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Some advantages of non-binary Galois fields for digital signal processing

10.11591/ijeecs.v23.i2.pp871-878
Inabat Moldakhan , Dinara K. Matrassulova , Dina B. Shaltykova , Ibragim E. Suleimenov
It is shown that the convenient processing facilities of digital signals that varying in a finite range of amplitudes are non-binary Galois fields, the numbers of which elements are equal to prime numbers. Within choosing a sampling interval which corresponding to such a Galois field, it becomes possible to construct a Galois field Fourier transform, a distinctive feature of which is the exact correspondence with the ranges of variation of the amplitudes of the original signal and its digital spectrum. This favorably distinguishes the Galois Field Fourier Transform of the proposed type from the spectra, which calculated using, for example, the Walsh basis. It is also shown, that Galois Field Fourier Transforms of the proposed type have the same properties as the Fourier transform associated with the expansion in terms of the basis of harmonic functions. In particular, an analogue of the classical correlation, which connected the signal spectrum and its derivative, was obtained. On this basis proved, that the using of the proposed type of Galois fields makes it possible to develop a complete analogue of the transfer function apparatus, but only for signals presented in digital form.
Volume: 23
Issue: 2
Page: 871-878
Publish at: 2021-08-01

A fully integrated violence detection system using CNN and LSTM

10.11591/ijece.v11i4.pp3374-3380
Sarthak Sharma , B. Sudharsan , Saamaja Naraharisetti , Vimarsh Trehan , Kayalvizhi Jayavel
Recently, the number of violence-related cases in places such as remote roads, pathways, shopping malls, elevators, sports stadiums, and liquor shops, has increased drastically which are unfortunately discovered only after it’s too late. The aim is to create a complete system that can perform real-time video analysis which will help recognize the presence of any violent activities and notify the same to the concerned authority, such as the police department of the corresponding area. Using the deep learning networks CNN and LSTM along with a well-defined system architecture, we have achieved an efficient solution that can be used for real-time analysis of video footage so that the concerned authority can monitor the situation through a mobile application that can notify about an occurrence of a violent event immediately.
Volume: 11
Issue: 4
Page: 3374-3380
Publish at: 2021-08-01

Performance analysis for three cases of outage probability in one-way DF full-duplex relaying network with presence of direct link

10.12928/telkomnika.v19i4.17477
Phu Tran; Industrial University of Ho Chi Minh City Tin , Van-Duc; Van Lang University Phan , Le Anh; Ton Duc Thang University Vu
In this paper, the one-way decode-and-forward (DF) full-duplex relaying network system with presence of direct link is investigated. In the analysis section, we derived the exact, lower, and upper bound for outage probability (OP) with maximal ratio combining (MRC) at the receiver. Furthermore, the system performance's analytical expressions are verified by using the Monte Carlo simulation. In addition, we investigated the effect of the main parameters on the OP of the proposed system. Finally, we can sate that the simulation curves overlap the analytical curves to convince the analysis section. This research can provide a novel recommendation for the communication network.
Volume: 19
Issue: 4
Page: 1059-1065
Publish at: 2021-08-01

Online traffic classification for malicious flows using efficient machine learning techniques

10.12928/telkomnika.v19i4.20402
Ying Yenn; Universiti Teknologi Malaysia Chan , Ismahani Bt; Universiti Teknologi Malaysia Ismail , Ban Mohammed; Al-Nahrain University Khammas
The rapid network technology growth causing various network problems, attacks are becoming more sophisticated than defenses. In this paper, we proposed traffic classification by using machine learning technique, and statistical flow features such as five tuples for the training dataset. A rule-based system, Snort is used to identify the severe harmfulness data packets and reduce the training set dimensionality to a manageable size. Comparison of performance between training dataset that consists of all priorities malicious flows with only has priority 1 malicious flows are done. Different machine learning (ML) algorithms performance in terms of accuracy and efficiency are analyzed. Results show that Naïve Bayes achieved accuracy up to 99.82% for all priorities while 99.92% for extracted priority 1 of malicious flows training dataset in 0.06 seconds and be chosen to classify traffic in real-time process. It is demonstrated that by taking just five tuples information as features and using Snort alert information to extract only important flows and reduce size of dataset is actually comprehensive enough to supply a classifier with high efficiency and accuracy which can sustain the safety of network.
Volume: 19
Issue: 4
Page: 1396-1406
Publish at: 2021-08-01

Improving bit error-rate based on adaptive Bose-Chaudhuri Hocquenghem concatenated with convolutional codes

10.11591/ijeecs.v23.i2.pp890-901
Ahmed Samy , Ashraf Y. Hassan , Hatem M. Zakaria
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.
Volume: 23
Issue: 2
Page: 890-901
Publish at: 2021-08-01

