http://localhost:80/xmlui/handle/123456789/1726 Pub Wed, 15 Apr 2026 03:09:03 GMT 2026-04-15T03:09:03Z http://localhost:80/xmlui/handle/123456789/4086 Title: Global Business & Economic Review Sat, 01 Dec 2018 00:00:00 GMT http://localhost:80/xmlui/handle/123456789/4086 2018-12-01T00:00:00Z http://localhost:80/xmlui/handle/123456789/3080 Title: A Study on Application of Artificial Neural Network And Genetic Algorithm in Pattern Recognition Authors: Srivastava, Nidhi; Singh, Angadraj; Keshri, Varsa; Shaw, Amit Kumar; Paul, Souvik Abstract: Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. Image processing is also an emerging field and lots of research had been performed for the past few years. Pattern recognition is an important part of image processing system. The aim of this paper is to study the use of artificial neural network and genetic algorithm in pattern recognition. Artificial neural network helps in training process where as the selection of various parameters for pattern recognition can be done in an optimized way by the genetic algorithm Fri, 01 Dec 2017 00:00:00 GMT http://localhost:80/xmlui/handle/123456789/3080 2017-12-01T00:00:00Z http://localhost:80/xmlui/handle/123456789/2559 Title: An improved recommender system based on clustering using representatives Authors: Das, Joydeep; Gupta, Harsh; Dugar, Shreya; Majumder, Subhashis; Gupta, Prosenjit Abstract: Recommender systems have proven to be valuable means for online users to cope up with the information overload and have become one of the most powerful and popular tools in electronic commerce. Collaborative Filtering (CF) is one of the most successful recommendation techniques that recommends by using the opinions of a community of users. However, the similarity computations associated with CF algorithms are very expensive and grow polynomially with the number of users and items in a database. To address this scalability problem, we propose a clustering based recommendation approach. Our proposed work partitions the users of the CF system using a CURE (Clustering using representatives) based data clustering algorithm and use the clusters to select the similar users of a target user. In this work, we further try to find the optimal number of clusters by using a binary search based technique. The cluster-based approach reduces the runtime of the system as we avoid similarity computations over the entire database. Experiments performed on MovieLens-1M dataset indicate that our method is efficient in reducing the runtime as well as maintains an acceptable recommendation quality. Thu, 01 Jan 2015 00:00:00 GMT http://localhost:80/xmlui/handle/123456789/2559 2015-01-01T00:00:00Z http://localhost:80/xmlui/handle/123456789/2558 Title: Green computing : practice of efficient and eco-friendly computing resources Authors: Chakraborty, Parichay; Bhattacharyya, Debnath; Nargiza Y., Sattarova Abstract: Green Computing is now under the attention of not only environmental organizations, but also businesses from other industries. In recent years, companies in the computer industry have come to realize that going green is in their best interest, both in terms of public relations and reduced costs. This paper will take a look at several green initiatives currently under way in the computer industry. Tue, 01 Sep 2009 00:00:00 GMT http://localhost:80/xmlui/handle/123456789/2558 2009-09-01T00:00:00Z