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Model reduction design for continuous systems with finite frequency specifications

10.11591/ijece.v11i5.pp3956-3963
Miloud Koumir , Abderrahim El-Amrani , Ismail Boumhidi
This paper is concerned with the problem of model reduction design for continuous systems in Takagi-Sugeno fuzzy model. Through the defined FF H∞ gain performance, sufficient conditions are derived to design model reduction and to assure the fuzzy error system to be asymptotically stable with a FF H∞ gain performance index. The explicit conditions of fuzzy model reduction are developed by solving linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.
Volume: 11
Issue: 5
Page: 3956-3963
Publish at: 2021-10-01

Deep learning for COVID-19 diagnosis based on chest X-ray images

10.11591/ijece.v11i5.pp4531-4541
Nashat Alrefai , Othman Ibrahim
Coronavirus disease 2019 (COVID-19) is a recent global pandemic that has affected many countries around the world, causing serious health problems, especially in the lungs. Although temperature testing is suggested as a firstline test for COVID-19, it was not reliable because many diseases have the same symptoms. Thus, we propose a deep learning method based on X-ray images that used a convolutional neural network (CNN) and transfer learning (TL) for COVID-19 diagnosis, and using gradient-weighted class activation mapping (Grad-CAM) technique for producing visual explanations for the COVID-19 infection area in the lung. The low sample size of coronavirus samples was considered a challenge, thus, this issue was overridden using data augmentation techniques. The study found that the proposed (CNN) and the modified pre-trained networks VGG16 and InceptionV3 achieved a promising result for COVID-19 diagnosis by using chest X-ray images. The proposed CNN was able to differentiate 284 patients with COVID-19 or normal with 98.2 percent for training accuracy and 96.66 percent for test accuracy and 100.0 percent sensitivity. The modified VGG16 achieved the best classification result between all with 100.0 percent for training accuracy and 98.33 percent for test accuracy and 100.0 percent sensitivity, but the proposed CNN overcame the others in the side of reducing the computational complexity and training time significantly.
Volume: 11
Issue: 5
Page: 4531-4541
Publish at: 2021-10-01

Visible light communication using new Flip-FBMC modulation system technique

10.12928/telkomnika.v19i5.18407
Abdullah Ali; University Tun Hussein Onn Malaysia Qasim , Husam Noman Mohammed; Al-Furat Al-Awsat Technical University Ali , Ali Kadhim; Al-Furat Al-Awsat Technical University Obaid , M. F. L.; University Tun Hussein Onn Malaysia Abdullah , R.; University Tun Hussein Onn Malaysia Talib , M. S. M.; University Tun Hussein Onn Malaysia Gismalla , Wafi A.; University Tun Hussein Onn Malaysia Mabrouk
Filter bank multi-carrier (FBMC) modulation in the visible light communication (VLC) system is one of the most promising modulation systems in optical wireless communications (OWC), especially in 5G and 6G future applications. FBMC has a wide bandwidth compared to other modulation systems. One of the highest degree essential conditions for utilising the signal in VLC is that the signal is real positive, the signal is agreeable with intensity modulation/direct detection (IM/DD), where Hermitian symmetry (H.S) is utilised to get a real signal (RE) and to be unipolar direct current (DC)-bias is used. Here the challenge arises as this method increases complicating, due to the modulation of the N number of frequency symbols, these symbols need 2N inverse fast fourier transform (IFFT) and fast fourier transform (FFT), in addition to energy consumption. This research focused on the time domain and not the frequency domain by using the traditional complex FBMC generation signal, and to obtain the RE signal by placing the RE signal side by side with the imaginary signal (IMs) in a row, and then using new Flip-FBMC technology, which saves more energy. The proposed technologies provide approximately 57% of the number of IFFT/FFT. The use of Flip-FBMC technology consumes less energy than traditional technologies with better bit error rate (BER) performance.
Volume: 19
Issue: 5
Page: 1439-1449
Publish at: 2021-10-01

Source side pre-ordering using recurrent neural networks for English-Myanmar machine translation

