scholarly journals Bags-of-Features for fish school cluster characterization in pelagic ecosystems: application to the discrimination of juvenile and adult anchovy (Engraulis ringens) clusters off Peru

2012 ◽  
Vol 69 (8) ◽  
pp. 1329-1339 ◽  
Author(s):  
Ronan Fablet ◽  
Paul Gay ◽  
Salvador Peraltilla ◽  
Cecilia Peña ◽  
Ramiro Castillo ◽  
...  
Keyword(s):  
Data Sets ◽  
Correct Classification ◽  
Classification Rate ◽  
Fish Schools ◽  
Fish School ◽  
Acoustic Data ◽  
School Based ◽  
Engraulis Ringens ◽  
Fisheries Acoustics

Whereas fisheries acoustics data processing mainly focused on the detection, characterization, and recognition of individual fish schools, here we addressed the characterization and discrimination of fish school clusters. The proposed scheme relied on the application of the Bags-of-Features (BoF) approach to acoustic echograms. This approach is widely exploited for pattern recognition issues and naturally applies here, considering fish schools as the relevant elementary objects. It relies on the extraction and categorization of fish schools in fisheries acoustic data. Echogram descriptors were computed per unit echogram length as the numbers of schools in different school categories. We applied this approach to the discrimination of juvenile and adult anchovy ( Engraulis ringens ) off Peru. Whereas the discrimination of individual schools is low (below 70%), the proposed BoF scheme achieved between 89% and 92% of correct classification of juvenile and adult echograms for different survey data sets and significantly outperformed classical school-based echogram characteristics (about 10% of improvement of the correct classification rate). We further illustrate the potential of the proposed scheme for the estimation of the spatial distribution of juvenile and adult anchovy populations.

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano

2018 ◽  
Author(s):  
Marco Abbatangelo ◽  
Estefanía Núñez-Carmona ◽  
Veronica Sberveglieri ◽  
Dario Zappa ◽  
Elisabetta Comini ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Correct Classification ◽  
Working Process ◽  
Classification Rate ◽  
Sensor Device ◽  
Novel Method ◽  
Two Stages ◽  
Parmigiano Reggiano ◽  
Made In

Parmigiano Reggiano cheese is one of the most appreciated and consumed food worldwide, especially in Italy, for its high content of nutrients and for its taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed in order to investigate the potentiality of this tool to distinguish rind percentage in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples in terms of percentage, seasoning and rind working process were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them with sensors responses. Data analysis consisted of two stages: multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results are promising in terms of correct classification of the samples. The classification rate is higher for ANNs than PLS-DA, reaching also 100% values.

scholarly journals Kernel methods for the detection and classification of fish schools in single-beam and multibeam acoustic data

2009 ◽  
Vol 66 (6) ◽  
pp. 1130-1135 ◽  
Author(s):  
Bart Buelens ◽  
Tim Pauly ◽  
Raymond Williams ◽  
Arthur Sale
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Age Groups ◽  
Routine Data ◽  
Acoustic Properties ◽  
Multibeam Sonar ◽  
Single Beam ◽  
Fish Schools ◽  
Acoustic Data

Abstract Buelens, B., Pauly, T., Williams, R., and Sale, A. 2009. Kernel methods for the detection and classification of fish schools in single-beam and multibeam acoustic data. – ICES Journal of Marine Science, 66: 1130–1135. A kernel method for clustering acoustic data from single-beam echosounder and multibeam sonar is presented. The algorithm is used to detect fish schools and to classify acoustic data into clusters of similar acoustic properties. In a preprocessing routine, data from single-beam echosounder and multibeam sonar are transformed into an abstracted representation by multidimensional nodes, which are datapoints with spatial, temporal, and acoustic features as components. Kernel methods combine these components to determine clusters based on joint spatial, temporal, and acoustic similarities. These clusters yield a classification of the data in groups of similar nodes. Including the spatial components results in clusters for each school and effectively detects fish schools. Ignoring the spatial components yields a classification according to acoustic similarities, corresponding to classes of different species or age groups. The method is described and two case studies are presented.

scholarly journals Pitfalls in stock discrimination by shape analysis of otolith contours

2015 ◽  
Vol 72 (7) ◽  
pp. 2090-2097 ◽  
Author(s):  
Alf Harbitz ◽  
Ole Thomas Albert
Keyword(s):  
Shape Analysis ◽  
Fourier Transforms ◽  
Influential Factor ◽  
Correct Classification ◽  
Success Rates ◽  
Greenland Halibut ◽  
Classification Rate ◽  
Stock Discrimination ◽  
The Impact

