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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
Bi-directional trust management system in fog computing using logistic regression Ramamurthy Priyadarshini; Nandagopal Malarvizhi
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.pp808-815

Abstract

Fog computing is a decentralised computing infrastructure that brings data, storage, computation, and communication resources closer to end users by extending typical cloud computing services to the network edge. A fog node can serve another fog node based on their processing power allowing fog-to-fog interaction. Fog nodes, being independent must be trusted for delegation because they collect sensitive data and share with other discrete fog nodes, where standard cryptographic solutions are ineffective against the internal attacks tossed by rogue fog node. This paper proposes a Bi-directional trust management system for secure transactions and fog-to-fog collaboration to address this problem in a fog environment, which allows a service requester to assess a service provider’s trustworthiness and the service provider to assess the service requester's level of trust before beginning a connection. This trust management system works based on the recommendation system, which is estimated using logistic regression by fog service provider and subjective logic by fog service requester for the establishment of secured connection between them. Using quality of security parameters, the proposed work yields the result of decision making between the fog service requester and fog service provider.
Stepwise regression of agarwood oil significant chemical compounds into four quality differentiation Siti Mariatul Hazwa Mohd Huzir; Aqib Fawwaz Mohd Amidon; Anis Hazirah ‘Izzati Hasnu Al-Hadi; Nurlaila Ismail; Zakiah Mohd Yusoff; Saiful Nizam Tajuddin; Mohd Nasir Taib
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.pp735-741

Abstract

This paper gives precise summary on the application of stepwise regression model based upon the pre-process analysis of boxplot for four chemical compounds into four different qualities of agarwood oil. In the global market, agarwood oil is acknowledged as a pricey and valuable nature product owing to its benefits. Unfortunately, there is no standard grading method for agarwood oil grade classification. Intelligent model in grading the quality of agarwood oil is crucial as one of the efforts to classify the agarwood quality. The main model chosen in this study is stepwise regression by concerned specific parameter which is the value of correlation coefficient, R2. To achieve this goal, four out of eleven significant compounds of agarwood oil that consist of 660 data samples from low, medium low, medium high and high quality are representing the input. The independent variables are X1, X2, X3 and X4 which refer to the ɤ-Eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin compounds, respectively. MATLAB software version r2015a has been chosen as the simulation platform for this research work. The result showed that the stepwise regression model has a correlation coefficient of 0.756 and p-value less than 0.05 significance level which successfully passed the performance criteria toward regression value.
Evaluation of object detectors in recognizing crossroad intersection triangle sign Shahad Jaafar Shahbaz; Ali Abid Dawood Al-Zuky; Fatin Ezzat Muhy Al-Dean Al-Obaidi
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.pp890-898

Abstract

Variations in perspective, illumination, occlusion, motion blur, and weatherworn degeneration of signs could all be crucial in identifying road signs. The goal of this work is to evaluate cascade object detector and Speed Up Robust Features in detecting, recognizing crossroad intersection triangle sign, and determining the optimum threshold value. The current work is executed in Baghdad's streets during the daytime. Results showed the effectiveness of cascade detector in detecting the triangle sign than SURF with precision lies in the range (0.9-0.98). Finally, the highest precision was recorded at fifteen and twenty-five threshold values for cascade and SURF approaches respectively.
Research on secure workload execution scheme in heterogeneous cloud environment Fairoz Pasha; Jayapandian Natarajan
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.pp1047-1054

Abstract

The increasing demand for the hardware, software and infrastructure is playing a big role in the information technology domain towards the need of customer’s specific requirements. Cloud computing is a major backbone for providing such services over the internet. It includes the services such as applications, storage, network, scalability, sharing, virtualization, confidentiality, security, authentication, and integrity. A large number of data intensive workflow applications uses heterogeneous cloud environment for communication and computation operation. An intruder/attacker will utilize these environments for their benefit by flooding malicious links, unwanted information and others. In cloud environment, detecting a malicious device/packet during workflow execution is a critical and challenging task. The various workflow method with security, service level agreement (SLA) and quality of service (QoS) have been modelled in recent time; However, these models are not efficient in detecting malicious users and maintaining high level of QoS or workflow applications. This article focus is on addressing research future direction, issues and challenges of work in meeting secure and efficient workflow execution model for heterogeneous cloud environment.
Brain signals analysis for sleep stages detection using virtual instrumentation platform Abdeljalil El Hadiri; Lhoussain Bahatti; Abdelmounime El Magri; Rachid Lajouad
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.pp761-771

Abstract

This paper discusses the use of the Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) software tool for analyzing brain waveforms (i.e EEG: electroencephalogram) to study sleep stages such as deep sleep, light sleep and so on. The used EEG signals are generated in order to span all sleeping phases. Indeed, a mandatory step of signal processing has been performed, such as sampling, filtering and features extraction. This analysis is carried out with the LabVIEW program, which is a popular virtual instrumentation platform. The EEG signals used in the analysis were obtained from an open-source database and went through several steps, including noise removal, classification and feature extraction. To extract the feature, different filters are employed and the outputs of all filters are compared, leading to a sleep level detection. The simulation results show clearly the performances of this analysis.
Analysis of benchmark program results of worst case execution time for multithreaded programs Padma Priya Dharishini Paraman; Prakriya V. Ramana Murthy
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.pp990-1005

