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29,922 Article Results

Attitudes and opinions of counselors about education of gifted students

10.11591/ijere.v9i4.20564
Nisa Gökden Kaya , Hasan Said Tortop
Counselors play effective roles not only in diagnosing but also in education of gifted students who lead the development of the societies. This study aimed to examine the attitudes and opinions of the counselors about the education of the gifted students. In the study, descriptive survey model is used. Both qualitative and quantitative data was collected within mixed method, according to the principles of pragmatist philosophy. “Attitude Scale towards Gifted Education” was applied to 250 counselors in order to collect quantitative data. The scale, which consists of 14 items, was developed by Gagné and Nadeau and adapted to Turkish by Tortop. The qualitative data was collected by semi-structured interview form consists of four questions about opinions on education of gifted students in Turkey, and was applied to 40 counselors. The mean of scores gathered from “Attitude Scale towards Gifted Education” was found 3.6 which is evaluated as slightly positive attitude. The scores were analyzed according to gender, seniority, having gifted students and institution of counselors, by using t test and ANOVA. Content analysis was performed for qualitative data that was gathered from interviews. The majority of counselors have stated that there are problems in education and diagnosis of gifted students.
Volume: 9
Issue: 4
Page: 1017-1024
Publish at: 2020-12-01

An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

10.12928/telkomnika.v18i6.15199
Kaushik; Vignan Institute of Technology & Science Sekaran , R.; Vignan's Foundation for Science Rajakumar , K.; Vellore Institute of Technology Dinesh , Y.; Vignan's Foundation for Science Rajkumar , T. P.; Vignan's Foundation for Science Latchoumi , Seifedine; Beirut Arab University Kadry , Sangsoon; Sungkyul University Lim
Clustering is considered as one of the most prominent solutions to preserve theenergy in the wireless sensor networks. However, for optimal clustering, anenergy efficient cluster head selection is quite important. Improper selectionofcluster heads(CHs) consumes high energy compared to other sensor nodesdue to the transmission of data packets between the cluster members and thesink node. Thereby, it reduces the network lifetime and performance of thenetwork. In order to overcome the issues, we propose a novelcluster headselection approach usinggrey wolf optimization algorithm(GWO) namelyGWO-CH which considers the residual energy, intra-cluster and sink distance.In addition to that, we formulated an objective function and weight parametersfor anefficient cluster head selection and cluster formation. The proposedalgorithm is tested in different wireless sensor network scenarios by varyingthe number of sensor nodes and cluster heads. The observed results conveythat the proposed algorithm outperforms in terms of achieving better networkperformance compare to other algorithms.
Volume: 18
Issue: 6
Page: 2822-2833
Publish at: 2020-12-01

Hiding text in speech signal using K-means, LSB techniques and chaotic maps

10.11591/ijece.v10i6.pp5726-5735
Iman Qays Abduljaleel , Amal Hameed Khaleel
In this paper, a new technique that hides a secret text inside a speech signal without any apparent noise is presented. The technique for encoding the secret text is through first scrambling the text using Chaotic Map, then encoding the scraped text using the Zaslavsky map, and finally hiding the text by breaking the speech signal into blocks and using only half of each block with the LSB, K-means algorithms. The measures (SNR, PSNR, Correlation, SSIM, and MSE) are used on various speech files (“.WAV”), and various secret texts. We observed that the suggested technique offers high security (SNR, PSNR, Correlation, and SSIM) of an encrypted text with low error (MSE). This indicates that the noise level in the speech signal is very low and the speech purity is high, so the suggested method is effective for embedding encrypted text into speech files.
Volume: 10
Issue: 6
Page: 5726-5735
Publish at: 2020-12-01

Compressive spectrum sensing using two-stage scheme for cognitive radio networks

10.11591/ijece.v10i6.pp5899-5908
Montadar Abas Taher , Mohammad Z. Ahmed , Emad Hmood Salman
The modern applications of communications that use wideband signals suffer the lacking since the resources of this kind of signals are limited especially for fifth generation (5G). The Compressive Spectrum Sensing (COMPSS) techniques address such issues to reuse the detected signals in the networks and applications of 5G. However, the raw techniques of COMPSS have low compression ratio and high computational complexity rather than high level of noise variance. In this paper, a hybrid COMPSS scheme has been developed for both non-cooperative and cooperative cognitive radio networks. The proposed scheme compiles on discrete wavelet transform – single resolution (DWT-SR) cascaded with discrete cosine transform (DCT). The first is constructed according to the pyramid algorithm to achieve 50% while the second performed 30% compression ratios. The simulation and analytic results reveal the significant detection performance of the proposed technique is better than that of the raw COMPSS techniques.
Volume: 10
Issue: 6
Page: 5899-5908
Publish at: 2020-12-01