Physical cyber-security algorithm for wireless sensor networks

10.12928/telkomnika.v19i4.18464
Dhuha Dheyaa; University of Technology Khudhur , Muayad Sadik; University of Technology Croock
Today, the wireless sensor network (WSN) plays an important role in our daily life. In addition, it is used in many applications such as military, medical, greenhouse, and transport. Due to the sending data between its nodes or to the base station requires a connection link, the sensor nodes can be exposed to the many attacks that exploit the weaknesses of the network. One of the most important types of these attacks is the denial of service (DoS). DoS attack exhausts the system's resources that lead the system to be out of service. In this paper, a cyber-security algorithm is proposed for physical level of WSN that adopts message queuing telemetry transport (MQTT) protocol for data transmission and networking. This algorithm predicts the DoS attacks at the first time of happening to be isolated from the WSN. It includes three stages of detecting the attack, predicting the effects of this attack and preventing the attacks by excluding the predicted nodes from the WSN. We applied a type of DoS attack that is a DoS injection attack (DoSIA) on the network protocol. The proposed algorithm is tested by adopting three case studies to cover the most common cases of attacks. The experiment results show the superior of the proposed algorithm in detecting and solving the cyber-attacks.
Volume: 19
Issue: 4
Page: 1177-1184
Publish at: 2021-08-01

Text classification model for methamphetamine-related tweets in Southeast Asia using dual data preprocessing techniques

10.11591/ijece.v11i4.pp3617-3628
Narongsak Chayangkoon , Anongnart Srivihok
Methamphetamine addiction is a prominent problem in Southeast Asia. Drug addicts often discuss illegal activities on popular social networking services. These individuals spread messages on social media as a means of both buying and selling drugs online. This paper proposes a model, the “text classification model of methamphetamine tweets in Southeast Asia” (TMTA), to identify whether a tweet from Southeast Asia is related to methamphetamine abuse. The research addresses the weakness of bag of words (BoW) by introducing BoW and Word2Vec feature selection (BWF) techniques. A domain-based feature selection method was performed using the BoW dataset and Word2Vec. The BWF dataset provided a smaller number of features than the BoW and TF–IDF dataset. We experimented with three candidate classifiers: Support vector machine (SVM), decision tree (J48) and naive bayes (NB). We found that the J48 classifier with the BWF dataset provided the best performance for the TMTA in terms of accuracy (0.815), F-measure (0.818), Kappa (0.528), Matthews correlation coefficient (0.529) and high area under the ROC Curve (0.763). Moreover, TMTA provided the lowest runtime (3.480 seconds) using the J48 with the BWF dataset.
Volume: 11
Issue: 4
Page: 3617-3628
Publish at: 2021-08-01

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

10.11591/ijece.v11i4.pp2820-2829
Omar Alshorman , Ahmad Alshorman
Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works.
Volume: 11
Issue: 4
Page: 2820-2829
Publish at: 2021-08-01

Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique

10.12928/telkomnika.v19i4.18874
Ahmad Zoebad; UIN Sunan Ampel Surabaya Foeady , Siti Ria; UIN Sunan Ampel Surabaya Riqmawatin , Dian Candra Rini; UIN Sunan Ampel Surabaya Novitasari
The number of people with lung cancer has reached approximately 2.09 million people worldwide. Out of 9.06 million cases of death, 1.76 million people die due to lung cancer. Lung cancer can be automatically identified using a computer-aided diagnosis system (CAD) such as image processing. The steps taken for early detection are pre-processing feature extraction, and classification. Pre-processing is carried out in several stages, namely grayscale images, noise removal, and contrast limited adaptive histogram equalization. This image feature extracted using GLCM and classified using 2 method of neural network which is feed forward neural network (FFNN) dan feed backward neural network (FBNN). This research aims to obtain the best neural network model to classify lung cancer a. Based on training time and accuracy, the best method of FFNN is kernel extreme learning machine (KELM), with a training time of 12 seconds and an accuracy of 93.45%, while the best method of FBNN is Backpropagation with a training time of 18 minutes 04 seconds and an accuracy of 97.5%.
Volume: 19
Issue: 4
Page: 1284-1290
Publish at: 2021-08-01

Development of a new system to detect denial of service attack using machine learning classification

10.11591/ijeecs.v23.i2.pp1068-1072
Mohammad M. Rasheed , Alaa K. Faieq , Ahmed A. Hashim
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.
Volume: 23
Issue: 2
Page: 1068-1072
Publish at: 2021-08-01