10.11591/ijece.v11i5.pp4513-4521
May Kyi Nyein , Khin Mar Soe
Word reordering has remained one of the challenging problems for machine translation when translating between language pairs with different word orders e.g. English and Myanmar. Without reordering between these languages, a source sentence may be translated directly with similar word order and translation can not be meaningful. Myanmar is a subject-objectverb (SOV) language and an effective reordering is essential for translation. In this paper, we applied a pre-ordering approach using recurrent neural networks to pre-order words of the source Myanmar sentence into target English’s word order. This neural pre-ordering model is automatically derived from parallel word-aligned data with syntactic and lexical features based on dependency parse trees of the source sentences. This can generate arbitrary permutations that may be non-local on the sentence and can be combined into English-Myanmar machine translation. We exploited the model to reorder English sentences into Myanmar-like word order as a preprocessing stage for machine translation, obtaining improvements quality comparable to baseline rule-based pre-ordering approach on asian language treebank (ALT) corpus.
Volume: 11
Issue: 5
Page: 4513-4521
Publish at: 2021-10-01

Improvements in space radiation-tolerant FPGA implementation of land surface temperature-split window algorithm

10.11591/ijece.v11i5.pp3844-3854
Assaad El Makhloufi , Nisrine Chekroun , Noha Tagmouti , Samir El Adib , Naoufal Raissouni
The trend in satellite remote sensing assignments has continuously been concerning using hardware devices with more flexibility, smaller size, and higher computational power. Therefore, field programmable gate arrays (FPGA) technology is often used by the developers of the scientific community and equipment for carrying out different satellite remote sensing algorithms. This article explains hardware implementation of land surface temperature split window (LST-SW) algorithm based on the FPGA. To get a high-speed process and real-time application, VHSIC hardware description language (VHDL) was employed to design the LST-SW algorithm. The paper presents the benefits of the used Virtex-4QV of radiation tolerant series FPGA. The experimental results revealed that the suggested implementation of the algorithm using Virtex4QV achieved higher throughput of 435.392 Mbps, and faster processing time with value of 2.95 ms. Furthermore, a comparison between the proposed implementation and existing work demonstrated that the proposed implementation has better performance in terms of area utilization; 1.17% reduction in number of Slice used and 1.06% reduction in of LUTs. Moreover, the significant advantage of area utilization would be the none use of block RAMs comparing to existing work using three blocks RAMs. Finally, comparison results show improvements using the proposed implementation with rates of 2.28% higher frequency, 3.66 x higher throughput, and 1.19% faster processing time.
Volume: 11
Issue: 5
Page: 3844-3854
Publish at: 2021-10-01

A novel secure biomedical data aggregation using fully homomorphic encryption in WSN

10.11591/ijeecs.v24.i1.pp428-443
Chethana G. , Padmaja K. V.
A new method of secure data aggregation for decimal data having integer as well as fractional part using homomorphic encryption is described. The proposed homomorphic encryption provides addition, subtraction, multiplication, division and averaging operations in the cipher domain for both positive and negative numbers. The scheme uses integer matrices in finite field Zp as encryption and decryption keys. An embedded Digital signature along with data provides data integrity and authentication by signature verification at the receiving end. The proposed scheme is immune to chosen plaintext and chosen ciphertext attacks. In the case of homomorphic multiplication, the ciphertext expansion ratio grows linearly with the data size. The computational complexity of the proposed method for multiplication and division is relatively less by 22.87% compared to Brakerski and Vaikantanathan method when the size of the plaintext data is ten decimal digits.
Volume: 24
Issue: 1
Page: 428-443
Publish at: 2021-10-01

Reliability assessment for electrical power generation system based on advanced Markov process combined with blocks diagram

10.11591/ijece.v11i5.pp3647-3659
A. A. Tawfiq , M. Osama abed el-Raouf , A. A. El-Gawad , M. A. Farahat
This paper presents the power generation system reliability assessment using an advanced Markov process combined with blocks diagram technique. The effectiveness of the suggested methodology is based on HL-I of IEEE_EPS_24_bus. The proposed method achieved the generation reliability and availability of an electrical power system using the Markov chain which based on the operational transition from state to state which represented in matrix. The proposed methodology has been presented for reliability performance evaluation of IEEE_EPS_24_bus. MATLAB code is developed using Markov chain construction. The transition between probability states is represented using changing the failure and repair rates. The reduced number of generation system are used with Markov process to assess the availability, unavailability, and reliability for the generation system. Additionally, the proposed technique calculates the frequency, time duration of states, the probability of generation capacity state which get out of service or remained in service for each state of failure, and reliability indices. A considerable improvement in reliability indices is found with using blocks diagram technique which is used to reduce the infinity number of transition states and assess the system reliability. The proposed technique succeeded at achieving accurate and faster reliability for the power system.
Volume: 11
Issue: 5
Page: 3647-3659
Publish at: 2021-10-01

