International Journal of Neural Networks and Advanced Applications
E-ISSN: 2313-0563
Volume 7, 2020
Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.
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Volume 7, 2020
Title of the Paper: Modelling Student’s Performance Using Data Mining Techniques in a Higher Learning Environment in the Pacific
Authors: Ravneil Nand, Ashneel Chand
Pages: 66-71
https://doi.org/10.46300/91016.2020.7.10
Abstract: The students’ performance in higher education has become one of the most widely studied area. Modelling student performance play a pivotal role in forecasting students’ performance where the data mining applications are now becoming most widely used techniques in this study. There are various factors, which determine the student performance. Eight attributes are used as input, which is considered most influential in determining students’ performance in the Pacific. Statistical analysis is done to see which attribute has the highest influence to student performance. In this research, different algorithms are utilized for building the classification model, each of them using various classification techniques. Some of classification techniques used are Artificial Neural Network, Decision Tree, Decision Table, and Naïve Bayes. The WEKA explorer application and R software are used for correlation test between different variables. The dataset used in this research is an imbalanced set, which is later transformed to balance set through under sampling. Neural Network is one of the classification techniques that has done well on both, imbalanced and balanced dataset. Another technique which has done well is Decision tree. Statistical analysis shows that internal assessment has weak positive relationship with student performance while demographic data is not. Further observations are reported in this research in relation to two types of datasets with application to different classification techniques.
Title of the Paper: Fault Detection and Classification for Slider Attachment Process using Convolution Neural Network
Authors: Thanaporn Thamcharoen, Jiraphon Srisertpol, Prathan Chommuangpuck, Jakawat Deeying
Pages: 60-65
https://doi.org/10.46300/91016.2020.7.9
Abstract: Hard Disk Drive (HDD) utilizes automation machines for the assembly processes used in the industry to achieve higher production rates and lower costs. The Head Gimbal Assembly (HGA) production process has two main parts: glue dispensing and slider attaching by an Auto Core Adhesion mounting Machine (ACAM). The slider attaching process produces a mounted head to the suspension utilizing vacuum pressure to hold and position a slider. The errors from a vacuum leak from any step trigger system alarms resulting in machine downtime and slider loss defective (SLD). This paper proposes a classification algorithm derived from 250x250 micron images of mounted heads are 4 different categories: Good, Fault I, Fault II and Fault III using Convolution Neural Networks (CNN). CNN is a performance model for predictive maintenance before failure. The method has achieved a 95 % accuracy for detection and classification
Title of the Paper: Analysis of Ultrasonic Pulse Generated by Piezoelectric Material (LiNbO3 Cut Y-X) Using SVM Classifier
Authors: Hafdaoui Hichem, Benatia Djamel
Pages: 55-59
https://doi.org/10.46300/91016.2020.7.8
Abstract: In this paper, we propose a new numerical method for ultrasonic pulse detection of an acoustics microwaves signal during the propagation of acoustics microwaves generated by piezoelectric substrate LiNbO3 Cut Y-X in ultrasonic transducer. We have used the classifications by support vector machines (SVM) , the originality of this method is it provides the accurate values and help us to identify undetectable waves that we can not identify with the classical methods; in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Ultrasonic Pulse or microwaves acoustics ( bulk waves ). By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity. This study will be very interesting in modeling and realization of acoustics microwaves devices (ultrasound) based on the propagation of acoustics microwaves.
Title of the Paper: A Compact Gradient Based Neural Network for Capon Spectral Estimation
Authors: Abderrazak Benchabane, Fella Charif
Pages: 49-54
https://doi.org/10.46300/91016.2020.7.7
Abstract: This paper describes the use of a novel gradient based recurrent neural network to perform Capon spectral estimation. Nowadays, in the fastest algorithm proposed by Marple et al., the computational burden still remains significant in the calculation of the autoregressive (AR) Parameters. In this paper we propose to use a gradient based neural network to compute the AR parameters by solving the Yule-Walker equations. Furthermore, to reduce the complexity of the neural network architecture, the weights matrixinputs vector product is performed efficiently using the fast Fourier transform. Simulation results show that proposed neural network and its simplified architecture lead to the same results as the original method which prove the correctness of the proposed scheme.
