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29,939 Article Results

Different analytical frameworks and bigdata model for Internet of Things

10.11591/ijeecs.v25.i2.pp1159-1166
Ayushi Chahal , Preeti Gulia , Nasib Singh Gill
Sensor devices used in internet of things (IoT) enabled environment produce large amount of data. This data plays a major role in bigdata landscape. In recent years, correlation, and implementation of bigdata and IoT is being extrapolated. Nowadays, predictive analytics is gaining attention of many researchers for big IoT data analytics. This paper summarizes different sort of IoT analytical platforms which consist in-built features for further use in machine learning, MATLAB, and data security. It emphasizes on different machine learning algorithms that plays important role in big IoT data analytics. Besides different analytical frameworks, this paper highlights the proposed model for bigdata in IoT domain and elaborates different forms of data analytical methods. Proposed model comprises different phases i.e., data storing, data cleaning, data analytics, and data visualization. These phases cover the basic characteristics of bigdata V’s model and most important phase is data analytics or big IoT analytics. This model is implemented using an IoT dataset and results are presented in graphical and tabular form using different machine learning techniques. This study enhances researchers’ knowledge about various IoT analytical platforms and usability of these platforms in their respective problem domains.
Volume: 25
Issue: 2
Page: 1159-1166
Publish at: 2022-02-01

Classification of three pathological voices based on specific features groups using support vector machine

10.11591/ijece.v12i1.pp946-956
Muneera Altayeb , Amani Al-Ghraibah
Determining and classifying pathological human sounds are still an interesting area of research in the field of speech processing. This paper explores different methods of voice features extraction, namely: Mel frequency cepstral coefficients (MFCCs), zero-crossing rate (ZCR) and discrete wavelet transform (DWT). A comparison is made between these methods in order to identify their ability in classifying any input sound as a normal or pathological voices using support vector machine (SVM). Firstly, the voice signal is processed and filtered, then vocal features are extracted using the proposed methods and finally six groups of features are used to classify the voice data as healthy, hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis using separate classification processes. The classification results reach 100% accuracy using the MFCC and kurtosis feature group. While the other classification accuracies range between~60% to~97%. The Wavelet features provide very good classification results in comparison with other common voice features like MFCC and ZCR features. This paper aims to improve the diagnosis of voice disorders without the need for surgical interventions and endoscopic procedures which consumes time and burden the patients. Also, the comparison between the proposed feature extraction methods offers a good reference for further researches in the voice classification area.
Volume: 12
Issue: 1
Page: 946-956
Publish at: 2022-02-01

Improving the design of super-lift Luo converter using hybrid switching capacitor-inductor cell for PV system

10.11591/ijeecs.v25.i2.pp710-720
Hussein Abdul-Khuder Hussein , Hassan Jassim Motlak
In this article, an improvement to the positive output super-lift Luo converter (POSLC) has been proposed to get high gain at a low duty cycle. Also, reduce the stress on the switch and diodes, reduce the current through the inductors to reduce loss, and increase efficiency. Using a hybrid switch unit composed of four inductors and two capacitors it is replaced by the main inductor in the elementary circuit. It’s charged in parallel with the same input voltage and discharged in series. The output voltage is increased according to the number of components. The gain equation is modeled. The boundary condition between Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM) has been derived. Passive components are designed to get high output voltage (8 times at D=0.5) and low ripple about (0.004). The circuit is simulated and analyzed using MATLAB/Simulink. Maximum power point tracker (MPPT) controls the converter to provide the most interest from solar energy.
Volume: 25
Issue: 2
Page: 710-720
Publish at: 2022-02-01

Classify arrhythmia by using 2D spectral images and deep neural network

10.11591/ijeecs.v25.i2.pp931-940
Tran Anh Vu , Hoang Quang Huy , Pham Duy Khanh , Nguyen Thi Minh Huyen , Trinh Thi Thu Uyen , Pham Thi Viet Huong
Electrocardiogram (ECG) is the most common method for monitoring the working of the heart. ECG signal is the basis to determine normal or abnormal rhythm, thereby helping to accurately diagnose cardiovascular diseases. Therefore, an automatic algorithm to detect and diagnose abnormal heart rhythms is essential. There are many methods of classifying arrhythmias using machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), based on the features extracted from the record of ECG signal. Actually, deep learning algorithms are evolving and highly effective in image analysis and processing. In this research, a dense neural network model is proposed to classify normal and abnormal beats. Input ECG signal presenting a time series is converted into 2-D spectral image by applying wavelet transform. Our research is evaluated based on using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. The accuracy of the classification algorithm we employ is 99.8%, demonstrating the model's validity when compared to other reports' findings. This is the foundation for our algorithm to prove it can be utilized as an efficient model for categorizing arrhythmia using ECG signals.
Volume: 25
Issue: 2
Page: 931-940
Publish at: 2022-02-01

Effect of optimal filtering parameters for autoregressive model AR(p) on motor unit action potential signal

