scholarly journals RST Analysis of Anomalous TIR Sequences in Relation with Earthquakes Occurred in Turkey in the Period 2004–2015

2022 ◽  
Vol 14 (2) ◽  
pp. 381
Author(s):  
Carolina Filizzola ◽  
Angelo Corrado ◽  
Nicola Genzano ◽  
Mariano Lisi ◽  
Nicola Pergola ◽  
...  
Keyword(s):  
Satellite Images ◽  
False Positive Rate ◽  
Time Dependent ◽  
Night Time ◽  
Thermal Anomalies ◽  
Positive Rate ◽  
Tectonic Settings ◽  
First Time ◽  
Random Guess

The paper provides, for the first time, a long-term (>10 years) analysis of anomalous transients in Earth’s emitted radiation over Turkey and neighbouring regions. The RST (Robust Satellite Techniques) approach is used to identify Significant Sequences of Thermal Anomalies (SSTAs) over about 12 years (May 2004 to October 2015) of night-time MSG-SEVIRI satellite images. The correlation analysis is performed with earthquakes with M ≥ 4, which occurred in the investigated period/region within a pre-defined space-time volume around SSTA occurrences. It confirms, also for Turkey, the possibility to qualify SSTAs among the candidate parameters of a multi-parametric system for time-Dependent Assessment of Seismic Hazard (t-DASH). After analysing about 4000 images (about 400 million of single satellite records), just 155 SSTAs (about 4 every 100 images) were isolated; 115 (74% out of the total) resulted in earthquake-related (false-positive rate 26%). Results of the error diagram confirms a non-casual correlation between RST-based SSTAs and earthquake occurrences, with probability gain values up to 2.2 in comparison with the random guess. The analysis, separately performed on Turkish areas characterized by different faults and earthquakes densities, demonstrates the SSTA correlation with a dynamic seismicity more than with static tectonic settings.

scholarly journals Night-Time Vehicle Sensing in Far Infrared Image with Deep Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Hai Wang ◽  
Yingfeng Cai ◽  
Xiaobo Chen ◽  
Long Chen
Keyword(s):  
Detection Rate ◽  
False Positive Rate ◽  
Infrared Image ◽  
Far Infrared ◽  
Night Vision ◽  
Infrared Sensors ◽  
Night Time ◽  
Detection Rates ◽  
Positive Rate ◽  
And Performance

The use of night vision systems in vehicles is becoming increasingly common. Several approaches using infrared sensors have been proposed in the literature to detect vehicles in far infrared (FIR) images. However, these systems still have low vehicle detection rates and performance could be improved. This paper presents a novel method to detect vehicles using a far infrared automotive sensor. Firstly, vehicle candidates are generated using a constant threshold from the infrared frame. Contours are then generated by using a local adaptive threshold based on maximum distance, which decreases the number of processing regions for classification and reduces the false positive rate. Finally, vehicle candidates are verified using a deep belief network (DBN) based classifier. The detection rate is 93.9% which is achieved on a database of 5000 images and video streams. This result is approximately a 2.5% improvement on previously reported methods and the false detection rate is also the lowest among them.

scholarly journals Epileptic Seizures Detection Using DCT-II and KNN Classifier in Long-Term EEG Signals

2020 ◽  
pp. 2687-2694
Author(s):  
Mahmood A. Jumaah ◽  
Ammar Ibrahim Shihab ◽  
Akeel Abdulkareem Farhan
Keyword(s):  
Euclidean Distance ◽  
False Positive Rate ◽  
Age Groups ◽  
Epileptic Seizures ◽  
Detection Algorithm ◽  
Discrete Cosine Transformation ◽  
Maximum Variance ◽  
Knn Classifier ◽  
Positive Rate

     Epilepsy is one of the most common diseases of the nervous system around the world, affecting all age groups and causing seizures leading to loss of control for a period of time. This study presents a seizure detection algorithm that uses Discrete Cosine Transformation (DCT) type II to transform the signal into frequency-domain and extracts energy features from 16 sub-bands. Also, an automatic channel selection method is proposed to select the best subset among 23 channels based on the maximum variance. Data are segmented into frames of  one Second length without overlapping between successive frames. K-Nearest Neighbour (KNN) model is used to detect those frames either to ictal (seizure) or interictal (non-seizure) based on Euclidean distance. The experimental results are tested on 21 patients included in the CHB-MIT dataset. The average F1-score was found to be 93.12, whereas the False-Positive Rate (FPR) average was determined to be 0.07.

scholarly journals Online Prediction of Lead Seizures from iEEG Data

2021 ◽  
Author(s):  
Hsiang-Han Chen ◽  
Han-Tai Shiao ◽  
Vladimir Cherkassky
Keyword(s):  
False Positive Rate ◽  
Group Learning ◽  
Processing Technique ◽  
Training Data ◽  
Svm Classifier ◽  
Naturally Occurring ◽  
Positive Rate ◽  
High Prediction ◽  
Online Prediction

