Unsupervised Learning & Map Formation – Foundations of Neural Computations (Paper Only)
In learning any new topic, the first stages are the most essential. The related topics should be properly introduced in order to hold the learner’s awareness. The learner then comprehends and appreciates the subject better. This is more so in the case of image processing in general. The goal of this book is to help the beginner in image processing from basics to the end of implementing complicated stages. This book describes the fundamental concepts of unsupervised learning method of content based image retrieval by discussing the implementation steps of the pre-processing methods of image processing. Also, showed and discussed the corresponding results of the recommended approaches in the last unit of this book. More effort has been made to write this book with simple language and easy to understand the basic concepts of images and image processing stages of any particular problem related to image retrieval methods.
We thank Allah, the almighty, for granting us the strength and courage to complete our book. This project was carried out at the school of engineering, Blekinge Institute of Technology, Karlskrona, Sweden. After that We would like to give our sincere regards to Sven Johansson and Anders Hultgren and our supervisor Raja Muhammad Khurram Shahzad for their support and help. Besides that we would like to say special thanks to our families for the encouragement and motivation they provided during the hard times.
This book contains seven chapters encircles comprehensive study on unsupervised change detection from remote sensing images for research scholar .Change detection (CD) is a very important topic in remote sensing for observing the earth .Change detection can be performed by two methods namely supervised and unsupervised Classification.We hope that the book will certainly adequate and systematic knowledge to the readers .Any suggestions to the improvement of this book will be highly appreciated.
The present book considers Wireless Ad-hoc Networks (WANETs) in the context of 4G network technologies and Ambient Intelligence. The book proposes how to reduce the power dissipation on mobile devices due to signal transmittion and increased computational requirements due to more sophisticated services. The book lustrates how to manage uncertainties and introduce some flexibility in determine service protocol algorithms by unsupervised learning from dynamic network topology context. Being context aware, services in WANETs enhance their underlying protocols to evolve in the future exploiting data-driven evolving fuzzy modeling.
Clustering can be considered the most important unsupervised learning problem. In briefly,the process of organizing objects into groups whose members are similar in some way. This book presents an overview of existing clustering algorithms with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.Determining the number of cluster is a running burning question for clustering. I tried to propose a method for clustering validation. I present taxonomy of clustering techniques, and identify crosscutting themes and recent advances. I also describe a big famous field of application of clustering- Image segmentation.
In this book, land use classification was done for the area corresponding to the toposheet 58 J/13.Two methods namely supervised and unsupervised methods were used. Ground truth verification was also done. The accuracy of classification for both the methods were also carried out. Area under each landuse cover in both supervised and unsupervised methods was calculated. Understanding the rate of land use changes in time and space is useful in planning for suitable management. Application of remote sensing and GIS was found helpful in quantifying past and present resources so that appropriate planning could be made.
Since human genome studies have brought out a huge number of biosequence data, computational techniques have been developed preventing the vast of cost and time in the management process of these data. In this book, new approaches on clustering and classification methods in biosequence –protein, enzyme sequences– analysis are studied. Classification is a supervised learning algorithm that aims at categorizing or assigning class labels to a pattern set under the supervision of an expert. Therefore, the prediction of subcellular location of proteins and the classification of enzymes have been solved via data mining techniques. Clustering is an unsupervised learning technique that aims at decomposing a given set of elements into clusters based on similarity. Due to the fact that protein sequences have evolutionary relationship, all protein sequences can be organized in terms of their sequence similarity. A graphical illustration called phylogenetic tree can summarize the relationship between the protein sequences. The construction of phylogenetic tree is based on hierarchical clustering. Thus, we have proposed a new method as a linkage method in construction phylogenetic tree.
Web Information Retrieval systems need to increase the user satisfaction while improving the quality of the results. The current trend in this direction focus on representing the information needs of the user along with the query. This representation need to be automatic and transparent to the furthest extent possible. In this work the focus is on identifying and understanding the user intent: What motivated the user to perform a search on the web? To this end, we apply machine learning models not requiring more information than the one provided by the very needs of the users, which in this work are represented by their queries. The knowledge and interpretation of this invaluable information can help search engines to obtain resources, especially relevant to users, and thus improve user satisfaction. By means of unsupervised learning techniques, which have been selected according to the context of the problem being solved, throughout this research work, we show that is not only possible to identify the user's intents but that this process can be done automatically.
Phishing is the act of using spoofed e-mails and fraudulent web sites to trick financial organizations and customers into revealing their personal or financial information. One of the main problems of phishing e-mail detection is the unknown “zero-day” phishing attack. A zero-day attack is one that phishers mount using hosts that do not appear in blacklists or using techniques that evade known approaches in phishing detection. Nowadays, phishers are creating different representation techniques to create unknown “zero-day” phishing e-mails to breach the defenses of detectors. This book proposes the Phishing Dynamic Evolving Neural Fuzzy Framework (PDENFF) that adapts the Evolving Connectionist System (ECoS) based online learning mode enhanced by offline learning mode. The proposed framework uses a hybrid supervised/unsupervised learning approach to speed up the system as well as to detect zero-day phishing e-mail attacks with a high level of accuracy and a low memory footprint. The proposed framework was tested, and a dynamic preprocessing and feature extraction system was implemented. MATLAB was used for the connectionist framework of the system engine as well as for computation.
Learning Networks – A Field Guide to Teaching & Learning Online
В комплект входят следующие книги: Early Learning: Colours and Shapes Early Learning: ABC Early Learning: Everyday Words Early Learning: What's the Time? Early Learning: Big and Little Early Learning: 123 First Words and Pictures: My Home Размер футляра: 125 мм х 185 мм.
Learning & Computational Neuroscience – Foundations of Adaptive Networks