10.11591/ijece.v12i1.pp229-238
Ayad Asaad lbrahim , Mohammed Ehsan Safi , Eyad Ibrahim Abbas
Error is one element of the autoregressive (AR) model, which is supposed to be white noise. Correspondingly assumption that white noise error is a normal distribution in electromyography (EMG) estimation is one of the common causes for error maximization. This paper presents the effect of a suitable choice of filtering function based on the non-invasive analysis properties of motor unit action potential signal, extracted from a non-invasive method-the high spatial resolution (HSR) electromyography (EMG), recorded during low-level isometric muscle contractions. The final prediction error procedure is used to find the number of parameters in the model. The error signal parameter, the simulated deviation from the actual signals, is suitably filtered to obtain optimally appropriate estimates of the parameters of the automatic regression model. It is filtered to acquire optimally appropriate estimates of the parameters of the automatic regression model. Then appropriate estimates of spectral power shapes are obtained with a high degree of efficiency compared with the robust method under investigation. Extensive experiment results for the proposed technique have shown that it provides a robust and reliable calculation of model parameters. Moreover, estimates of power spectral profiles were evaluated efficiently.
Volume: 12
Issue: 1
Page: 229-238
Publish at: 2022-02-01

Measurement of an electroretinogram signal and display waves on graphical user interface by laboratory virtual instrument engineering workbench

10.11591/ijeecs.v25.i2.pp980-988
Mustafa F. Mahmood , Huda Farooq Jameel , Mayss Alreem Nizar Hammed
The electroretinogram (ERG) is an electrophysiological recording method that measures the retinal electrical potential. The electrical reaction is quantified by electrical interaction of the indicator electrode with the cornea or at various levels inside the retina. However, such ERG systems suffer from certain limitations and challenges, such as high cost, low a/b-wave amplitude, and the outcomes do not provide any information about patients. In this work, we designed and implemented a real-time prototype for an ERG system for measuring eye waves via diode-transistor logic (DTL)- electrode and AD624AD-model. In addition, a graphical user interface (GUI) via virtual instrument engineering workbench (LabVIEW) was used. The developed system achieved high amplitude for ERG a/b-waves of about 100 and 700 mV. In terms of a/b-waves in the system, the findings show that this study has good results for optimizing the measurement of ERG signals. The method showed satisfactory accuracy of about 92.5% for 10 participants aged 20-60 years and comprising both genders
Volume: 25
Issue: 2
Page: 980-988
Publish at: 2022-02-01

Agriculture data visualization and analysis using data mining techniques: application of unsupervised machine learning

10.12928/telkomnika.v20i1.18938
Kunal; IIIT Bhubaneswar, Odisha, India Badapanda , Debani Prasad; IIIT Bhubaneswar, Odisha, India Mishra , Surender Reddy; Woosong University Salkuti
Unsupervised machine learning is one of the accepted platforms for applying a broad data analytics challenge that involves the way to identify secret trends, unexplained associations, and other significant data from a wide dispersed dataset. The precise yield estimate for the various crops involved in the planning is a critical problem for agricultural planning. To achieve realistic and effective solutions to this problem, data mining techniques are an essential approach. Applying distplot combined with kernel density estimate (KDE) in this paper to visualize the probability density of disseminated datasets of vast crop deals for crop planning. This paper focuses on analyzing and segmenting agricultural data and determining optimal parameters to maximize crop yield using data mining techniques such as K-means clustering and principal component analysis (PCA)
Volume: 20
Issue: 1
Page: 98-108
Publish at: 2022-02-01

Performance enhancement of a high-speed railway supply system with multi module converter: a laboratory prototype model for Indian railways

10.11591/ijeecs.v25.i2.pp680-689
Venkatasupura Vemulapati , Yerram N. Vijaykumar , Nagalamadaka Visali
The present Indian traction supply system’s complications (neutral sections of the catenary line and issues of power quality) restrict the growth of railway transportation, particularly high-speed rail networks that are fast growing globally. The neutral sections (NS) results in loss of speed, momentum and mechanical failures that are all threatening the fast and stable operation of trains and systems. In the meantime, issues with the power quality such as the negative sequence currents (NSC), the reactive power and harmonics may create problems on the three phase grid side that cannot be overlooked. To address these two issues concurrently, a new traction power supply system is designed in this paper. The proposal will also analyses the theory of operation, build the mathematical model and develop the control system for back to back converters. Small scale prototype is also made for validation of simulation results. The results shows that it can fulfil the practical requirements. The experimental results shows that the overall system is practically more appropriate for the high speed railway.
Volume: 25
Issue: 2
Page: 680-689
Publish at: 2022-02-01

A fast and non-trainable facial recognition system for schools

10.11591/ijeecs.v25.i2.pp989-994
Kazeem Oyebode , Kingsley Chiwuike Ukaoha
Deep learning models have been at the forefront of facial recognition because they deliver improved classification accuracy over traditional ones. Regardless, deep learning models require an extensive dataset for training. To significantly cut down on its training time and dataset volume, pretrained models, have been used although, they are still required to undergo the usual training process for custom facial recognition tasks. This research focuses on an improved facial recognition system that lacks the training and retraining requirements. The system uses an existing deep learning feature extraction model. First, a user stands before a camera-enabled system. After that, the user supplies a unique identification number to fetch a corresponding face image from the database. This process generates two face feature vectors. One from the camera and that retrieved from the database. The cosine distance function determines the similarity value of these vectors. When the cosine distance value falls below a set threshold, the face is recognized and access granted. If the cosine distance of the two vectors gives a value above this threshold, access is denied. The proposed model performs satisfactorily on publicly available datasets.
Volume: 25
Issue: 2
Page: 989-994
Publish at: 2022-02-01

