Search results for "machine"
showing 10 items of 2592 documents
Breast Ultra-Sound image segmentation: an optimization approach based on super-pixels and high-level descriptors
2015
International audience; Breast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the b…
Khmer character recognition using artificial neural network
2014
Character Recognition has become an interesting and a challenge topic research in the field of pattern recognition in recent decade. It has numerous applications including bank cheques, address sorting and conversion of handwritten or printed character into machine-readable form. Artificial neural network including self-organization map and multilayer perceptron network with the learning ability could offer the solution to character recognition problem. In this paper presents Khmer Character Recognition (KCR) system implemented in Matlab environment using artificial neural networks. The KCR system described the utilization of integrated self-organization map (SOM) network and multilayer per…
An optimization approach to segment breast lesions in ultra-sound images using clinically validated visual cues
2015
International audience; As long as breast cancer remains the leading cause of cancer deaths among female population world wide, developing tools to assist radiologists during the diagnosis process is necessary. However, most of the technologies developed in the imaging laboratories are rarely integrated in this assessing process, as they are based on information cues differing from those used by clinicians. In order to grant Computer Aided Diagnosis (CAD) systems with these information cues when performing non-aided diagnosis, better segmentation strategies are needed to automatically produce accurate delineations of the breast structures. This paper proposes a highly modular and flexible f…
Iterative pairs and multitape automata
1996
In this paper we prove that if every iterative k-tuple of a language L recognized by a k-tape automaton is very degenerate, then L is recognizable. Moreover, we prove that if L is an aperiodic langnage recognized by a deterministic k-tape automaton, then L is recognizable.
SMEs' heterogeneity at the extensive margin and within the intensive margin of trade
2021
In this paper, we contribute to the literature on firm-heterogeneity and trade, by looking not only at the firm-level determinants of trade participation (i.e. extensive margin) but also at differences between firms with different levels of trade intensity (i.e. intensive margin). Further, we compare firms that are born ‘local’ and display different scales of international exposure to firms that are born ‘global’, i.e. access international markets soon after their birth. Using a large World Bank dataset of SMEs from 112 countries and qualitative dependent variable models, our analysis uncovers the heterogeneity of SMEs not only at the extensive margin but also within the intensive margin of…
Handling local concept drift with dynamic integration of classifiers : domain of antibiotic resistance in nosocomial infections
2006
In the real world concepts and data distributions are often not stable but change with time. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the target concept. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined. In this paper we consider the use of an ensemble integration technique that helps to better handle concept drift at t…
Concepts, proto-concepts, and shades of reasoning in neural networks
2019
One of the most important functions of concepts is that of producing classifications; and since there are at least two different types of such things, we better give a preliminary short description of them both. The first kind of classification is based on the existence of a property common to all the things that fall under a concept. The second, instead, relies on similarities between the objects belonging to a certain class A and certain elements of a subclass AS of A, the so-called ‘stereotypes.’ In what follows, we are going to call ‘proto-concepts’ all those concepts whose power of classification depends on stereotypes, leaving the term ‘concepts’ for all the others. The main aim of th…
Arabic Named Entity Recognition: A Feature-Driven Study
2009
The named entity recognition task aims at identifying and classifying named entities within an open-domain text. This task has been garnering significant attention recently as it has been shown to help improve the performance of many natural language processing applications. In this paper, we investigate the impact of using different sets of features in three discriminative machine learning frameworks, namely, support vector machines, maximum entropy and conditional random fields for the task of named entity recognition. Our language of interest is Arabic. We explore lexical, contextual and morphological features and nine data-sets of different genres and annotations. We measure the impact …
Energy saving in WWTP: Daily benchmarking under uncertainty and data availability limitations
2016
Efficient management of Waste Water Treatment Plants (WWTPs) can produce significant environmental and economic benefits. Energy benchmarking can be used to compare WWTPs, identify targets and use these to improve their performance. Different authors have performed benchmark analysis on monthly or yearly basis but their approaches suffer from a time lag between an event, its detection, interpretation and potential actions. The availability of on-line measurement data on many WWTPs should theoretically enable the decrease of the management response time by daily benchmarking. Unfortunately this approach is often impossible because of limited data availability. This paper proposes a methodolo…
Jalapeno or jalapeño: Do diacritics in consonant letters modulate visual similarity effects during word recognition?
2020
AbstractPrior research has shown that word identification times to DENTIST are faster when briefly preceded by a visually similar prime (dentjst; i↔j) than when preceded by a visually dissimilar prime (dentgst). However, these effects of visual similarity do not occur in the Arabic alphabet when the critical letter differs in the diacritical signs: for the target the visually similar one-letter replaced prime (compare and is no more effective than the visually dissimilar one-letter replaced prime Here we examined whether this dissociative pattern is due to the special role of diacritics during word processing. We conducted a masked priming lexical decision experiment in Spanish using target…