scholarly journals Advanced Principal Component Analysis for Analysis of Optimized Credit Card Fraud Detection

2019 ◽  
Vol 8 (11) ◽  
pp. 318-322
Keyword(s):  
Principal Component Analysis ◽  
Credit Card ◽  
Detection System ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Security Systems ◽  
Positive Rate

The information has turned out to be increasingly more imperative to people, associations, and organizations, and thusly, shielding this delicate information in social databases has turned into a basic issue. In any case, in spite of customary security systems, assaults coordinated to databases still happen. In this way, an intrusion detection system (IDS) explicitly for the database that can give security from all conceivable malignant clients is important. In this paper, we present the Principal Component Analysis (PCA) technique with weighted voting in favor of the assignment of inconsistency location. PCA is a diagram based procedure reasonable for demonstrating bunching questions, and weighted casting a ballot improves its capacities by adjusting the casting a ballot effect of each tree. Trials demonstrate that RF with weighted casting a ballot shows a progressively predominant presentation consistency, just as better blunder rates with an expanding number of trees, contrasted with traditional grouping approaches. Besides, it outflanks all other best in class information mining calculations as far as false positive rate and false negative rate.

scholarly journals Facial Features Based Hybrid Methods for Emotion Recognition

2019 ◽  
Vol 8 (2S10) ◽  
pp. 885-889
Keyword(s):  
Principal Component Analysis ◽  
Emotion Recognition ◽  
Hybrid Methods ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Descriptor ◽  
Human Machine Interaction

Effective human machine interaction systems are need of the time so the work carried out deals with one of such significant HMI tasks- automatic emotion recognition. The experimentation carried out for this study is focused to facial expressions based emotion recognition. Two techniques of emotion recognition based on hybrid features are designed and experimented using JAFFE database. The first technique referred as "Hybrid Method1" is designed around feature descriptor obtained through local directional number & principal component analysis and feed forward neural network used as classifier. The second technique referred as "Hybrid Method 2" is designed around feature descriptor obtained through histogram of oriented gradients, local binary pattern and Gabor filters. PCA- principal component analysis is used for dimensionality reduction of feature descriptor and k-nearest neighbors as classifier. The average emotion recognition accuracy achieved through method 1 and method 2 is 85.24% and 93.86% respectively. Effectiveness of both the techniques is compared on the basis of performance parameters such as accuracy, false positive rate, false negative rate and emotion recognition time. Emotion recognition has wide application areas so the work carried out can be applied for suitable application development.

Video Copy Detection Based on Principal Component Analysis

2014 ◽  
Vol 548-549 ◽  
pp. 693-697
Author(s):  
Yan Yan Hou
Keyword(s):  
Principal Component Analysis ◽  
Spatial Information ◽  
False Positive Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Video Copy Detection ◽  
Copy Detection ◽  
Key Frame ◽  
Detection Algorithms ◽  
Positive Rate

Content-based video hashing was proposed for the purpose of video copy detection. Conventional video copy detection algorithms apply image hashing algorithm to either every frame or key frame which is sensitive to video variation. In our proposed algorithm, key frames including temporal and spatial information are used to video copy detection, Discrete cosine transform (DCT) is done for video key frame and feature vector is extracted by principal component analysis ( PCA ). An average true positive rate of 99.31% and false positive rate of 0.37% demonstrate the robustness and uniqueness of the proposed algorithm. Experiments indicate that it is easy to implement and more efficient than other video copy detection algorithms.

Empirical mean curve decomposition with multiwavelet transformation for eye movements recognition using electrooculogram signals

2020 ◽  
Vol 234 (8) ◽  
pp. 794-811
Author(s):  
Harikrishna Mulam ◽  
Malini Mudigonda
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Principal Component ◽  
The Neural Network ◽  
Marquardt Algorithm ◽  
Positive Rate ◽  
Sensitivity Specificity

Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human–computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg–Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1 score, and Mathews correlation coefficient.

BEASF-Based Image Enhancement for Caries Detection Using Multidimensional Projection and Neural Network

2018 ◽  
Vol 8 (2) ◽  
pp. 47-66
Author(s):  
Shashikant Patil ◽  
Vaishali Kulkarni ◽  
Archana Bhise
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
False Negative ◽  
Research Work ◽  
False Negative Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Oral Disease ◽  
Caries Detection ◽  
Linear Discriminant

Tooth caries or cavities diagnosing are concerned as the most significant research work, as this is the common oral disease suffered by humans. Many approaches have been proposed under the topics including demineralization and decaying as well. However, the imaging modalities often suffer from various critical or complex aspects that struggles the methods to attain accurate diagnosis. This article turns to introduce a new cavity diagnosis model with three phases: (i) pre-processing (ii) feature extraction (iii) classification. In the first phase, a new bi-histogram equalization with adaptive sigmoid functions (BEASF) is introduced to enhance the image quality followed by other enhancements models like grey thresholding and active contour. Then, the features are extracted using multilinear principal component analysis (MPCA). Further, the classification is done via neural network (NN) classifier. After the implementation, the proposed model compares its performance over other conventional methods like principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) and the performance of the approach is analyzed in terms of measures such as accuracy, sensitivity, specificity, precision, false positive rate (FPR), false negative rate (FNR), negative predictive value (NPV), false discovery rate (FDR), F1Score and Mathews correlation coefficient (MCC), and proves the superiority of proposed work.

