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Search Results: 1 - 10 of 559787 matches for " DR. A. K. WADHWANI "
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HYBRID AND INTEGRATED APPROACH TO SHORT TERM LOAD FORECASTING
J. P. Rothe,,Dr. A. K. Wadhwani,Dr. S. Wadhwani
International Journal of Engineering Science and Technology , 2010,
Abstract: The forecasting of electricity demand has become one of the major research fields in Electrical Engineering. In recent years, much research has been carried out on the application of artificial intelligence techniques to the Load-Forecasting problem. Various Artificial Intelligence (AI) techniques used for load forecasting are Expert systems, Fuzzy, Genetic Algorithm, Artificial Neural Network (ANN). This research work is an attempt to apply hybrid and ntegrated effort to forecast load. Regression, Fuzzy and Neural alongwith Genetic Algorithm will empower the analysts to strongly forecast fairly accurate load demand on hourly base.
A REAL TIME QRS DETECTION SYSTEM USED ERROR BACK PROPAGATION NEURAL NETWORK
Sulaxana pant,Dr. A.K. Wadhwani
International Journal of Engineering Science and Technology , 2011,
Abstract: In this paper a real-time signal processing technique adopting a fast Error Back Propagation Neural Network (EBP-NN) algorithm for QRS complex locations is presented. The obtained performance shows the method validity as results with minimum interferences from noise and artifacts have been obtained. The accurate detection of QRS complexes is important for ECG signal analysis. This paper presents an improved version of a QRS detector based on a Back propagation neural network. We have to use the Butterworth filter for eliminating noise which created problem or not detecting the accurate QRS complex. . Butterworth filter to filtering the signal then we detected the QRS complex. We are taking ten patients to detect the QRS complex is normal or abnormal or the patients suffering from unknown diseases. The aim of this is to achieve high QRS detection performance in terms of time accuracy and reliability. This method can make the prediction much easier and more accurate. Algorithm performance was evaluated against MIT-BIH arrhythmia database. We get the overall output of back propagation neural network is 98.26%.
Analysis of Different EMG signals by Segmentation,Classification and Feature extraction phase
Deepika Chaudhary,Dr. A.k Wadhwani
International Journal of Engineering Science and Technology , 2012,
Abstract: EMG signal is a complicated signal, which is controlled by the nervous system.Quantitative analysis in clinical electromyography (EMG) is very desirable because it allows a more standardized, sensitive and specific evaluation of the neurophysiological findings, especially for the assessment of neuromuscular disorders.. In thisstudy, we have investigated that, The analysis of different electromayography signals (NOR & MYO). This paper basically deals with the basic steps for recording ,analysis of EMG signal,.For recording of EMG of a muscles or facial muscles two electrodes are used one is surface electrodes and second one is needle electrode,after comparing both electrodes we found surface electrode is better than needle electrode .The analysis the EMG signal during three phase segmentation ,classification and feature extraction.We extracted both time domain (TPDs) and frequency domain parameters (FDPs),by which we get some important information of MUAP abnormality and muscular change.and we also concluded its best application forrecognition of Facial Expression . Facial expression analysis is rapidly becoming an area of intense interest in computer science and human-computer interaction design communities. The most expressive way humans display emotions is through facial expressions.
FILTERING OF ECG SIGNAL USING BUTTERWORTH FILTER AND ITS FEATURE EXTRACTION
BHARTI SALSEKAR,DR. A. K. WADHWANI
International Journal of Engineering Science and Technology , 2012,
Abstract: This paper represents filtering of ECG signal of a healthy person and also an unhealthy person using Butterworth filter and then we extract the features of the resultant noise free ECG signals. The analysis of ECG can benefit from the wide availability of computing technology as far as features and performance as well. The overall process has been subdivided into the process of filtering and then its feature extraction using MATLAB. The features of both the ECG signals are calculated and by comparing these results together we can find theabnormalities in the heart of the unhealthy person. The features calculated are: amplitude of P wave, amplitude of R wave, amplitude of T wave, RR interval etc.
Short Term Load Forecasting Using Multi Parameter Regression
Mrs. J. P. Rothe,Dr. A. K. Wadhwani,Dr. Mrs. S. Wadhwani
Computer Science , 2009,
Abstract: Short Term Load forecasting in this paper uses input data dependent on parameters such as load for current hour and previous two hours, temperature for current hour and previous two hours, wind for current hour and previous two hours, cloud for current hour and previous two hours. Forecasting will be of load demand for coming hour based on input parameters at that hour. In this paper we are using multiparameter regression method for forecasting which has error within tolerable range. Algorithms implementing these forecasting techniques have been programmed using MATLAB and applied to the case study. Other methodologies in this area are ANN, Fuzzy and Evolutionary Algorithms for which investigations are under process. Adaptive multiparameter regression for load forecasting, in near future will be possible.
