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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 65 Documents
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Search results for , issue "Vol 25, No 1: January 2022" : 65 Documents clear
Feature extraction to predict quality of segregating sweet tamarind using image processing Panana Tangwannawit; Sakchai Tangwannawit
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp339-346

Abstract

In this modern age, several new methods have been developed, especially in image processing for agriculture business, which consists of technologies derived from artificial intelligence (AI) capabilities called machine learning. Classify is a widely used method to analyze patterns, trends, as well as the body of knowledge from the data visualization. Image classification application improves discrimination and prediction efficiency. The objective of this research was to feature extraction of sweet tamarind and compare the algorithm for classification. This research used images from golden sweet tamarind species with the use of MATLAB and Python language. The steps of this research consisted of 1) preprocessing step for finding the distance to appropriate of the image quality, 2) feature extracting for finding the number of black pixels and the number of white pixels, perimeter, diameter, and centroid, and 3) classifying for algorithms' comparison. The results showed that the camera's distance to the image was 60 cm. The coefficient of determination was at 0.9956, and the Standard Error of Estimate was 7,424.736 pixels. The conclusion of classification found that the random forest had the highest accuracy at 92.00%, SD. = 8.06, precision = 90.12, recall = 92.86, and F1-score = 91.36.
The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network Ahmed Shihab Ahmed; Hussein Ali Salah
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp273-280

Abstract

The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
Digital image processing methods for estimating leaf area of cucumber plants Uoc Quang Ngo; Duong Tri Ngo; Hoc Thai Nguyen; Thanh Dang Bui
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp317-328

Abstract

Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.
Fixed point theorem between cone metric space and quasi-cone metric space Abdullah Al-Yaari; Hamzah Sakidin; Yousif Alyousifi; Qasem Al-Tashi
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp540-549

Abstract

This study involves new notions of continuity of mapping between quasi-cone metrics spaces (QCMSs), cone metric spaces (CMSs), and vice versa. The relation between all notions of continuity were thoroughly studied and supported with the help of examples. In addition, these new continuities were compared with various types of continuities of mapping between two QCMSs. The continuity types are ????????-continuous, ????????-continuous, ????????-continuous, and ????????-continuous. The results demonstrated that the new notions of continuity could be generalized to the continuity of mapping between two QCMSs. It also showed a fixed point for this continuity map between a complete Hausdorff CMS and QCMS. Overall, this study supports recent research results.
Development of computer-based learning system for learning behavior analytics Kanyalag Phodong; Thepchai Supnithi; Rachada Kongkachandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp460-473

Abstract

This paper aims to analyze the learning behavior of Thai learners by using a computer-based learning system for English writing. Three main objectives were set: the development of a computer-based learning system, automatic behavior data collection, and learning behavior analytics. Firstly, the system is developed under a multidisciplinary idea that is designed to integrate two concepts between the self-regulated learning model and components of natural language processing. The integration design encourages self-learning in the digital learning environment and supports appropriate English writing by the provided component selection. Second, the system automatically collects the writing behavior of a group of Thai learners. The data collected are necessary input for the process of learning analytics. Third, the writing behaviors data were analyzed to find the learning behavioral patterns of the learners. For learning analytics, behavior sequential analysis was used to analyze the learning logs from the system. The 31 undergraduate students are participated to record writing behaviors via the system. The learning patterns in relation to grammatical skills were compared between three groups: basic, intermediate, and advanced levels. The learning behavior patterns of the three groups are different that use for reflecting learners and improving the learning materials or curriculum.
An efficient look up table based approximate adder for field programmable gate array Hadise Ramezani; Majid Mohammadi; Amir Sabbagh Molahosseini
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp144-151

Abstract

The approximate computing is an alternative computing approach which can lead to high-performance implementation of audio and image processing as well as deep learning applications. However, most of the available approximate adders have been designed using application specific integrated circuits (ASICs), and they would not result in an efficient implementation on field programmable gate arrays (FPGAs). In this paper, we have designed a new approximate adder customized for efficient implementation on FPGAs, and then it has been used to build the Gaussian filter. The experimental results of the implementation of Gaussian filter based on the proposed approximate adder on a Virtex-7 FPGA, indicated that the resource utilization has decreased by 20-51%, and the designed filter delay based on the modified design methodology for building approximate adders for FPGA-based systems (MDeMAS) adder has improved 10-35%, due to the obtained output quality.
Enhancement of observability using Kubernetes operator Prerana Shenoy S. P.; Sai Vishnu Soudri; Ramakanth Kumar P.; Sahana Bailuguttu
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp496-503

