Browsing by Author "K.K. Shukla"

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A Mathematical Model of Consumers' Buying Behaviour Based on Multiresolution Analysis
(Elsevier B.V., 2017) Prateep Upadhyay; S.K. Upadhyay; K.K. Shukla
In this paper we have presented a generalized mathematical model of consumers' buying behaviour. This model provides better insights and perceptions that can be used to take many important managerial decisions for any product to improve the buying behaviour of consumers towards that product. In this paper we have proved that consumers' buying behaviour is a L2(ℝ) function. Such functions can take two values. 1 (if the buying behaviour is satisfied) or 0 (if the buying behaviour is not satisfied). Through multiresolution analysis (MRA), we have proved that all the factors affecting consumers' buying behaviour are the subspaces of L2(ℝ). We have also proved that the satisfaction of consumers' buying behaviour is convex with respect to all the factors that affect it. We have given a relationship among all the factors influencing consumers' buying behaviour.We have provided a way by which the overall inclination of buying behaviour of any consumer or his inclination towards any particular product can be investigated.
Automated medical image segmentation techniques
(2010) Neeraj Sharma; Amit K. Ray; K.K. Shukla; Shiru Sharma; Satyajit Pradhan; Arvind Srivastva; Lalit Aggarwal
Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.
Classification of histopathological images of breast cancerous and non cancerous cells based on morphological features
(Oriental Scientific Publishing Company, 2017) Anuranjeeta; K.K. Shukla; Anoop Tiwari; Shiru Sharma
This paper presents the automated detection and classification of histopathological images of cancer cells using morphological features. The manual assessment of disease is time-consuming and varies with the perception and the level of expertise of the pathologists. The judgment is based on the tissue structures, distribution of cells in tissue and the irregularities of cell shape and size. To overcome the limitation of manual diagnosis, a computer aided diagnosis based on the morphological features has been implemented for accurate and reliable detection of cancer. A dataset of 70 histopathological images of non-cancerous and cancerous tissues are randomly selected. The proposed work aims at developing the technique that uses reliable quantitative measures for providing objective and reproducible information complementary to that of a pathologist.
Convergence rate analysis of proximal gradient methods with applications to composite minimization problems
(Taylor and Francis Ltd., 2021) D.R. Sahu; J.C. Yao; M. Verma; K.K. Shukla
First-order methods such as proximal gradient, which use Forward–Backward Splitting techniques have proved to be very effective in solving nonsmooth convex minimization problem, which is useful in solving various practical problems in different fields such as machine learning and image processing. In this paper, we propose few new forward–backward splitting algorithms, which consume less number of iterations to converge to an optimum. In addition, we derive convergence rates for the proposed formulations and show that the speed of convergence of these algorithms is significantly better than the traditional forward–backward algorithm. To demonstrate the practical applicability, we apply them to two real-world problems of machine learning and image processing. The first issue deals with the regression on high-dimensional datasets, whereas the second one is the image deblurring problem. Numerical experiments have been conducted on several publicly available real datasets to verify the obtained theoretical results. Results demonstrate the superiority of our algorithms in terms of accuracy, the number of iterations required to converge and the rate of convergence against the classical first-order methods. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Crop parameters estimation by fuzzy inference system using X-band scatterometer data
(Elsevier Ltd, 2013) Abhishek Pandey; R. Prasad; V.P. Singh; S.K. Jha; K.K. Shukla
Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level. © 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.
Denoising 1D signal using wavelets
(Inderscience Publishers, 2020) Prateep Upadhay; S.K. Upadhyay; K.K. Shukla
Signal denoising is one of the most important areas in signal processing. In the present paper, we have denoised a 1D piecewise constant (PWC) signal corrupted by additive white Gaussian noise (AWGN) using the thresholded Haar wavelet denoising method. The central idea of the wavelet transform is the multiresolution decomposition of signals. This becomes advantageous because small objects require high resolution and low resolution is suitable for large objects. In multiresolution decomposition, an approximation component is created using a scaling function, which is often termed as a lowpass filter. Detail components are obtained using wavelet functions, which are often known as highpass filters. A series of approximations of a signal is thus obtained, which has difference in resolution among them by a factor 2. Detail components contain the difference between adjacent approximations. Proper thresholding of the transformed signal results in reduced noise because, in general, the noise component of a signal has a relatively smaller magnitude and wider bandwidth. We show that our method outperforms recently reported non-convex regularisation-based convex 1D total variation denoising method on PWC signals. © 2020 Inderscience Enterprises Ltd.