Abstract

Worst case execution time (WCET) estimation by static analyzers is being investigated with keen interest in view of their importance in designing applications for embedded systems that have real- time requirements. Recent work reported on improving precision of estimates of WCET of multithreaded programs, by improving precision of shared instruction cache analysis, shows significant improvement in WCET estimates. An abstraction of a multithreaded program as Hoare’s communicating sequential processes (CSP) specification program is realized to enable higher precision in micro-architectural modelling unit of WCET analyzer of multithreaded programs. A thread is viewed as a composition of CSP. The WCET of a thread may be viewed as dependent on WCET of processes in a thread and in turn WCET of each process is the WCET of the sub-graph of basic block nodes in the process. Corresponding CSP in interacting threads, based on calls to synchronization primitives wait and notify, generate shared cache interferences to the process in a thread whose WCET is being estimated by the analyzer. A detailed study of how partitioning of a thread into processes yields higher reduction in WCET is performed on benchmark programs. Furthermore, which processes in a thread yield higher reduction in WCET is performed.
Secure fragile watermarking based on Huffman encoding and optimal embedding strategy Mourad Zairi; Tarik Boujiha; Abdelhaq Ouelli
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.pp1132-1139

Abstract

Image watermarking is one of the most popular techniques used to assure information security, integrity, and authenticity. Watermarking algorithms can be categorised, according to the domain of insertion, as either spatial or spectral domain-based watermarking approaches. The resulted watermark can also be classified as either strong designed to withstand malicious attacks or fragile designed to detect every possible alteration. In order to combine the advantages of the two categories of watermarking algorithms, this paper proposes a new fragile hybrid domain-based watermarking scheme to get both robustness and imperceptibility using an optimal embedding strategy according to the entropy values of the host images blocs. To enhance security and safety, the watermark undergoes an encryption using Huffman encoding to produce a scrambled watermark. This scheme is evaluated based on different metrics like the peak signal to noise ratio, the structural index similarity, and the normalized correlation coefficient, satisfactory results are attained. The experimental results show that Huffman encryption and optimal blocs selection offer good features of security and imperceptibility.
Web-based autism screening using facial images and convolutional neural network Mohamed Ikermane; Abdelkrim El Mouatasim
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.pp1140-1147

Abstract

Developmental disabilities such as autism spectrum disorder (ASD) affect a person’s ability to interact socially, and communicate effectively and also cause behavioral issues. Children with ASD cannot be cured but they might benefit from early intervention to enhance their cognitive abilities, favorite their growth , and affect their lives and families in a positive way. Multiple standard ASD screening tools are used such as the autism diagnostic observational schedule (ADOS) and the autism diagnostic interview (ADI), which are known to be lengthy and challenging without specialist training to administrate and score. The process of ASD assessment can be time-consuming and costly, and the growing number of autistic cases worldwide indicates an urgent need for a quick, simple, and dependable self-administered autism screening tool that may be used if a child displays some of the common signs of autism, and to ensure whether or not he should seek professional full ASD diagnosis. According to a number of studies, ASD individuals exhibit facial phenotypes that are distinct from those of normally developing children. Furthermore, convolutional neural networks (CNN) have mostly found utility in image classification applications due to their high classification accuracy. Using facial images, a dense convolutional network (Densenet) model, and cloud-based advantages, in this paper we proposed a practical, fast, and easy-to-use ASD online screening approach. Easily available through the internet via the link “https://asd-detector.herokuapp.com/”, our suggested web-based screening instrument may be a practical and trustworthy tool for practitioners in their ASD diagnostic procedures with a 98 percent testing dataset classification accuracy.
Large dataset partitioning using ensemble partition-based clustering with majority voting technique Vunnava Dinesh Babu; Karunakaran Malathi
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.pp838-844

Abstract

Large datasets have become useful in data mining for processing, storing, and handling vast amounts of data. However, handling and processing large datasets is time-consuming and memory intensive. As a result, the researchers adopted a partitioning strategy to improve controllability and performance and reduce the time and memory required to handle large datasets. Unfortunately, the numerous clustering techniques available in the literature could confuse experts in choosing the best techniques for a given dataset. Furthermore, no clustering technique can tackle all problems, such as cluster structure, noise, or density. To manage large datasets, existing clustering techniques need scalable solutions. Therefore, this paper proposes an ensemble partition-based clustering with a majority voting technique for large dataset partitioning using the aggregation of k-means, k-medoids, fuzzy c-means, expectation-maximization (EM) and density-based spatial clustering of applications with noise (DBSCAN) techniques. These techniques cluster the large dataset individually in the first stage. The final clusters are discovered in the next stage through a majority voting technique among the five clustering algorithms. These five clustering algorithms assigned data instances to the cluster with the most votes. The experimental findings demonstrate that the ensemble partition-based clustering method surpasses the other five clustering algorithms in terms of execution time and accuracy.
Markov random field model and expectation of maximization for images segmentation Lalaoui Lahouaoui; Djaalab Abdelhak
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.pp772-779

Abstract

Image segmentation is a significant issue in image processing. Among the various models and approaches that have been developed, some are commonly used the Markov Random Field (MRF) model, statistical techniques (MRF). In this study a Markov random field proposed is based on an EM Modified (EMM) model. In this paper, The local optimization is based on a modified Expectation-Maximization (EM) method for parameter estimation and the ICM method for finding the solution given a fixed set of these parameters. To select the combination strategy, it is necessary to carry out a comparative study to find the best result. The effectiveness of our proposed methods has been proven by experimentation. We have applied this segmented algorithm to different types of images, exhibiting the algorithm's image segmentation strength with its best values criteria for EM statics and other methods.

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