An assessment of cybersecurity awareness level among Northeastern University students in Nigeria

10.11591/ijece.v12i1.pp572-584
Adamu Abdullahi Garba , Maheyzah Muhamad Siraj , Siti Hajar Othman
The world economy today has adopted the internet as a medium of transactions, this has made many organizations use the internet for their daily activities. With this, there is an urgent need to have knowledge in cybersecurity and also how to defend critical assets. The objective of this paper is to identify the level of cybersecurity awareness of students in Northeastern Nigeria. A quantitative approach was used for data collection and cyberbully, personal information, internet banking, internet addiction, and Self-protection were the items ask for cybersecurity awareness level identification. Descriptive analysis was performed for initial result findings using SPSS and OriginPro for graphical design. the preliminary result shows of the students have some basic knowledge of cybersecurity in an item like internet banking, while other items like cyberbully, self-protection and, internet addiction result show moderate awareness, the students' participation based on gender, males constitute 77.1% i.e. (N=340) and females constitute 22.9% i.e. (N=101). Future research would concentrate on designing awareness programs that would increase the level of their awareness especially the students in the Northeastern part of Nigeria.
Volume: 12
Issue: 1
Page: 572-584
Publish at: 2022-02-01

Asymmetric image encryption scheme based on Massey Omura scheme

10.11591/ijece.v12i1.pp1040-1047
Najlae Falah Hameed Al Saffar , Inaam R. Al-Saiq , Rewayda Razaq Mohsin Abo Alsabeh
Asymmetric image encryption schemes have shown high resistance against modern cryptanalysis. Massey Omura scheme is one of the popular asymmetric key cryptosystems based on the hard mathematical problem which is discrete logarithm problem. This system is more secure and efficient since there is no exchange of keys during the protocols of encryption and decryption. Thus, this work tried to use this fact to propose a secure asymmetric image encryption scheme. In this scheme the sender and receiver agree on public parameters, then the scheme begin deal with image using Massey Omura scheme to encrypt it by the sender and then decrypted it by the receiver. The proposed scheme tested using peak signal to noise ratio, and unified average changing intensity to prove that it is fast and has high security.
Volume: 12
Issue: 1
Page: 1040-1047
Publish at: 2022-02-01

On the computation of the automorphisms group of low density parity check codes using genetic algorithm

10.11591/ijeecs.v25.i2.pp1059-1066
El Mehdi Bellfkih , Said Nouh , Imrane Chemseddine Idrissi , Abdelaziz Ettaoufik , Khalid Louartiti , Jamal Mouline
The genetic algorithm (GA) is an adaptive metaheuristic search method based on the process of evolution and natural selection theory. It is an efficient algorithm used for solving the combinatorial optimization problems, e.g., travel salesman problem (TSP), linear ordering problem (LOP), and job-shop scheduling problem (JSP). The simple GA applied takes a long time to reach the optimal solution, the configuration of the GA parameters is vital for a successful GA search and convergence to optimal solutions, it includes population size, crossover operator, and mutation operator rates. Also, very recently, many research papers involved the GA in coding theory, In particular, in the decoding linear block codes case, which has heavily contributed to reducing the complexity, and guaranting the convergence of searching in fewer iterations. In this paper, an efficient method based on the genetic algorithm is proposed, and it is used for computing the Automorphisms groups of low density parity check (LDPC) codes, the results of the aforementioned method show a significant efficiency in finding an important set of Automorphisms set of LDPC codes.
Volume: 25
Issue: 2
Page: 1059-1066
Publish at: 2022-02-01

Optimal linear quadratic Gaussian control based frequency regulation with communication delays in power system

10.11591/ijece.v12i1.pp157-165
Hoan Bao Lai , Anh-Tuan Tran , Van Van Huynh , Emmanuel Nduka Amaefule , Phong Thanh Tran , Van-Duc Phan
In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the MAPS with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the LMI technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.
Volume: 12
Issue: 1
Page: 157-165
Publish at: 2022-02-01

A novel weather parameters prediction scheme and their effects on crops

10.11591/ijece.v12i1.pp639-648
Naveen Lingaraju , Hosaagrahara Savalegowda Mohan
Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work.
Volume: 12
Issue: 1
Page: 639-648
Publish at: 2022-02-01

Text pre-processing of multilingual for sentiment analysis based on social network data

10.11591/ijece.v12i1.pp776-784
Neha Garg , Kamlesh Sharma
Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text pre-processing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition.
Volume: 12
Issue: 1
Page: 776-784
Publish at: 2022-02-01
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