Hybrid basis vector based underdetermined beamforming algorithm in optimized antenna reconfiguration

10.11591/ijeecs.v24.i1.pp367-375
Krupa Prasad K. R. , H. D. Maheshappa
Optimized positioning of antenna to obtain the best beam forming solution is adopted in this research. Non-uniform linear array-based beamforming algorithms have the challenge of placing the array of antennas in positions that would implement best beamforming outputs. This paper attempts to obtain the optimized beam forming by tuning the sparse Bayesian learning based algorithm. The parameters used for tuning involve choosing the hybrid basis vector for creating the steering vector while at the same time developing the optimized position of the antennas. Basis vectors are the building blocks of the steering vector developed for the beamforming algorithm that finds the angle of arrival in antennas. Reconfiguration of antennas is carried out using particle swarm optimization (PSO) algorithm and the basis vectors are generated using two different ways. One by cumulating similar basis vectors and another by cumulating two different basis vectors. The performance of accurate detection of angle of arrival in the beamforming algorithm is analyzed and results are discussed. This basis vector and antenna distance optimization is adopted on the sparse Bayesian learning paradigm. Performance evaluation of these optimizations in the algorithm is realised by validating the mean square error (MSE) versus signal to noise ratio (SNR) graphs for both the cumulative basis vector and hybrid basis vector cases.
Volume: 24
Issue: 1
Page: 367-375
Publish at: 2021-10-01

Lifetime centric load balancing mechanism in wireless sensor network based IoT environment

10.11591/ijece.v11i5.pp4183-4193
Veerabadrappa Veerabadrappa , Booma Poolan Marikannan
Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model.
Volume: 11
Issue: 5
Page: 4183-4193
Publish at: 2021-10-01

Optimized machine learning algorithm for intrusion detection

10.11591/ijeecs.v24.i1.pp590-599
Royida A. Ibrahem Alhayali , Mohammad Aljanabi , Ahmed Hussein Ali , Mostafa Abdulghfoor Mohammed , Tole Sutikno
Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in terms of their prolonged training time and classification accuracy. This article presents an improved intrusion detection technique for binary classification. The proposal is a combination of different optimizers, including Rao optimization algorithm, extreme learning machine (ELM), support vector machine (SVM), and logistic regression (LR) (for feature selection & weighting), as well as a hybrid Rao-SVM algorithm with supervised machine learning (ML) techniques for feature subset selection (FSS). The process of selecting the least number of features without sacrificing the FSS accuracy was considered a multi-objective optimization problem. The algorithm-specific, parameter-less concept of the proposed Rao-SVM was also explored in this study. The KDDCup 99 and CICIDS 2017 were used as the intrusion dataset for the experiments, where significant improvements were noted with the new Rao-SVM compared to the other algorithms. Rao-SVM presented better results than many existing works by reaching 100% accuracy for KDDCup 99 dataset and 97% for CICIDS dataset.
Volume: 24
Issue: 1
Page: 590-599
Publish at: 2021-10-01

Bone cancer detection using electrical impedance tomography

10.11591/ijeecs.v24.i1.pp245-252
Nabanita Saha , Mohammad Anisur Rahaman
Bone cancer is an uncommon sort of malignancy that alludes to irregular development of tissue inside the bone, with high opportunity to spread to different pieces of the body. It is extremely important to distinguish bone cancer at the beginning phase to cure it productively. Presently, in addition to a physical examination, magnetic resonance imaging, blood tests, positron emission tomography (PET), computed tomography (CT) or PET-CT scan, X-ray, Bone scan, Biopsy and computed tomography scan, are used to diagnose or determine the stage (or extent) of bone sarcoma. But these methods are costly and not free of radiation. Moreover, these machines are bulky. Electrical impedance tomography approach was proposed in this research for identifying bone cancer as this detection technique is able to distinguish between cancerous and non-cancerous cells by differentiating between their conductivity and it has the possibility to remove the limitations of conventional medical imaging techniques. Here, equivalent bone models were generated using (electrical impedance and diffused optical reconstruction software (EIDORS) which had been implemented in MATLAB, and three different image reconstruction algorithms-GREIT, Sheffield Backprojection, Gauss-Newton inverse algorithm had been used to detect the cancerous cells.
Volume: 24
Issue: 1
Page: 245-252
Publish at: 2021-10-01

