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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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An Otsu thresholding for images based on a nature-inspired optimization algorithm Khamael Raqim Raheem; Hafedh Ali Shabat
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp933-944

Abstract

Thresholding is a type of image segmentation, where the pixels change to make the image easier to analyze. In bi-level thresholding, the image in grayscale format is transformed into a binary format. The traditional methods for image thresholding may be inefficient in finding the best threshold and take longer computation time. Recently, metaheuristic swarm-based algorithms were applied for optimization in different applications to find optimal solutions with minimum computational time. The proposed work aims to optimize the fitness function obtained by the Otsu algorithm using a metaheuristic swarm-based algorithm called the bat algorithm. As a result, the optimal threshold value for bi-level images in cloud detection was obtained. Also, one of the trajectory-based algorithms called hill climbing was applied to optimize the fitness function taken from the Otsu algorithm. The HYTA dataset was used to evaluate the work, which was later confirmed through testing. The findings of experiments indicated that the developed algorithm is promising and the performance of the metaheuristic population-based algorithm is better than the trajectory-based algorithm in terms of efficiency and computational time for image thresholding.
Preparation of silver sulphide (Ag2S) thin films by chemical bath deposition for photocatalytic application Nedal A. Hussain; Lamyaa A. Latif; Haidar J. Mohamad
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp692-699

Abstract

Semiconductor silver sulphide (Ag2S) thin films were grown on the glass substrate by chemical bath deposition (CBD). Five films deposited at the room temperature in a bath containing an aqueous solution of silver sulphide. The chemically synthesized Ag2S films are annealed at 573K for one hour for the second sample, and two hours for the third sample. The fourth sample exposed to microwave irradiation for one hour, and two hours for the fifth sample. Surface morphology, photocatalytic, optical and structural properties of all Ag2S samples investigated. The optical parameters such as transmittance, absorbance, absorption coefficient and energy bandgap of the films with thermal annealing and expose microwave irradiation presented. Optical measurement shows high absorbance in the ultraviolet-visible (UV-Vis) region. The difference in bandgap values of Ag2S samples was located to be in the range of (1.5-2.05) eV. X-ray diffraction (XRD) measurements reflect the existence of polycrystalline, and the scanning electron microscopy images showed that the morphologies of Ag2S thin films have various microspheres. The summary of the samples in is in the annealing Ag2S thin film which exhibits the best photocatalytic application.
Nexus between Iraqi SMEs cloud computing adoption intention and firm performance: moderating role of risk factors Ali Salah Alasady; Hayder Salah Hashim; Wid Akeel Awadh
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1128-1135

Abstract

Cloud computing has been adopted in many developing nations, namely Iraq and Ghana, for its multiple benefits. Nonetheless, there is a dearth of studies on cloud computing adoption in developing nations. This study aims to investigate the factors influencing cloud computing adoption among Iraqi small and medium-sized enterprises (SMEs) and the implication of cloud computing on the SME firm performance. The diffusion of innovation (DOI) theory served as the foundation for the research model. Primary data from 396 SMEs were analysed using the structural equation modelling (SEM) approach through SmartPls 3.0 software for model testing. Resultantly, the use of cloud computing enhances firm performance. The technological factor also positively affects firm performance and cloud computing. The findings provide cloud service providers, managers, and government regulators with important recommendations to encourage the adoption and effective implementation of cloud computing in Iraq.
Novel extraction and tracking method used in multiplelevel for computer vision Ekhlas Watan Ghindawi; Sally Ali Abdulateef
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1061-1069

Abstract

Various scientific applications, including cosmological simulation, fluid simulation, and molecular dynamics, depend heavily on the analysis of particle data. Although there are techniques for feature extraction and tracking regarding volumetric data, it is more difficult to do such tasks for particle data due to the lack of explicit connectivity information. Even though one could transform the particle data to volume beforehand, doing so runs the risk of incurring error and growing the data size. In order to facilitate feature extraction and tracking for scientific particle data, we adopt a deep learning (DL) method in this research. In order to capture the relation between physical features and spatial locations in a neighborhood, we use a DL model that generates latent vectors. Through clustering the latent vectors, characteristics could be retrieved from the vectors. The Cam-shift tracking algorithm, which just needs inference of the latent vector for chosen regions of interest, is implemented in the feature space to accomplish quick feature tracking. With the use of two datasets, we test our approach and contrast it with other approaches already in use.
A novel and distributed three phase consensus based secured data sharing in internet of things environment Rashmi H. Chamarajappa; Guruprakash C. Dyamanna
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp636-646

