Implementation of Bagged SVM Ensemble Model for Classification of Epileptic States Using EEG

2019 ◽  
Vol 20 (9) ◽  
pp. 755-765
Author(s):  
Arshpreet Kaur ◽  
Karan Verma ◽  
Amol P. Bhondekar ◽  
Kumar Shashvat
Keyword(s):  
Approximate Entropy ◽  
Classification Problem ◽  
Training Data ◽  
Discrete Wavelet ◽  
Ensemble Model ◽  
Data Set ◽  
Testing Data ◽  
Ictal Activity ◽  
Group 2 ◽  
Group 1

Background: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy and locating the seizure focus, is a critical task. The aim of this work was to find how the ensemble model distinguishes between two different sets of problems which are group 1: inter-ictal and ictal, group 2: controlled and inter-ictal using approximate entropy as a parameter. Methods: This work addresses the classification problem for two groups; Group 1: “inter-ictal vs. ictal” for which case 1(C-E), and case 2(D-E) are included and Group 2; “activity from controlled vs. inter-ictal activity” considering four cases which are case 3 (A-C), case 4(B-C), case 5 (A-D) and case 6(B-D) respectively. To divide the EEG into sub-bands, DWT (Discrete Wavelet Transform) was used and approximate Entropy was extracted out of all the five sub-bands of EEG for each case. Bagged SVM was used to classify the different groups considered. Results: The highest accuracy for Group 1 using Bagged SVM Ensemble model for case 1 was observed to be 96.83% with testing data; which was similar to 97% achieved by using training data. For case 2 (D-E) 93.92% accuracy with training and 84.83% with testing data were obtained. For Group 2, there was a large disparity between SVM and Bagged Ensemble model, where 76%, 81.66%, 72.835% and 71.16% for case 3, case 4, case 5 and case 6 were obtained. While for training data set, 92.87%, 91.74%, 92% and 92.64% accuracy was attained, respectively. The results obtained by SVM for Group 2 showed a huge difference from the highest accuracy achieved by bagged SVM for both the training and the test data. Conclusion: Bagged Ensemble model outperformed SVM model for every case with a huge difference with both training as well as test dataset for Group 2 and marginally better for Group 1.

scholarly journals On Realistically Attacking Tor with Website Fingerprinting

2016 ◽  
Vol 2016 (4) ◽  
pp. 21-36 ◽  
Author(s):  
Tao Wang ◽  
Ian Goldberg
Keyword(s):  
Background Noise ◽  
Laboratory Tests ◽  
Training Data ◽  
Web Traffic ◽  
Training Set ◽  
Data Set ◽  
Laboratory Conditions ◽  
Testing Data ◽  
In The Wild ◽  
New Algorithms

Abstract Website fingerprinting allows a local, passive observer monitoring a web-browsing client’s encrypted channel to determine her web activity. Previous attacks have shown that website fingerprinting could be a threat to anonymity networks such as Tor under laboratory conditions. However, there are significant differences between laboratory conditions and realistic conditions. First, in laboratory tests we collect the training data set together with the testing data set, so the training data set is fresh, but an attacker may not be able to maintain a fresh data set. Second, laboratory packet sequences correspond to a single page each, but for realistic packet sequences the split between pages is not obvious. Third, packet sequences may include background noise from other types of web traffic. These differences adversely affect website fingerprinting under realistic conditions. In this paper, we tackle these three problems to bridge the gap between laboratory and realistic conditions for website fingerprinting. We show that we can maintain a fresh training set with minimal resources. We demonstrate several classification-based techniques that allow us to split full packet sequences effectively into sequences corresponding to a single page each. We describe several new algorithms for tackling background noise. With our techniques, we are able to build the first website fingerprinting system that can operate directly on packet sequences collected in the wild.

scholarly journals Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

2018 ◽  
Vol 13 (3) ◽  
pp. 408-428 ◽  
Author(s):  
Phu Vo Ngoc
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Sentiment Classification ◽  
Training Data ◽  
The Novel ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Novel Model ◽  
Better Than

We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.

scholarly journals An improved method to determine basic probability assignment with interval number and its application in classification

