1,721,060 research outputs found
Exploiting AUC for Optimal Linear Combination of Dichotomizers
No full textThe combination of classifiers is an established technique to improve the classification performance. The possible combination rules proposed up to now generally try to decrease the classification error rate, which is a performance measure not suitable in many real situations and particularly when dealing with two-class problems. In this case, a good alternative is given by the area under the receiver operating characteristic curve (AUC), whose effectiveness in measuring the classification quality has been proved in many recent papers.
In this paper, we propose a method to achieve the optimal linear combination of two dichotomizers based on the maximization of the AUC of the resulting classification system. The effectiveness of the approach has been confirmed by the tests performed on standard datasets
An End to End Indoor Air Monitoring System Based on Machine Learning and SENSIPLUS Platform
Full text linkIn the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low-power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed through a microchip, named SENSIPLUS, while the processing phase is mainly based on Machine Learning techniques, embedded in a low power and low resources micro controller unit, for classification purposes. An extensive experimental campaign on different contaminants has been carried out and raw sensor data have been processed through a lightweight Multi Layer Perceptron for embedded implementation. More complex and computationally costly Deep Learning techniques, as Convolutional Neural Network and Long Short Term Memory, have been adopted as a reference for the validation of Multi Layer Perceptron performance. Results prove good classification capabilities, obtaining an accuracy greater than 75% in average. The obtained results, jointly with the reduced computational costs of the solution, highlight that this proposal is a proof of concept for a pervasive IoT air monitoring system
Metrological characterization of a novel microsensor platform for activated carbon filters monitoring
No full textNowadays, an increasing concern about people and environmental health and safety is spreading all over the world, and technologies such as efficient monitoring systems are furthered. In the field of air quality, filtering systems, especially based on activated carbons (ACs), are commonly used. In most cases, their state of health is not monitored, and their time of life is based on statistical and a priori evaluations. This paper proposes a novel microsensor platform for the real-time monitoring of AC filters based on the impedance measurements during gas exposition. A metrological characterization of the novel proposed instrument is provided in this paper, by comparing its output to a reference RLC meter. A calibration and adjustment procedure is developed in order to analyze the device measurement capabilities and obtain correction coefficients. Experiments with different gas typologies are presented, and the analysis of filters impedance dynamics is provided
A multi-context CNN ensemble for small lesion detection
No full textIn this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion. Experiments were focused on two well-known medical detection problems that have been recently faced with CNNs: microcalcification detection on full-field digital mammograms and microaneurysm detection on ocular fundus images. To this end, we used two publicly available datasets, INbreast and E-ophtha. Statistically significantly better detection performance were obtained by the proposed ensemble with respect to other approaches in the literature, demonstrating its effectiveness in the detection of small abnormalities
A Computer-Aided Detection System for Clustered Microcalcifications
No full textObjective
The aim of this paper is to describe a novel system for computer-aided detection of clusters of microcalcifications on digital mammograms.
Methods and material
Mammograms are first segmented by means of a tree-structured Markov random field algorithm that extracts the elementary homogeneous regions of interest. An analysis of such regions is then performed by means of a two-stage, coarse-to-fine classification based on both heuristic rules and classifier combination. In this phase, we avoid taking a decision on the single microcalcifications and forward it to the successive phase of clustering realized through a sequential approach.
Results
The system has been tested on a publicly available database of mammograms and compared with previous approaches. The obtained results show that the system is very effective, especially in terms of sensitivity.
Conclusions
The proposed approach exhibits some remarkable advantages both in segmentation and classification phases. The segmentation phase employs an image model that reduces the computational burden, preserving the small details in the image through an adaptive local estimation of all model parameters. The classification stage combines the results of the classifiers focused on the single microcalcification and the cluster as a whole. Such an approach makes a detection system particularly effective and robust with respect to the large variations exhibited by the clusters of microcalcifications
SVM Based Regression Schemes for Instruments Fault Accommodation in Automotive Systems
No full textThe paper deals with the use of Support Vector Machines (SVMs) and performance comparisons with Artificial Neural Networks (ANNs) in software-based Instrument Fault Accommodation schemes. As an example, a real case study on an automotive systems is presented. The ANNs and SVMs regression capability are employed to accommodate faults that could occur on main sensors involved in the operating engine. The obtained results prove the good behaviour of both tools and similar performances have been achieved in terms of accuracy
Crack Shape Reconstruction in Eddy Current Testing using Machine Learning Systems for Regression
No full textNondestructive testing techniques for the diagnosis of defects in solid materials can follow three steps, i.e., detection, location, and characterization. The solutions currently on the market allow for good detection and location of defects, but their characterization in terms of the exact determination of defect shape and dimensions is still an open question. This paper proposes a method for the reliable estimation of crack shape and dimensions in conductive materials using a suitable nondestructive instrument based on the eddy current principle and machine learning system postprocessing. After the design and tuning stages, a performance comparison between the two machine learning systems [artificial neural network (ANN) and support vector machine (SVM)] was carried out. An experimental validation carried out on a number of specimens with different known cracks confirmed the suitability of the proposed approach for defect characterization
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