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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
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Some results on χ-single valued neutrosophic subgroups M. Shazib Hameed; Zaheer Ahmad; Salman Mukhtar; Asad Ullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1583-1589

Abstract

In this study, we develop a novel structure χ-single valued neutrosophic set, which is a generalization of the intuitionistic set, inconsistent intuitionistic fuzzy set, Pythagorean fuzzy set, spherical fuzzy set, paraconsistent set, etc. Fuzzy subgroups play a vital role in vagueness structure, it differ from regular subgroups in that it is impossible to determine which group elements belong and which do not. In this paper, we investigate the concept of a χ-single valued neutrosophic set and χ-single valued neutrosophic subgroups. We explore the idea of χ-single valued neutrosophic set on fuzzy subgroups and several characterizations related to χ-single valued neutrosophic subgroups are suggested.
Mobile fitness application for beginners Mohamed Imran Mohamed Ariff; Nabil Farhan Roslan; Khairulliza Ahmad Salleh; Masurah Mohamad
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp500-506

Abstract

The present project is motivated by the recognition that the use of mobile fitness application is increasingly popular among sports and exercise participants in recent years. However, an extensive research on mobile fitness application indicates that most of them are not suitable for beginners. Thus, this project paper describes the development process of a mobile fitness application for beginners, who are looking at enhancing their physical fitness level. This mobile fitness application is developed using android studio and java language. Upon the development of this mobile fitness application, a user testing was conducted and analyzed. The result shows that users were satisfied with the applications as most test scores were above average. Based on these results, the usage of this newly developed mobile fitness application can be suggested to be used by beginner exercisers.
Enhancement of medical images using fuzzy logic Yousra Ahmed Fadil; Baidaa Al-Bander; Hussein Y. Radhi
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1478-1484

Abstract

Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy c-means clustering (FCM) technique is utilized to enhance the medical images. The method of enhancement consists of two stages. The proposed algorithm conducts a cluster test on the image pixels. It then increases the difference of gray level between the diverse objects to accomplish the enhancement purpose of the medical images. The experimental results have been tested using various images. The algorithm enhanced the small target of the image to a reasonable limit and revealed favorable performance. The results of image enhancement techniques were evaluated by using terms of different criteria such as peak signal to noise ratio (PSNR), mean square error (MSE) and average information contents (AIC), showing promising performance.
Analysis and design of the biasing network for 1 GHz bandwidth RF power amplifier Md. Golam Sadeque; Zubaida Yusoff; Mardeni Roslee; Shaiful Jahari Hashim; Azah Syafiah Mohd Marzuki
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp308-316

Abstract

The bandwidth of the wireless communication has increased due to the various applications of the wireless devices. A radio frequency power amplifier (RFPA) is one of the crucial components of the transceiver. So, to meet the requirement of the bandwidth, wideband RFPA is needed. The RFPA not only requires a wideband matching network but importantly the biasing network. For the next-generation communication system, a wideband biasing network is needed to operate in the wide GHz bandwidth range. In this paper, a wideband biasing network for the power amplifier is designed using a quarter-wave transmission line and a butterfly stub for the frequency band of 3.3 GHz to 4.3 GHz. Roger’s RO3006 is used as the substrate for the design of the biasing network. The designed network performed well in the required frequency range. The performances of the biasing network have shown 9 dB to 19 dB return loss, the radio frequency (RF) isolation has more than 35 dB, and 0 dB to 1.5 dB insertion loss. This wideband biasing network can be used for the next generation communication system.
Engaging students to fill surveys using chatbots: University case study Nadir Belhaj; Abdemounaime Hamdane; Nour El Houda Chaoui; Habiba Chaoui; Moulhime El Bekkali
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp473-483

Abstract

The use of chatbot or conversational agents is becoming common these days by the companies in many fields to make smart conversations with users. Backed by artificial intelligence and natural language processing they provide a strong platform to engage users. These positive aspects of chatbots can be beneficial in the educational sector, especially in conducting online survey. This study aims to explore the feasibility of a new chatbot approach survey as a new survey method in Moroccan university to overcome the web survey’s common response quality problems. Indeed, having student feedback before and after graduation is essential for university assessment. This new approach keeps students engaged, supportive, and even excited to offer feedback without getting bored and dropping the conversation, especially in Moroccan universities known by an overcrowding of students where it is difficult to get their feedback. This feedback feeds into our university' databases for further reporting and decision making to improve the quality of educational content and student-oriented services. Finally, we have shown the effectiveness of our approach by a comparative data study between the traditional online survey and the use of this chatbot.
Hybrid basis vector based underdetermined beamforming algorithm in optimized antenna reconfiguration Krupa Prasad K. R.; H. D. Maheshappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp367-375

