Search results for "Machine Learning"
showing 10 items of 1464 documents
Embedded System Study for Real Time Boosting Based Face Detection
2006
This paper describes a study for a real time embedded face detection system. Recently, the boosting based face detection algorithms proposed by [(Viola, P and Jone, M, 2001); (Lienhart, R, et al., 2003)] have gained a lot of attention and are considered as the fastest accurate face detection algorithms today. However, the embedded implementation of such algorithms into hardware is still a challenge, since these algorithms are heavily based on memory access. A sequential implementation model is built showing its lack of regularity in time consuming and speed of detection. We propose a parallel implementation that exploits the parallelism and the pipelining in these algorithms. This implement…
Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-…
2006
We present a classification work performed on industrial parts using artificial vision, a support vector machine (SVM), boost- ing, and a combination of classifiers. The object to be controlled is a coated heater used in television sets. Our project consists of detect- ing anomalies under manufacturer production, as well as in classi- fying the anomalies among 20 listed categories. Manufacturer speci- fications require a minimum of ten inspections per second without a decrease in the quality of the produced parts. This problem is ad- dressed by using a classification system relying on real-time ma- chine vision. To fulfill both real-time and quality constraints, three classification algorit…
Real Time Robust Embedded Face Detection Using High Level Description
2011
Face detection is a fundamental prerequisite step in the process of face recognition. It consists of automatically finding all the faces in an image despite the considerable variations of lighting, background, appearance of people, position/orientation of faces, and their sizes. This type of object detection has the distinction of having a very large intra-class, making it a particularly difficult problem to solve, especially when one wishes to achieve real time processing. A human being has a great ability to analyze images. He can extract the information about it and focus only on areas of interest (the phenomenon of attention). Thereafter he can detect faces in an extremely reliable way.…
Statistical Learning Algorithms to Forecast the Equity Risk Premium in the European Union
2018
With the explosion of “Big Data”, the application of statistical learning models has become popular in multiple scientific areas as well as in marketing, finance or other business disciplines. Nonetheless, there is not yet an abundant literature that covers the application of these learning algorithms to forecast the equity risk premium. In this paper we investigate whether Classification and Regression Trees (CART) algorithms and several ensemble methods, such as bagging, random forests and boosting, improve traditional parametric models to forecast the equity risk premium. In particular, we work with European Monetary Union data for a period that spans from the EMU foundation at the begin…
Class discovery from semi-structured EEG data for affective computing and personalisation
2017
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not exp…
3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients
2022
Rationale and Objectives: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radio-mics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).Materials and Methods: 107 radiomic features were extracted from a …
Investigating Centrality Measures in Social Networks with Community Structure
2021
Centrality measures are crucial in quantifying the influence of the members of a social network. Although there has been a great deal of work dealing with this issue, the vast majority of classical centrality measures are agnostic of the community structure characterizing many social networks. Recent works have developed community-aware centrality measures that exploit features of the community structure information encountered in most real-world complex networks. In this paper, we investigate the interactions between 5 popular classical centrality measures and 5 community-aware centrality measures using 8 real-world online networks. Correlation as well as similarity measures between both t…
A new reading of Bronze Age funerary structures in Mongolia through the analysis of their architectural elements, arrangement and form
2022
The archaeological landscape of Mongolia is marked by the presence of numerous funerary monuments (khirigsuurs) and engraved stelae (deer stones), erected by nomadic populations at the end of the Bronze Age. The first observations indicate great regularity in the choice and arrangement of the architectural and iconographic elements at the time when these structures were built. This regularity suggests the existence of shared traditions and social coherence between nomadic communities in a territory covering almost twice that of France. The data available today do not provide sufficient perspective to establish social models at the level of a province, and even less so at the scale of the co…
Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model
2021
Predicting student performance as early as possible and analysing to which extent initial student behaviour could lead to failure or success is critical in introductory programming (CS1) courses, for allowing prompt intervention in a move towards alleviating their high failure rate. However, in CS1 performance prediction, there is a serious lack of studies that interpret the predictive model’s decisions. In this sense, we designed a long-term study using very fine-grained log-data of 2056 students, collected from the first two weeks of CS1 courses. We extract features that measure how students deal with deadlines, how they fix errors, how much time they spend programming, and so forth. Subs…
A Statistical Study to Analyze the Impact of External Weather Change on Chronic Pulmonary Infection in South Norway with Machine Learning Algorithms
2021
In this paper, we analyzed the holistic impact of external weather on chronic pulmonary infection in the Agder region with traditional machine learning algorithms. Millions of people are diagnosed with Chronic Obstructive Pulmonary Disease (COPD). Our study is dedicated in the Agder region, the Southern part of Norway. Norway has four seasons – winter (December-February), late winter/spring (March-May), Summer (June-August), and Autumn (September-November) in a year with average annual temperature approx. 7.5 °C | 45.5 °F and an annual rainfall of 1260 mm or 49.6 in. in Kristiansand. As predicted by the World Health Organization (WHO), in 2016, Norway suffered from 8% mortality due to c(1)h…