Most recent issue published online in the International Journal of Intelligent Systems Design and Computing.
International Journal of Intelligent Systems Design and Computing
http://www.inderscience.com/browse/index.php?journalID=428&year=2020&vol=3&issue=2
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International Journal of Intelligent Systems Design and Computing
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International Journal of Intelligent Systems Design and Computing
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http://www.inderscience.com/browse/index.php?journalID=428&year=2020&vol=3&issue=2
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Increased theta activity after Om mantra meditation with Fourier and wavelet transform
http://www.inderscience.com/link.php?id=115166
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. 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. 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. The study confirms that this 30 minutes of Om mediation offers relaxation; then it could be the way to de-stress if adopted as a daily routine.
Increased theta activity after Om mantra meditation with Fourier and wavelet transform
Bhavna P. Harne; Anil S. Hiwale
International Journal of Intelligent Systems Design and Computing, Vol. 3, No. 2 (2020) pp. 101 - 116
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. 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. 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. The study confirms that this 30 minutes of Om mediation offers relaxation; then it could be the way to de-stress if adopted as a daily routine.]]>
10.1504/IJISDC.2020.115166
International Journal of Intelligent Systems Design and Computing, Vol. 3, No. 2 (2020) pp. 101 - 116
Bhavna P. Harne
Anil S. Hiwale
Department of Electronics, S.S.G.M. College of Engineering Shegaon, 444203, India ' Department of Electronics, MIT college of Engineering, Pune, 411038, India
mantra meditation
EEG
Om
2021-05-21T23:20:50-05:00
Copyright © 2021 Inderscience Enterprises Ltd.
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2
101
116
2021-05-21T23:20:50-05:00
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A review on the interval type-2 fuzzy systems
http://www.inderscience.com/link.php?id=115168
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.
A review on the interval type-2 fuzzy systems
Prabhash Chandra; Devendra Agarwal; Praveen Kumar Shukla
International Journal of Intelligent Systems Design and Computing, Vol. 3, No. 2 (2020) pp. 117 - 132
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.]]>
10.1504/IJISDC.2020.115168
International Journal of Intelligent Systems Design and Computing, Vol. 3, No. 2 (2020) pp. 117 - 132
Prabhash Chandra
Devendra Agarwal
Praveen Kumar Shukla
Department of Computer Science and Engineering, School of Engineering, Babu Banarasi Das University, Lucknow, India ' Department of Computer Science and Engineering, School of Engineering, Babu Banarasi Das University, Lucknow, India ' Department of Computer Science and Engineering, School of Engineering, Babu Banarasi Das University, Lucknow, India
crisp sets
fuzzy sets
T-norm
S-norm
type-2 fuzzy systems
interval type-2 fuzzy systems
2021-05-21T23:20:50-05:00
Copyright © 2021 Inderscience Enterprises Ltd.
3
2
117
132
2021-05-21T23:20:50-05:00
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Breast cancer data classification using deep neural network
http://www.inderscience.com/link.php?id=115169
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 in the last few years. Worldwide breast cancer is a major disease for women; it is one of the most challenging jobs to detect 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 pre-processing 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 compared to others.
Breast cancer data classification using deep neural network
Vipul Sharma; Saumendra Kumar Mohapatra; Mihir Narayan Mohanty
International Journal of Intelligent Systems Design and Computing, Vol. 3, No. 2 (2020) pp. 133 - 144
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 in the last few years. Worldwide breast cancer is a major disease for women; it is one of the most challenging jobs to detect 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 pre-processing 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 compared to others.]]>
10.1504/IJISDC.2020.115169
International Journal of Intelligent Systems Design and Computing, Vol. 3, No. 2 (2020) pp. 133 - 144
Vipul Sharma
Saumendra Kumar Mohapatra
Mihir Narayan Mohanty
Department of Computer Science and Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Electronics and Communication Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
artificial neural network
ANN
deep learning
deep neural network
DNN
breast cancer
classification
2021-05-21T23:20:50-05:00
Copyright © 2021 Inderscience Enterprises Ltd.
