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Mapari R.B.,Anuradha Engineering College
Proceedings of 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2015 | Year: 2015

Researchers have used one of the means for acquiring hand Signs like Web Camera, instrumented Glove, Depth Camera and Kinect sensor. In this paper, an Indian Sign Language recognition system is developed which recognizes ISL (with both hands) using Leap Motion sensor. This sensor overcomes the major issues in real time environment like background, lightening condition, and occlusion. The leap motion sensor captures the hand gesture and gives finger position in 3D format (X, Y, Z axis values). The positional information of five finger tips along with center of palm for both the hand is used to recognize sign posture based on Euclidean distance and Cosine similarity. The system was tested for ISL (Indian Sign Language alphabets) signs with 10 different signers. The average recognition accuracy of ISL is 88.39 % for Euclidean Distance method and 90.32 % for Cosine similarity. While performing sign, the Leap Motion Camera is kept about 10 degree inclined so that depth information is properly extracted. Although Leap Motion sensor tracks both the hand accurately it can't track non manual signs which involve other body parts and facial expressions. © 2015 IEEE.


Jinturkar A.M.,Anuradha Engineering College | Deshmukh S.S.,University of Surrey
Expert Systems with Applications | Year: 2011

In this study, a fuzzy mixed integer goal programming model (FMIGP) has been developed for rural cooking and heating energy planning in the Chikhli taluka of Buldhana district, Maharashtra, Central India. The model considers various scenarios such as economical, environmental, social acceptance and local resources to trade off between socio-economical and environmental issues related to cooking and heating energy in two villages namely Malshemba and Muradpur. Due to uncertainty involved in real world energy planning problems, exact input data is impossible to acquire. Hence FMIGP model is used to consider four fuzzy objectives. The solutions provide energy resource allocations at micro level with minimized cost, minimized emission, maximized social acceptance and maximized use of local resources. The proposed approach can handle fuzzy environmental realistic situation and can provide better solution to decision maker for rural energy planning. © 2011 Elsevier Ltd. All rights reserved.


Bhatkar A.P.,Anuradha Engineering College
ACM International Conference Proceeding Series | Year: 2016

We presented neural network based classifier for diabetic retinopathy detection in fundus images. The Multi Layer Perception Neural Network (MLPNN) based classifier is used to categorize fundus retinal images as normal and abnormal. Feature vector is composed of transform domain features and different statistical parameters of fundus retinal images. 64-point Fast Fourier Transform (FFT) is used as transform domain feature and Entropy, mean, standard deviation, average, Euler number, contrast, correlation, energy and homogeneity are used as statistical parameters. This feature vector is used as input to IvILPNN based classifier. The classifier performance is calculated on DJ.ARETDBO database. The 00 classification accuracy on train and CV data sets is 99.48°o and 100 respectively. © 2016 ACM.


Mapari R.B.,Anuradha Engineering College
ACM International Conference Proceeding Series | Year: 2016

Advancement in technology has opened new ways in the field of Human Machine Interaction. A Novel method for recognition of American Sign Language (ASL) is proposed using Leap Motion Sensor. Static signs (A to Z and numbers from 1 to 10) excluding J and Z are used for processing. However 2 and 6 also excluded from dataset as the posture of these is similar to V and W respectively. Features Set consist of positional values (fingers and palm), distance and angle values. Total 48 features are used to recognize ASL using Multilayer Perceptron (MLP) which is a feed forward artificial neural network. Dataset consists of 146 users who have performed 32 signs resulting in total dataset of 4672 signs. Out of this 90% dataset is used for training and 10% dataset is used for CV (Cross Validation)/testing. The average classification accuracy obtained is near about 90%. © 2016 ACM.


Basu B.,Anuradha Engineering College
Journal of the Textile Association | Year: 2011

The role of secondary heater is very often a disputed matter. The temperature is kept by the PTY manufacturers depending upon their end use. Experimental works were carried out with latest POY, latest texturising machine reveals the fact that there is marginal difference found the denier, tenacity and elongation. The main area is HCC % which is known to all. No difference in fabric width was found nor in dye uptake although the same is very often doubted by the end users. The bulk variation starts only at the difference of 20°C between the primary and secondary heater. There is no remarkable difference in dye uptake also. The X-Ray and intrinsic Viscosity (IV) also do not show any difference as there is no change in molecular structure. This experiment is an ideal guideline for the PTY manufacturers.