We describe a novel system for online prediction of lead seizures from long-term intracranial electroencephalogram (iEEG) recordings for canines with naturally occurring epilepsy. This study adopts new specification of lead seizures, reflecting strong clustering of seizures in observed data. This clustering results in fewer lead seizures (~7 lead seizures per dog), and hence new challenges for online seizure prediction, that are addressed in the proposed system. In particular, machine learning part of the system is implemented using the Group Learning method suitable for modeling sparse and noisy seizure data. In addition, several modifications for the proposed system are introduced to cope with non-stationarity of noisy iEEG signal. They include: (1) periodic re-training of SVM classifier using most recent training data; (2) removing samples with noisy labels from training data; (3) introducing new adaptive post-processing technique for combining many predictions made for 20-second windows into a single prediction for 4 hr segment. Application of the proposed system requires only 2 lead seizures for training the initial model, and results in high prediction performance for all four dogs (with mean 0.84 sensitivity, 0.27 time-in-warning, and 0.78 false-positive rate per day). Proposed system achieves accurate prediction of lead seizures during long-term test periods, 3–16 lead seizures during 169–364 days test period, whereas earlier studies did not differentiate between lead vs. non-lead seizures and used much shorter test periods (~few days long).

scholarly journals Online Prediction of Lead Seizures from iEEG Data

2021 ◽  
Vol 11 (12) ◽  
pp. 1554
Author(s):  
Hsiang-Han Chen ◽  
Han-Tai Shiao ◽  
Vladimir Cherkassky
Keyword(s):  
False Positive Rate ◽  
Group Learning ◽  
Processing Technique ◽  
Training Data ◽  
Svm Classifier ◽  
Naturally Occurring ◽  
Positive Rate ◽  
High Prediction ◽  
Online Prediction

We describe a novel system for online prediction of lead seizures from long-term intracranial electroencephalogram (iEEG) recordings for canines with naturally occurring epilepsy. This study adopts new specification of lead seizures, reflecting strong clustering of seizures in observed data. This clustering results in fewer lead seizures (~7 lead seizures per dog), and hence new challenges for online seizure prediction, that are addressed in the proposed system. In particular, the machine learning part of the system is implemented using the group learning method suitable for modeling sparse and noisy seizure data. In addition, several modifications for the proposed system are introduced to cope with the non-stationarity of a noisy iEEG signal. They include: (1) periodic retraining of the SVM classifier using most recent training data; (2) removing samples with noisy labels from training data; and (3) introducing a new adaptive post-processing technique for combining many predictions made for 20 s windows into a single prediction for a 4 h segment. Application of the proposed system requires only two lead seizures for training the initial model, and results in high prediction performance for all four dogs (with mean 0.84 sensitivity, 0.27 time-in-warning, and 0.78 false-positive rate per day). The proposed system achieves accurate prediction of lead seizures during long-term test periods, 3–16 lead seizures during a 169–364 day test period, whereas earlier studies did not differentiate between lead vs. non-lead seizures and used much shorter test periods (~few days long).

scholarly journals Neuronal Subcompartment Classification and Merge Error Correction

2020 ◽  
Author(s):  
Hanyu Li ◽  
Michał Januszewski ◽  
Viren Jain ◽  
Peter H. Li
Keyword(s):  
State Of The Art ◽  
False Positive Rate ◽  
Automated Analysis ◽  
Automated Detection ◽  
Volume Reconstruction ◽  
3D Electron Microscopy ◽  
Positive Rate ◽  
Previous State ◽  
First Time ◽  
Multiple Analysis

AbstractRecent advances in 3d electron microscopy are yielding ever larger reconstructions of brain tissue, encompassing thousands of individual neurons interconnected by millions of synapses. Interpreting reconstructions at this scale demands advances in the automated analysis of neuronal morphologies, for example by identifying morphological and functional subcompartments within neurons. We present a method that for the first time uses full 3d input (voxels) to automatically classify reconstructed neuron fragments as axon, dendrite, or somal subcompartments. Based on 3d convolutional neural networks, this method achieves a mean f1-score of 0.972, exceeding the previous state of the art of 0.955. The resulting predictions can support multiple analysis and proofreading applications. In particular, we leverage finely localized subcompartment predictions for automated detection and correction of merge errors in the volume reconstruction, successfully detecting 90.6% of inter-class merge errors with a false positive rate of only 2.7%.

scholarly journals Longitudinal Monitoring of SARS-CoV-2 IgM and IgG Seropositivity to Detect COVID-19

2020 ◽  
Vol 5 (5) ◽  
pp. 908-920 ◽  
Author(s):  
Raymond T Suhandynata ◽  
Melissa A Hoffman ◽  
Michael J Kelner ◽  
Ronald W McLawhon ◽  
Sharon L Reed ◽  
...  
Keyword(s):  
False Positive Rate ◽  
The United States ◽  
Performance Parameters ◽  
Chain Reaction ◽  
Viral Antibodies ◽  
Positive Rate ◽  
Polymerase Chain ◽  
First Time ◽  
Kinetics Of ◽  
Disease Free

