Department of Computer Science

Recognition system using some statistical features like zone density, projection histograms , 8 directional zone density features in combination with some geometric features like area, perimeter, eccentricity, etc. The image document is first pre-processed by using many techniques like binarization, morphological operations (erosion and dilation) applied to remove noise and then segmented into isolated characters. The highest accuracy obtained by using these features and back propagation classifier is 98%. The character recognition system consists of the different phase's digitization, pre-processing, feature

We have developed a CS1 curriculum that uses a robotics context to teach introductory programming [1]. Core to our approach is that each student has their own personal robot. Our robot and software have been specifically developed to support the needs of a CS1 curriculum. We frame traditional problems (robot control) in terms that are personal, relevant, and fun. Initial

Data mining is the computer assisted process of digging through and analysing enormous sets of data and then extracting the meaningful data. Data mining tools predicts behaviours and future trends, allowing businesses to make proactive decisions. It can answer questions that traditionally were very time consuming to resolve. Therefore they can be used to predict meteorological data that is weather prediction. Weather prediction is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems across the world in the last century. Predicting the weather is essential to help preparing for the best and the worst of the climate. Accurate Weather Prediction has been one of the most challenging problems around the world. Many weather predictions like rainfall prediction, thunderstorm prediction, predicting cloud conditions are major challenges for atmospheric research. This paper presents the review of Data Mining Techniques for Weather Prediction and studies the benefit of using it. The paper provides a survey of available literatures of some algorithms employed by different researchers to utilize various data mining techniques, for Weather Prediction. The work that has been done by various researchers in this field has been reviewed and compared in a tabular form. For weather prediction, decision tree and kmean clustering proves to be good with higher prediction accuracy than other techniques of data mining.

Data mining is the computer assisted process which predicts behaviors and future trends by digging through and analyzing enormous sets of data and then extracting the meaningful data. It is used to answer questions that traditionally were very time consuming to resolve. This feature makes it useful to predict meteorological data that is weather prediction. Weather prediction is a necessary application in meteorology and has been one of the most scientifically and technologically challenging problems across the world in the last century. Predicting the weather is essential to help people preparing for the best and the worst of the climate. Accurately predicting the weather has been one of the most challenging problems around the world. Many weather predictions like thunderstorm prediction, rainfall prediction, predicting wind conditions, and cloud burst are major challenges for atmospheric research. The data for this work is authentic and has been collected from India Meteorological Department for city Shimla (India) from the period of January 2010 to December 2013. This paper compared three decision tree algorithms named C4.5, CART, LMT without boosting and with boosting their performance by using boosting algorithm AdaBoost. AdaBoost was run with three different iterations. It has been observed that with every increase in iteration, the accuracy of decision tree algorithm is improved. It has been also observed that LMT shows the best improvement among the decision tree algorithms after boosting. Therefore, boosted LMT is used to predict the weather of Shimla. The prediction accuracy of boosted LMT is nearly 100% when taken yearly and comparatively better than un-boosted LMT when the dataset is taken separately for each month.

The goal of Big Data is to help organizations make better business decisions based on information by enabling researchers, data scientists and other analytics professionals to analyze large volumes of operational data, as well as other forms of data that may not be discovered by conventional Business Intelligence (BI) programs. This gives rise to the need for an analytical review of recent developments in the big data technology. Cloud services were used to process huge amount of data and it has turned into new Big Data model to meet the on demand services. This paper aims to provide a comprehensive review of big data applications and challenges, which it is facing. In addition to that, several research areas have been highlighted for future directions. The survey will be beneficial for the further enhancement and enrichment of Big Data Analytics in various research perspectives.