Title of the Paper: Detection of Brain Tumor Using K-Nearest Neighbor (KNN) Based Classification Model and Self Organizing Map (SOM) Algorithm
Authors: S.G.Raja, K.Nirmala
Pages: 42-48
https://doi.org/10.46300/91016.2020.7.6
Abstract: Knowledge discovery is also known as Data mining in databases, in recent years that technique plays a major role in research area. Data mining in healthcare domain has noteworthy usage in real world. The mining method can enable the healthcare field for the enhancement of institutionalization of its administrations and become quicker with best in class technologies. Innovation utilization isn't restricted to basic leadership in undertakings, yet spread to different social statuses in all fields. In this paper a novel approach for the detection of brain tumor is proposed. The novel approach uses the classification technique of K-nearest neighbor (KNN) and for ignoring the error of the dataset image SOM (self-organizing map) algorithm has been used. Discrete wavelet transform (DWT) is used for transforming input image data set, in which RGB color of input data image has been converted into gray scale. Then it has been classified using KNN after that the error avoiding algorithm has been carried out. This will help to differentiate tumor cells and the normal cells. The presence of tumor in brain image is detected using parametric analysis by simulation.
Title of the Paper: Neural Network Model for Heart Disease Diagnosis
Authors: Seda Postalcıoğlu
Pages: 39-41
https://doi.org/10.46300/91016.2020.7.5
Abstract: The heart disease diagnosis system is proposed inthis study. This kind of diagnosis systems enhance medical careand helps doctors. In this paper, heart disease dataset fromkaggle web site is used. Neural Network is examined andanalyzed for different structures as an optimizer, loss function,and batch size. The simulation results show that the proposedneural network model has 90,16% accuracy.
Title of the Paper: Time Series Simulation by Conditional Generative Adversarial Net
Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto
Pages: 25-38
https://doi.org/10.46300/91016.2020.7.4
Abstract: Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.
Title of the Paper: Rule Insertion Technique for Dynamic Cell Structure Neural Network
Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin
Pages: 19-24
https://doi.org/10.46300/91016.2020.7.3
Abstract: This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.
Title of the Paper: An Overview of Data Mining with Neural Network
Authors: U. C. Jha
Pages: 16-18
https://doi.org/10.46300/91016.2020.7.2
Abstract: With increasing data base management systems applications, large amounts of important data are available much of its knowledge is preserved and concealed. The methods used to extract data from is Data Mining. Various tools are available to forecast the trends that will support decision of people. Neural Networks or Artificial Neural Networks (ANN) have been a promising system in many applications due to their learning ability from data and generalization ability. Neural Networks are used for prediction, classification, forecasting, and pattern recognition. This paper provides a brief overview of Data Mining with the Neural Network, its tools and process
Title of the Paper: A Novel Adaptive Speed Observer using Neural Network and Sliding-Mode for SPIM Drives
Authors: Ngoc Thuy Pham
Pages: 1-15
https://doi.org/10.46300/91016.2020.7.1
Abstract: In this paper, a novel Stator Current Based Model Reference Adaptive System (SC_MRAS) speed estimation scheme using neural network (NN) and Sliding Mode (SM) is proposed to improve the performance of the MRAS speed observer for high-performance Six Phases Induction Motor (SPIM) drives, especially at low and zero speed region, where the poor performance of observers is still always a large challenge. In this novel SC_MRAS scheme, a two-layer linear NN, which has been trained online by means of the Total Least Squares (TLS) algorithm, is used as an adaptive model to estimate the stator current and this model is employed in prediction mode. These novel proposed can ensure that the whole drive system achieves faster satisfactory torque and speed control and strong robustness, the observer operate better accuracy and stability both in transient and steady-state operation. Especially, in this proposed observer, the rotor flux, which is needed for the stator current estimation of the adaptive model and providing to the controller, is identified based on adaptive SM technique. The improvement of Rotor Flux Estimation for SC_MRAS-Based Sensorless SPIM Drives help to eliminate the disadvantages in SC_MRAS based observer such as stator resistance sensitivity, and flux open loop integration which may cause dc drift and initial condition problems or instability in the regenerating mode of operation, therefore, enhancing the rotor flux estimation, speed estimation and control accuracy at very low and zero stator frequency operation help improve the overall observer and drive system performance. The indirect field oriented control (IFOC) for speed control of a sensorless SPIM drive using the proposed observer is built by MATLAB/ Simulink. The simulation results are presented under sensorless speed control performance to validate the effectiveness of the proposed estimation and control algorithms.
