Cancer is a life-threatening disease with high mortality rates. In the Indian subcontinent, women have a higher possibility to be diagnosed with cancer than men. The most common cancers identified in Indian women are Breast Cancer and Cervical Cancer. Both these cancers have high survival rates in case of early prediction. This paper reviews the attributes which are used in the existing datasets for prediction of these two cancers. The paper also proposes new attributes to overcome the limitations of existing ones, which will further increase the effectiveness of cancer prediction systems. The efficiency of existing and proposed attributes is compared by processing datasets through data mining algorithms using WEKA tool. The algorithms used for this study are – J48 (Decision Tree), Na?ve Bayes, Random Forest, Random Tree, KStar and Bagging Algorithm. The empirical analysis done in the paper reported improvement in the efficiency of cancer prediction over existing prediction systems.

Robust clustering techniques are real life clustering techniques for noisy data. They work efficiently in the presence of noise. Fuzzy C-means (FCM) is the first clustering algorithm, based upon fuzzy sets, proposed by J C Bezdek but it does not give accurate results in the presence of noise. In this paper, FCM and various robust clustering algorithms namely: Possibilistic C-Means (PCM), Possibilistic Fuzzy C-means (PFCM), Credibilistic Fuzzy C-means (CFCM), Noise Clustering (NC) and Density Oriented Fuzzy C-Means (DOFCM) are studied and compared based upon robust characteristics of a clustering algorithm. For the performance analysis of these algorithms in noisy environment, they are applied on various noisy synthetic data sets, standard data sets like DUNN data-set, Bensaid data set. In comparison to FCM, PCM, PFCM, CFCM, and NC, DOFCM clustering method identified outliers very well and selected more desirable cluster centroids.

Touch-less fingerprint recognition system is a reliable alternative to conventional touch-based fingerprint recognition system. Touch-less system is different from conventional system in the sense that they make use of digital camera to acquire the fingerprint image where as conventional system uses live-acquisition techniques. The conventional fingerprint systems are simple but they suffer from various problems such as hygienic, maintenance and latent fingerprints. In this paper we present a review of touch-less fingerprint recognition systems that use digital camera. We present some challenging problems that occur while developing the touch-less system. These problems are low contrast between the ridge and the valley pattern on fingerprint image, non-uniform lighting, motion blurriness and defocus, due to less depth of field of digital camera. The touch-less fingerprint recognition system can be divided into three main modules: preprocessing, feature extraction and matching. Preprocessing is an important step prior to fingerprint feature extraction and matching. In this paper we put our more emphasis on preprocessing so that the drawbacks stated earlier can be removed. Further preprocessing is divided into four parts: first is normalization, second is fingerprint Segmentation, third is fingerprint enhancement and last is the core point detection. Feature extraction can be done by Gabor filter or by minutia extraction and the matching can be done by Support Vector Machine or Principal Component Analysis and three distance method.

This paper presents a comparison of the three fuzzy based image segmentation methods namely Fuzzy C-Means (FCM), TYPE-II Fuzzy C-Means (T2FCM), and Intuitionistic Fuzzy C-Means (IFCM) for digital images with varied levels of noise. Apart from qualitative performance, the paper also presents quantitative analysis of these three algorithms using four validity functions-Partition coefficient (V_pc), Partition entropy (V_pe), Fukuyama-Sugeno (V_fs), and Xie-Beni (V_xb) functions and also compared the performance on the basis of their execution time.

This paper presents a detailed study and comparison of some Kernelized Fuzzy C-means Clustering based image segmentation algorithms Four algorithms have been used Fuzzy Clustering, Fuzzy C-Means(FCM) algorithm, Kernel Fuzzy C-Means(KFCM), Intuitionistic Kernelized Fuzzy C-Means(KIFCM), Kernelized Type-II Fuzzy C-Means(KT2FCM).The four algorithms are studied and analyzed both quantitatively and qualitatively. These algorithms are implemented on synthetic images in case of without noise along with Gaussian and salt and pepper noise for better review and comparison. Based on outputs best algorithm is suggested.