Data Mining is the process of analyzing data from different viewpoints and summarizing it into useful information. By using Data mining tool, the user can analyze data from different dimensions or angles, categorize it, and process the relations recognized. Clustering is one of most widely used techniques in data mining. Clustering is the process of grouping data by finding similarities between data based on their features. Similar Items are grouped in one cluster and dissimilar in another. In this paper, a comparative study of nine clustering algorithms is performed. For comparison three datasets are used. The main objective of the study is to observe the effect of size of different dataset on data mining tool and clustering algorithms. The dataset chosen for comparison are diverse in terms of number of attributes and instances. All the nine algorithms are compared according to the factors such as size of the dataset, number of clusters and time taken to form clusters. For performing comparison, data mining tool Weka is used. Also the performance of Weka for handling large datasets is analyzed.With the development of information technology, the large amount of databases and huge data in various areas has been generated. The research in the areas of databases and information technology has given rise to a new approach to store and manipulate this precious data which can be used for further decision making. Data mining is a process of extracting useful information and patterns from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data /pattern analysis [2]. Data mining is a powerful concept for data analysis and process of discovery interesting pattern from the huge amount of data, data stored at various sources such as data warehouse, World Wide Web, external sources, interesting pattern that is easy to understand, unknown, valid, potential useful. Data mining is a sorting technique which is mainly used to extract hidden patterns and data from large databases. The goals of data mining are fast retrieval of data or information, knowledge discovery from the databases, to identify hidden patterns and those patterns which are previously not explored, to reduce the level of complexity, time saving, etc [3]. Sometimes data mining is treated as knowledge in the databases (KDD) [1]. The ultimate goal of knowledge discovery and data mining process is to find the patterns that are hidden among the huge dataset of data and interpret them to useful knowledge and information [2].

Big data holds great promises to change health care for better. Various advancements and digitalization of processes involved in healthcare organizations give rise to adopt big data analytic techniques to acquire, store, process healthcare data. Hive has been widely used in organizations like Facebook, eBay, LinkedIn, Taobao, Yahoo, Spotify and Tencent, to manage and process large volumes of data. It is an open source project used as data warehouse for Hadoop. With all the technical advancements, healthcare also needs the financial support to deliver all the facilities to patients and healthcare industries, which is provided by many grant programs. The paper focuses on analysing the data which contains the financial assistance provided to healthcare by HRSA in U.S. for the period of last 8 years, using hive.

Classification and prediction of the images are fairly easy task for humans, but it takes more effort for a machine to do the same. Machine learning helps to attain this goal. It automates the task of classifying a large collection of images into different classes by labelling the incoming data and recognizes patterns in it, which is subsequently translated into valuable insights. The aim of this paper is to classify the image data set of five cancer types, namely Osteosarcoma, Prostate Cancer, Brain Cancer, Breast Cancer and Acute Myeloid Leukaemia. Furthermore, the prediction of Osteosarcoma case for one of the four classes of tumor namely Non tumor, Non-Viable tumor, viable tumor, Viable: Non-Viable tumor has to be done. The quantitative analysis is done using various machine learning libraries of python. The three classification algorithms used for image analysis are random forest, SVM, and logistic regression. The metrics used for performing perspective analysis are precision, ...

This paper focuses on the problem of using machine learning techniques on cancer/tumor prediction. One of the biggest applications of big data and machine learning is in the field of medical domain. Consequently, a health care organization that uses the techniques of machine learning and big data to treat patients see fewer mishaps or gets enough time to deal with them in advance. Classification and prediction of the images are fairly easy task for humans, but it takes more effort for a machine to do the same. Machine learning helps to attain this goal. It automates the task of classifying a large collection of images into different classes by labelling the incoming data and recognizes patterns in it, which is
subsequently translated into valuable insights. Furthermore, the prediction of Osteosarcoma case for one of the four classes of tumor namely Non tumor, Non-Viable tumor, viable tumor, Viable: Non-Viable tumor has to be done. The quantitative analysis is done using various machine learning libraries of python. The three classification algorithms used for image analysis are random forest, SVM, and logistic regression. The metrics used for performing perspective analysis are precision, recall and F1 Score. The domain of medical imaging helps providing important information on anatomy and organ function subsequently detecting disease states. Although the characteristics of medical data make its analysis a big challenge notwithstanding that machine learning techniques could make the task easier. The results show that the random forest algorithm has performed best amongst the three classification algorithms when given with less  complicated scenario, with prediction accuracy, precision, recall and f1 score of 100%. But the performance of every classification algorithm degrades when provided with the cases of Osteosarcoma which has got more complicated scatter graph. However, the logistic regression retains its performance by predicting tumor cases with 99% accuracy. For future scope, various other machine learning algorithms can be applied to observe their performance on the same set of features extracted from the cancer image data set