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882052 ◽  
Author(s):  
Bowen Qin ◽  
Fuyuan Xiao
Keyword(s):  
Evidence Theory ◽  
Classification Problem ◽  
Interval Number ◽  
Training Data ◽  
Cluster Method ◽  
Uncertain Information ◽  
Data Set ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability

Due to its efficiency to handle uncertain information, Dempster–Shafer evidence theory has become the most important tool in many information fusion systems. However, how to determine basic probability assignment, which is the first step in evidence theory, is still an open issue. In this article, a new method integrating interval number theory and k-means++ cluster method is proposed to determine basic probability assignment. At first, k-means++ clustering method is used to calculate lower and upper bound values of interval number with training data. Then, the differentiation degree based on distance and similarity of interval number between the test sample and constructed models are defined to generate basic probability assignment. Finally, Dempster’s combination rule is used to combine multiple basic probability assignments to get the final basic probability assignment. The experiments on Iris data set that is widely used in classification problem illustrated that the proposed method is effective in determining basic probability assignment and classification problem, and the proposed method shows more accurate results in which the classification accuracy reaches 96.7%.

Selection of optimal external filter for colorimetric camera

2021 ◽  
Vol 2021 (29) ◽  
pp. 141-147
Author(s):  
Michael J. Vrhel ◽  
H. Joel Trussell
Keyword(s):  
Figure Of Merit ◽  
Band System ◽  
Image Data ◽  
Optimal Filter ◽  
Training Data ◽  
Noise Model ◽  
Data Set ◽  
Testing Data ◽  
And Training ◽  
Selection Of

A database of realizable filters is created and searched to obtain the best filter that, when placed in front of an existing camera, results in improved colorimetric capabilities for the system. The image data with the external filter is combined with image data without the filter to provide a six-band system. The colorimetric accuracy of the system is quantified using simulations that include a realistic signal-dependent noise model. Using a training data set, we selected the optimal filter based on four criteria: Vora Value, Figure of Merit, training average ΔE, and training maximum ΔE. Each selected filter was used on testing data. The filters chosen using the training ΔE criteria consistently outperformed the theoretical criteria.

STING Algorithm Used English Sentiment Classification in a Parallel Environment

2017 ◽  
Vol 31 (07) ◽  
pp. 1750021 ◽  
Author(s):  
Nguyen Duy Dat ◽  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran ◽  
Vo Thi Ngoc Chau ◽  
Tuan A. Nguyen
Keyword(s):  
Sentiment Classification ◽  
Training Data ◽  
Network Environment ◽  
Data Set ◽  
Political Activities ◽  
New Model ◽  
Commercial Activities ◽  
Testing Data ◽  
Parallel Network ◽  
English Training

Sentiment classification is significant in everyday life of everyone, in political activities, activities of commodity production, commercial activities. In this research, we propose a new model for Big Data sentiment classification in the parallel network environment. Our new model uses STING Algorithm (SA) (in the data mining field) for English document-level sentiment classification with Hadoop Map (M)/Reduce (R) based on the 90,000 English sentences of the training data set in a Cloudera parallel network environment — a distributed system. In the world there is not any scientific study which is similar to this survey. Our new model can classify sentiment of millions of English documents with the shortest execution time in the parallel network environment. We test our new model on the 25,000 English documents of the testing data set and achieved on 61.2% accuracy. Our English training data set includes 45,000 positive English sentences and 45,000 negative English sentences.

scholarly journals 1520. Respiratory Syncytial Virus Hospitalizations (RSVH) and All-Cause Bronchiolitis Hospitalizations (BH) Among Children Aged ≤ 24 Months at the Start of RSV Season With Bronchopulmonary Dysplasia/Chronic Lung Disease of Prematurity (BPD/CLDP) Before and After the 2014 American Academy of Pediatrics (AAP) Policy

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S762-S762
Author(s):  
Jaime Fergie ◽  
Tara Gonzales ◽  
Mina Suh ◽  
Xiaohui Jiang ◽  
Jon Fryzek ◽  
...  
Keyword(s):  
Health Information System ◽  
International Health ◽  
International Classification Of Diseases ◽  
Data Set ◽  
Before And After ◽  
Classification Of Diseases ◽  
Group 2 ◽  
Syncytial Virus ◽  
The Impact ◽  
Group 1