Letters to the Editor

PEDIATRICS ◽  
1981 ◽  
Vol 68 (1) ◽  
pp. 144-145
Author(s):  
Lachlan Ch De Crespigny ◽  
Hugh P. Robinson
Keyword(s):  
Real Time ◽  
Linear Array ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Letters To The Editor ◽  
Premature Babies ◽  
Computed Tomography Scans ◽  
Positive Rate ◽  
Time Linear

We read with interest the report which suggested that the diagnosis of cerebroventricular hemorrhage ([CVH] including both subependymal [SEH] and intraventricular) with real time ultrasound was unreliable.1 Ultrasound, when compared with computed tomography scans, had a 35% false-positive rate and a 21% false-negative rate. In our institution over a 12-month period more than 200 premature babies have been examined (ADR real time linear array scanner with a 7-MHz transducer).

Pathologic Assessment of Computerized Tomography Accuracy for the Evaluation of the Laryngeal Cartilaginous Frame Work in Laryngopharyngeal Carcinomas

1989 ◽  
Vol 75 (2) ◽  
pp. 156-162 ◽  
Author(s):  
Sandro Sulfaro ◽  
Francesco Querin ◽  
Luigi Barzan ◽  
Mario Lutman ◽  
Roberto Comoretto ◽  
...  
Keyword(s):  
Computerized Tomography ◽  
False Positive ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Cartilage Destruction ◽  
High False Negative Rate ◽  
Positive Rate ◽  
Pathologic Assessment ◽  
Low Sensitivity

Sixty-six whole-organ sectioned laryngopharyngectomy specimens removed for cancer during a seven-year period were uniformly examined to determine the accuracy of preoperative high resolution computerized tomography (CT) for detection of cartilaginous involvement. Our results indicate that CT has a high overall specificity (88.2%) but a low sensitivity (47.1 %); we observed a high false-negative rate (26.5%) and a fairly low false-positive rate (5.9%). Massive cartilage destruction was easily assessed by CT, whereas both small macroscopic and microscopic neoplastic foci of cartilaginous invasion were missed on CT scans. Moreover, false-positive cases were mainly due to proximity of the tumor to the cartilage. Clinical implications of these results are discussed.

scholarly journals Macromolecule Particle Picking and Segmentation of a KLH Database by Unsupervised Cryo-EM Image Processing

Biomolecules ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 809
Author(s):  
Miguel Carrasco ◽  
Patricio Toledo ◽  
Nicole D. Tischler
Keyword(s):  
Signal To Noise Ratio ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Keyhole Limpet Hemocyanin ◽  
True Positive Rate ◽  
Cryo Electron Microscopy ◽  
Positive Rate ◽  
Top View ◽  
Non Negative Matrix Factorization

Segmentation is one of the most important stages in the 3D reconstruction of macromolecule structures in cryo-electron microscopy. Due to the variability of macromolecules and the low signal-to-noise ratio of the structures present, there is no generally satisfactory solution to this process. This work proposes a new unsupervised particle picking and segmentation algorithm based on the composition of two well-known image filters: Anisotropic (Perona–Malik) diffusion and non-negative matrix factorization. This study focused on keyhole limpet hemocyanin (KLH) macromolecules which offer both a top view and a side view. Our proposal was able to detect both types of views and separate them automatically. In our experiments, we used 30 images from the KLH dataset of 680 positive classified regions. The true positive rate was 95.1% for top views and 77.8% for side views. The false negative rate was 14.3%. Although the false positive rate was high at 21.8%, it can be lowered with a supervised classification technique.

Validity and Reliability of the Hooper Visual Organization Test

1974 ◽  
Vol 39 (1) ◽  
pp. 95-100 ◽  
Author(s):  
Allan Gerson
Keyword(s):  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Functional Disorders ◽  
Validity And Reliability ◽  
Clinical Value ◽  
Clear Cut ◽  
Positive Rate ◽  
Organic Brain ◽  
Visual Organization

To assess the validity and reliability of the Hooper Visual Organization Test, 68 Ss, of whom 16 were clinically and psychometrically determined to be suffering from organic brain damage, 19 had functional disorders, and 33 were without organic or functional disorders (normal), were given the test. The instrument was shown to be reliable ( r = .80), however, clear-cut discriminations between groups were not achieved. There were significant differences in scores of normal and damaged groups, functional and damaged Ss, but not functional and normal Ss. The qualitative signs said to aid in differentiations were totally absent from all protocols. Performance was affected in part by IQ and other aspects of recognition of meaning. There was a 19% false negative rate for the functionals and a 51% false positive rate for normals. The conclusion was that this device is of dubious clinical value.

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