104,286 research outputs found
Use of a Confusion Network to Detect and Correct Errors in an On-Line Handwritten Sentence Recognition System
International audienceIn this paper we investigate the integration of a confusion network into an on-line handwritten sentence recognition system. The word posterior probabilities from the confusion network are used as confidence scored to detect potential errors in the output sentence from the Maximum A Posteriori decoding on a word graph. Dedicated classifiers (here, SVMs) are then trained to correct these errors and combine the word posterior probabilities with other sources of knowledge. A rejection phase is also introduced in the detection process. Experiments on handwritten sentences show a 28.5i% relative reduction of the word error rate
Development of an intelligent system for the detection of corona virus using artificial neural network
This paper presents the development of an intelligent system for the
detection of coronavirus using artificial neural network. This was done after
series of literature review which indicated that high fever accounts for 87.9%
of the COVID-19 symptoms. 683 temperature data of COVID-19 patients at >= 38C^o
were collected from Colliery hospital Enugu, Nigeria and used to train an
artificial neural network detective model for the detection of COVID-19. The
reference model generated was used converted into Verilog codes using Hardware
Description Language (HDL) and then burn into a Field Programming Gate Array
(FPGA) controller using FPGA tool in Matlab. The performance of the model when
evaluated using confusion matrix, regression and means square error (MSE)
showed that the regression value is 0.967; the accuracy is 97% and then MSE is
0.00100Mu. These results all implied that the new detection system for is
reliable and very effective for the detection of COVID-19.Comment: 13 pages, 8 Figure
Evaluation of Intelligent Intrusion Detection Models
This paper discusses an evaluation methodology that can be used to assess the performance of intelligent techniques at detecting, as well as predicting, unauthorised activities in networks. The effectiveness and the performance of any developed intrusion detection model will be determined by means of evaluation and validation. The evaluation and the learning prediction performance for this task will be discussed, together with a description of validation procedures. The performance of developed detection models that incorporate intelligent elements can be evaluated using well known standard methods, such as matrix confusion, ROC curves and Lift charts. In this paper these methods, as well as other useful evaluation approaches, are discussed.Peer reviewe
Robust Detection, Association, and Localization of Vehicle Lights: A Context-Based Cascaded CNN Approach and Evaluations
Vehicle light detection, association, and localization are required for
important downstream safe autonomous driving tasks, such as predicting a
vehicle's light state to determine if the vehicle is making a lane change or
turning. Currently, many vehicle light detectors use single-stage detectors
which predict bounding boxes to identify a vehicle light, in a manner decoupled
from vehicle instances. In this paper, we present a method for detecting a
vehicle light given an upstream vehicle detection and approximation of a
visible light's center. Our method predicts four approximate corners associated
with each vehicle light. We experiment with CNN architectures, data
augmentation, and contextual preprocessing methods designed to reduce
surrounding-vehicle confusion. We achieve an average distance error from the
ground truth corner of 4.77 pixels, about 16.33% of the size of the vehicle
light on average. We train and evaluate our model on the LISA Lights Dataset,
allowing us to thoroughly evaluate our vehicle light corner detection model on
a large variety of vehicle light shapes and lighting conditions. We propose
that this model can be integrated into a pipeline with vehicle detection and
vehicle light center detection to make a fully-formed vehicle light detection
network, valuable to identifying trajectory-informative signals in driving
scenes
LSDA: Large Scale Detection Through Adaptation
A major challenge in scaling object detection is the difficulty of obtaining
labeled images for large numbers of categories. Recently, deep convolutional
neural networks (CNNs) have emerged as clear winners on object classification
benchmarks, in part due to training with 1.2M+ labeled classification images.
Unfortunately, only a small fraction of those labels are available for the
detection task. It is much cheaper and easier to collect large quantities of
image-level labels from search engines than it is to collect detection data and
label it with precise bounding boxes. In this paper, we propose Large Scale
Detection through Adaptation (LSDA), an algorithm which learns the difference
between the two tasks and transfers this knowledge to classifiers for
categories without bounding box annotated data, turning them into detectors.
Our method has the potential to enable detection for the tens of thousands of
categories that lack bounding box annotations, yet have plenty of
classification data. Evaluation on the ImageNet LSVRC-2013 detection challenge
demonstrates the efficacy of our approach. This algorithm enables us to produce
a >7.6K detector by using available classification data from leaf nodes in the
ImageNet tree. We additionally demonstrate how to modify our architecture to
produce a fast detector (running at 2fps for the 7.6K detector). Models and
software are available a
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