Search results for "artificial intelligence"
showing 10 items of 6122 documents
Expert system for predicting unstable angina based on Bayesian networks
2013
The use of computer-based clinical decision support (CDS) tools is growing significantly in recent years. These tools help reduce waiting lists, minimise patient risks and, at the same time, optimise the cost health resources. In this paper, we present a CDS application that predicts the probability of having unstable angina based on clinical data. Due to the characteristics of the variables (mostly binary) a Bayesian network model was chosen to support the system. Bayesian-network model was constructed using a population of 1164 patients, and subsequently was validated with a population of 103 patients. The validation results, with a negative predictive value (NPV) of 91%, demonstrate its …
A Method Based on Multi-source Feature Detection for Counting People in Crowded Areas
2019
We propose a crowd counting method for multisource feature fusion. Image features are extracted from multiple sources, and the population is estimated by image feature extraction and texture feature analysis, along with for crowd image edge detection. We count people in high-density still images. For instance, in the city’s squares, sports fields, subway stations, etc. Our approach uses a still image taken by a camera on a drone to appraise the count in the population density image, using a kind of sources of information: HOG, LBP, CANNY. We furnish separate estimates of counts and other statistical measurements through several types of sources. Support vector machine SVM, classification an…
Anomaly Detection in Dynamic Social Systems Using Weak Estimators
2009
Anomaly detection involves identifying observationsthat deviate from the normal behavior of a system. One ofthe ways to achieve this is by identifying the phenomena thatcharacterize “normal” observations. Subsequently, based on thecharacteristics of data learned from the “normal” observations,new observations are classified as being either “normal” or not.Most state-of-the-art approaches, especially those which belongto the family parameterized statistical schemes, work under theassumption that the underlying distributions of the observationsare stationary. That is, they assume that the distributions thatare learned during the training (or learning) phase, thoughunknown, are not time-varyin…
Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination
2015
This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive…
3D inter-subject medical image registration by scatter search
2005
Image registration is a very active research area in computer vision, namely it is used to find a transformation between two images taken under different conditions. Point matching is an image registration approach based on searching for the right pairing of points between the two images. From this matching, the registration transformation we are searching, can be inferred by means of numerical methods. In this paper, we propose a scatter search (SS) algorithm to solve the matching problem. SS is a hybrid metaheuristic with a good trade-off between search space diversification and intensification. On the one hand, diversity is basically introduced from a population-based approach where syst…
Thermographic quantitative variables for diabetic foot assessment: preliminary results
2018
The aim of this study was to define aspects of a protocol for a diabetic population by obtaining and evaluating thermographic images following thermal stress (cooling of the sole of the foot with c...
Fuzzy-based Kernel Regression Approaches for Free Form Deformation and Elastic Registration of Medical Images
2009
In modern medicine, a largely diffused method for gathering knowledge about organs and tissues is obtained by means of merging information from several datasets. Such data are provided from multimodal or sequential acquisitions. As a consequence, a pre-processing step that is called “image registration” is required to achieve data integration. Image registration aims to obtain the best possible spatial correspondence between misaligned datasets. This procedure is also useful to correct distortions induced by magnetic interferences with the acquisition equipment signals or the ones due patient’s involuntary movements such as heartbeat or breathing. The problem can be regarded as finding the …
Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Tech…
2020
Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period …
Standardization of islet isolation outcome- A new automatic system to determine pancreatic islet viability
2011
Pancreatic islet transplantation is emerging as a therapeutic approach for patients affected by diabetes. This approach consists of a minimally invasive procedure replacing insulin-producing cells (pancreatic islets). The technique has been proven successful, but limitations have been identified. One of the major challenges of the procedure is the counting of the isolated pancreatic islets, which is currently jeopardized by subjectivity and inaccuracy. Determination of the accurate islet number is a crucial factor in determining the correlation between the isolation product and clinical outcome. In the proposed study, we have developed software capable of objectively evaluating islet number…
Generalization Capacity Analysis of Non- Intrusive Load Monitoring using Deep Learning
2020
Appliance Load Monitoring is a technique used to monitor devices existing in homes, industry or naval vessels. Acquisition of device-level data can provide great benefits in many areas such as energy management, demand response, and load forecasting. However, the monitoring process is often provided with a costly installation, as it requires a large number of sensors and a data center. Non-Intrusive Load Monitoring (NILM) is an alternative and cost-efficient load monitoring solution. Simply put, NILM is the process of obtaining device-level data by analyzing the aggregated data read from the main meter that measures the electricity consumption of the whole house. Before NILM analysis is per…