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

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.

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Handling Imbalanced Class Problem of Measles InfectionRisk Prediction Model

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a2649.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 3431-3435

Keyword(s):

Logistic Regression ◽

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Sampling Technique ◽

True Positive Rate ◽

True Positive ◽

Positive Rate ◽

Imbalanced Class ◽

Medical Dataset

Measles is an emerging infectious disease with increasing number of reported cases. It is a vaccine-preventable disease;thus, it is common to have imbalanced class problem in the dataset. This study aims to resolve the imbalanced class problem for the prediction of measles infection risk and to compare the predictive results on a balanced dataset based on three machine learningtechniques. The data that was utilized in this study contained 37,884 records of suspected measles casesthat were highly imbalanced towards negative measles cases. The Synthetic Minority Over-Sampling Technique (SMOTE) was performed to balance thedistribution of the target attribute. The balanced dataset was then modelled using logistic regression, decision tree and Naïve Bayes. The predicted results indicated that logistic regression executed on the balanced dataset by SMOTE has the highest and most accurateclassification with 94.5% overall accuracy, 93.9% true positive rate, 5.8% false positive rate and 5.1% false negative rate. Therefore, SMOTE and other over-sampling approaches may be applicable to overcome imbalanced class issues in the medical dataset.

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LINKCALCULATOR – AN EFFICIENT LINK-BASED PHISHING DETECTION TOOL

Acta Informatica Malaysia ◽

10.26480/aim.02.2020.37.44 ◽

2020 ◽

Vol 4 (2) ◽

pp. 37-44

Author(s):

Orunsolu Abiodun ◽

Sodiya A.S ◽

Kareem S.O

Keyword(s):

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

True Positive Rate ◽

True Negative ◽

Negative Rate ◽

Positive Rate ◽

Link Information ◽

Phishing Detection ◽

Mobile Market

The problem of phishing attacks continues to demand new solutions as existing solutions are limited by various challenges such as high computational requirements, zero-day attacks, needs for updates, complex ruled-based, etc. Besides, the emerging mobile market demands simple solutions to phishing due to several factors such as memory, fragmentation, etc. In response to the above challenges, a simple anti-phishing tool called LinkCalculator is presented. The proposed LinkCalculator anti-phishing scheme is based on an algorithm designed to extract link characteristics from loading URLs to determine their legitimacy. Unlike the other link-based extraction approaches, the proposed approach introduced the concept of weight to represent the different links found in a URL. This is because certain link information within parsed webpages or requests is sufficient to classify them as phishing without loss of generality. The approach is experimented using a dataset of 300 instances consisting of 150 legitimate URLs and 150 phishing URLs from openly-available research datasets. The experimental results indicate a significance performance of 100%. True Negative Rate and 0.00% False Positive Rate for legitimate instances and True Positive Rate of 96.67% with 0.03 % False Negative Rate for phishing instances which indicate that the approach offers a more efficient lightweight approach to phishing detection.

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Optimized Double-Regional Filtering Algorithm on MRI Three-Dimensional Reconstructed Images for the Evaluation of Effects of Delivery on the Pelvis of Primiparas

Scientific Programming ◽

10.1155/2021/7985624 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Qiong Jin ◽

Mian Huang ◽

Jun Lin ◽

Shansan Wu ◽

Zhang Shen ◽

...

Keyword(s):

Signal To Noise Ratio ◽

False Positive Rate ◽

False Negative ◽

Three Dimensional ◽

False Negative Rate ◽

Galois Field ◽

Pelvic Organ ◽

Control Group ◽

Nonlocal Means ◽

Positive Rate

This study was to explore the denoising and segmentation effect of dual-domain image denoising (DDID) algorithm, and the Galois field (GF) and nonlocal means (NLM) algorithms were introduced for comparative analysis. 40 primiparas in the hospital from January 2018 to January 2020 were divided into an experimental group (caesarean section (CS), group E) and a control group (vaginal delivery (VD), group C). The peak signal-to-noise ratio (PSNR) and segmentation parameters of DDID algorithm were compared with GF algorithm and NLM algorithm. It was found that the DDID showed higher overall accuracy (OA) and lower false positive rate (FPR) and false negative rate (FNR). The PSNR of DDID was higher than the other two algorithms. GF algorithm showed the highest edge retention index (ERI). The incidence of pelvic organ prolapse (POP) in group E and group C was 9/20 (45%) and 5/20 (25%), respectively, with extreme difference ( P < 0.05 ). Evaluation of the effects of delivery on the pelvis of primiparas with MRI three-dimensional (3D) reconstructed images based on the optimized DDID showed a superior and stable denoising effect and good segmentation, so it was worthy of clinical promotion and application.

