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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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Biometric face recognition method using graphics processing unit system Abeer A. Mohamad Alshiha; Mohammed Wajid Al-Neama; Abdalrahman R. Qubaa
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp183-191

Abstract

The expansion of biometric applications and databases is worrying. Processing extensive or sophisticated biometric data results in longer wait times, which might restrict application usefulness. This work focuses on accelerating the processing of biometric data and proposes a parallel method of data processing that exceeds the capabilities of a central processing unit (CPU). The combination of the graphics processing unit (GPU) and compute unified device architecture (CUDA) results in at least three times the processing speed of a published accurate and secure multimodal biometric system. The GPU-assisted approach beats the CPU-only implementation when saturating the CPU-only performance with more people than the available thread count. The GPU-assisted solution is also proven to have the same accuracy as the original system, indicating accuracy and processing performance improvements in the demanding big data environment.
Search and classify topics in a corpus of text using the latent dirichlet allocation model Orlando Iparraguirre-Villanueva; Fernando Sierra-Liñan; Jose Luis Herrera Salazar; Saul Beltozar-Clemente; Félix Pucuhuayla-Revatta; Joselyn Zapata-Paulini; Michael Cabanillas-Carbonell
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp246-256

Abstract

This work aims at discovering topics in a text corpus and classifying the most relevant terms for each of the discovered topics. The process was performed in four steps: first, document extraction and data processing; second, labeling and training of the data; third, labeling of the unseen data; and fourth, evaluation of the model performance. For processing, a total of 10,322 "curriculum" documents related to data science were collected from the web during 2018-2022. The latent dirichlet allocation (LDA) model was used for the analysis and structure of the subjects. After processing, 12 themes were generated, which allowed ranking the most relevant terms to identify the skills of each of the candidates. This work concludes that candidates interested in data science must have skills in the following topics: first, they must be technical, they must have mastery of structured query language, mastery of programming languages such as R, Python, java, and data management, among other tools associated with the technology.
Quality of performance evaluation of ten machine learning algorithms in classifying thirteen types of apple fruits Nashaat M. Hussain Hassan; Basma Ramadan Gamal Elshoky; A. M. M. Mabrouk
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp102-109

Abstract

Recently, computer vision technology has become essential for the automatic, accurate, and fast classification of fruits. Actually, there are many challenges in separating the types of fruits that are somewhat similar, such as apples, pears, and peaches. However, the challenges become more difficult if the separation is on different varieties of the same fruit. While the difficulty doubles if the classification takes place with a large number of different varieties of the same fruit. Most of the literature which is presented in this regard, and which is relied on the use of machine learning techniques lacked the following: first; the focus was on certain technologies such as k-nearest neighbor (KNN), support vector machine (SVM) without looking at many other machine learning techniques. Second; the literature was concerned only with measuring the accuracy of the techniques that are used, without looking at the relationship between the accuracy and processing speed (computation times). This manuscript aims to study and analyze the results of measuring accuracy and computation times for ten machine-learning techniques in order to identify and classify thirteen types of apples. After studying and analyzing the results, many observations were made, which will be referred to in the results section.
Utilization of deep learning and semantic analysis for opinion mining in information extraction: a review Mekala Susmitha; Shaik Razia
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp469-480

Abstract

In concern of the increasing availability and popularity of the opinion information sources at a different platform like individual blogs, online feedback, and social network are proliferating and gaining new opportunities and challenges that can be actively exploited using information technology to seek and comprehend people's opinions. In today's field people or entrepreneurs before taking any decision they must be considering the opinion of peoples or information networks. Most of them express the view or opinion through social media platform like Tweeter, Facebook, or blogs on the internet. Therefore, it is essential to analyze to automatically analyze the immense amount of social data available on the internet. Deep learning (DL) has appeared as an influential machine learning (ML) method that studies the properties of different layers or data and provides more advanced predictive results. The study of DL, along with success in many other practical fields, has been widely used in opinion and emotion analysis in recent years. This review explores new challenges in brainstorming and facilitates DL study and the use of semantic analysis. The analysis focuses mainly on methods that try to solve new challenges identified by empathy programs, compared to those already in place. It also conducts extensive research on its current uses in emotion analysis.
Improved grey wolf optimizer for multiple unmanned aerial vehicles task allocation Yu Wang; Qifang Luo; Yongquan Zhou
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp577-585

Abstract

Grey wolf optimizer (GWO) is a metaheuristic optimization algorithm proposed in 2014, which has already been applied in many fields. However, there are still two problems in GWO: i) during the optimization process, there are three leading wolves to lead the population for search, resulting in poor population diversity and ii) because of its position updated equation which not only brings strong convergence ability but also makes it easily fall into local optimal. In this paper, to overcome this, the following contributions were made: i) an improved GWO (IGWO) with two strategies was proposed to solve the above problems and ii) for verifying the effectiveness of IGWO, it was applied in solving multiple UAVs task allocation problems. The experimental results show that IGWO can solve this problem well and suit for large-scale complex examples.
An optimal model for classification of lung cancer using grey wolf optimizer and deep hybrid learning Rashmi Mothkur; Veerappa Budhihal Nagendrappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp406-413

