Intelligent Techniques for Data Science

This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions./p The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for realworld applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.

[1]  Philip S. Yu,et al.  Classifying Data Streams with Skewed Class Distributions and Concept Drifts , 2008, IEEE Internet Computing.

[2]  Serge Andrianov,et al.  Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks , 2014 .

[3]  Sanjeev Dhawan,et al.  A Framework for Polarity Classification and Emotion Mining from Text , 2014 .

[4]  Peter D. Turney Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[5]  Javad Rezaie,et al.  Reducing the Dimensionality of Geophysical Data in Conjunction with Seismic History Matching , 2012 .

[6]  Stan Szpakowicz,et al.  Identifying Expressions of Emotion in Text , 2007, TSD.

[7]  Rajendra Akerkar,et al.  Big Data Computing , 2013 .

[8]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[9]  Andy Konwinski,et al.  Chukwa: A large-scale monitoring system , 2008 .

[10]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[11]  J. Leon Zhao,et al.  Message Sense Maker: engineering a tool set for customer relationship management , 2003, 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.

[12]  Yong Yu,et al.  A hybrid SARIMA wavelet transform method for sales forecasting , 2011, Decis. Support Syst..

[13]  T. Mitsuishi Basic Properties of Fuzzy Set Operation and Membership Function , 2004 .

[14]  Abraham Duarte,et al.  Adaptive Memory Programming for Global Optimization , .

[15]  Ellen Spertus,et al.  Smokey: Automatic Recognition of Hostile Messages , 1997, AAAI/IAAI.

[16]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[17]  Yuhui Shi,et al.  Swarm Intelligence in Big Data Analytics , 2013, IDEAL.

[18]  C. von Altrock,et al.  Fuzzy logic data analysis of environmental data for traffic control , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[19]  Vili Podgorelec,et al.  Genetic Algorithm Based System for Patient Scheduling in Highly Constrained Situations , 1997, Journal of Medical Systems.

[20]  Tom White,et al.  Hadoop: The Definitive Guide , 2009 .

[21]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[22]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[23]  Wei-Chang Yeh,et al.  Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm , 2011, Appl. Soft Comput..

[24]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[25]  Rafael Martí,et al.  Scatter Search: Diseño Básico y Estrategias avanzadas , 2002, Inteligencia Artif..

[26]  Kunihiko Fukushima,et al.  A neural network for visual pattern recognition , 1988, Computer.

[27]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[28]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[29]  B. E. Reddy,et al.  Predictive data mining on Average Global Temperature using variants of ARIMA models , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).

[30]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[31]  El-Ghazali Talbi A unified view of multi-objective metaheuristics , 2011 .

[32]  Li Deng,et al.  A tutorial survey of architectures, algorithms, and applications for deep learning , 2014, APSIPA Transactions on Signal and Information Processing.

[33]  Zhengyou Zhang,et al.  Improving multiview face detection with multi-task deep convolutional neural networks , 2014, IEEE Winter Conference on Applications of Computer Vision.

[34]  Stephen Grossberg,et al.  The ART of Adaptive Pattern Recognition Self-organizing by a Neu Network , 1988 .

[35]  Rajendra Akerkar,et al.  Mining Sentiment Using Conversation Ontology , 2013 .

[36]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[37]  Francisco Martínez-Álvarez,et al.  Neural networks to predict earthquakes in Chile , 2013, Appl. Soft Comput..

[38]  Tara N. Sainath,et al.  Improvements to Deep Convolutional Neural Networks for LVCSR , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[39]  Mitsuru Ishizuka,et al.  EmoHeart: Conveying Emotions in Second Life Based on Affect Sensing from Text , 2010, Adv. Hum. Comput. Interact..

[40]  Byron Ellis Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data , 2014 .

[41]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[42]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[43]  Václav Snásel,et al.  Social Networks Analysis: Tools, Measures and Visualization , 2012, Computational Social Networks.

[44]  Priti Srinivas Sajja,et al.  Type-2 Fuzzy Interface for Artificial Neural Network , 2010 .

[45]  Yang Shen,et al.  Emotion mining research on micro-blog , 2009, 2009 1st IEEE Symposium on Web Society.

[46]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[47]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[48]  Muthu Dayalan,et al.  MapReduce : Simplified Data Processing on Large Cluster , 2018 .

[49]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.