Effect of y doping on magnetic and transport properties of La 0.7Sr0.3CoO3
(2013) G.D. Dwivedi; K.K. Shukla; P. Shahi; O.K. Jha; A.K. Ghosh; A.K. Nigam; Sandip Chatterjee
The temperature variation of magnetization, resistivity and thermo electric power of undoped and Y-doped La0.7Sr0.3CoO3 samples have been investigated. Y-doping decreases the magnetization possibly due to the spin state transition of Co-ions. The low temperature conduction in (La1-yYy)0.7Sr0.3CoO3 is consistent with the variable range hopping. With Y doping, value of the Seebeck coefficient increases, as Y doping decreases bandwidth and increases distortion. © 2013 American Institute of Physics.
Effect of Y-doping on the transport and magnetic properties of La 0.5Sr0.5CoO3 and La0.7Sr 0.3CoO3
(2013) G.D. Dwivedi; K.K. Shukla; P. Shahi; A.K. Ghosh; A.K. Nigam; Sandip Chatterjee
The temperature variation of magnetization, resistivity, and thermoelectric power of undoped and Y-doped La0.7Sr0.3CoO3 and La0.5Sr0.5CoO3 samples have been investigated. Y-doping decreases the magnetization possibly due to the spin-state transition of Co ions. The low temperature conduction in (La 1-y Y y )0.7Sr0.3CoO3 is consistent with the variable range hopping. With Y-doping, value of the Seebeck coefficient increases as Y-doping decreases bandwidth and increases distortion. Seebeck coefficient value also reflects that the orbital stability increases with Sr concentration. © 2012 Springer Science+Business Media New York.
Effect of Zn doping on the magneto-caloric effect and critical constants of Mott insulator MnV2O4
(American Institute of Physics Inc., 2014) Prashant Shahi; Harishchandra Singh; A. Kumar; K.K. Shukla; A.K. Ghosh; A.K. Yadav; A.K. Nigam; Sandip Chatterjee
X-ray absorption near edge spectra (XANES) and magnetization of Zn doped MnV2O4 have been measured and from the magnetic measurement the critical exponents and magnetocaloric effect have been estimated. The XANES study indicates that Zn doping does not change the valence states in Mn and V. It has been shown that the obtained values of critical exponents β, γ and δ do not belong to universal class and the values are in between the 3D Heisenberg model and the mean field interaction model. The magnetization data follow the scaling equation and collapse into two branches indicating that the calculated critical exponents and critical temperature are unambiguous and intrinsic to the system. All the samples show large magneto-caloric effect. The second peak in magneto-caloric curve of Mn0.95Zn0.05V2O4 is due to the strong coupling between orbital and spin degrees of freedom. But 10% Zn doping reduces the residual spins on the V-V pairs resulting the decrease of coupling between orbital and spin degrees of freedom. © 2014 Author(s).
Genetic evolution of neural network based on a new three-parents crossover operator
(2000) A.K. Srivastava; S.K. Srivastava; K.K. Shukla
Among the emerging technologies now a days Genetic Algorithm, a powerful optimization technique, is becoming the subject of new craze among neural network researchers. Genetic Algorithms (GA's) for training and designing artificial neural networks (ANN's) heve proved to be useful integration. This paper reports an improvement over earlier work on Genetic evolution of neural network weights using two-parents multipoint restricted crossover (Double-MRX) operator proposed by Srivastava, Shukla and Srivastava. In this research a methodology to improve network convergence is presented by introducing a new concept contrary to natural law i.e. crossover among three-parents in place of usual two-parents crossover with randomly selected multiple crossover sites restricted to lie within individual weight boundaries, hence termed as Triple-MRX. In GA search strategy relies more on exchange of information between individual building blocks by exploiting crossover operator. The use of Triple-MRX promotes cooperation among individuals, that better exploits the new genotypic information contained in genome variation. This ensures a much more effective search, both in terms of quality of the solution and speed of convergence as shown by the simulation experiments. Fitness function used in our study is 1/MSE (Mean Square Error). Effectiveness of the proposed technique is tested by evaluating the capability of neural network to learn real-world gas identification problem.