NLP-based personal learning assistant for school education

10.11591/ijece.v11i5.pp4522-4530
Ann Neethu Mathew , Rohini V. , Joy Paulose
Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future.
Volume: 11
Issue: 5
Page: 4522-4530
Publish at: 2021-10-01

Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population

10.11591/ijece.v11i5.pp4471-4480
Md. Ariful Islam Arif , Saiful Islam Sany , Farah Sharmin , Md. Sadekur Rahman , Md. Tarek Habib
Nowadays addiction to drugs and alcohol has become a significant threat to the youth of the society as Bangladesh’s population. So, being a conscientious member of society, we must go ahead to prevent these young minds from life-threatening addiction. In this paper, we approach a machinelearning-based way to forecast the risk of becoming addicted to drugs using machine-learning algorithms. First, we find some significant factors for addiction by talking to doctors, drug-addicted people, and read relevant articles and write-ups. Then we collect data from both addicted and nonaddicted people. After preprocessing the data set, we apply nine conspicuous machine learning algorithms, namely k-nearest neighbors, logistic regression, SVM, naïve bayes, classification, and regression trees, random forest, multilayer perception, adaptive boosting, and gradient boosting machine on our processed data set and measure the performances of each of these classifiers in terms of some prominent performance metrics. Logistic regression is found outperforming all other classifiers in terms of all metrics used by attaining an accuracy approaching 97.91%. On the contrary, CART shows poor results of an accuracy approaching 59.37% after applying principal component analysis.
Volume: 11
Issue: 5
Page: 4471-4480
Publish at: 2021-10-01

The security of RC4 algorithm using keys generation depending on user's retina

10.11591/ijeecs.v24.i1.pp452-463
Huda M. Salih , Raghda Salam Al Mahdawi
Digital technologies grow more rapidly; information security threats are becoming increasingly dangerous. Advanced and various cyber-attacks and security threats, like targeted emails, and information exploitation, pose a critical threat that basically undermines our trust in the digital society. Rivest cipher 4 (RC4) algorithm is a significant cipher of a stream that could be utilized with protocols of the internet, the advantage of the RC4 algorithm is that it is simple and effective. There are several weak, especially after the pseudo-random generation algorithm (PRGA), PRGA's initially 256 rounds (the amount of the RC4 permutation). Several modified RC4 studies have been published thus far, however, they all face either standard privacy or achievement evaluation issues. This paper proposes a new RC4 algorithm that is based on the user's retina (RC4-Retina), which has solved both of these weak points it was indicated in the standard RC4 algorithm. The novelty of retina key scheduling algorithm (RKSA), which is generated by relying on the user's retina of the algorithm will modify the matrix of permutation used to configure the keys. The efficiency of the improved algorithm was measured by depending on the average security of ciphertext of different keys and different messages, results were good compared to the standard algorithm.
Volume: 24
Issue: 1
Page: 452-463
Publish at: 2021-10-01

Forecasting and communication key elements for low-cost fluvial flooding early warning system in urban areas

10.11591/ijece.v11i5.pp4143-4156
Melisa Acosta-Coll , Andres Solano-Escorcia , Lilia Ortega-Gonzalez , Ronald Zamora-Musa
Fluvial flooding occurs when a river overspills its banks due to excessive rainfall, and it is the most common flood event. In urban areas, the increment of urbanization makes communities more susceptible to fluvial flooding since the excess of impervious surfaces reduced the natural permeable areas. As flood prevention strategies, early warning systems (EWS) are used to reduce damage and protect people, but key elements need to be selected. This manuscript proposes the monitoring instruments, communication protocols, and media to forecast and disseminate EWS alerts efficiently during fluvial floods in urban areas. First, we conducted a systematic review of different EWS architectures for fluvial floods in urban areas and identified that not all projects monitor the most important variables related to the formation of fluvial floods and most use communication protocols with high-energy consumption. ZigBee and LoRaWAN are the communication protocols with lower power consumption from the review, and to determine which technology has better performance in urban areas, two wireless sensor networks were deployed and simulated in two urban areas susceptible to fluvial floods using Radio Mobile software. The results showed that although Zigbee technology has better-received signal strength, the difference with LoRAWAN is lower than 2 dBm, but LoRaWAN has a better signal-to-noise ratio, power consumption, coverage, and deployment cost.
Volume: 11
Issue: 5
Page: 4143-4156
Publish at: 2021-10-01
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