Enhance the chromatic uniformity and luminous efficiency of WLEDs with triple-layer remote phosphor structures

10.11591/ijece.v10i6.pp6244-6250
Nguyen Thi Phuong Loan , Anh Tuan Le
The angular color uniformity (ACU) with the ability to evaluate chromatic performance of WLED has become an important target to achieve in producing higher-quality WLEDs. This paper studies the ACU enhancing effects of novel triple-phosphor configuration in lighting devices with remote phosphor structure. Moreover, the optical influences of remote phosphor structure with three phosphor layers (TL) on WLEDs properties are calculated and compared to the dual-layer (DL) one for reference. The experiments are applied to devices at 5 distinct correlated color temperature ranging from 5600-8500 K. The results presented that DL structure attains better color rendering index (CRI) than the TL one. Meanwhile, in terms of color quality scales (CQS), TL model shows higher values at all ACCTs, compared to the DL. Moreover, the luminous flux of DL configuration is lower than that of TL structure. In addition, the diversion of color temperature depicts as D-CCT in TL structure is much better than the value in DL structure, especially at high ACCT as 8500 K, which means TL is good for chromatic uniformity of high ACCTs WLEDs. These results proved that the triple-layer structure is superior and more effective to apply for acquiring the enhancement of WLEDs package.
Volume: 10
Issue: 6
Page: 6244-6250
Publish at: 2020-12-01

Detection and classification of various pest attacks and infection on plants using RBPN with GA based PSO algorithm

10.11591/ijeecs.v20.i3.pp1278-1288
Kapilya Gangadharan , G. Rosline Nesa Kumari , D. Dhanasekaran , K. Malathi
Machine learning methodologies are commonly used in the field of precession farming. It prospects greatly in the plant safety measure like disease detection and classification of pest attacks. It highly influences the crop production and management. The venture of our system is to produce healthy plantation. The proposed system involves enhanced feature fractal texture analysis, Statistical feature selection and machine learning methodology for classification. Hence more than ever there is a need for such a tool that combines image processing methodologies and the neural network concepts and that is supported by huge cloud of structured data which makes the diagnosis and classification part much easier and convenient. The proposed system recognizes and classifies the plant taxonomy and the infection based on the selected statistical features. The neural network concept followed in our proposed system is focused on artificial neural network which uses recursive back propagation neural network to speed up the training process as well as reduce multiclass problem in the network and optimize the weights on hidden layers of the Network using Genetic algorithm based particle swarm optimization technique. We have used MATLAB to implement the concept and obtained better accuracy in disease/pest detection and proved to be an efficient method.
Volume: 20
Issue: 3
Page: 1278-1288
Publish at: 2020-12-01

Machine learning algorithms for fall detection using kinematic and heart rate parameters-a comprehensive analysis

10.11591/ijai.v9.i4.pp772-780
Anita Ramachandran , Adarsh Ramesh , Aditya Sukhlecha , Avtansh Pandey , Anupama Karuppiah
The application of machine learning techniques to detect and classify falls is a prominent area of research in the domain of intelligent assisted living systems. Machine learning (ML) based solutions for fall detection systems built on wearable devices use various sources of information such inertial motion units (IMU), vital signs, acoustic or channel state information parameters. Most existing research rely on only one of these sources; however, a need to do more experimenation to observe the efficiency of the ML classifiers while coupling features from diverse sources, was felt. In addition, fall detection systems based on wearable devices, require intelligent feature engineering and selection for dimensionality reduction, so as to reduce the computational complexity of the devices. In this paper we do a comprehensive performance analysis of ML classifiers for fall detection, on a dataset we collected. The analysis includes the impact of the following aspects on the performance of ML classifiers for fall detection: (i) using a combination of features from 2 sensors-an IMU sensor and a heart rate sensor, (ii) feature engineering and feature selection based on statistical methods, and (iii) using ensemble techniques for fall detection. We find that the inclusion of heart rate along with IMU sensor parameters improves the accuracy of fall detection. The conclusions from our experimentations on feature selection and ensemble analysis can serve as inputs for researchers designing wearable device-based fall detection systems.
Volume: 9
Issue: 4
Page: 772-780
Publish at: 2020-12-01

