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2012 International Conference on Software and Computer Applications(ICSCA 2012)

作者： H.B.kekre tanuja k.sarode Sanjay R.Sange Pallavi Halarnkar Senior Professor Department of Computer Science MPSTMENMIMS University Professor Department of Computer Science TSECBandraMumbai University Mumbai

Optimization of bandwidth is major challenge in networking field for transmission of data in application like video *** compress such a huge video data,combination of lossy half tone and lossless Run-Length-Encoding（RLE） technique is proposed so as to obtain low-bit rate video data *** tone is the binary image,in which long strings of 1’s and 0’s are present,on it RLE is *** experimental result shows the higher compression ratio（CR）,minimum Root Mean Square Error（RMSE） and high Peak Signal-to-Noise Ratio（PSNR） with acceptable image *** of the paper is that,this technique can be used for video *** video conferencing person’s face is the object and usually background is constant where RLE effectively gives higher CR for long string of *** sample images of different people captured by Nikon camera are used for *** images are bitmap（.BMP） 256 X 256 in *** same technique can be used for storage of movies,CCTV footage etc.

关键词： half toning Run-Length-Encoding Low-Bit rate video data compression

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Analyzing the Impact of Feature Selection on Crop Yield Prediction

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Journal of Computer and Communications 2024年第8期12卷 278-291页

作者： Jayshree Hajgude tanuja sarode Department of Computer Engineering Thadomal Sahani Engineering College Mumbai University Mumbai India

In agriculture sector, machine learning has been widely used by researchers for crop yield prediction. However, it is quite difficult to identify the most critical features from a dataset. Feature selection techniques allow us to remove the extraneous and noisy features from the original feature set. The feature selection techniques help the model to focus only on the important features of the data, thus reducing execution time and improving efficiency of the model. The aim of this study is to determine relevant subset features for achieving high predictive performance by using different feature selection techniques like Filter methods, Wrapper methods and embedded methods. In this work, different feature selection techniques like Rank-based feature selection technique, weighted feature selection technique and Hybrid Feature Selection Technique have been applied to the agricultural data. The optimal feature set returned by different feature selection techniques is used for yield prediction using Linear regression, Random Forest, and Decision Tree Regressor. The accuracy of prediction obtained using the above three methods has been analyzed by using different evaluation parameters. This study helps in increasing predictive accuracy with the minimum number of features.

关键词： Crop Yield Feature Selection Hybrid Embedded Analysis

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Performance Comparison of DCT and VQ Based Techniques for Iris Recognition

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Journal of Electronic Science and Technology 2010年第3期8卷 223-229页

作者： H.B.kekre tanuja k. sarode Vinayak Ashok Bharadi Abhishek A. Agrawal Rohan J. Arora Mahesh C. Nair MPSTME NMIMS University Thadoamal Shahani Engg.College Bandra (w) MPSTME NMIMS Universityand Thakur Engg.College

Iris recognition enjoys universality, high degree of uniqueness and moderate user co-operation. This makes iris recognition systems unavoidable in emerging security ＆ authentication mechanisms. An iris recognition system based on vector quantization （VQ） techniques is proposed and its performance is compared with the discrete cosine transform （DCT）. The proposed system does not need any pre-processing and segmentation of the iris. We have tested Linde-Buzo- Gray （LBG）, kekre＇s proportionate error （kPE） algorithm and kekre＇s fast codebook generation （kFCG） algorithm for the clustering purpose. Proposed vector quantization based method using kFCG requires 99.99% less computations as that of full 2-dimensional DCT. Further, the kFCG method gives better performance with the accuracy of 89.10% outperforming DCT that gives accuracy around 66.10%.

关键词： Biometrics discrete cosine transform iris recognition kekre's fast codebook generation kekre's proportionate error Linde Buzonad Gray vector quantization.

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