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Advanced Information Systems Engineering ; 20th International Conference, CAiSE 2008 Montpellier, France, June 16-20, 2008 Proceedings
This book constitutes the refereed proceedings of the 20th International Conference on Advanced Information Systems Engineering, CAiSE 2008, held in Montpellier, France, in June 2008.
Advanced data mining and applications ; Vol. 4093 : 2nd International Conference, ADMA 2006, Xi'an, China, August 14-16, 2006, Proceedings
This book constitutes the refereed proceedings of the Second International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China in August 2006. The 41 revised full papers and 74 revised short papers presented together with 4 invited papers were carefully reviewed and selected from 515 submissions. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, text mining, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, security and privacy issues, spatial data mining, and streaming data mining.
Advanced data mining and applications ; Vol. 3584 ; 1st International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005, Proceedings
This book constitutes the refereed proceedings of the First International Conference on Advanced Data Mining and Applications, ADMA 2005, held in Wuhan, China in July 2005. The 25 revised full papers and 75 revised short papers presented were carefully peer-reviewed and The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, text mining, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, security and privacy issues, spatial data mining, and streaming data mining.
Advanced Data Mining and Applications ; 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings
This book constitutes the refereed proceedings of the 4th International Conference on Advanced Data Mining and Applications, ADMA 2008, held in Chengdu, China, in October 2008.
Advanced Data Mining and Applications ; 3rd International Conference, ADMA 2007, Harbin, China, August 6-8, 2007 Proceedings
The Third International Conference on Advanced Data Mining and Applications (ADMA) organized in Harbin, China continued the tradition already established by the first two ADMA conferences in Wuhan in 2005 and Xi’an in 2006. One major goal of ADMA is to create a respectable identity in the data mining research com- nity. This feat has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. The impact of a conference is measured by the citations the conference papers receive. Some have used this measure to rank conferences.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
Adaptive Business Intelligence
In the modern information era, managers must recognize the competitive opportunities represented by decision-support tools. Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address the fundamental questions: What is likely to happen in the future? And what is the best decision right now? Adaptive Business Intelligence includes elements of data mining, predictive modeling, forecasting, optimization, and adaptability.
Active mining ; 2nd International workshop, AM 2003, Maebashi, Japan, October 28, 2003, revised selected papers
"This volume contains the papers selected for presentation at the 2nd Inter- tional Workshop on Active Mining (AM 2003) which was organized in conju- tion with the 14th International Symposium on Methodologies for Intelligent Systems (ISMIS 2003), The workshop was organized by the Maebashi Institute of Technology for shed light on the future development of active mining. "This volume contains : Topics Database Management / Artificial Intelligence / Algorithm Analysis and Problem Complexity / Health Informatics / Bioinformatics
Active Conceptual Modeling of Learning : Next Generation Learning-Base System Development
This volume contains a collection of the papers presented during the 1st International ACM-L Workshop, which was held on November 8, 2006 during the 25th International Conference on Conceptual Modeling, ER 2006, held November 6–9,2006, in Tucson, Arizona, plus several invited papers.These papers plus the invited papers represent the current thinking in conceptual modeling research, The active model can only be realized through technology integration (e.g., AI, software engineering, information technology,cognitive science, art and sciences, philosophy, etc.)
A Practical Programming Model for the Multi-Core Era ; 3rd International Workshop on OpenMP, IWOMP 2007, Beijing, China, June 3-7, 2007 Proceedings
This book constitutes the thoroughly refereed post-workshop proceedings of the Third International Workshop on OpenMP, IWOMP 2007, held in Beijing, China, in June 2007.The 14 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 28 submissions. The papers address all topics related to OpenMP, such as OpenMP performance analysis and modeling, OpenMP performance and correctness tools and proposed OpenMP extensions, as well as applications in various domains, e.g., scientific computation, video games, computer graphics, multimedia, information retrieval, optimization, text processing, data mining, finance, signal and image processing, and numerical solvers.
A General introduction to data analytics
A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming. A guide to the reasoning behind data mining techniques. A unique illustrative example that extends throughout all the chapters. Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts
3D Segmentation for medical images (OsteoVision) = التقطيع ثلاثي الأبعاد للصور الطبية
With the increasing integration of AI across various sectors, artificial intelligence (AI) is already playing a significant role in the healthcare industry, and its use is expected to grow further. AI systems used in image processing and computer vision algorithms have shown a significant ability to perform many operations such as segmentation, classification, and detection. This project presents the application of computer vision algorithms in the field of medical imaging for diagnostic, therapeutic, and interventional purposes. This thesis explores the use of several computer vision algorithms to address different pathologies, specifically brain tumors (glioma) (see Appendix A) and knee osteoarthritis (OA), as well as tracking the progression of knee osteoarthritis using the Kellgren and Lawrence (KL) grading system, a common method for classifying the severity of OA into five grades. To achieve the desired impact, the project employs various techniques, including 3D segmentation for brain tumors, 2D segmentation for knee joints, and multinomial classification for determining the severity of knee OA injuries. The primary aims of the project are to enhance diagnostic accuracy, assist in creating treatment plans, provide an assistive tool for healthcare providers to make more informed decisions, leverage AI's capabilities to detect abnormalities that might escape the human eye, and streamline workflow. To facilitate these goals, the project incorporates a user-friendly UI, a website, and a Flutter-based mobile application, enabling healthcare providers to efficiently integrate these tools into their practice and improve patient care.
