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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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New memoryless self-scaling quasi Newton strategy on large scale unconstrained optimization problems Aseel M. Qasim; Zinah F. Salih
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp339-345

Abstract

In unconstrained optimization algorithms, we employ the memoryless quasi Newton procedure to construct a new conjugacy coefficient for the conjugate gradient approaches. This newer updating formula was adapted by scaling the well-known broyden fletcher glodfarb shanno (BFGS) formula by a selfscaling factor in order to reach to the new form of the conjugacy coefficient which makes a satisfactory result in the descent direction and satisfies the globally convergent features when compared the proposed method to HS standard conjugate gradient approach. The theorems are studied in detail and moreover the numerical results of this paper is depend on a Fortran programming which are extremely stable.
Intelligent voltage controller based on firefly algorithm for DC-DC boost converter Darmansyah Darmansyah; Imam Robandi
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp735-743

Abstract

DC-DC boost converter is one of the important tools of photovoltaic (PV) power plant. DC-DC boost converter can be used to adjust the voltage output of photovoltaic before going to the inverter. In addition, DC-DC boost converter can be used as additional devices to make the PV plant operated in maximum power point condition. To get the optimal voltage adjusment, an appropriate controller is essential. Generally, PI controller is commonly used to control the switching procedure of the boost converter. However, with uncertainty of the source (uncertainty of the PV output), PI controller is out off dated. Hence designing PI controller based on the metaheuristic algorithm such as firefly algorithm is essential. This paper is proposed an intelligent voltage controller for DC-DC boost converter based on firefly algorithm (FA). From the simulation results it is noticeable that the PI based on FA could provide better control signal (indicated by the fastest settling time of the boost converter).
Fuzzy spider monkey optimization routing protocol to balance energy consumption in heterogeneous wireless sensor networks Khalid Hameed Zaboon; Nagham Mumtaz Kudhair; Imad S. Alshawi
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.pp921-930

Abstract

Wireless sensor network (WSN) nodes have high computation limitations, limited communication capabilities, and limited power resources because of the difficulty or impossibility of replacing or recharging the sensor battery. Energy consumption in nodes is a critical issue to consider while developing WSNs. Many routing protocols are proposed for energy conservation as an important goal for improvement. Nonetheless, just delivering energy is not enough to prolong the life of a WSN. Unbalanced energy depletion is a significant problem in WSNs, often resulting in network splits and a reduction in network lifetime, as well as performance retrogression. This article, therefore, proposes a robust protocol called the fuzzy spider monkey optimization routing protocol (FSMORP) to determine the best data path routing for heterogeneous WSNs (HWSNs). In this case, an FSMORP computes the best path across the cluster heads from a sensor to the sink. This work uses the clustering method to organize heterogeneous nodes in HWSNs. The simulation result indicates that the FSMORP considerably enhances data latency reduction, energy balancing, and lifetime maximization for the network.
Topic modelling of legal documents using NLP and bidirectional encoder representations from transformers Amar Jeet Rawat; Sunil Ghildiyal; Anil Kumar Dixit
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1749-1755

Abstract

Modeling legal text is a difficult task because of its unique features, such as lengthy texts, complex language structures, and technical terms. During the last decade, there has been a big rise in the number of legislative documents, which makes it hard for law professionals to keep up with legislation like analyzing judgements and implementing acts. The relevancy of topics is heavily influenced by the processing and presentation of legal documents in some contexts. The objective of this work is to understand the legal judgement corpus related to cases under the Hindu Marriage Act of India. The study looked into various methods to generate sentence embeddings from the judgement. This paper employs the power of the BERTopic algorithm for generating significant topics.
A disaster classification application using convolutional neural network by performing data augmentation Mummaneni Sobhana; Smitha Chowdary Chaparala; Devaganugula N. V. S. L. S. Indira; Konduru Kranthi Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1712-1720

Abstract

Natural disasters are catastrophic events and cause havoc to human life. These events occur in the most unpredictable times and are beyond human control. The aftermath of the disasters is devastating ranging from loss of life to relocation of large groups of the population. With the development in the domains of computer vision (CV) and Image processing, machine learning and deep learning models can integrate images and perform predictions. Deep learning techniques employ many robust techniques and provide significant results even in the case of images. The detection of natural disasters without human intervention requires the help of deep learning techniques. The project aims to employ a multi-layered convolutional neural network (CNN) organization to classify the images related to natural disasters related to earthquakes, floods, cyclones, and wildfires.
Communication frame work in an electric vehicle charging station supporting solar energy management Victor George; Pradipkumar Dixit; Soman Dawnee; Kushagra Agarwal; Vismayi Venkataramu; Deeksha B. Giridhar
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp49-57

