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Data mining is one of the emerging technologies used in many applications such as Market analysis and Machine learning. Temporal data mining is used to get a clear knowledge about current trend and to predict the upcoming future. The rudimentary challenge in introducing a data mining procedure is, processing time and memory consumption are highly increasing while trying to improve the accuracy, precision or recall. As well as, while trying to reduce the processing time or memory consumption, accuracy, precision and recall values are reducing significantly. So, for improving the performance of the system and to preserve the memory and processing time, Three-Dimensional Fuzzy FP-Tree (TDFFPT) is proposed for Temporal data mining. Three functional modules namely, Three-Dimensional Temporal data FP-Tree (TTDFPT), Fuzzy Logic based Temporal Data Tree Analyzer (FTDTA) and Temporal Data Frequent Itemset Miner (TDFIM) are integrated in the proposed method. This algorithm scans the database and generates frequent patterns as per the business need. Every time a client purchases a new item, it gets stored in the recent database layer instead of rescanning the entire records which are placed in the old layer. The results obtained shows that the performance of the proposed model is more efficient than that of the existing algorithm in terms of overall accuracy, processing time, reduction in the memory utilization, and the number of databases scans. In addition, the proposed model also provides improved decision making and accurate pattern prediction in the time series data. [ABSTRACT FROM AUTHOR]

Breast cancer is the second highest death causing disease in female. From the data it's clear that one of the eight women is affected by breast cancer. The most effective way to detect the cancer is by using mammography technique and also it detects masses and abnormal conditions. The cluster shaped white spots on mammograms are used the cancer in the early stages. The importance of breast cancer detection to reduce different noises which are commonly found in mammogram images and provides better image quality. Here the multi-scale and the multi-directional analysis would produce optimal approximation and detail coefficients of Non-subsampled shearlet transform (NSST). Hence in this paper, the noisy image is amended into piecewise smooth function in different subbands of frequency. NSST coefficients are amended into information and noise related coefficients are noise is removed by using the adaptive threshold. To evaluate the performance of this proposed algorithm, Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are used. Testing these experimental results show that the proposed algorithm can preserve the edges and textures very well while weakening the noise can obtain better suppressed noise and enhances the objective of evaluations than other noise removal methods. [ABSTRACT FROM AUTHOR]

Cryptography is one of the most accepted and successful data security methods used at present by most of the organizations. Encrypting the message using natural languages reduces the encryption time and improves the performance. Tamil is a most ancient Dravidian language spoken in the southern part of India. Here we use Tamil language for encrypting the text named as Tamilian Cryptography. Since Tamil language has 247 characters it is very difficult to crack the message. The message to be encrypted is translated to Tamil language, this translated text is then mapped to a randomly generated 2-bit combination of English alphabets. The result of this process is called intermediate cipher (inter cipher). This is an hybrid algorithm since the inter cipher is encrypted using Advanced Encryption Standard algorithm to enhance the confidentiality of the text. Since the Tamilian Cryptography makes use of three phases namely Translation, Mapping and Encryption, it makes the data much more secure than the existing algorithms like blowfish and Data Encryption Standard (DES) and also the person who tries to decrypt must have the knowledge of Tamil language as well the mapping details to see the original data and This algorithm exhibits stronger avalanche effect of 95% which is greater than blowfish and DES. The evaluation of the proposed algorithm shows that it executes faster and has comparatively lesser encryption time, less memory overhead. [ABSTRACT FROM AUTHOR]