Search results for "artificial intelligence"
showing 10 items of 6122 documents
Using Attribute Grammars for Description of Inductive Inference Search Space
1998
The problem of practically feasible inductive inference of functions or other objects that can be described by means of an attribute grammar is studied in this paper. In our approach based on attribute grammars various kinds of knowledge about the object to be found can be encoded, ranging from usual input/output examples to assumptions about unknown object's syntactic structure to some dynamic object's properties. We present theoretical results as well as describe the architecture of a practical inductive synthesis system based on theoretical findings.
A Survey of Bayesian Techniques in Computer Vision
2010
The Bayesian approach to classification is intended to solve questions concerning how to assign a class to an observed pattern using probability estimations. Red, green and blue (RGB) or hue, saturation and lightness (HSL) values of pixels in digital colour images can be considered as feature vectors to be classified, thus leading to Bayesian colour image segmentation. Bayesian classifiers are also used to sort objects but, in this case, reduction of the dimensionality of the feature vector is often required prior to the analysis. This chapter shows some applications of Bayesian learning techniques in computer vision in the agriculture and agri-food sectors. Inspection and classification of…
Text Classification Using Novel “Anti-Bayesian” Techniques
2015
This paper presents a non-traditional “Anti-Bayesian” solution for the traditional Text Classification (TC) problem. Historically, all the recorded TC schemes work using the fundamental paradigm that once the statistical features are inferred from the syntactic/semantic indicators, the classifiers themselves are the well-established statistical ones. In this paper, we shall demonstrate that by virtue of the skewed distributions of the features, one could advantageously work with information latent in certain “non-central” quantiles (i.e., those distant from the mean) of the distributions. We, indeed, demonstrate that such classifiers exist and are attainable, and show that the design and im…
Predictive and Contextual Feature Separation for Bayesian Metanetworks
2007
Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of conditional dependency. However, depending on a context, many attributes of the model might not be relevant. If a Bayesian Network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all contexts and cannot guarantee high predictive accuracy when applied to a concrete case. We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on "relevance" of the predictive attributes towards target attribut…
Integration of cloud computing tools and knowledge bodies for the management of programming projects
2018
A Body of Knowledge (BOK) is a set of concepts and skills that represent the knowledge of a specific area of engineering or scientific discipline and ensure their common understanding [1]. A BOK can include technical terms and theoretical concepts as well as best practices [2] so in this document we will focus on the context of software engineering specifically in Software Project Management. In addition, Cloud Computing will is becoming the best way to deliver solutions that meet the current need for greater collaboration between companies, education and society. In this context, the learning of Software Project Management is important during the professional life of Informatica since it e…
Prediction of Disease–lncRNA Associations via Machine Learning and Big Data Approaches
2021
This chapter introduces long non-coding RNAs and their role in the occurrence and progress of diseases. The discovery of novel lncRNA-disease associations may provide valuable input to the understanding of disease mechanisms at the lncRNA level, as well as to the detection of biomarkers for disease diagnosis, treatment, prognosis, and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of potential disease-lncRNA associations can effectively decrease the time and cost of biological experiments. We first review the main computatio…
Binarization of a super-resolving graytone pupil filter by digital halftoning
1995
— Six digital-halftoning procedures, including one algorithm proposed by us, are compared to determine which one is best suited to binarization of a parabolic super-resolving pupil filter. The procedures we deal with include iterative, error-diffusion, error-convergence, and 1-pixel algorithms. We carry out a numerically simulated experiment in which an object that consists of either one point source or two coherent point sources is imaged in a 4f imaging system with either a continuous super-resolving parabolic filter or one of its six different binary versions. The performance of binary filters is examined in terms of two parameters: the resemblance of their amplitude impulse response (AI…
Detection of Duplicated Regions in Tampered Digital Images by Bit-Plane Analysis
2009
In this paper we present a new method for searching duplicated areas in a digital image. The goal is to detect if an image has been tampered by a copy-move process. Our method works within a convenient domain. The image to be analyzed is decomposed in its bit-plane representation. Then, for each bitplane, block of bits are encoded with an ASCII code, and a sequence of strings is analyzed rather than the original bit-plane. The sequence is lexicographically sorted and similar groups of bits are extracted as candidate areas, and passed to the following plane to be processed. Output of the last planes indicates if, and where, the image has been altered.
Retinal vasculature segmentation and measurement framework for color fundus and SLO images
2020
Abstract The change in vascular geometry is an indicator of various health issues linked with vision and cardiovascular risk factors. Early detection and diagnosis of these changes can help patients to select an appropriate treatment option when the disease is in its primary phase. Automatic segmentation and quantification of these vessels would decrease the cost and eliminate inconsistency related to manual grading. However, automatic detection of the vessels is challenging in the presence of retinal pathologies and non-uniform illumination, two common occurrences in clinical settings. This paper presents a novel framework to address the issue of retinal blood vessel detection and width me…