Abstract

Optimized positioning of antenna to obtain the best beam forming solution is adopted in this research. Non-uniform linear array-based beamforming algorithms have the challenge of placing the array of antennas in positions that would implement best beamforming outputs. This paper attempts to obtain the optimized beam forming by tuning the sparse Bayesian learning based algorithm. The parameters used for tuning involve choosing the hybrid basis vector for creating the steering vector while at the same time developing the optimized position of the antennas. Basis vectors are the building blocks of the steering vector developed for the beamforming algorithm that finds the angle of arrival in antennas. Reconfiguration of antennas is carried out using particle swarm optimization (PSO) algorithm and the basis vectors are generated using two different ways. One by cumulating similar basis vectors and another by cumulating two different basis vectors. The performance of accurate detection of angle of arrival in the beamforming algorithm is analyzed and results are discussed. This basis vector and antenna distance optimization is adopted on the sparse Bayesian learning paradigm. Performance evaluation of these optimizations in the algorithm is realised by validating the mean square error (MSE) versus signal to noise ratio (SNR) graphs for both the cumulative basis vector and hybrid basis vector cases.
Machine learning based outlier detection for medical data R. Vijaya Kumar Reddy; Shaik Subhani; B. Srinivasa Rao; N. Lakshmipathi Anantha
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp564-569

Abstract

The concept of machine learning generate best results in health care data, it also reduce the work load of health care industry. This algorithm potentially overcome the issues and find out the novel knowledge for development of medical date in health care industry. In this paper propose a new algorithm for finding the outliers using different datasets. Considering that medical data are analytic of mutually health problems and an activity. The proposed algorithm is working based on supervised and unsupervised learning. This algorithm detects the outliers in medical data. The effectiveness of local and global data factor for outlier detection for medical data in real time. Whatever, the model used in this scenario from their training and testing of medical data. The cleaning process based on the complete attributes of dataset of similarity operations. Experiments are conducted in built in various medical datasets. The statistical outcome describe that the machine learning based outlier finding algorithm given that best accurateness.
Digital agriculture based on big data analytics: a focus on predictive irrigation for smart farming in Morocco Loubna Rabhi; Noureddine Falih; Lekbir Afraites; Belaid Bouikhalene
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp581-589

Abstract

Due to the spead of objects connected to the internet and objects connected to each other, agriculture nowadays knows a huge volume of data exchanged called big data. Therefore, this paper discusses connected agriculture or agriculture 4.0 instead of a traditional one. As irrigation is one of the foremost challenges in agriculture, it is also moved from manual watering towards smart watering based on big data analytics where the farmer can water crops regularly and without wastage even remotely. The method used in this paper combines big data, remote sensing and data mining algorithms (neural network and support vector machine). In this paper, we are interfacing the databricks platform based on the apache Spark tool for using machine learning to predict the soil drought based on detecting the soil moisture and temperature.
Design and simulation of cascaded H-bridge multilevel inverter with energy storage Tan Chee Ting; Zulhani Rasin; Chan Sia Ching
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1289-1298

Abstract

Stand-alone power system provides a solution for the user in rural areas that are disconnected from the utility grid which requires power electronics device for the power conversion. This work proposes a design of 5-level cascaded H-bridge inverter with energy storage to realize DC-AC power conversion for such system. The DC-DC bidirectional converter is designed to control the charging and discharging of current into/from the battery during the buck and boost mode of operation. At the DC side, dual-loop control strategy using PI controllers is designed to control the current and voltage. The inner loop current controller controls the recharging/discharging of current for the battery, while the outer voltage controller controls the DC link voltage at 200 V for each of the H-bridge unit. At the AC side, multiple feedback loop control strategy regulates the inverter output voltage at 240 Vrms under various load change. The modelling and design of the system is implemented under Matlab Simulink environment. From the results, the battery storage unit works well with the DC link voltage to achieve a balance power transfer within the system between the PV source, load and battery storage under variation of PV power and loading condition.
Big transfer learning for automated skin cancer classification Zinah Mohsin Arkah; Dalya S. Al-Dulaimi; Ahlam R. Khekan
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1611-1619

Abstract

Skin cancer is an example of the most dangerous disease. Early diagnosis of skin cancer can save many people’s lives. Manual classification methods are time-consuming and costly. Deep learning has been proposed for the automated classification of skin cancer. Although deep learning showed impressive performance in several medical imaging tasks, it requires a big number of images to achieve a good performance. The skin cancer classification task suffers from providing deep learning with sufficient data due to the expensive annotation process and required experts. One of the most used solutions is transfer learning of pre-trained models of the ImageNet dataset. However, the learned features of pre-trained models are different from skin cancer image features. To end this, we introduce a novel approach of transfer learning by training the pre-trained models of the ImageNet (VGG, GoogleNet, and ResNet50) on a large number of unlabelled skin cancer images, first. We then train them on a small number of labeled skin images. Our experimental results proved that the proposed method is efficient by achieving an accuracy of 84% with ResNet50 when directly trained with a small number of labeled skin and 93.7% when trained with the proposed approach.

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