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133
144
2021-05-21T23:20:50-05:00
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A hybrid incremental conductance and sliding mode controller for a PV microgrid system
http://www.inderscience.com/link.php?id=115170
A new controller is designed for performance improvement of a photovoltaic based microgrid (PVMG) system in this paper. The photovoltaic system is integrated to grid via an H-bridge voltage-source inverter (VSI). To enhance the power conversion from the solar panel, an incremental conductance (I&C) Maximum-Power-Point-Tracking (MPPT) controller is designed. The proposed controller comprises of two units namely for accomplishing power quality improvement and MPPT tasks. We design a sliding mode controller for improving power quality. This controller is designed to control the power-flow injection to the PVMG. The results are compared with that of the proportional-integral+proportional-integral-derivative (PI+PID) hybrid controller to evaluate its effectiveness. From the transient performance analysis, it is found that with the proposed controller, it is 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.
A hybrid incremental conductance and sliding mode controller for a PV microgrid system
Swati Sucharita Pradhan; Raseswari Pradhan; Bidyadhar Subudhi
International Journal of Intelligent Systems Design and Computing, Vol. 3, No. 2 (2020) pp. 145 - 160
A new controller is designed for performance improvement of a photovoltaic based microgrid (PVMG) system in this paper. The photovoltaic system is integrated to grid via an H-bridge voltage-source inverter (VSI). To enhance the power conversion from the solar panel, an incremental conductance (I&C) Maximum-Power-Point-Tracking (MPPT) controller is designed. The proposed controller comprises of two units namely for accomplishing power quality improvement and MPPT tasks. We design a sliding mode controller for improving power quality. This controller is designed to control the power-flow injection to the PVMG. The results are compared with that of the proportional-integral+proportional-integral-derivative (PI+PID) hybrid controller to evaluate its effectiveness. From the transient performance analysis, it is found that with the proposed controller, it is 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.]]>
10.1504/IJISDC.2020.115170
International Journal of Intelligent Systems Design and Computing, Vol. 3, No. 2 (2020) pp. 145 - 160
Swati Sucharita Pradhan
Raseswari Pradhan
Bidyadhar Subudhi
Department of Electrical Engineering, VSSUT Burla, Odisha, India ' Department of Electrical Engineering, VSSUT Burla, Odisha, India ' Electrical Engineering Department, IIT Goa, India
PVMG system
grid integration
robust control
PI +
SMC
PI +
PID
I&C MPPT
microgrid
2021-05-21T23:20:50-05:00
Copyright © 2021 Inderscience Enterprises Ltd.
3
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145
160
2021-05-21T23:20:50-05:00
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Real-time automatic tracking of hand motion in RGB videos using local feature SIFT
http://www.inderscience.com/link.php?id=115175
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 YC<SUB align="right">gC<SUB align="right">r converted version of YC<SUB align="right">bC<SUB align="right">r colour space for a more compact representation of the initial region of moving hand and then local feature SIFT to detect and track hand simultaneously. YC<SUB align="right">gC<SUB align="right">r has a high tendency for skin colour accretion and can effectively discriminate between the skin and non-skin colour 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 YC<SUB align="right">gC<SUB align="right">r 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.
Real-time automatic tracking of hand motion in RGB videos using local feature SIFT
Richa Golash; Yogendra Kumar Jain
International Journal of Intelligent Systems Design and Computing, Vol. 3, No. 2 (2020) pp. 161 - 177
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 YC<SUB align="right">gC<SUB align="right">r converted version of YC<SUB align="right">bC<SUB align="right">r colour space for a more compact representation of the initial region of moving hand and then local feature SIFT to detect and track hand simultaneously. YC<SUB align="right">gC<SUB align="right">r has a high tendency for skin colour accretion and can effectively discriminate between the skin and non-skin colour 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 YC<SUB align="right">gC<SUB align="right">r 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.]]>
10.1504/IJISDC.2020.115175
International Journal of Intelligent Systems Design and Computing, Vol. 3, No. 2 (2020) pp. 161 - 177
Richa Golash
Yogendra Kumar Jain
Samrat Ashok Technological Institute, Vidisha, MP, 464001, India ' Samrat Ashok Technological Institute, Vidisha, MP, 464001, India
hand gesture recognition
HGR
feature extraction
scale invariant feature transform
SIFT
Kalman filter
tracking
2021-05-21T23:20:50-05:00
Copyright © 2021 Inderscience Enterprises Ltd.
3
2
161
177
2021-05-21T23:20:50-05:00