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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 64 Documents
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Search results for , issue "Vol 29, No 2: February 2023" : 64 Documents clear
Automatic recognition of color sensation with controlled phosphene brightness using pre-trained CNNs framework Muthyala Veera Venkata Satyanarayana Chowdary; Venkata Ramana
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1174-1182

Abstract

Argus-II is one of the most successful epiretinal implantation for providing visual acuity those who lost their vision sight due to retinitis pigmentosa (RP) problem. However, this model faces color recognition issue is observed from implanted patients. Hence, it arises whenever electrode fail to retain same electrical stimuli property during sensitivity color transition state is occurred (especially, blue and purple colors). To resolve this problem, a proper handling of electrical stimuli parameters (amplitude, frequency and pulse width) is required during patient under every visual impact is possible. Addition to this, the individual patient color sensation is recorded in the observation state and creates Argus-II dataset to train the machine learning algorithm for maintaining phosphene brightness through controlled generation of the electrical stimuli. Therefore, in this paper, an automatic recognition of color sensation with controlled phosphene brightness using pre-trained CNNs framework is proposed. The frequency modulated electrical stimulation of retina is purely influence by trained CNNs for adjusting amplitude that can retain maximum brightness along with clarity in the color sensation. The experimental results shows that the proposed system is achieved reasonable improvement in the transition color sensation as well as controlled brightness when compared with other existing systems.
Improving WSNs execution using energy-efficient clustering algorithms with consumed energy and lifetime maximization Mohanad Sameer Jabbar; Samer Saeed Issa; Adnan Hussein Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1122-1131

Abstract

Wireless sensor networks (WSNs) has a major designing feature representing by energy. Specifically, the sensor nodes have limited battery energy and are deployed remote from base station (BS); therefore, the actual enhancement dealing with energy turns into the Clustering routing protocols fundamentals which concerned in network lifetime improvement. Though, unexpected and energy insensible of the clusters head (CH) selection is not the best of WSN for greatly lowering lifetime network. A presentation article of an WSNs incoming routing approach using a mix of the fuzzy approach besides hybrid energy-efficient distributing (HEED) algorithm for increasing the lifetime and node’s energy. The FLH-P proposal algorithm is split into two parts. The stable election protocol HEED approach is used to arrange WSNs into clusters. Then, using a combination of fuzzy inference and the low energy adaptive clustering hierarchy (LEACH) algorithm, metrics like residual energy, minimal hops, with node traffic counts are taken into account. A comparison of FLH-P proposal algorithm with LEACH algorithm, fuzzy approach, and HEED utilizing identical guiding standards was used for demonstrating the performance of the suggested technique from where corresponding consumed energy as well as lifetime maximization. The suggested routing strategy considerably increases the network lifetime and transmitted packet throughput, according to simulation findings.
Generating himawari-8 time series data for meteorological application Ahmad Luthfi Hadiyanto; Ketut Wikantika; Ary Setijadi Prihatmanto; Nurjanna Joko Trilaksono; Dedi Irawadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp780-787

Abstract

Optical remote sensing images have been widely used for temporal monitoring. The data is acquired by sensors on satellites with better spatial resolution compared to in-situ measurements by meteorological stations. The problem with utilizing optical images is the cloud, which blocks the ground and near-ground information collected by satellites. To overcome this problem, especially when dealing with thermal bands, we propose a procedure including aggregation and spatial interpolation methods to obtain time series data over a region. There is still no reference to selecting the data period to calculate the aggregate value and apply spatial interpolation. An assessment is proposed by applying Yamane’s formula in the time domain and thresholding the number of pixels in the spatial domain. Himawari-8 data was utilized and collected on an hourly basis over Java Island. This algorithm is applied to a sequence of periodic datasets to obtain a time series of aggregate data for meteorological applications. The result of this study is a recommendation to use three-month periods of data over the eastern part of Java.
Social spider optimization algorithm-based energy management of photovoltaic powered textile industry Preetha Pujar Somashekharappa; Ashok Kusagur
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp963-972

Abstract

The competitive nature of business, especially in the production industries like the textile industry, increases the importance of the economic operation of the production setup. Energy management is one important tool that supports the economic operation of any industry. This paper attempts to develop an optimized energy management algorithm using Social Spider optimization in a textile industry environment. Energy management for one full day is analyzedin the environment, considering fixed, shift-able, and uninterruptible loads for energy management implementation. The benefit of implementing photovoltaic power on the premises is discussed with an appropriate calculation. The results are compared with the bat algorithm and the ant lion algorithm. MATLAB 2017b is used to program the concept.
A crypto-steganography healthcare management: towards a secure communication channel for data COVID-19 updating Mohanad Sameer Jabbar; Samer Saeed Issa
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1102-1112

Abstract

Nowadays, secure transmission massive volumes of medical data (such as COVID-19 data) are crucial but yet difficult in communication between hospitals. The confidentiality and integrity are two concerning challenges must be addressing to healthcare data. Also, the data availability challenge that related to network fail which may reason concerns to the arrival the COVID-19 data. The second challenge solved with the different tools such as virtual privet network (VPN) or blockchain technology. Towards overcoming the aforementioned for first challenges, a new scheme based on crypto steganography is proposed to secure updating (COVID-19) data. Three main contributions have been consisted within this study. The first contribution is responsible to encrypt the COVID-19 data prior to the embedding process, called hybrid cryptography (HC). The second contribution is related with the security in random blocks and pixels selection in hosting image. Three iterations of the Hénon Map function used with this contribution. The last contribution called inversing method which used with embedding process. Three important measurements were used the peak signal-to-noise ratio (PSNR), the Histogram analysis and structural similarity index measure (SSIM). Based on the findings, the present scheme gives evidence to increase capacity, imperceptibility, and security to ovoid the existing methods problem.
Adaptation of powerline communications-based smart metering deployments with IoT cloud platform Shaimaa Mudhafar Hashim; Israa Bader Al-Mashhadani
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp825-837