Abstract

Exploiting Renewable energy to the maximum extent possible in an electric vehicle charging station (EVCS) is the key in supporting the anticipated carbon reduction from the electric vehicles (EVs). Knowing the expected load and the solar energy in advance at the EVCS can be crucial in framing a proper energy management strategy. Selection of suitable parameters associated with the participating EVs and EVCS are vital in utilizing them for predicting the probable EV load and expected solar energy for a given period under consideration. A prototype EVCS with smart communication infrastructure is developed considering solar pv as the energy source. Real time communication of the parameters between multiple agents has been established effectively using an interactive website, cloud server and an short message service (SMS) application programming interface (API). The data generated from the prototype models have been utilized in a random forest regression (RFR) classifier model in order to predict the probable solar energy and the expected EV load for every minute duration. The integrated communication frame work is found to be less complex to implement for an autonomous direct current (DC EVCS). The details provided at the graphical user interface (GUI) designed at the EVCS can be instrumental in developing a proper energy management strategy.
Target selection method on the occluded and distant object in handheld augmented reality Ajune Wanis Ismail; Nur Ameerah Abdul Halim; Rohaya Talib; Ahmad Johari Sihes
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.pp1157-1165

Abstract

Existing interaction techniques within handheld augmented reality (AR) have frequently used touchscreen input (pure two-dimensional (2D) pointing and clicking) from the handheld device's display for target selection on the virtual object. However, performing accurate target selection on a distant target object becomes challenging as the target object will appear smaller when the distance increases. Aside from that, the difficulty increases in performing target selection when another virtual object obscures the distant virtual object. Therefore, this study aims to present a target selection method to perform the target selection. We enable the raycasting technique with real hand gesture for the target selection method on the occluded and distant object in handheld AR. The leap motion device is mounted at the back of the handheld device to track the real hand gesture. The markerless tracking technology of simultaneous localization and mapping (SLAM) is implemented to enable the AR environment. Based on the results, the aim of this study was achieved.
Comparative analysis and feature importance of machine learning and deep learning for heart disease prediction Priyanka Gupta; Dambarudhar Seth
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp451-459

Abstract

Cardiovascular disease (CVD) or heart disease is one of the main reasons for early death, even at young age and that too often sudden. If it is detected more accurately, much before it seriously affects the individual, life can be saved through proper medication and changes in lifestyles. In this work different machine learning classifiers and a deep learning algorithm multi-layer perceptron (MLP) were applied on two different datasets, Framingham heart study dataset and UCI heart disease dataset for prediction of heart disease. These algorithms were optimized using hyperparameter tuning and compared for their performance measures and prediction accuracies. For different features, feature importance scores were calculated using machine learning algorithms. The features were ranked according to their scores. Out of various classification algorithms, random forest algorithm has shown the best results with prediction accuracy of 97.13% for Framingham dataset. MLP has shown good performance for both the datasets.
Supervised learning using support vector machine applied to sentiment analysis of teacher performance satisfaction Omar Chamorro-Atalaya; Dora Arce-Santillan; José Antonio Arévalo-Tuesta; Lilia Rodas-Camacho; Ronald Fernando Dávila-Laguna; Rufino Alejos-Ipanaque; Lilly Rocío Moreno-Chinchay
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp516-524

Abstract

Satisfaction with teaching performance is an important measurement process in higher education institutions, for this reason, applying sentiment analysis to the opinions of university students through the support vector machine (SVM) Fine Gaussian supervised learning algorithm represents an important contribution to the academic literature. This article identifies the best classification algorithm according to performance parameters for predicting student satisfaction with teaching performance through sentiment analysis; the subsequent implementation of the research has the purpose of strengthening teaching practices, in addition to allowing continuous training of teaching for the benefit of student learning. This article has provided a compact predictive model, with literature review based on SVM and sentiment analysis techniques. Through the machine learning classification learner technique, it is identified that the SVM algorithm: Fine Gaussian SVM is the one with the best accuracy equal to 98.3%. Likewise, the performance metrics for the four classes of the model were identified, which have a sensitivity equal to 88.89%, a specificity of 98.04%, a precision of 99.21% and an accuracy of 98.85%.
Reputation-based security model for detecting biased attacks in big data Vinod Desai; Dinesha Hagare Annappaiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1567-1576

Abstract

As internet of things (IoT) devices are increasing since the emergence of these devices in 2010, the data stored by these devices should have a proper security measure so that it can be stored without getting in hands of an attacker. The data stored has to be analyzed whether the data is safe or malicious, as the malicious data can corrupt the whole information. The security model in big data has many challenges such as vulnerability to fake data generation, troubles with cryptographic protection, and absent security audits. As cyber-attacks are increasing the main objective of each organization is to secure the data efficiently. This paper presents a model of reputation security for the detection of biased attacks on big data. The proposed model provides various evaluation models to identify biased attack in malicious IoT devices and provide a secure communication metric for big data. The results show better rates in terms of attack detection rate, attack detection failure rata, system throughput and number of dead nodes when the attack rate is increased when compared with the existing reputation-based security (ERS) model. Moreover, this model reputation-based biased attack detection (RBAD) increases the security of the IoT devices in the big data and reduces the biased attack coming from various malicious nodes.

Page 711 of 918 | Total Record : 9174

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