International Journal of Intelligent Systems Design and Computing
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International Journal of Intelligent Systems Design and Computing (5 papers in press)
Increased theta activity after Om mantra meditation with Fourier and wavelet transform by Bhavna Harne Abstract: The Om mantra is found to be the most powerful of all the mantras in the scriptures of olden India. Om mantra is used meditation. The present study is based on Om mantra meditation. Little is known about the neural mechanisms by which this meditation works, and there is a need for more rigorous EEG investigations of the underlying neurobiology. Discrete Wavelet Transform (DWT) and Fast Fourier transform (FFT) are two tools of spectral analysis of the EEG signal. In our previous study, FFT analysis has been used for spectral analysis of the EEG signal to investigate the effect of OM mantra meditation. It was proved that this mediation plays a role in providing relaxation. In the present study, we continued our work with wavelet analysis to firmly establish this benefit. The dataset of our previous study was used in this work. Two way repeated measures ANOVA was used on relative power obtained by FFT and DWT. The comparative results of both methods are presented. The same increasing and decreasing pattern of relative power are observed in each band with FFT and DWT. A significant increase in theta power when averaged across all brain regions was found only with the method of FFT. But an increase in theta power in all regions of the brain has been observed with both the methods. Raised theta is a sign of deep relaxation. Once again results of this study confirm that 30 minutes Om mediation offers relaxation. The study suggests that if such a small duration of Om meditation has such a relaxing effect then it could be the way to destress if adopted as a daily routine. Keywords: Mantra meditation; EEG; OM.
A Review on the Interval Type-2 Fuzzy Systems by Prabhash Chandra, Devendra Agarwal, Praveen Shukla Abstract: Considering the benefits of the human decision making, the efforts have been executed to implement it in machines. The chronic problem addressed in this implementation is the representation and manipulation of human knowledge which is full of uncertainties and imprecision due to its subjective nature. To deal with this problem a strong mathematical framework is investigated known as fuzzy logic. Initially the concept of fuzzy set has been developed by extending the boolean crisp set logic. Further, type-2 fuzzy systems and interval type-2 fuzzy systems are investigated. This paper reviews the approaches and systems developed under the category of interval type-2 fuzzy systems along with the interpretability and accuracy issues in fuzzy systems. Keywords: Crisp Sets; Fuzzy Sets; T-Norm; S-Norm; Type-2 Fuzzy Systems; Interval Type-2rnFuzzy Systems.
Breast Cancer Data Classification using Deep Neural Network by Vipul Sharma, Saumendra Kumar Mohapatra, Mihir Narayan Mohanty Abstract: Artificial neural networks and their variants play an important role in the analysis and classification of different biomedical data. Deep learning is an advanced machine learning approach
which has been used in many applications since last few years. Worldwide breast cancer is a major disease for woman and still it is one of the challenging job to detect it at an early stage. The authors in this work have taken an attempt to classify the breast cancer data collected from the UCI machine learning repository. Malignant and Benign two different types of breast cancer tumours are classified using deep neural network (DNN). Before classification two preprocessing steps are done for improving the accuracy. The correlation and one-hot encoding of the dataset was done for getting some relevant features that can be used as the input to the DNN. Around 94% of classification accuracy is achieved by using a six-layer DNN classifier. The result is also compared with some earlier works and it is found that the proposed classifier is providing better results as compare to others.
Keywords: ANN; Deep learning; DNN; Breast cancer; Classification.
A Hybrid Incremental Conductance and Sliding Mode Controller For A PV Microgrid System by Swati Sucharita Pradhan, Raseswari Pradhan Abstract: A new controller is designed for performance improvement of a grid-integrated photovoltaic based microgrid (PVMG) system in this paper. The solar photovoltaic (SPV) system is integrated to grid by a single-stage converter such as an H-bridge voltage-source inverter (VSI). The input side of the studied power system is connected to SPV system. To enhance the power conversion from solar panel, a maximum-power-point-tracking (MPPT) such as incremental conductance (I&C) technique is included. The proposed controller comprises of two functional units namely for accomplshing power quality improvement and maximum power extraction tasks. We design a sliding mode controller for improving power quality. This controller is designed to control the power-flow injection to the microgrid (MG). The studied system is built and the simulation and obtained results are analysed. The controller is found to be robust, efficient and easy to implement. Further, the results are compared with that of the proportional-integral+proportional-integral-derivative (PI+PID) hybrid controller to evalaute its effectiveness. From the transient performance analysis, it is found that the studied system with the proposed controller is found to be faster in response, with lesser harmonics and more robust compared to the (PI+PID) hybrid controller. Also, this system is capable controlling both active and reactive power of the line. Keywords: PVMG system; grid integration; robust control; PI+SMC; PI+PID; I&C MPPT.
Real-Time Automatic Tracking of Hand Motion in RGB Videos Using Local feature SIFT by Richa Golash, Yogendra Kumar Jain Abstract: This paper proposes a method for real-time visual tracking of moving hand in RGB videos without any segmentation process and background subtraction. We have used YCgCr converted version of YCbCr color space for a more compact representation of the initial region of moving hand and then local feature SIFT to detect and track hand simultaneously. YCgCr has a high tendency for skin color accretion and can effectively discriminate between the skin and non-skin color regions. The approach demonstrates that using local features (SIFT) of only active region reduces the computation as well as make the method free from the challenges of freedom factor of hand and thus the methodology can detect the hand of any shape and size without being affected by background conditions. In general, researchers avoid using a normal camera for applications based on hand tracking, as RGB images are sensitive to illumination. Our work exhibits that the combination of YCgCr and two-stage feature matching through SIFT algorithm is successful in tracking non-rigid objects with less computation. The methodology is further evaluated with Kalman tracking in Hand Gesture Recognition and is also compared with contemporary works. Keywords: Hand Gesture Recognition; Feature extraction; SIFT; Kalman Filter; Tracking; Centroid.