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a novel beta-coronavirus that has recently emerged as the cause of the 2019 coronavirus pandemic (COVID-19). Polymerase chain reaction (PCR) based tests are optimal and recommended for the diagnosis of an acute SARS-CoV-2 infection. Serology tests for viral antibodies provide an important tool to diagnose previous exposure to the virus. Here we evaluate the analytical performance parameters of the Diazyme SARS-CoV-2 IgM/IgG serology assays and describe the kinetics of IgM and IgG seroconversion observed in patients with PCR-confirmed COVID-19 who were admitted to our hospital. Methods We validated the performance of the Diazyme assay in 235 presumed SARS-CoV-2 negative subjects to determine specificity. Subsequently, we evaluated the SARS-CoV-2 IgM and IgG seroconversion of 54 PCR-confirmed COVID-19 patients and determined sensitivity of the assay at three different timeframes. Result Sensitivity and specificity for detecting seropositivity at ≥15 days following a positive SARS-CoV-2 PCR result, was 100.0% and 98.7% when assaying for the panel of IgM and IgG. The median time to seropositivity observed for a reactive IgM and IgG result from the date of a positive PCR was 5 days (IQR: 2.75–9 days) and 4 days (IQR: 2.75–6.75 days), respectively. Conclusions Our data demonstrate that the Diazyme IgM/IgG assays are suited for the purpose of detecting SARS-CoV-2 IgG and IgM in patients with suspected SARS-CoV-2 infections. For the first time, we report longitudinal data showing the evolution of seroconversion for both IgG and IgM in a cohort of acutely ill patients in the United States. We also demonstrate a low false positive rate in patients who were presumed to be disease free.

Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

2002 ◽  
Vol 41 (01) ◽  
pp. 37-41 ◽  
Author(s):  
S. Shung-Shung ◽  
S. Yu-Chien ◽  
Y. Mei-Due ◽  
W. Hwei-Chung ◽  
A. Kao
Keyword(s):  
Acute Appendicitis ◽  
False Positive ◽  
False Positive Rate ◽  
Accurate Method ◽  
Clinical Findings ◽  
Pathological Findings ◽  
Lower Quadrant ◽  
Predictive Values ◽  
Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

Somatic Embryogenesis and In vitro Plant Regeneration in Vigna trilobata (L.) Verd. - A Potential Pasture Legume

1970 ◽  
Vol 19 (1) ◽  
pp. 89-99
Author(s):  
K. Choudhary ◽  
M. Singh ◽  
M. S. Rathore ◽  
N. S. Shekhawat
Keyword(s):  
Somatic Embryogenesis ◽  
Growth Regulators ◽  
Somatic Embryos ◽  
Basal Medium ◽  
Long Term Study ◽  
Continuous Application ◽  
First Time ◽  
Pasture Legume

This long term study demonstrates for the first time that it is possible to propagate embryogenic Vigna trilobata and to subsequently initiate the differentiation of embryos into complete plantlets. Initiation of callus was possible on 2,4-D. Somatic embryos differentiated on modified MS basal nutrient medium with 1.0 mg/l  of 2,4-D and 0.5 mg/l  of Kn. Sustained cell division resulted in globular and heart shape stages of somatic embryos. Transfer of embryos on to a fresh modified MS basal medium with 0.5 mg/l of Kn and 0.5 mg/l of GA3 helped them to attain maturation and germination. However, the propagation of cells, as well as the differentiation of embryos, were inhibited by a continuous application of these growth regulators. For this reason, a long period on medium lacking these growth regulators was necessary before the differentiation of embryos occurred again. The consequences for improving the propagation of embryogenic cultures in Vigna species are discussed. Key words: Pasture  legume, Vigna trilobata, Globular, Heart shape, somatic embryogenesis D.O.I. 10.3329/ptcb.v19i1.4990 Plant Tissue Cult. & Biotech. 19(1): 89-99, 2009 (June)

An Evaluation of Provincial Macroeconomic Performance in Vietnam

2017 ◽  
pp. 34-47
Author(s):  
Hoi Le Quoc ◽  
Nam Pham Xuan ◽  
Tuan Nguyen Anh
Keyword(s):  
Development Strategy ◽  
Institutional Transformation ◽  
Economic Indicators ◽  
Macroeconomic Performance ◽  
Economic Development Strategy ◽  
Socio Economic Development ◽  
The Cost ◽  
Development Objectives ◽  
First Time

The study was targeted at developing a methodology for constructing a macroeconomic performance index at a provincial level for the first time in Vietnam based on 4 groups of measurements: (i) Economic indicators; (ii) oriented economic indicators; (iii) socio-economic indicators; and (iv) economic - social – institutional indicators. Applying the methodology to the 2011 - 2015 empirical data of all provinces in Vietnam, the research shows that the socio-economic development strategy implemented by those provinces did not provide balanced outcomes between growth and social objectives, sustainability and inclusiveness. Many provinces focused on economic growth at the cost of structural change, equality and institutional transformation. In contrast, many provinces were successful in improving equality but not growth. Those facts threaten the long-term development objectives of the provinces.

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