Abstract

The necessity of energy management and optimization through smart devices has an essential role in sustainable energy. Smart grid features and cutting-edge technologies are progressively integrated into traditional electricity networks. One of these features is the interference between power line communications and IoT. The introduction and deployment of these grids are mainly focused on the development of the field of smart metering. A new proposed module for smart meter design within the existing infrastructure grid system using power line communication (PLC) is presented. The system will include a transmitter with a microcontroller (MCU) and numerous sensors, as well as communication channels that include PLC and an in-house powerline network, and a receiver with an MCU. The suggested system interacts with the IoT system, characterized by a free web interface showing the data directly in real-time. Based on real-world experience, this paper develops guidelines for various aspects of PLC Smart Metering network deployment via the cloud environment. The practical result of packet losses is about 0, 1, or 2 characters of received data, and the time difference between transmitter and receiver is about 5000 milliseconds for a fixed transmission line.
Identifying corn leaves diseases by extensive use of transfer learning: a comparative study Ahmed Samit Hatem; Maha Sabri Altememe; Mohammed Abdulraheem Fadhel
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1030-1038

Abstract

Deep learning is currently playing an important role in image analysis and classification. Diseases in maize diminish productivity, which is a major cause of economic damages in the agricultural business throughout the world. Researchers have previously utilized hand-crafted characteristics to classify images and identify leaf illnesses in Maize plants. With the advancement of deep learning, researchers can now significantly enhance the accuracy of object classification and identification. Using the "Corn or Maize Leaf Disease Dataset" from the Kaggle website, four forms of maize leaf diseases were investigated: blight, common rust, gray leaf spot, and healthy. The pictures obtained from these corn leaf illnesses are categorized using four deep convolutional neural network (CNN) models that have been pre-trained (GoogleNet, AlexNet, ResNet50 and VGG16). Accuracy, precision, specificity, recall, F-score, and time are the six metrics used to assess the performance of any transfer learning (TL) model. MATLAB programming software is used to design and train the TL models. The accuracy of each item in the dataset has been checked. It has been determined that GoogleNet, AlexNet, VGG16, and ResNet50 each have an accuracy of 98.57%, 98.81%, 99.05%, and 99.36%, respectively.
Inpatient WiFi-enabled medication dispenser for improving ward-based clinical pharmacy services Khaleel Nawfeal Khaleel; Mazin N. Farhan; Mohammed G. Ayoub; Mohammed Sabah Jarjees
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp687-693

Abstract

Medications are vital for patients and especially for those who are receiving treatment in hospitals. Providing medications for these people is essential to maintain their health. On the other hand, medication dispensing error is one of the most common challenges that face clinical pharmacists and medical staff. These errors frequently occurred due to poor medication systems and/or human factors (i.e. environmental conditions, fatigue or staff shortage). These factors may affect prescribing, transcribing, administration, dispensing and monitoring practices which can result in disability, severe harm and even death. Avoiding medication dispensing errors is the key motivation of this paper. Consequently, a biometric-based dispensing system has been designed and implemented. The system can be installed at hospital wards and used for delivering and monitoring inpatients doses. It consists of three parts; hardware, software and mechanical part. Three 4-phase stepper motors are used for controlling the mechanical part of this system. An optical fingerprint sensor is used which is compatible with the ESP32 low-power SoC for scanning patients’ fingerprints to recognize and store their data. The system directly updates its database whenever is used by the inpatients, so that nobody can get additional doses. This system is cost-effective, reliable and easy to use.
An investigation of machine learning techniques in speech emotion recognition Anu Saini; Amit Ramesh Khaparde; Sunita Kumari; Salim Shamsher; Jeevanandam Joteeswaran; Seifedine Kadry
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp875-882

Abstract

The natural languages are medium of communication from the inception of civilization. As the technology improves, the text messages, voice messages and videos are the addons in medium of communication. In long distance communication, the analysis of expression is modern area of research. The parameters of assessment are subjective hence the emotion recognition is challenging task. This article furnishes the investigation of various machine learning techniques and novel methods for speech emotion recognition (SER) to determine the feeling/sentiments in a speech. Here, we investigate the three machine learning methods named multinominal Naive Bayes (MNB), logistic regression (LR), and linear support vector machine (LSVM). Further, these techniques are incorporated with the proposed method. The performance of these machine learning techniques is investigated on two different datasets.  The datasets consist of voice and text data samples. The prosed method is trained and tested on these datasets. As per the experimentation, it has been observed that the LSVM has outperformed the other two machine learning techniques.
Refurbished and improvised model using convolution network for autism disorder detection in facial images Narinder Kaur; Ganesh Gupta
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp883-889

Abstract

The main quality of deep learning over conventional machine learning (ML) techniques empowers firsthand uses in processing of images, speech recognition, medical imaging, machine translation and robotics, computer vision, and numerous other fields. The purpose of this study is to assess algorithms of deep Learning for person with the disorder of autism. This disorder is developing disorder that causes significant communicative, social and behavioral difficulties in those who have it. In this research paper, the Enhanced version of convolution network is discussed. Visual geometry group (VGG), is one of model of the convolution neural network which has essential features of convolution neural network (CNN). The VGG 16 net is employed to calculate the processes that can be used to classify this disorder with improved accuracy. The preprocessing of the image data is done. The VGG 16 convolution network is used to classify between autism spectrum disorder (ASD) and Non-ASD. Finally, the algorithm's efficacy is considered based on its accuracy performance. The VGG 16 net algorithm produces better results with an accuracy of 68.54%, compared with the normal CNN algorithm.

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