Abstract Background The AAP, in 2014, stopped endorsing palivizumab for use in children with BPD/CLDP born at < 32 weeks’ gestational age (wGA) between the ages of 12 to 24 months not requiring medical support during the 6 months before the start of RSV season and all children with BPD/CLDP born at > 32 wGA. We sought to understand the impact of the guidance change on RSVH and BH in children no longer advised for RSV immunoprophylaxis with palivizumab. Methods Children with BPD/CLDP aged ≤ 24 months at the RSV season start and hospitalized for RSV or bronchiolitis during the 2010-2017 RSV seasons (November-March) were studied. RSVH, BH, and BPD/CLDP were defined by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and ICD-10-CM codes. ICD-9 codes for wGA combine 31 and 32 wGA into one code. Therefore, for BPD/CLDP, we classified group 1 as children aged 12 to 24 months who were born at < 31 wGA and group 2 as those born at ≥ 31 wGA. The Children’s Hospital Association’s Pediatric Health Information System® (PHIS) data set was used to describe frequency and characteristics of RSVH and BH and disease severity (including intensive care unit [ICU] admission and mechanical ventilation [MV]) before and after the 2014 AAP policy. Statistical analyses were done using z-tests; SAS version 9.4. Results Among children with BPD/CLDP, RSVH rates were 1.7% (1035/59,217) before 2014 and 2.1% (973/45,470) after 2014 (P< 0.0001). RSVH rose after the policy change vs before among children with BPD/CLDP in both group 1 (0.40% vs 0.26%; P< 0.0001) and group 2 (0.22% vs 0.14%; P=0.002). Similarly, BH also increased for both group 1 (P< 0.0001) and group 2 (P=0.002) after the guidance change vs before. Although ICU admissions increased significantly for children with BPD/CLDP in both group 1 (P< 0.0001) and group 2 (P=0.0004), use of MV (P=0.002) increased after 2014 for children with BPD/CLDP in group 1 only. Similar results were observed for BH. Conclusion This analysis highlights the increase in RSVH, BH, and associated severity among BPD/CLDP subgroups within the PHIS health system after 2014. Further study of long-term complications associated with RSVH in these children is warranted. Disclosures Jaime Fergie, MD, AstraZeneca (Speaker’s Bureau)Sobi, Inc. (Speaker’s Bureau) Tara Gonzales, MD, Sobi, Inc. (Employee) Mina Suh, MPH, International Health, EpidStrategies (Employee) Xiaohui Jiang, MS, EpidStrategies (Employee) Jon Fryzek, PhD, MPH, EpidStrategies (Employee) Adam Bloomfield, MD, FAAP, Sobi, Inc. (Employee)

scholarly journals Skin Cancer Detection using CNN Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 45-49
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Well Being ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Sequential Model ◽  
Pigmented Lesions ◽  
Testing Data

The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is well known as HAM10000 dataset, it consists of massive amounts of information regarding several dermatoscopic images of most trivial pigmented lesions of skin which are collected from different sufferers. Once the dataset is collected, cleaned, it is split into training and testing data sets. We used CNN to build our model and using the training data we trained the model , later using the testing data we tested the model. Once the model is implemented over the testing data, plots are made in order to analyze the relation between the echos and loss function. It is also used to analyse accuracy and echos for both training and testing data.

scholarly journals AI Testing: Ensuring a Good Data Split Between Data Sets (Training and Test) using K-means Clustering and Decision Tree Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu
Keyword(s):  
Decision Tree ◽  
Software Testing ◽  
Training Data ◽  
Data Sets ◽  
Full Data ◽  
Data Set ◽  
Full Dataset ◽  
Development Methodology ◽  
Testing Data ◽  
Long Time

Artificial Intelligence and Machine Learning have been around for a long time. In recent years, there has been a surge in popularity for applications integrating AI and ML technology. As with traditional development, software testing is a critical component of a successful AI/ML application. The development methodology used in AI/ML contrasts significantly from traditional development. In light of these distinctions, various software testing challenges arise. The emphasis of this paper is on the challenge of effectively splitting the data into training and testing data sets. By applying a k-Means clustering strategy to the data set followed by a decision tree, we can significantly increase the likelihood of the training data set to represent the domain of the full dataset and thus avoid training a model that is likely to fail because it has only learned a subset of the full data domain.