WAVELET DE-COMPOSITION AND FEATURE EXTRACTION FOR HRV AND ECG SIGNAL ANALYSIS FOR SMOKERS AND NON-SMOKERS
Ankita Rajput,Dr. A.K.Wadhwani
International Journal of Engineering Science and Technology , 2012,
Abstract: This current approach deals with study of Heart Rate Variability (HRV) and wavelet-based ECG signal analysis for some volunteers, who were divided into two group’s smokers and non-smokers. Although the preliminary results of frequency domain analysis of HRV showed some dominance towards the sympathetic nervous system activity in smokers, they were not found to be statistically significant. The wavelet decomposition of the ECG signal was done using the Daubechies (Db 6) wavelet family. No significant difference was observed between the smokers and nonsmokers which apparently suggested that HRV does not affect the conduction pathway of heart. Normally clinicians compute the overall heart rate by counting the number of QRS complexes over an interval of one minute. The ‘Heart Rate Variability’ has become the conventionally accepted term to describe variations for both instantaneous HR and R-R intervals.
Expert System design and analysis for Breast cancer
Tripty Singh,,Dr Sarita Bhadaouria,,Dr S.Wadhwani,,Dr A K Wadwani
International Journal of Engineering Science and Technology , 2010,
Abstract: In this paper Algorithm for rule-based reasoning is presented. This is developed for mammographic findings to provide support for the clinical decision to perform biopsy of the breast. The system was evaluated using a round-robin sampling scheme and performed with an area under the receiver operating characteristic curve of 0.83,comparable with the performance of a neural network model. If only the cases returning a malignancy fraction of greater than a threshold of 0.10 are sent to biopsy, no malignancies would be missed, and the number of benign biopsies would be decreased by 25%. At a threshold of 0.21, 98%, of the malignancies would be biopsied, and the number of benign biopsies would be decreased by 41%.This paper is based on clinical trials done at Gwalior Cancer Research Hospital By the author.
Analysis of EEG Signals for Epilepsy and Seizure by decomposition with Wavelet Transform
Sonal Jain,A. K Wadhwani
International Journal of Engineering and Advanced Technology , 2012,
Abstract: The Electroencephalogram (EEG) is a complex signal that indicates the electrical activity of brain. EEG is a signal that represents that effect of the superimposition of diverse processes in the brain. Epilepsy is a common brain disorder. Out of hundred one person is suffering from this problem. Here we study a novel scheme for detecting epileptic seizure from EEG data recorded for healthy subjects and Epileptic patients. EEG is obtained by International 10-20 electrodes system. Wavelet transform is used for feature extraction. Wavelet Transform (WT) provides a flexible way of time-frequency representation of a signal.
DISEASE DIAGNOSIS OF HEART MUSCLES USING ERROR BACK PROPAGATION NEURAL NETWORK
Sunita Pachekhiya,A.K. Wadhwani
International Journal of Engineering Science and Technology , 2011,
Abstract: The death caused by heart diseases has become a serious problem, how to diagnose heart diseases efficiently plays a more important role recently. This study presents a comparative study of the classification, accuracy of ECG signals using a well-known neural network architecture named multi-layered perception (MLP) with errorback propagation training algorithm. A MATLAB based analytical method for feature extraction and disease diagnosis using ANN has been successfully develop.
Data Compression of ECG Signals Using Error Back Propagation (EBP) Algorithm
Anuradha Pathak,A.K. Wadhwani
International Journal of Engineering and Advanced Technology , 2012,
Abstract: Heart is one of the vital parts of our human body, which maintains life line. The paper deals with an efficient composite method which has been developed for data compression and signal reconstruct of ECG signals. ECG data compression algorithm is needed that will reduce the amount of data to be transmitted, stored and analyzed, but without losing the clinical information content. After carrying out detailed studies and by training different topologies of error back propagation (EBP) artificial neural network (ANN) with respect to variation in number of hidden layers and number of elements, the topology with single hidden layer and four elements in each hidden layer has been finalized for ECG data compression using a Physionet.org data base. The compression ratio (CR) in ANN method increases with increase in number of ECG cycles. The entire programming in this paper is carried out on the version of MATLAB 7.8.
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