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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 66 Documents
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Search results for , issue "Vol 23, No 2: August 2021" : 66 Documents clear
Levenberg–Marquart logistic deep neural learning based energy efficient and load balanced routing in MANET A. Sangeetha; T. Rajendran
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1002-1010

Abstract

As the advent of new technologies grows, the deployment of mobile ad hoc networks (MANET) becomes increasingly popular in many application areas. In addition, all the nodes in MANET are battery operated and the node mobility affects the path stability and creates excessive traffic leads to higher utilization of energy, data loss which degrades the performance of routing. So, in this paper we propose Levenberg–Marquardt logistic deep neural learning based energy efficient and load balanced routing (LLDNL-EELBR) which is a machine learning method to deeply analyze the mobile nodes to calculate residual load and energy and it also uses logistic activation function to select the mobile node having higher residual energy and residual load to route the data packet. Experimental evaluations of three methods (LLDNL-EELBR, multipath battery and mobility-aware routing scheme (MBMA-OLSR) and opportunistic routing with gradient forwarding for MANETs (ORGMA)) were done and the result reveals that LLDNL-EELBR method is able to increase the through put and minimizes the delay and energy consumption in MANET when compared to works under consideration. 
Internet of things based vital signs monitoring system: A prototype validity test Osman Yakubu; Emmanuel Wireko
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp962-972

Abstract

The advent of the internet of things (IoT) has resulted in an upsurge in the deployment of digital health care systems enabling patients’ health conditions to be remotely monitored. This article presents an intelligent and automated IoT-based vital signs monitoring system to aid in patient care. A the oretical framework was established to guide the development of a prototype. It encompasses the patient, IoT sensors, input and storage unit and data processing, analysis and data transmission. The prototype is equipped with the capability of sensing a patient’s body temperature, heart rate, and respiration rate in real time and transmits the data to a cloud data repository for storage and analysis. Alerts are sent to caregivers using SMS, email and voice calls where urgent attention is required for the patient. The voice call isto ensure a caregiver does not miss the alert since SMS and email may not be checked on time. To ensure privacy of patients, a caregiver has to be biometrically verified by either fingerprint or facial pattern. The experimental results confirmed the accuracy of the data gathered by the prototype, privacy of patients is also guaranteed compared to other benchmark systems.
Design and implementation of bi-directional converter with internet of things control based reading Wisam Dawood Abdullah; Raad Z. Homod; Abdulbasit H. Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp938-952

Abstract

In this paper, a new technique to monitor and control bidirectional DC-DC converter was designed and implemented precisely. A prototype of a complete system is verified with efficient communication capabilities. This system is realized by integrating the internet of things (IoT) operating system and the bidirectional DC-DC converter. The IoT communication facilities further develop and extend the platform for this system. The DC-DC converter with the soft switching technique will then convert the battery voltage to a high voltage of 380V inverter bus in emergencies via boost converter mode. High-frequency toroidal transformer has been used for power level shifting and isolation between the primary and secondary sides of the transformer. The closed-loop control scheme is implemented in software by using a high-performance 32-bit STM32 micro controller. IoT technique is used to find current, voltage and perform the communication smoothly through Wi-Fi sensors to complete the design of the system. The results of the proposed system prove the effectiveness of the proposed system with high-performance specifications.
Data security using random dynamic salting and AES based on master-slave keys for Iraqi dam management system Hussam J. Ali; Talib M. Jawad; Hiba Zuhair
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1018-1029

Abstract

In the present time, dam management is considered one of the important challenges for e-government in Iraq, becuase it needs information technology infrastructure, data integrity and protection of user privacy against Internet threats that render such vital infrastructure ineffective. This struggle between the proposed dam management system (DMS) and a multi-tier secure model specifically for the Fallujah dam (and generally for all dams) which is addressed in this paper as a case study. To do this, a relational database design will discuss the development of a multi-tier secure model for integration of the dam management framework with its functions. This paper will discusse encryption and decryption of the dam data using the advanced encryption standard (AES) algorithm with derived keys via PBKDF2 and RNG sequences generator and Slave key for salting protection. The experimental results and analysis on the speed of encryption/decryption process, entropy value, plain text sensitivity, key sensitivity, keyspaceanalysis and histogram analysis will prove the the proposed scheme can impede the known attacks like brute force attacks, statistical and differential.Thus, the encryption scheme can be implemented on the proposed DMS and any other information system, as the implementation which will be presented in the results.
A review on supervised learning methodologies for detection of exudates in diabetic retinopathy Ujwala W. Wasekar; R. K. Bathla
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp837-846

Abstract

Diabetic retinopathy has become one of the major reasons for blindness in the world. Early and precise diagnosis of the disease may save one’s eyesight from irreversible damage. Manual detection of lesions is time consuming and may not be as accurate as desirable. Many automated systems have been developed recently to help ophthalmologists in their endeavors. Exudates are one of the early signs of manifestation of diabetic retinopathy. In this paper, the methodologies detecting exudates in retinal fundus images were reviewed. These methods were categorized into deep learning, machine learning and methods primarily focusing on image processing techniques. The comprehensive view of the performances of the methods was given. Several datasets were described briefly. Most of the researchers preferred combination of multiple publically available databases. Also, the potential areas of research were discussed. It was found that sensitivity which identifies the abnormal images correctly, is the most widely used performance measure. The study will be helpful to the researchers wanting to explore more in this field.
Improving bit error-rate based on adaptive Bose-Chaudhuri Hocquenghem concatenated with convolutional codes Ahmed Samy; Ashraf Y. Hassan; Hatem M. Zakaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp890-901