Abstract T MP100: Improving Timeliness of TPA Administration: Re-vamping Emergency Department Processes to Decrease Door to Needle Times

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Julie B Jurf ◽  
Eema Hemmen ◽  
Tenielle Delima ◽  
Eileen Virrey ◽  
Linda Ojeda ◽  
...  
Keyword(s):  
Respiratory Arrest ◽  
Continuous Variables ◽  
Neurological Assessment ◽  
Data Set ◽  
Hospital Arrival ◽  
Before And After ◽  
Neurological Exam ◽  
Group 2 ◽  
Iv Tpa ◽  
Group 1

Introduction: Time to neuroimaging is one of the critical measures for fast thrombolysis in acute ischemic stroke (AIS). While many aim to give IV tPA under 60 minutes from hospital arrival (door-to-needle or DTN), every minute of delay decreases the chances of good outcomes. Following previous reports, we established a streamlined triage system (STS) for all out-of-hospital stroke alerts and report on the initial experience of this new system and its effect on DTN. Methods: We included all AIS with last known well (LKW) under 2 hours who were treated with IV tPA from 11/2012 to 06/2014 at our facilities. We excluded telestroke or patients who were transferred to us, implemented STS on November 01, 2013 and analyzed time to CT (start of the exam) and DTN for patients before (group 1) after (Group 2) that date, using the UCSD GetWithTheGuidelines® data set. STS includes rapid triage on the EMS stretcher in the ED to assess patient safety for immediate transfer by EMS to CT, without a detailed neurological assessment. Previously patients were roomed in the ED first and received a triage including a neurological exam. We assessed treatment times, ED arrival to CT (DTC) and DTN for each Group and assessed serious neurological and medical complications (episodes of increased intracranial pressure, cardiac or respiratory arrest) in radiology before and after STS. We used unpaired t-test for comparing means (continuous variables). Results: A total of 36 patients received IV tPA (Group 1: 22, Group 2: 14). Mean (±SD) times (minutes) for DTC was 15.9(7.3) versus 11.5(6.5) (NS), for DTN 62.6(17.7) versus 53.8(19.6) (NS). We saw no serious complication before or after STS. Conclusions: Our streamlined stroke triage is safe and we aim to continue our analysis to identify additional opportunities for shorten treatment times. Expanding STS to include all stroke patients may further shorten DTN.

scholarly journals Off-Grid DOA Estimation Based on Circularly Fully Convolutional Networks (CFCN) Using Space-Frequency Pseudo-Spectrum

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2767
Author(s):  
Wenqiong Zhang ◽  
Yiwei Huang ◽  
Jianfei Tong ◽  
Ming Bao ◽  
Xiaodong Li
Keyword(s):  
Low Frequency ◽  
Doa Estimation ◽  
Classification Problem ◽  
Training Data ◽  
Model Parameters ◽  
Data Set ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Space Frequency ◽  
Multi Classification

Low-frequency multi-source direction-of-arrival (DOA) estimation has been challenging for micro-aperture arrays. Deep learning (DL)-based models have been introduced to this problem. Generally, existing DL-based methods formulate DOA estimation as a multi-label multi-classification problem. However, the accuracy of these methods is limited by the number of grids, and the performance is overly dependent on the training data set. In this paper, we propose an off-grid DL-based DOA estimation. The backbone is based on circularly fully convolutional networks (CFCN), trained by the data set labeled by space-frequency pseudo-spectra, and provides on-grid DOA proposals. Then, the regressor is developed to estimate the precise DOAs according to corresponding proposals and features. In this framework, spatial phase features are extracted by the circular convolution calculation. The improvement in spatial resolution is converted to increasing the dimensionality of features by rotating convolutional networks. This model ensures that the DOA estimations at different sub-bands have the same interpretation ability and effectively reduce network model parameters. The simulation and semi-anechoic chamber experiment results show that CFCN-based DOA is superior to existing methods in terms of generalization ability, resolution, and accuracy.

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