Research in character recognition is an old application in the area of pattern recognition and has attracted many researchers during the last few decades. Handwritten character recognition (HCR) is of two types namely, Online and Offline. The recognition accuracy for HCR is less than 60% as per the literature survey. Also the non existence of standard database for Indian languages is another reason for motivation of this work. This work describes Offline HCR by extracting features using 2D FFT and using the support vector machines for Telugu documents. The best percentage recognition accuracy for Telugu handwritten characters is 71%.

Many diversified applications do exist in science & technology, which make use of the primary theory of a recognition phenomenon as one of its solutions. Recognition scenario is incorporated with a set of decisions and the action according to the decision purely relies on the quality of extracted information on utmost applications. Thus, the quality decision making absolutely reckons on processing momentum and precision which are entirely coupled with recognition methodology. In this article, a latest rule is formulated based on the degree of correlation to characterize the generalized recognition constraint and the application is explored with respect to image based information extraction. Machine learning based perception called feed forward architecture of Artificial Neural Network has been applied to attain the expected eminence of elucidation. The proposed method furnishes extraordinary advantages such as less memory requirements, extremely high level security for storing data, exceptional speed and gentle implementation approach.

An organization often uses forums and social media channels for getting feedback from costumers or users. The ratings of products on rating platforms are a useful feedback to make a product better. The feedback from a customer is in the form of a suggestion which appears in a rating text or is directly asked from the customer. Suggestion mining is a binary classification problem that labels sentences as Suggestion or Non-suggestion. The suggestion mining is similar to sentiment analysis which is associated with common linguistic properties and challenges irrespective of the domain and application. Most of the previous works of suggestion mining proposed rule based methods and a very few developed statistical classifiers by using manually identified features. Recently, several researchers paid attention on deep learning technique based solutions to suggestion mining where features are automatically learned. In this work, various deep learning techniques like RNN, LSTM, Attention based LSTM and GRU are used in the experimentation of suggestion mining. The experiment carried out on the dataset provided in SemEval 2019 suggestion mining competition. The Attention based LSTM achieved best accuracies for suggestion mining when compared with other deep learning techniques.

Data mining is the essential step which identifies hidden patterns from large repositories. Medical diagnosis became a major area of current research in data mining. Machine learning technique which use statistical methods to enable machine to improve with experiences and identify hidden patterns in data like regression algorithms, clustering algorithms, classification algorithms, neural networks(ANN,CNN,DL),recommender system algorithms, Apriori algorithms, page ranking algorithms, text search and NLP(natural language processing) etc.., but due to lack of evaluation, these algorithms are unsuccessful in finding a better classifier for images to estimate accuracy of classification in medical image processing. Classification is an supervised learning which predicts the future class for an unknown object. The main purpose is to identify an unknown class by consulting with the neighbor class characteristics. Clustering can be known as unsupervised learning as it label the objects based on the scale of similar characteristics without consulting its class label. Main principle of clustering is find the distance like nearby and faraway based on their similarities and dissimilarities and groups the objects and hence can be used to identify outliers (which are far away from from the object). Feature extraction, variable selection is a method of obtaining a subset of relevant characteristics from large dataset. Too many features of a class may affect the accuracy of classifier. Therefore, feature extraction technique can be used to eliminate irrelevant attributes and increases the accuracy of classifier. In this paper we performed an induction to increase the accuracy of classifier by applying mining techniques in WEKA tool. Breast Cancer dataset is chosen from learning repository to analyze and an experimental analysis was conducted with WEKA tool using training dataset by applying naïve bayes, bayesnet, and PART, ZeroR, J48 and Random Forest techniques on the Wisconsin's dataset on Breast cancer. Finally presented the best classifier where the accuracy is more

Now a day's every second trillion of bytes of data is being generated by enterprises especially in internet. To achieve the best decision for business profits, access to that data in a well-situated and interactive way is always a dream of business executives and managers. Data warehouse is the only viable solution that can bring the dream into veracity. The enhancement of future endeavours to make decisions depends on the availability of correct information that is based on quality of data underlying. The quality data can only be produced by cleaning data prior to loading into data warehouse since the data collected from different sources will be dirty. Once the data have been pre-processed and cleansed then it produces accurate results on applying the data mining query. Therefore the accuracy of data is vital for well-formed and reliable decision making. In this paper, we propose a framework which implements robust data quality to ensure consistent and correct loading of data into data warehouses which ensures accurate and reliable data analysis, data mining and knowledge discovery.