Abstract

Observability is the ability for us to monitor the state of the system, which involves monitoring standard metrics like central processing unit (CPU) utilization, memory usage, and network bandwidth. The more we can understand the state of the system, the better we can improve the performance by recognizing unwanted behavior, improving the stability and reliability of the system. To achieve this, it is essential to build an automated monitoring system that is easy to use and efficient in its working. To do so, we have built a Kubernetes operator that automates the deployment and monitoring of applications and notifies unwanted behavior in real time. It also enables the visualization of the metrics generated by the application and allows standardizing these visualization dashboards for each type of application. Thus, it improves the system's productivity and vastly saves time and resources in deploying monitored applications, upgrading Kubernetes resources for each application deployed, and migration of applications.
Automated breast cancer detection system from breast mammogram using deep neural network Suneetha Chittineni; Sai Sandeep Edara
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp580-588

Abstract

All over the world breast cancer is a major disease which mostly affects the women and it may also cause death if it is not diagnosed in its early stage. But nowadays, several screening methods like magnetic resonance imaging (MRI), ultrasound imaging, thermography and mammography are available to detect the breast cancer. In this article mammography images are used to detect the breast cancer. In mammography image the cancerous lumps/microcalcifications are seen to be tiny with low contrast therefore it is difficult for the doctors/radiologist to detect it. Hence, to help the doctors/radiologist a novel system based on deep neural network is introduced in this article that detects the cancerous lumps/microcalcifications automatically from the mammogram images. The system acquires the mammographic images from the mammographic image analysis society (MIAS) data set. After pre-processing these images by 2D median image filter, cancerous features are extracted from the images by the hybridization of convolutional neural network with rat swarm optimization algorithm. Finally, the breast cancer patients are classified by integrating random forest with arithmetic optimization algorithm. This system identifies the breast cancer patients accurately and its performance is relatively high compared to other approaches.
Design of an environmental management information system for the Universidad Distrital Edwin Arturo Quintero Torres; William Andrés León Beltrán; Juan Manuel Sánchez Céspedes
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp529-539

Abstract

This article presents the design, development and implementation of a software tool, serving as an alternative to the problems involving management, control and reporting of processes within the institutional plan for environmental management (known as plan institucional de gestión ambiental (PIGA) by its Spanish acronym) for the Universidad Distrital Francisco José de Caldas. The software is focused on carrying out such processes to the automation setting, based on the extreme programming (XP) Agile methodology that mainly centers on the continuous development of the customer requirements to offer a more assertive tool, in line with the plan institucional de gestión ambiental in Spanish (PIGA) processes. The result is a complete satisfaction of users and a highly usable, adaptable and efficient software, inherently optimizing and automating the environmental management processes of the PIGA program. This work delivers an applet that meets the design and implementation requirements of environmental management policies. The proposed tool manages to reduce process-related times by 97%, therefore, allowing to aim efforts in other missional functions and increase the overall value offer of the organization.
Intelligent aquaculture system for pisciculture simulation using deep learning algorithm Sherwin B. Sapin; Bryan A. Alibudbud; Paulo B. Molleno; Maureen B. Veluz; Jonardo R. Asor
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp561-568

Abstract

The project aims to develop an intelligent system for simulating pisciculture in Taal Lake in the Philippines through geographical information system and deep learning algorithm. Records of 2018-2020 from the database of Bureau of fisheries and aquatic resources IV-A-protected area management board (BFAR IVA-PAMB) was collected for model development. Deep learning algorithm model was developed and integrated to the system for time series analysis and simulation. Different technologies including tensorflow.js were used to successfully developed the intelligent system. It is found on this paper that recurrent neural network (RNN) is a good deep learning algorithm for predicting pisciculture in Taal lake. Further, it is also shown in the initial visualization of the system that barangay Sampaloc in Taal has highest rate of fish production in Taal while Tilapia nilotica sp. is the major product of the latter.

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