Abstract

The most essential part of any internet of things (IoT) model is the wireless sensor networks (WSN); latest technologies are combined with the applications of WSN results in fast, efficient, flexible as well as economical models. These networks are highly prone to attacks considering their characteristic nature, which includes self-organization, a topology that is dynamic, large-scale and constrained on the resources. Various models have been proposed for the detection of attacks in these wireless sensor networks. This research work proposes three-phase consensus based secured data sharing (TCSDS) in IoT environment. TCSDS adopts the consensus-based protocol for designing the security model in this research. Furthermore, TCSDS comprises three distinctive phases, each phase consisting of novel algorithm. First phase includes the setting up threshold value for sensor nodes. Second phase includes the efficient data packet transmission and third phase includes the efficient and secure routing, which tends to discard the unsecured nodes. TCSDS is evaluated considering the different parameter like energy consumption, malicious packet detection and throughput. Further comparison with the existing model is carried out based on the classified and misclassified packet; through the comparative analysis, it is observed that the TCSDS approach simply outperforms the existing model.
Rectangular and radial region of interests on the edge of cylindrical phantom for spatial resolution measurement Choirul Anam; Nazil Ainurrofik; Heri Sutanto; Ariij Naufal; Mohammad Haekal
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp747-754

Abstract

The purpose of this study was to evaluate the effect of rectangular region of interest (ROI) size on modulation transfer function (MTF), to develop the radial ROI, and to compare both ROIs performances for MTF measurement using a cylindrical polymethyl methacrylate (PMMA) phantom. The PMMA phantom used in this study was rotated 45°. Four rectangular ROIs and a radial ROI were created to measure the MTF value. The rectangular ROI sizes were 3×41, 21×41, 41×41, and 61×41 pixels; each was placed at upper phantom edge. The radial ROI’s length was 41 pixels and placed at several points in phantom edge. The MTF calculation was automatically conducted using MATLAB. The MTFs from rectangular ROIs and radial ROI were then compared. The comparison of the MTF measurement was also conducted using three different filters. The MTF which used radial ROI was smoother than those of rectangular ROI for all filters. This indicated that radial ROI was more resistant to noise than rectangular ROI. Rectangular ROI with the 41×41 pixels had similar 50% and 10% MTF values with the radial ROI. The MTF value which was obtained using radial ROI is more accurate and robust than those obtained using rectangular ROI.
Comparative evaluation of data mining algorithms in breast cancer Mustafa Qahtan Alsudani; Hassan Falah Fakhruldeen; Israa Fayez Yousif
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp777-784

Abstract

Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. The authors of this review studied numerous data mining algorithms and implemented them such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, additive regression (AR), back propagation (BP) neural network, and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the authors found that SVM and Bagging had the highest precision and accuracy, respectively. Also assess the number of studies that provide machine learning techniques for breast cancer detection.
Anomaly detection for software defined datacenter networks using X-Pack Sneha Mahabaleshwar; Shobha Gangadhar; Sharath Krishnamurthy
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp995-1007

Abstract

The global data center market is growing as more and more enterprises are increasingly adopting cloud computing services and applications. Data centers are evolving towards highly virtualized architectures where transformation to software defined network (SDN) based solutions provides benefits in terms of network programmability, automation, and flow visibility. With the benefits, the need for securing network becomes essential as many critical applications are hosted on to such networking platforms. Anomaly detection is a continuous process of monitoring the traffic pattern and alerting the user about the anomalies if detected. For such real time analysis NoSQL and relational databases are less efficient. This paper proposes a framework for anomaly detection and alerting system using Elasticsearch database for SDN. Traffic patterns generated from SDN devices are continuously monitored and predefined actions are taken immediately if an anomaly is detected. The proof of concept is implemented in NOKIAs Nuage Networks Laboratory and the results showed a real time anomaly detection and took relevant actions within minimum time.
Grouping of Twitter users according to contents of their tweets Farha Naznin; Anjana Kakoti Mahanta
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp876-884

Abstract

In today’s world most of the people use social networking sites such as Twitter. They share their opinions and their views. through these media. Grouping these users will help us in different ways such as product recommendation, opinion mining, characterization of users based on their way of expressing their feelings. In this work, we present a technique to group the users based on the textual contents of the tweets. This technique is based on an unsupervised approach of machine learning that is clustering. A method is presented for representing the users using vector space model and TF-IDF weight scheme. K-means algorithm is employed for grouping the users using cosine distance as a distance measure. For the evaluation of this method, we construct a Twitter user dataset by using the Twitter application programming interface (API). A new technique is also proposed for characterization of the clusters formed. The experimental results are promising and from the study, it is found that the users in the clusters formed could be well defined by using the proposed cluster characterization technique.
A literature review for measuring maintainability of code clone Shahbaa I. Khaleel; Ghassan Khaleel Al-Khatouni
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1118-1127

Abstract

Software organizations face constant pressure due to stakeholder requirements and the increasing complexity of software systems. This complexity, combined with defects in code quality and failures, can pose risks to software systems. To ensure code is understood before maintenance, developers must spend over 60% of their time modifying and improving code quality, which is costly. This study examines the impact of code refactoring activities on software maintainability and quality by reviewing relevant research and explaining key terms. The research finds that refactoring activities can enhance specific quality characteristics, including maintainability, understandability, and testability. The study also identifies important factors that should be considered when developing refactoring tools. Refactoring enables code improvement without altering program behavior and can be applied multiple times to source code.

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