Haemostatic status in prostatectomy & the effect of paraaminomethyl benzoic acid on anti-fibrinolysis
(1988) S.S. Ambasta; K.K. Shukla; R.K. Dube; B. Dube
[No abstract available]
Heat equation-based ECG signal denoising in the presence of white, colored, and muscle artifact noises
(Springer Verlag, 2019) Prateep Upadhyay; S.K. Upadhyay; K.K. Shukla
In this paper, we have derived a novel solution of heat equation which comes out in the form of wavelet transformation and we have applied this solution to the signals of the MIT-BIH normal sinus rhythm database from PhysioBank in the presence of white Gaussian noise, colored noises, and muscle artifact (MA) noise respectively. It was found that the proposed method outperforms the recently reported method by Hamed Danandeh Hesar et al. in their specified SNR range of noises. © Springer Nature Singapore Pte Ltd. 2019.
In search of a good neuro-genetic computational paradigm
(IEEE, 2000) A.K. Srivastava; S.K. Srivastava; K.K. Shukla
This paper reports effect of some advanced Genetic operators like two-parents multipoint restricted crossover (Double-MRX), three-parents multipoint restricted crossover (Triple-MRX), Elitist selection and Scheduled Mutation on the adaptability of feedforward neural network trained over complex and computationally expensive Electronic Nose data. We show that performance of Triple-MRX is better that Double-MRX. Upon applying Elitist Selection with Double-MRX and Scheduled Mutation with Triple-MRX, performance of Genetic training of neural network improves upto some extent, but Triple-MRX is still better than Double-MRX as far as quality of solution and speed of convergence are concerned. It is also showed that performance level of these hybrid techniques far exceeds than that of commonly used backpropagation model. Search of a good neuro-genetic hybrid computational paradigm based on advanced genetic operators is a frontier research area in the evolution of sixth generation computing system.
Large power factor and anomalous Hall effect and their correlation with observed linear magneto resistance in Co-doped Bi2Se3 3D topological insulator
(Institute of Physics Publishing, 2016) Rahul Singh; K.K. Shukla; A. Kumar; G.S. Okram; D. Singh; V. Ganeshan; Archana Lakhani; A.K. Ghosh; Sandip Chatterjee
Magnetoresistance (MR), thermo power, magnetization and Hall effect measurements have been performed on Co-doped Bi2Se3 topological insulators. The undoped sample shows that the maximum MR as a destructive interference due to a π-Berry phase leads to a decrease of MR. As the Co is doped, the linearity in MR is increased. The observed MR of Bi2Se3 can be explained with the classical model. The low temperature MR behavior of Co doped samples cannot be explained with the same model, but can be explained with the quantum linear MR model. Magnetization behavior indicates the establishment of ferromagnetic ordering with Co doping. Hall effect data also supports the establishment of ferromagnetic ordering in Co-doped Bi2Se3 samples by showing the anomalous Hall effect. Furthermore, when spectral weight suppression is insignificant, Bi2Se3 behaves as a dilute magnetic semiconductor. Moreover, the maximum power factor is observed when time reversal symmetry (TRS) is maintained. As the TRS is broken the power factor value is decreased, which indicates that with the rise of Dirac cone above the Fermi level the anomalous Hall effect and linearity in MR increase and the power factor decreases. © 2016 IOP Publishing Ltd.
Magnetic and structural properties of Zn doped MnV2O4
(American Institute of Physics Inc., 2014) Prashant Shahi; K.K. Shukla; Rahul Singh; A. Das; A.K. Ghosh; A.K. Nigam; Sandip Chatterjee
The magnetization, Neutron diffraction and X-ray diffraction of Zn doped MnV2O4 as a function of temperature have been measured. It has been observed, with increase of Zn the non-linear orientation of Mn spins with the V spins will decrease which effectively decrease the structural transition temperature more rapidly than Curie Temperature. © 2014 AIP Publishing LLC.
