http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Data mining and machine learning applications
Data, the latest currency of today's world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.
Introduction to Data Mining / Santosh R Durugkar, Rohit Raja, Kapil Kumar Nagwanshi, and Sandeep Kumar -- Classification and Mining Behavior of Data / Srinivas Konda, Kavitarani Balmuri, and Kishore Kumar Mamidala -- A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects / Rakhi Seth and Aakanksha Sharaff -- Stream Mining: Introduction, Tools & Techniques and Applications / Naresh Kumar Nagwani -- Data Mining Tools and Techniques: Clustering Analysis / Rohit Miri, Amit Kumar Dewangan, SR Tandan, and Priya Bhatnagar, Hiral Raja -- Data Mining Implementation Process / Kamal K Mehta, Rajesh Tiwari, and Nishant Behar -- Predictive Analytics in IT Service Management (ITSM) / Christa IL Sharon and V Suma -- Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques / K Ramya Laxmi, Sumit Srivastava, K Madhuravani, S Pallavi, and Omprakash Dewangan -- Inductive Learning Including Decision Tree and Rule Induction Learning / Raj Kumar Patra, A Mahendar, and G Madhukar -- Data Mining for Cyber-Physical Systems / M Varaprasad Rao, D Anji Reddy, Anusha Ampavathi, and Shaik Munawar -- Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining / Vivek Parganiha, Soorya Prakash Shukla, and Lokesh Kumar Sharma -- Human-Machine Interaction and Visual Data Mining / Sinha Upasana, Gupta Akanksha, Samera Khan, Shilpa Rani, and Swati Jain -- MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection / Lokesh Singh, Rekh Ram Janghe, and Satya Prakash Sahu -- New Algorithms and Technologies for Data Mining / Padma Bonde, Latika Pinjarkar, Korhan Cengiz, Aditi Shukla, and Maguluri Sudeep Joel -- Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier / Sudesh Kumar, Rekh Ram Janghel, and Satya Prakash Sahu -- An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques / Nanda R Wagh and Sanjay R Sutar -- Conclusion and Future Direction in Data Mining and Machine Learning / Santosh R Durugkar, Rohit Raja, Kapil Kumar Nagwanshi, and Ramakant Chandrakar.
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Includes bibliographical references and index.