Abstract

Several algorithms have been proposed to avoid the error floor region, such as the concatenation codes that requires high computational demands in addition to high complexity. This paper proposes a technique based on using cascaded BCH and convolutional codes that leads to better error correction performance. Moreover, an adaptive method based on sensing the channel's noise to determine the number of the parity bits that will be added to the used BCH that reduces the consumed bandwidth and the transmitted parity bits is presented. A further enhancement is fulfilled by using parallel processing branches, resulting in reducing the consumed time and speed up the performance. The results show that the proposed code presents a better performance. A high reduction in the number of cycles that will be used in the encoding and decoding compared with the classical method and finally a flexible parity bits method based on the signal-to-noise ratio of the channel that reduced the parity bits which leads to reduce the consumed bandwidth. The MATLAB simulation and the field programmable gate array (FPGA) implementation will be provided in this paper to validate the proposed concept.
Development of a new system to detect denial of service attack using machine learning classification Mohammad M. Rasheed; Alaa K. Faieq; Ahmed A. Hashim
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1068-1072

Abstract

Denial of service (DoS) attack is among the most significant types of attacks in cyber security. The objective of this research is to introduce a new algorithm to distinguish normal service requests from the denial of service attacks. Our proposed approach can detect the denial of service attacks by the analysis of the packets sent from the client to the server, which depend on machine learning. Our algorithm collects different datasets of benign network traffic and different types of denial of service attacks, such as DDoS, DoS Hulk, DoS GoldenEye, DoS Slowhttptest and DoS Slowloris, that were used for training. Moreover, our algorithm monitors the network every specific time to find denial of service attack. Our results show that the algorithm can detect the benign cases and distinguish the types of denial of service attack. Furthermore, the results could achieve 99 percentage of correct classification of all selected cases.
Implementing optimization of PID controller for DC motor speed control Yasir G. Rashid; Ahmed Mohammed Abdul Hussain
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp657-664

Abstract

The point of this paper presents an optimization technique which is flexible and quick tuning by using a genetic algorithm (GA) to obtain the optimum proportional-integral-derivative (PID) parameters for speed control of aseparately excited DC motor as a benchmark for performance analysis. The optimization method is used for searching for the proper value of PID parameters. The speed controller of DC motor using PID tuning method sincludes three types: MATALB PID tunner app., modified Ziegler-Nicholsmethod and genetic algorithm (GA). PID controller parameters (Kp, Ki and Kd) will be obtained by GA to produce optimal performance for the DC motor control system. Simulation results indicate that the tuning method of PID by using a genetic algorithm is shown to create the finest result in system performance such as settling time, rise time, percentage of overshoot and steady state error. The MATLAB/Simulink software is used to model and simulate the proposed DC motor controller system.
Towards developing a pocket therapist: an intelligent adaptive psychological support chatbot against mental health disorders in a pandemic situation Intissar Salhi; Kamal El Guemmat; Mohammed Qbadou; Khalifa Mansouri
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1200-1211

Abstract

Nowadays with COVID-19 ongoing epidemic outbreak, containment for weeks was one of the most effective measures adopted to deal with the spread of the virus until a vaccine could be efficient. Over that period, increased anxiety, depression, suicide attempts and post-traumatic stress disorder are accumulated. Several studies referred to the need of using chatbots, which recognizes human emotions in such pandemic contexts. More recently, numerous research papers improved the ability of artificial intelligence methods to recognize human emotion. However, they are still limited. The aim of this paper is the development of a chatbot against the disturbing psychic consequences of the pandemic, taking human emotion recognition into account. The object is to help people; especially students; suffering from mental disorders, by progressively understanding the reasonsbehind them. This innovative chatbot was developed by using the natural language processing model of deep learning. An advanced model of deep learning has been elaborated the intention for people and that to help them to regulate their mood and to reduce distortion of negative thoughts, that why a collection of a new database was done. The sequence-to-sequence model encoder and decoder consist of Long short-term memory cells and it is defined with the bi-directional dynamic recurrent neural network packets.
Comparison of levels and fusion approaches for multimodal biometrics S. Sujana; V. S. K. Reddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp791-801

Abstract

The biometric-based authentication system occupies maximal space in the field of security administration. Biometric applications are swiftly accelerating in day-to-day life such as computer login, smart homes, online banking, hospitals, border areas, industries, forensics, e-voting attendance system and investigation of crime. A reliable and accurate recognition body can be achieved with multimodal biometric methodologies. In this paper, we discuss starting with an introduction to biometric systems followed by their classification, and advantages as well as disadvantages. In today’s world, most of the systems are unimodal biometrics having a lot of limitations to overcome those multimodal biometrics comes in to picture. In this paper we have discussed comprehensive representation on the system of multimodal biometric, various modes of undertakings, the significance of information fusion, a different section is allotted on the various possible levels of fusion involving sensor-level, feature-level, score-level, and decision -level as well as different rules of fusion.

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