Abstract

In recent years, metaheuristic methods have shown major advantages in the field of feature selection due to its comprehensibility and possible extensive search competence. However, the majority of evolutionary computation-based feature selection algorithms in use today are wrapper approaches, which are expensive to compute, particularly for extensive biomedical data. Developing an effective evaluation strategy is crucial for significant reduction of computational cost. The proposed framework extracts deep feature from ResNet-50 and VGG-16 based convolutional neural models with initial segmentation process based on marker-controlled watershed method. Next the feature reduction is a two-fold approach with principal component analysis applied to reduce the dimensionality of large feature space from convolutional neural network (CNN) models as first step. The second step is optimal feature subset selection using a swarm intelligence method referred as modified grey wolf optimization. Finally, the selected feature subset is fed to various machine learning classifiers. The experimental result reveals that the proposed algorithm outperforms the other state-of-the-art methods with classification accuracy of 96.56%, thus upholding the dependability of the approach.
Prediction on field crops yield based on analysis of deep learning model Iniyan Shanmugam; Jebakumar Rethnaraj; Srinivasan Rajendran; Senthilraja Manickam
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp518-527

Abstract

Agriculture has a key role in the overall economic development of the country. Climate change, irregular rainfall, changes in the nutrient content of the soil, and other environmental changes are seen as a severe problem in crop yield prediction. Using deep learning (DL) models that incorporate multiple factors can be viewed as an essential strategy for attaining accurate and effective solutions to this issue. The crop yield can be predicted using yield data obtained from a historical source that includes information about the weather, soil nutrient content, soil type, the season in which the crop was grown, and its yield. In order to train the model and achieve high accuracy, a large set of data including multiple factors would be required. This research aims to forecast the yield of a certain crop using long short-term memory (LSTM) time series analysis and the information currently available. The data used to construct the models was obtained from a reputable source and contains correct numbers. Before growing a crop that has been sown on a piece of agricultural land, the yield prediction utilizing advanced methodologies can assist farmers predict the yield of a specific crop.
A survey: medical health record data security based on interplanetary file system and blockchain technologies Rana Abbas Al-Kaabi; Alharith A. Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp586-597

Abstract

The adoption of modern health records is growing more mature, yet security issues always accompany it. Interplanetary file system (IPFS) and blockchain are developing technologies with decentralization, distributed fault tolerance, and trustworthiness. Using IPFS and blockchain technology to tackle medical health record data security issues is a very promising trend, and it is presently being utilized to secure medical health record data security. This article first explains the idea of IPFS and highlights the classification of existing IPFS and blockchain techniques before briefly discussing distributed ledger to tackle the existing medical health record data security challenges and faults. Finally, to preserve medical health records, a new medical health record storage architectural model based on IPFS and blockchain technologies is presented.
Facial action coding-based facial sub-structures for anxiety emotion classification Rawinan Praditsangthong; Pattarasinee Bhattarakosol
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp208-218

Abstract

Most stroke patients usually have problems in communication and body movement, such as speaking, sitting, walking, and picking up items. Moreover, the number of caregivers is smaller than the number of stroke patients. Nevertheless, these patients need 24-hour caregivers for the patients’ safety. Therefore, the objective of this research is to determine patterns of anxiety emotion via facial expressions from the sub-structures on the face, such as the inner brow raiser, brow lower, lid raiser, and lip part. Random samples of 360 facial images from horror-thriller movies based on the internet movie database (IMDb) website were selected. Then, 68 facial landmarks for classifying the emotions were applied to each facial image. The differences in these 68 positions before and after the changed emotions were used as the emotional indicators. Furthermore, these different values are applied to implement a decision tree with all the boundaries of the sub-structures disclosed as a suitable classification model for emotion detection. Consequently, the accuracy when applying this decision tree with other facial images is 83.33%.
Approach by modeling to generate an e-commerce web code from laravel model M'hamed Rahmouni; Mouad Bouzaidi; Samir Mbarki
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp257-266

Abstract

The world of web is constantly evolving. Today, we no longer speak of a website but of a web application. The growing difficulty of designing web applications has given rise to solutions and tools. The framework is one of them. Providing a serious framework for development by offering strict development rules, as well as generic and out-of-the-box components, PHP laravel framework is one of them. This paper aims to present the different stages of modeling and development of an enterprise resource planning (ERP) system that will be implemented by PHP laravel by defining the main concepts involved in this information system modeling and development process. To lead and implement the end-to-end development of the said system, we apply the model-driven architecture (MDA) approach. This approach is based on atlas transformation language (ATL). The result of this paper is an Ecore file, a reliable MVC2 web model of e-commerce ERP, which will be the input file to generate the aforementioned system code. To validate this approach, we implemented a case study. The result of this work is very satisfying. Thus, we arrived to generate all necessary elements for this ERP code generation by respecting the MVC2 model and the PHP coding.

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