing similar models considered. The developed model can be adapted for deployment on mobile computing devices, paving the way for its practical implementation in agriculture and horticulture.
Self-steering Yagi-Uda antenna positioning system for television Federis Montañez, John Joshua; Alipante Vargas, James; Francisco Palibino, Mary Grace; Esplana Rebedorial, Rustom Jim; Bio Borilla, Louise Deanna
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp435-442

Abstract

The aim of this study is to develop a prototype that automatically improves the position of a Yagi-Uda antenna using a microcontroller and to illustrate its radiation pattern through the use of MATLAB®. This study is intended for students and professors in the electronics engineering field. This served as their educational materials for teaching antenna system principles and theories. Developmental and experimental methods were used to achieve the objectives. The materials and components generally used in this study are a Yagi-Uda antenna, stepper motor, Arduino Uno, L293D motor shield, USB TV stick tuner, slotted optocoupler, ADS1115, coax cable splitter, speaker stand, and timing belt. The statistical tool used in this study was a Z-test to find out if the experiment results were significant. In testing the effectiveness of the automatic antenna system, the TV display in every increment of 1.8° was taken. It was the basis for the effectiveness of the study. At 5% α/2 level (1.96), the computed z value is 1.76, which is less than 1.96. Therefore, there is no significant difference between the picture quality of the TV display at every angle and the desired angle with maximum reception of the signal with the integration of MATLAB®.
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.

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