The feasibility of obstacle awareness forwarding scheme in a visible light communication vehicular network

10.11591/ijece.v10i6.pp6453-6460
Lisa Kristiana , Arsyad Ramadhan Darlis , Irma Amelia Dewi
A vehicular-to-vehicular (V2V) communication is a part of a vehicular ad-hoc network (VANET) that emerges recently due to the heavy traffic environment. V2V is a frequently changing network since it implements vehicles as mobile nodes. The challenges in implementing V2V are the relatively short duration of possible communication and the uneven city environment caused by high rise buildings or other objects that distract the signal transmission. The limited transmitting duration between vehicles requires efficient coordination and communication. This work focuses on the utility of visible light communication in vehicular network (VLC-VN) in data transmitting and the obstacle awareness in the forwarding scheme based on our knowledge in previous researches. The result of evaluating the feasibility of VLC-VN forwarding in a freeway environment the transmission delay is lower than 1 second in 500 byte data transmission, however it reaches to only about 4% in throughput as a drawback.
Volume: 10
Issue: 6
Page: 6453-6460
Publish at: 2020-12-01

Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification

10.11591/ijeecs.v20.i3.pp1626-1633
M. H Jopri , MR Ab Ghani , A.R Abdullah , Tole Sutikno , M Manap , J. Too
The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the Naïve Bayes (NB) and linear discriminate analysis (LDA) in identifying and diagnosing the harmonic sources.  The MLs inputs are the voltage and current feature sets that estimated from the time-frequency representation (TFR) of S-transform analysis. Four specific cases of harmonic source location are considered in this research, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. The sufficiency of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and to prevent overfitting, the K-fold cross-validation technique is implemented for performance evaluation. To identify the best ML, the performance measurement consist of the accuracy, precision, geometric mean, F-measure, sensitivity, and specificity are conducted.
Volume: 20
Issue: 3
Page: 1626-1633
Publish at: 2020-12-01

Towards 5G millimeter-wave wireless networks: a comparative study on electro-optical upconversion techniques

10.11591/ijeecs.v20.i3.pp1471-1478
Nael A. Al-Shareefi , Jaafar A. Aldhaibaini , Sura Adil Abbas , Hadeel S. Obaid
Fifth-generation (5G) wireless networks that use the MM−W hold a great promise to revolutionize wireless industry. However, the difficulty in generating and transmitting these high‐frequency signals in the electrical-domain due to bandwidth limitation of electronic components, and high absorption loss limits current applications. Consequently, ptical generation and transmission of MM-W signals are a viable option. In this paper, a comparative study is carried out on three electro-optical upconversion (EOU) techniques to generate 60-GHz MM-W signal, namely 4-tupling, 6-tupling and 8-tupling. The paper briefly describes the three techniques and analyses the optical harmonic distortion suppression ratio (OHDSR) and electrical spurious suppression ratio (ESSR) generated by each one of the techniques. OHDSR and ESSR have been compared to show the trade-off between the techniques. In addition, the paper compares the implementation of non−ideal phase shifting on OHDSR for the three EOU techniques. Finally, the performance of the three EOU techniques after transmission over optical fiber is evaluated by quality factor (Q-factor) and eye pattern test. The results of the simulation illustrate well the benefits of the performed study and confirm that the 4-tupling constitutes a cost-effective technique to generate MM-W signals.
Volume: 20
Issue: 3
Page: 1471-1478
Publish at: 2020-12-01

Power saving and optimal hybrid precoding in millimeter wave massive MIMO systems for 5G

10.12928/telkomnika.v18i6.15952
Abdul Haq; REVA University Nalband , Mrinal; REVA University Sarvagy , Mohammed Riyaz; REVA University Ahmed
The proliferation of wireless services emerging from use cases offifth-generation(5G) technology is posing many challenges on cellular communicationinfrastructure. They demand to connect a massive number of devices withenhanced data rates. The massive multiple-input multiple-output (MIMO)technology at millimeter-wave (mmWave) in combination with hybrid precodingemerges as a concrete tool to address the requirements of 5G networkdevelopments. But Massive MIMO systems consume significant power fornetwork operations. Hence the prior role is to improve the energy efficiency byreducing the power consumption. This paper presents the power optimizationmodels for massive MIMO systems considering perfect channel state information(CSI) and imperfect CSI. Further, this work proposes an optimal hybrid precodingsolution named extended simultaneous orthogonal matchingpursuit (ESOMP).Simulation results reveal that a constant sum-rate can be achieved in massiveMIMO systems while significantly reducing the power consumption. Theproposed extended SOMPhybrid precoder performsclose to the conventionaldigital beamforming method. Further, modulation schemes compatible withmassive MIMO systems are outlined and their bit error rate (BER) performance isinvestigated
Volume: 18
Issue: 6
Page: 2842-2851
Publish at: 2020-12-01

