Suresh N. Mali

Work place: Sinhgad Institute of Technology and Science, Pune, India



Research Interests: Data Structures, Data Compression, Multimedia Information System, Network Security, Information Security, Information-Theoretic Security, Online information processing


Suresh N. Mali has completed his Ph.D. in Computer Science from Bharati Vidyapeeth, Pune and presently he is working as Principal in Sinhgad Institute of Technology and Science, Narhe, Pune, Maharashtra, India. He is the author of 3 books and has more than 40 research papers in referred international and national journals and conferences. His research interests mainly include Image Processing, Security, and Machine Learning.

Author Articles
A Hybrid Approach for Class Imbalance Problem in Customer Churn Prediction: A Novel Extension to Under-sampling

By Uma R. Salunkhe Suresh N. Mali

DOI:, Pub. Date: 8 May 2018

Customer retention is becoming a key success factor for many business applications due to increasing market competition. Especially telecom companies are facing this challenge with a rapidly increasing number of service providers. Hence there is need to focus on customer churn prediction in order to detect the customers that are likely to churn i.e. switch from one service provider to another. Several data mining techniques are applied for classifying customers into the churn and non-churn category. But churn prediction applications comprise an imbalanced distribution of the dataset.
One of the commonly used techniques to handle imbalanced data is re-sampling of data as it is independent of the classifier being used. In this paper, we develop a hybrid re-sampling approach named SOS-BUS by combining well known oversampling technique SMOTE with our novel under-sampling technique. Our methodology aims to focus on the necessary data of majority class and avoid their removal in order to overcome the limitation of random under-sampling. Experimental results show that the proposed approach outperforms the other reference techniques in terms of Area under ROC Curve (AUC).

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Evaluation of Shape and Color Features for Classification of Four Paddy Varieties

By Archana A. Chaugule Suresh N. Mali

DOI:, Pub. Date: 8 Nov. 2014

This research is aimed at evaluating the shape and color features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of shape and color features and neural network was done to classify four Paddy (Rice) grains, viz. Karjat-6, Ratnagiri-2, Ratnagiri-4 and Ratnagiri-24. Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and use them as input features for classification. Different feature models were tested for their ability to classify these cereal grains. Effect of using different parameters on the accuracy of classification was studied. The most suitable feature set from the features was identified for accurate classification. The Shape-n-Color feature set outperformed in almost all the instances of classification.

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Strongly Robust and Highly Secured DWT-SVD Based Color Image Watermarking: Embedding Data in All Y, U, V Color Spaces

By Baisa L. Gunjal Suresh N. Mali

DOI:, Pub. Date: 8 Apr. 2012

In this paper ‘DWT-SVD’ based Color Image Watermarking technique in YUV color space using Arnold Transform is proposed. The RGB color image is converted into YUV color space. Image is decomposed by 3 level DWT and then SVD is applied. The security is increased with watermark scrambling using Arnold Transform. The watermark is embedded in all Y,U and V color spaces in HL3 region. The decomposition is done with ‘Haar’ which is simple, symmetric and orthogonal wavelet and the direct weighting factor is used in watermark embedding and extraction process is used. PSNR and Normalized Correlations (NC) values are tested for 10 different values of flexing factor. We got maximum PSNR up to 52.3337 for Y channel and average value of NC equal to 0.99 indicating best recovery of watermark. The proposed scheme is non blind and strongly robust to different attacks like compression, scaling, rotation, cropping and Noise addition which is tested with standard database image of size 512x512 and watermark of size 64X64.

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