Magnetic resonance images denoising using a wavelet solution to laplace equation associated with a new variational model
(Elsevier Inc., 2021) Prateep Upadhyay; S.K. Upadhyay; K.K. Shukla
In this paper by exploiting the theory of wavelet transform, a solution of Laplace equation, after changing certain initial conditions in terms of wavelet transformation is obtained. We have further applied this solution of Laplace equation to denoise magnetic resonance (MR) images from brain web dataset at different noise levels. The denoised MR images are again denoised with the help of a new proposed variational model with certain wavelet. We compared our results with the reported results of Yadav et al along with some other recently reported results. It was found that the proposed method not only outperforms the reported methods of Yadav et al but some other recently reported results also. © 2021
Neurocomputer based learning controller for critical industrial applications
(IEEE, 1995) K.K. Shukla
Because of their remarkable ability to learn nonlinear mappings from a nonexhaustive training set and generalize, neurocomputer can readily play the role of learning controllers for complex plants. Due to parallel distributed nature of processing they introduce little time delay in the control loop. In this paper a new generalized feedforward neural network model is applied to the task of controlling an aircraft engine model. A more effective learning algorithm based on adaptive optimization is presented which compares favorably with the classical backpropagation method. Similar controllers can be designed for critical industrial applications such as nuclear power plants etc.
New approaches to intuitionistic fuzzy-rough attribute reduction
(IOS Press, 2018) Anoop Kumar Tiwari; Shivam Shreevastava; K.K. Shukla; Karthikeyan Subbiah
Technological advancement in the area of computing has led to production of huge amount of structured as well as unstructured data. This high dimensional data is very complex to process. Feature selection is one of the widely used techniques for preprocessing of this huge data in predictive analytics. Rough set based feature selection is an approach for handling the vagueness in data and works fine on discrete data but struggles in the continuous case as it requires discretization. This process of discretization leads to information loss. Solution for this problem was given by various authors in form of fuzzy rough set as well as intuitionistic fuzzy rough set based approaches for feature selection. Intuitionistic fuzzy set has certain benefits over the theory of traditional fuzzy sets such as its ability in a better expression of underlying information as well as its aptness to recite fragile ambiguities of the uncertainty of the objective world. The benefits offered by Intuitionistic fuzzy sets is due to the concurrent contemplation of positive, negative and hesitancy degrees for an object to belong to a set. In this paper, three novel approaches of feature reduction based on intuitionistic fuzzy rough set are presented. For this, a new intuitionistic fuzzy rough set model is established by defining a pair of lower and upper approximations. Furthermore, three new approaches of feature selection based on the degree of dependency by using score function, membership grade and cardinality of intuitionistic fuzzy numbers are introduced. Moreover, the basic results on lower and upper approximations based on rough sets are extended for intuitionistic fuzzy rough sets and analogous results are established. Moreover, a suitable algorithm is given based on our proposed approaches. Finally, the proposed algorithm is applied to an arbitrary example data set and comparison has been made with the previous fuzzy rough set based technique. The proposed algorithm is found to be better performing in terms of selected features. © 2018-IOS Press and the authors. All rights reserved.
On the design issue of intelligent electronic nose system
(2000) A.K. Srivastava; S.K. Srivastava; K.K. Shukla
Intelligent electronic nose (ENOSE) system technology is gaining importance in several industrial applications. These include process control and quality control in industries such as foodstuffs, beverages, tobacco, perfumery and pharmaceutical. ENOSE is also crucial component in industrial safety as well as environmental pollution control. The design of an intelligent ENOSE system for the identification of gas/odors using sensor array and neural network pattern classifier is described.
On the performance evaluation of hybrid-trained neural classifier for the detection of hazardous vapours using responses from SAW sensors array
(IEEE, 2000) A.K. Srivastava; S.K. Srivastava; K.K. Shukla
A neural classifier has been designed by a new two - phase hybrid training algorithm introduced by us for classification of hazardous vapours. The neural network is trained using Genetic Algorithm in initial phase. This is followed by a second phase of backpropagation training that uses weight matrix determined by first phase for initialization. For establishing the superior performance of our classifier, published data from polymer - coated surface - acoustic wave (SAW) sensors array exposed to varying concentration of each of nine vapours belonging to two different classes have been used. Vapours of class I are toxic vapours of interest in ambient air that contains common interferents (class II vapours) at much higher concentration. Performance of the classifier is evaluated by reducing dimensionality of resulting data matrix from 4 to 1 by taking different set of sensors. We show that as the dimension is reduced, the gas identification problem becomes harder for backpropagation. Whereas the same set of problems when solved using Genetic Algorithm with heuristic switch over to backpropagation as training paradigm, significantly better results are obtained in predicting class and type of test vapours.