Improved anti-noise attack ability of image encryption algorithm using de-noising technique

10.12928/telkomnika.v18i6.16384
Mohanad Najm; University of Technology Abdulwahed , Ali Kamil; University of Technology Ahmed
Information security is considered as one of the important issues in the information age used to preserve the secret information through out transmissions in practical applications. With regard to image encryption, a lot of schemes related to information security were applied. Such approaches might be categorized into 2 domains; domain frequency and domain spatial. The presented work develops an encryption technique on the basis of conventional watermarking system with the use of singular value decomposition (SVD), discrete cosine transform (DCT), and discrete wavelet transform (DWT) together, the suggested DWT-DCT-SVD method has high robustness in comparison to the other conventional approaches and enhanced approach for having high robustness against Gaussian noise attacks with using denoising approach according to DWT. MSE in addition to the peak signal-to-noise ratio (PSNR) specified the performance measures which are the base of this study’s results, as they are showing that the algorithm utilized in this study has high robustness against Gaussian noise attacks.
Volume: 18
Issue: 6
Page: 3080-3087
Publish at: 2020-12-01

Cost implications analysis of grid supplied electricity and solar source of electricity in Nigeria

10.12928/telkomnika.v18i6.13558
Tobiloba; Covenant University Somefun , Claudius; Covenant University Awosope , Ademola; Covenant University Abdulkareem , Joseph; Covenant University Ojo , Elizabeth; Covenant University Amuta , Timilehin; Covenant University Sanni
Energy is a key component in the overall growth of every nation. Insufficient energy delivery hinders political growth, restricts social growth, limits economic growth, and negatively affects the standard of living of citizens, bothin urban and rural areas. Sufficient energy delivery increases food production, improves the standards of living of citizens, improves healthcare and enhancements in other human services, enhances industrial output, provides effective and efficient transportation not forgetting adequate shelter to the citizens of the nation. Currently, there is a significant level of deficiency in Nigeria’s energy sector. This study seeks to address this issue by analysing cost implications of conventional energy source and solar energy source. This study brings to focus the payback period of a solar powered home and the return on investment that might accrue during this time to the residential home owners. Furthermore, the best cost-effective load sharing option for residential owners considering two energy sources is also obtained.
Volume: 18
Issue: 6
Page: 3258-3265
Publish at: 2020-12-01

Data mining in web personalization using the blended deep learning model

10.11591/ijeecs.v20.i3.pp1507-1512
Qusay Abdullah Abed , Osamah Mohammed Fadhil , Wathiq Laftah Al-Yaseen
In general, multidimensional data (mobile application for example) contain a large number of unnecessary information. Web app users find it difficult to get the information needed quickly and effectively due to the sheer volume of data (big data produced per second). In this paper, we tend to study the data mining in web personalization using blended deep learning model. So, one of the effective solutions to this problem is web personalization. As well as, explore how this model helps to analyze and estimate the huge amounts of operations. Providing personalized recommendations to improve reliability depends on the web application using useful information in the web application. The results of this research are important for the training and testing of large data sets for a map of deep mixed learning based on the model of back-spread neural network. The HADOOP framework is using to perform a number of experiments in a different environment with a learning rate between -1 and +1. Also, using the number of techniques to evaluate the number of parameters, true positive cases are represent and fall into positive cases in this example to evaluate the proposed model
Volume: 20
Issue: 3
Page: 1507-1512
Publish at: 2020-12-01

Survey on Deep Learning applied to predictive maintenance

10.11591/ijece.v10i6.pp5592-5598
Youssef Maher , Boujemaa Danouj
Prognosis Health Monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model. In addition, post-RUL reasoning and the use of distributed computing with cloud technology is presented, which will potentially improve the classification accuracy in maintenance activities. Deep learning will undoubtedly prove to have a major impact in upgrading companies at the lowest cost in the new industrial revolution, Industry 4.0.
Volume: 10
Issue: 6
Page: 5592-5598
Publish at: 2020-12-01
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