Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

On Educational psychology: A cognitive view.

This paper presents an original low-level system for color image segmentation considering the Hue-Saturation-Value (HSV) color space. Many difficulties of color image segmentation may be resolved using the correct color space in order to increase the effectiveness of color components to discriminate color data. The technique proposed in the article uses new data structures that lead to simpler and more efficient segmentation algorithms. We introduce a flexible hexagonal network structure on the pixels image and we extract for each segmented region the syntactic features that can be used in the shape recognition process. Our technique has a time complexity lower than the methods studied from specialized literature and the experimental results on Berkeley Segmentation Dataset color image database show that the performance of method is robust.

A New Method for Segmentation of Images Represented in a HSV Color Space

This paper presents a Web platform on which we developed an e-Learning application. The platform represents a web framework in which web applications may be developed. On the platform an e-Learning application is deployed. The e-Learning application brings together professors and students in a collaborative environment managed by secretaries and supervised by administrators. This paper introduces a novel Web application frame based on MVC. This frame separates the business logic from the view and model. Employed technologies, software architecture and software development process improve and ensure the application maintainability, scalability and performance.

Tesys: e-learning application built on a web platform

The paper presents a framework that implements our original algorithm of automatic mapping a MySQL relational database to a MongoDB NoSQL database. The algorithm uses the metadata stored in the MySQL system tables. It takes into consideration the concepts from Entity-Relationship (ER) model: entity type represented by a relation in the Relational Model (RM), 1∶1 and 1∶M relationship type represented with Foreign Keys (FK) in the RM and N∶M relationship type represented in RM with a join table that contains the Primary Keys (PK) from the original tables, each representing a FK and two 1∶M relationships between the original tables and the join table. The initial results of our algorithm that was tested on small size databases (10–15 tables with many relationships and 100 records/ table) are presented in this paper.

/pdf/automatic-mapping-of-mysql-databases-to-nosql-mongodb-1h3hjr81uc.pdf

Automatic mapping of MySQL databases to NoSQL MongoDB

Automatic image annotation and semantic based image retrieval for medical domain

This paper presents an efficient graph-based method to detect salient objects from color images and to extract their color and geometric features. Despite of the majority of the segmentation methods our method is totally adaptive and it do not require any parameter to be chosen in order to produce a better segmentation. The proposed segmentation method uses a hexagonal structure defined on the set of the image pixels ant it performs two different steps: a pre-segmentation step that will produce a maximum spanning tree of the connected components of the visual graph constructed on the hexagonal structure of an image, and the final segmentation step that will produce a minimum spanning tree of the connected components, representing the visual objects, by using dynamic weights based on the geometric features of the regions. Experimental results are presented indicating a good performance of our method.

Dumitru Dan Burdescu

Papers

A New Method for Segmentation of Images Represented in a HSV Color Space

Tesys: e-learning application built on a web platform

Automatic mapping of MySQL databases to NoSQL MongoDB

Automatic image annotation and semantic based image retrieval for medical domain

An Adaptive Method for Efficient Detection of Salient Visual Object from Color Images