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Pretherapeutic Imaging for Axillary Staging in Breast Cancer: A Systematic Review and Meta-Analysis of Ultrasound, MRI and FDG PET

Journal of Clinical Medicine ◽

10.3390/jcm10071543 ◽

2021 ◽

Vol 10 (7) ◽

pp. 1543

Author(s):

Morwenn Le Boulc’h ◽

Julia Gilhodes ◽

Zara Steinmeyer ◽

Sébastien Molière ◽

Carole Mathelin

Keyword(s):

Systematic Review ◽

Sensitivity And Specificity ◽

Meta Analysis ◽

False Negative ◽

Significant Proportion ◽

False Negative Rate ◽

True Positive Rate ◽

Axillary Staging ◽

Positron Emission ◽

Positive Rate

Background: This systematic review aimed at comparing performances of ultrasonography (US), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography (PET) for axillary staging, with a focus on micro- or micrometastases. Methods: A search for relevant studies published between January 2002 and March 2018 was conducted in MEDLINE database. Study quality was assessed using the QUality Assessment of Diagnostic Accuracy Studies checklist. Sensitivity and specificity were meta-analyzed using a bivariate random effects approach; Results: Across 62 studies (n = 10,374 patients), sensitivity and specificity to detect metastatic ALN were, respectively, 51% (95% CI: 43–59%) and 100% (95% CI: 99–100%) for US, 83% (95% CI: 72–91%) and 85% (95% CI: 72–92%) for MRI, and 49% (95% CI: 39–59%) and 94% (95% CI: 91–96%) for PET. Interestingly, US detects a significant proportion of macrometastases (false negative rate was 0.28 (0.22, 0.34) for more than 2 metastatic ALN and 0.96 (0.86, 0.99) for micrometastases). In contrast, PET tends to detect a significant proportion of micrometastases (true positive rate = 0.41 (0.29, 0.54)). Data are not available for MRI. Conclusions: In comparison with MRI and PET Fluorodeoxyglucose (FDG), US is an effective technique for axillary triage, especially to detect high metastatic burden without upstaging majority of micrometastases.

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Letters to the Editor

PEDIATRICS ◽

10.1542/peds.68.1.144a ◽

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).

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

Tumori Journal ◽

10.1177/030089168907500216 ◽

1989 ◽

Vol 75 (2) ◽

pp. 156-162 ◽

Cited By ~ 5

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.

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Validity and Reliability of the Hooper Visual Organization Test

Perceptual and Motor Skills ◽

10.2466/pms.1974.39.1.95 ◽

1974 ◽

Vol 39 (1) ◽

pp. 95-100 ◽

Cited By ~ 13

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

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/0954411920924496 ◽

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.

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An Enhanced Multimodal Stacking Scheme for Online Pornographic Content Detection

Applied Sciences ◽

10.3390/app10082943 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2943

Author(s):

Kwangho Song ◽

Yoo-Sung Kim

Keyword(s):

Average Rate ◽

False Negative ◽

False Negative Rate ◽

Serial Order ◽

True Positive Rate ◽

Online Detection ◽

Convolutional Network ◽

Signal Change ◽

Positive Rate ◽

Auditory Features

An enhanced multimodal stacking scheme is proposed for quick and accurate online detection of harmful pornographic contents on the Internet. To accurately detect harmful contents, the implicative visual features (auditory features) are extracted using a bi-directional RNN (recurrent neural network) with VGG-16 (a multilayered dilated convolutional network) to implicitly express the signal change patterns over time within each input. Using only the implicative visual and auditory features, a video classifier and an audio classifier are trained, respectively. By using both features together, one fusion classifier is also trained. Then, these three component classifiers are stacked in the enhanced ensemble scheme to reduce the false negative errors in a serial order of the fusion classifier, video classifier, and audio classifier for a quick online detection. The proposed multimodal stacking scheme yields an improved true positive rate of 95.40% and a false negative rate of 4.60%, which are superior values to previous studies. In addition, the proposed stacking scheme can accurately detect harmful contents up to 74.58% and an average rate of 62.16% faster than the previous stacking scheme. Therefore, the proposed enhanced multimodal stacking scheme can be used to quickly and accurately filter out harmful contents in the online environments.

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Malicious Domain Names Detection Algorithm Based on N-Gram

Journal of Computer Networks and Communications ◽

10.1155/2019/4612474 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Hong Zhao ◽

Zhaobin Chang ◽

Guangbin Bao ◽

Xiangyan Zeng

Keyword(s):

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Detection Algorithm ◽

Internet Security ◽

Domain Name ◽

Domain Names ◽

Malicious Attack ◽

Positive Rate ◽

N Gram

Malicious domain name attacks have become a serious issue for Internet security. In this study, a malicious domain names detection algorithm based on N-Gram is proposed. The top 100,000 domain names in Alexa 2013 are used in the N-Gram method. Each domain name excluding the top-level domain is segmented into substrings according to its domain level with the lengths of 3, 4, 5, 6, and 7. The substring set of the 100,000 domain names is established, and the weight value of a substring is calculated according to its occurrence number in the substring set. To detect a malicious attack, the domain name is also segmented by the N-Gram method and its reputation value is calculated based on the weight values of its substrings. Finally, the judgment of whether the domain name is malicious is made by thresholding. In the experiments on Alexa 2017 and Malware domain list, the proposed detection algorithm yielded an accuracy rate of 94.04%, a false negative rate of 7.42%, and a false positive rate of 6.14%. The time complexity is lower than other popular malicious domain names detection algorithms.

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