Application of big data for distribution and consumption of power

10.12928/telkomnika.v19i4.16285
Olagunju; Federal University Mukaila , Adeniyi Abidemi; Landmark University Emmanuel , Ogundokun Roseline; Landmark University Oluwaseun , Ojo Olufemi; Landmark University Samuel , Kolawole Paul; University of Ilorin Oluwatoba
The exponentially growing and tremendous collection of data stored in the power sector, combined with the need for data analysis, has produced an urgent need for powerful tools to extract hidden data as to effectively distribute the power for proper consumption for the household. This research work was embarked on to show the business value of big data analytics in Energy and utilities with a focus on how analytics can help solve problems of inefficiency and wastages in electricity generation, production and distribution and how raw energy datasets can be converted into insights that can be used by energy policy makers to make major business decisions. To explicitly show how raw data can be turned into insights, the study deploys the use of the Hadoop on Hortonworks’ open-source apache-Hive licensed data warehousing framework run on a windows operating system to turn raw datasets (in excel formats converted to .csv format) gotten from the prepaid meters of 196,000 consumers (households and businesses) in 11 business units of Ikeja Electricity Distribution Company (IKEDC, Nigeria) to analyze the distribution and consumption of power.
Volume: 19
Issue: 4
Page: 1090-1099
Publish at: 2021-08-01

An optimized power allocation algorithm for cognitive radio NOMA communication

10.12928/telkomnika.v19i4.20366
Madan H.; REVA University T. , P. I.; REVA University Basarkod
The primary objective of cognitive radio network is to effectively utilize the unused spectrum bands. In cognitive radio networks, spectrum sharing between primary and secondary users is accomplished using either underlay or interweave cognitive radio approach. Non orthogonal multiple access (NOMA) is the proven technology in the present wireless developments, which allows the coexistence of multiple users in the same orthogonal block. The new paradigm cognitive radio NOMA (CR-NOMA) is one of the potential solutions to fulfill the demands of future wireless communication. This paper emphasizes on practical implementation of NOMA in cognitive radio networks to enhance the spectral efficiency. The goal is to increase the throughput of the secondary users satisfying the quality of service (QOS) requirements of primary users. To achieve this, we have presented the optimized power allocation strategy for underlay downlink scenario to support the simultaneous transmission of primary and secondary users. Furthermore, we have proposed QOS based power allocation scheme for CR-NOMA interweave model to support the coexistence of multiple secondary networks. Also, the changes adopted in implementing superposition coding (SC) and successive interference cancellation (SIC) for CR-NOMA are highlighted. Finally, simulation results validate the mathematical expressions that are derived for power allocation coefficient and outage probability.
Volume: 19
Issue: 4
Page: 1066-1077
Publish at: 2021-08-01

Implementing optimization of PID controller for DC motor speed control

10.11591/ijeecs.v23.i2.pp657-664
Yasir G. Rashid , Ahmed Mohammed Abdul Hussain
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.
Volume: 23
Issue: 2
Page: 657-664
Publish at: 2021-08-01

Small intestine bleeding detection using color threshold and morphological operation in WCE images

10.11591/ijece.v11i4.pp3040-3048
A. Al Mamun , M. S. Hossain , P. P. Em , A. Tahabilder , R. Sultana , M. A. Islam
Wireless capsule endoscopy (WCE) is a significant modern technique for observing the whole gastroenterological tract to diagnose various diseases like bleeding, ulcer, tumor, Crohn's disease, polyps etc in a non-invasive manner. However, it will make a substantial onus for physicians like human oversight errors with time consumption for manual checking of a vast amount of image frames. These problems motivate the researchers to employ a computer-aided system to classify the particular information from the image frames. Therefore, a computer-aided system based on the color threshold and morphological operation has been proposed in this research to recognize specified bleeding images from the WCE. Besides, A unique classifier, quadratic support vector machine (QSVM) has been employed for classifying the bleeding and non-bleeding images with the statistical feature vector in HSV color space. After extensive experiments on clinical data, 95.8% accuracy, 95% sensitivity, 97% specificity, 80% precision, 99% negative predicted value and 85% F1 score has been achieved, which outperforms some of the existing methods in this regard. It is expected that this methodology would bring a significant contribution to the WCE technology. 
Volume: 11
Issue: 4
Page: 3040-3048
Publish at: 2021-08-01

A new technology on translating Indonesian spoken language into Indonesian sign language system

10.11591/ijece.v11i4.pp3338-3346
Risky Aswi Ramadhani , I Ketut Gede Dharma Putra , Made Sudarma , Ida Ayu Dwi Giriantari
People with hearing disabilities are those who are unable to hear, resulted in their disability to communicate using spoken language. The solution offered in this research is by creating a one way translation technology to interpret spoken language to Indonesian sign language system (SIBI). The mechanism applied here is by catching the sentences (audio) spoken by common society to be converted to texts, by using speech recognition. The texts are then processed in text processing to select the input texts. The next stage is stemming the texts into prefixes, basic words, and suffixes. Each words are then being indexed and matched to SIBI. Afterwards, the system will arrange the words into SIBI sentences based on the original sentences, so that the people with hearing disabilities can get the information contained within the spoken language. This technology success rate were tested using Confusion Matrix, which resulted in precision value of 76%, accuracy value of 78%, and recall value of 79%. This technology has been tested in SMP-LB Karya Mulya on the 7th grader students with the total of 9 students. From the test, it is obtained that 86% of students stated that this technology runs very well.
Volume: 11
Issue: 4
Page: 3338-3346
Publish at: 2021-08-01
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