Abstract This paper focuses on artefacts that may corrupt stock discrimination by shape analysis of otolith contours, how one can examine if such artefacts are important, and how they can be avoided. The scope focuses on Fourier transforms of contour points, the linear Fisher discrimination technique, and success rates based on cross validation by the “leave one out at a time” technique. The “zero-score” technique is introduced as a tool to examine the importance of a possible artefact, based on the theoretical result that the probability of correct classification of any otolith from either of two identical groups is zero. If one of the identical groups is exposed to a possible influential factor, e.g. a different smoothing, a high classification rate will reveal that this factor is an important artefact. The concept of a “lasso contour” is introduced that drastically reduces the impact of smoothing and provides a non-concave shape that enables a one-dimensional representation of the contour without ambiguities. Results are illustrated by comparison between Greenland halibut (Reinhardtius hippoglossoides) otolith contours from southern Greenland and Northeast Arctic waters. The conclusion is that the probability of correct classification of locality based on the original contours is too optimistic (77–79%), while the scores based on lasso contours are insensitive to smoothing and still optimistically high (68–70%).

Performance Evaluation of Neural Networks in Concrete Condition Assessment

2000 ◽  
Vol 1739 (1) ◽  
pp. 76-82 ◽  
Author(s):  
D. R. Martinelli ◽  
Samir N. Shoukry
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Neural Nets ◽  
Training Data ◽  
Neural Network Modeling ◽  
Data Sets ◽  
Neural Net ◽  
Correct Classification ◽  
Data Set

A neural network modeling approach is used to identify concrete specimens that contain internal cracks. Different types of neural nets are used and their performance is evaluated. Correct classification of the signals received from a cracked specimen could be achieved with an accuracy of 75 percent for the test set and 95 percent for the training set. These recognition rates lead to the correct classification of all the individual test specimens. Although some neural net architectures may show high performance with a particular training data set, their results might be inconsistent. In situations in which the number of data sets is small, consistent performance of a neural network may be achieved by shuffling the training and testing data sets.

Measuring the Certainty of a Decision Rule with Applications in Electrocardiography

1988 ◽  
Vol 27 (04) ◽  
pp. 155-160 ◽  
Author(s):  
E. Lesaffre ◽  
J. L. Willems
Keyword(s):  
Recent Work ◽  
Decision Rule ◽  
Decision Process ◽  
Data Sets ◽  
Interval Estimate ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Interval Estimates ◽  
Prediction Modelling

SummaryAs an extension of recent work, several types of uncertainty involved in a decision process are further clarified in this paper. The concept of allocation with ß-confidence and the ß-doubt matrix are illustrated by two electrocardiographic data sets. These data sets illustrate the importance and the novelty of the above concepts. We argue for augmenting the classical correct classification rate with an interval estimate. We think that interval estimates are essential in the area of prediction modelling to refrain the user from being too optimistic.

scholarly journals Inheritance metrics feats in unsupervised learning to classify unlabeled datasets and clusters in fault prediction

2021 ◽  
Vol 7 ◽  
pp. e722
Author(s):  
Syed Rashid Aziz ◽  
Tamim Ahmed Khan ◽  
Aamer Nadeem
Keyword(s):  
Object Oriented ◽  
Fault Prediction ◽  
Training Data ◽  
Data Sets ◽  
Correct Classification ◽  
High Quality ◽  
Software Fault Prediction ◽  
Public Data ◽  
Software Fault

Fault prediction is a necessity to deliver high-quality software. The absence of training data and mechanism to labeling a cluster faulty or fault-free is a topic of concern in software fault prediction (SFP). Inheritance is an important feature of object-oriented development, and its metrics measure the complexity, depth, and breadth of software. In this paper, we aim to experimentally validate how much inheritance metrics are helpful to classify unlabeled data sets besides conceiving a novel mechanism to label a cluster as faulty or fault-free. We have collected ten public data sets that have inheritance and C&K metrics. Then, these base datasets are further split into two datasets labeled as C&K with inheritance and the C&K dataset for evaluation. K-means clustering is applied, Euclidean formula to compute distances and then label clusters through the average mechanism. Finally, TPR, Recall, Precision, F1 measures, and ROC are computed to measure performance which showed an adequate impact of inheritance metrics in SFP specifically classifying unlabeled datasets and correct classification of instances. The experiment also reveals that the average mechanism is suitable to label clusters in SFP. The quality assurance practitioners can benefit from the utilization of metrics associated with inheritance for labeling datasets and clusters.

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

Detection and Classification of Anomalies in Large Data Sets on the Basis of Information Granules

2021 ◽  
pp. 1-1
Author(s):  
Adam Kiersztyn ◽  
Pawe Karczmarek ◽  
Krystyna Kiersztyn ◽  
Witold Pedrycz
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Information Granules
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