Kharat P.A.,Anuradha Engineering College | Dudul S.V.,Sant Gadge Baba Amravati University
WSEAS Transactions on Biology and Biomedicine | Year: 2012

Epilepsy is one of the major fields of application of EEG. Now a days, identification of epilepsy is accomplished manually by skilled neurologist. Those are very small in number. In this work, we propose a methodology for automatic detection of normal, interictal and ictal conditions from recorded of EEG signals. We used the wavelet transform for the feature extraction and obtained statistical parameters from the decomposed wavelet coefficients. The Generalized Feed Forward Neural Network (GFFNN), Multilayer Perceptron (MLP), Elman Neural Network (ENN) and Support Vector Machine (SVM) are used for the classification. The performance of the proposed system was evaluated in terms of classification accuracy, sensitivity, specificity and overall accuracy.


Bhatkar A.P.,Anuradha Engineering College | Kharat G.U.,College of Engineering, Pune
Proceedings - 2015 IEEE International Symposium on Nanoelectronic and Information Systems, iNIS 2015 | Year: 2015

The rising situation in the developing world suggests diabetic retinopathy may soon be a major problem in the clinical world [1]. Hence, detection of diabetic retinopathy is important. This paper focuses on Multi Layer Perception Neural Network (MLPNN) to detect diabetic retinopathy in retinal images. In this paper the MLPNN classifier is presented to classify retinal images as normal and abnormal. A feature vector is formed with 64-point Discrete Cosine Transform (DCT) with different 09 statistical parameters namely Entropy, mean, standard deviation, average, Euler number, contrast, correlation, energy and homogeneity. The Train N Times method was used to train the MLPNN to find best feature subset. The training and cross validation rates by the MLP NN are 100% for detection of normal and abnormal retinal images. © 2015 IEEE.


Some new 3,5 diaryl-4-benzoyl-1-pyridoyl pyrazoles have been synthesized by the oxidation of pyrazolines, by using I2 in DMSO solvent. The structures of these compounds have been established by spectral analysis (IR, NMR and UV) and elemental analysis. The reactions were carried out in microwave oven.


Rajput D.,Anuradha Engineering College | Bhagade S.S.,Anuradha Engineering College | Raut S.P.,VNIT | Ralegaonkar R.V.,VNIT | Mandavgane S.A.,VNIT
Construction and Building Materials | Year: 2012

Cotton waste results from the mechanical processing of raw cotton in yarn mills. Recycle Paper Mills constitute 30% of total pulp and paper mill segment in India. With 85% average efficiency of Recycle Paper Mills, 15% waste is produced annually. Recycle Paper Mills waste and cotton waste has been utilized to make Waste Crete Bricks. It helps in solid waste management, generate additional revenue and help in earning carbon credits. Waste Crete Bricks with varying content of cotton waste (1-5 wt.%), Recycle Paper Mills waste (89-85 wt.%) and fixed content of Portland cement (10 wt.%) have been prepared and tested as per IS 3495 (Part 1-3): 1992 standards. The characteristics of raw materials, which is the base material for Waste Crete Bricks, have been determined using XRF, TG-DTA, and SEM. TG-DTA indicate that bricks is thermally stable up to a temperature of 280°C while SEM monographs show its porous and fibrous nature. The bricks meet of IS 3495 (Part 1-3): 1992. © 2012 Elsevier Ltd. All rights reserved.


Kharat P.A.,Anuradha Engineering College | Dudul S.V.,Sgb Amaravati University
Interdisciplinary Sciences: Computational Life Sciences | Year: 2012

Epilepsy is a common neurological disorder that is characterized by recurrent unprovoked seizures. Epilepsy can develop in any person at any age. 0.5% to 2% of people will develop epilepsy during their lifetime. This paper aims to develop the clinical decision support system (DSS) for the diagnosis of epilepsy. In this paper a simple, reliable and economical Neural Network (NN) based DSS was proposed for the diagnosis of epilepsy. The generalized feed forward neural network (GFFNN) was designed for the diagnosis. Eleven statistical parameters along with the 64 FFT were extracted for the electroencephalogram (EEG) signal. Data used for the experimentation purpose was obtained from the University of Bonn. The classification rate of GFFNN was 100 % for the training data and 86.67% for the cross validation data. © 2012 International Association of Scientists in the Interdisciplinary Areas and Springer-Verlag Berlin Heidelberg.

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