Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper

Knowledge discovery for friction stir welding via data driven approaches: Part 2 – multiobjective modelling using fuzzy rule based systems

In this final part of this extensive study, a new systematic data-driven fuzzy modelling approach has been developed, taking into account both the modelling accuracy and its interpretability (transparency) as attributes. For the first time, a data-driven modelling framework has been proposed designed and implemented in order to model the intricate FSW behaviours relating to AA5083 aluminium alloy, consisting of the grain size, mechanical properties, as well as internal process properties. As a result, ‘Pareto-optimal’ predictive models have been successfully elicited which, through validations on real data for the aluminium alloy AA5083, have been shown to be accurate, transparent and generic despite the conservative number of data points used for model training and testing. Compared with analytically based methods, the proposed data-driven modelling approach provides a more effective way to construct prediction models for FSW when there is an apparent lack of fundamental process knowledge

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

O.R.,Statistics,A.I.- the potential for interdisciplinary progress

This paper examines the need for O.R. workers to become more involved in the development of A.I. A brief outline of A.I. is provided noting problems, techniques and objectives similar to those found in O.R. This outline gives an indication of how interdisciplinary development might proceed and indicates the direction in which O.R. training should be progressin

On the suitability and development of layout templates for analog layout reuse and layout-aware synthesis

Accelerating the synthesis of increasingly complex analog integrated circuits is key to bridge the widening gap between what we can integrate and what we can design while meeting ever-tightening time-to-market constraints. It is a well-known fact in the semiconductor industry that such goal can only be attained by means of adequate CAD methodologies, techniques, and accompanying tools. This is particularly important in analog physical synthesis (a.k.a. layout generation), where large sensitivities of the circuit performances to the many subtle details of layout implementation (device matching, loading and coupling effects, reliability, and area features are of utmost importance to analog designers), render complete automation a truly challenging task. To approach the problem, two directions have been traditionally considered, knowledge-based and optimization-based, both with their own pros and cons. Besides, recently reported solutions oriented to speed up the overall design flow by means of reuse-based practices or by cutting off time-consuming, error-prone spins between electrical and layout synthesis (a technique known as layout-aware synthesis), rely on a outstandingly rapid yet efficient layout generation method. This paper analyses the suitability of procedural layout generation based on templates (a knowledge-based approach) by examining the requirements that both layout reuse and layout-aware solutions impose, and how layout templates face them. The ability to capture the know-how of experienced layout designers and the turnaround times for layout instancing are considered main comparative aspects in relation to other layout generation approaches. A discussion on the benefit-cost trade-off of using layout templates is also included. In addition to this analysis, the paper delves deeper into systematic techniques to develop fully reusable layout templates for analog circuits, either for a change of the circuit sizing (i.e., layout retargeting) or a change of the fabrication process (i.e., layout migration). Several examples implemented with the Cadence's Virtuoso tool suite are provided as demonstration of the paper's contributions.Ministerio de Educación y Ciencia TEC2004-0175