All textile industries aim to produce competitive materials and the competition enhancement depends mainly on designs and quality of the dresses produced by each industry. Every day, a vast amount of textile images are being generated such as images of shirts, jeans, t-shirts and sarees. A principal driver of innovation is World Wide Web, unleashing publication at the scale of tens and millions of content creators. Images play an important role as a picture is worth thousand words in the field of textile design and marketing. A retrieving of images needs special concepts such as image annotation, context, and image content and image values. This research work aimed at studying the image mining process in detail and analyzes the methods for retrieval. The textile images analyze various methods for clustering the images and developing an algorithm for the same. The retrieval method considered is based on relevance feedback, scalable method, edge histogram and color layout. The image clustering algorithm is designed based on color descriptors and k-means clustering algorithm. A software prototype to prove the proposed algorithm has been developed using net beans integrated development environment and found successful.
Weather forecasting is the prediction of atmosphere state for particular location by using principles of physics provided by many statistical and empirical techniques. Weather forecasts are frequently made by collecting quantitative data about current state of atmosphere through scientific
understanding of atmospheric processes to illustrate how atmosphere changes in future. Current weather conditions are collected through the observation from the ground, ships, aircraft, radio sounds and satellites. The information is transmitted to the meteorological centers where the data
are collected and examined for prediction. There are diverse techniques included in weather forecasting, from relatively simple observation of sky to complex computerized mathematical models. But, the existing techniques failed to predict the weather with higher accuracy and lesser time. In
order to improve the prediction performance, the machine learning and ensemble techniques are introduced.
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