scholarly journals PreNNsem: A Heterogeneous Ensemble Learning Framework for Vulnerability Detection in Software

2020 ◽  
Vol 10 (22) ◽  
pp. 7954
Author(s):  
Lu Wang ◽  
Xin Li ◽  
Ruiheng Wang ◽  
Yang Xin ◽  
Mingcheng Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ensemble Learning ◽  
Detection Efficiency ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Training Methods ◽  
Vulnerability Detection ◽  
Sample Data ◽  
Heterogeneous Ensemble

Automated vulnerability detection is one of the critical issues in the realm of software security. Existing solutions to this problem are mostly based on features that are defined by human experts and directly lead to missed potential vulnerability. Deep learning is an effective method for automating the extraction of vulnerability characteristics. Our paper proposes intelligent and automated vulnerability detection while using deep representation learning and heterogeneous ensemble learning. Firstly, we transform sample data from source code by removing segments that are unrelated to the vulnerability in order to reduce code analysis and improve detection efficiency in our experiments. Secondly, we represent the sample data as real vectors by pre-training on the corpus and maintaining its semantic information. Thirdly, the vectors are fed to a deep learning model to obtain the features of vulnerability. Lastly, we train a heterogeneous ensemble classifier. We analyze the effectiveness and resource consumption of different network models, pre-training methods, classifiers, and vulnerabilities separately in order to evaluate the detection method. We also compare our approach with some well-known vulnerability detection commercial tools and academic methods. The experimental results show that our proposed method provides improvements in false positive rate, false negative rate, precision, recall, and F1 score.

scholarly journals Early Detection of The Advanced Persistent Threats Attacks Using Performance Analysis of Deep Learning

2020 ◽  
Author(s):  
Javad Hassannataj Joloudari ◽  
Mojtaba Haderbadi ◽  
Amir Mashmool ◽  
Mohammad GhasemiGol ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Tree ◽  
Bayesian Network ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Learning Models ◽  
Data Set ◽  
Advanced Persistent Threats ◽  
C5.0 Decision Tree

One of the most common and important destructive attacks on the victim system is Advanced Persistent Threats (APT)-attack. The APT attacker can achieve his hostile goals by obtaining information and gaining financial benefits regarding the infrastructure of a network. One of the solutions to detect a secret APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the network may crash because of high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods such as C5.0 decision tree, Bayesian network and deep neural network are used for timely detection and classification of APT-attacks on the NSL-KDD data set. Moreover, 10-fold cross validation method is used to experiment these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 95.64%, 88.37% and 98.85%, respectively, and also, in terms of the important criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 2.56, 10.47 and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false negative rate and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.

scholarly journals Machine Learning Based Classification Models for Financial Crisis Prediction

2019 ◽  
Vol 8 (4) ◽  
pp. 4887-4893
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Financial Crisis ◽  
Large Scale ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Small Scale ◽  
Early Prediction ◽  
Rbf Network

Financial Crisis Prediction (FCP) being the most complicated and expected problem to be solved from the context of corporate organization, small scale to large scale industries, investors, bank organizations and government agencies, it is important to design a framework to determine a methodology that will reveal a solution for early prediction of the Financial Crisis Prediction (FCP). Earlier methods are reviewed through the various works in statistical techniques applied to solve the problem. However, it is not sufficient to predict the results with much more intelligence and automated manner. The major objective of this paper is to enhance the early prediction of Financial Crisis in any organization based on machine learning models like Multilayer Perceptron, Radial basis Function (RBF) Network, Logistic regression and Deep Learning methods and conduct a comparative analysis of them to determine the best methods for Financial Crisis Prediction (FDP). The testing is conducted with globalized benchmark datasets namely German dataset, Weislaw dataset and Polish Dataset. The testing is performed in both WEKA and Rapid Miner Framework design and obtained with accuracies and other performance measures like False Positive Rate (FPR), False Negative Rate (FNR), Precision, Recall, F-score and Kappa that would determine the best result from specific algorithm that will intelligently identify the financial crisis before it actually occurs in an organization. The results achieved the algorithms DL, MLP, LR and RBF Network with accuracies 96%, 72.10%, 75.20% and 74% on German Dataset, 91.25%, 85.83%, 83.75% and 73.75% on Weislaw dataset, 99.70%, 96.30%, 96.21% and 96.14 on Polish dataset respectively. It is evident from all the predictive results and the analytics in Rapid Miner that Deep Learning (DL) is the best classifier and performer among other machine learners and classifiers. This method will enhance the future predictions and would provide efficient solutions for financial crisis predictions.

Comparative study of ultrasonographic findings with the operative findings of biliary surgery

2020 ◽  
Vol 22 (1) ◽  
pp. 25-29
Author(s):  
Zubayer Ahmad ◽  
Mohammad Ali ◽  
Kazi lsrat Jahan ◽  
ABM Khurshid Alam ◽  
G M Morshed
Keyword(s):  
Bile Duct ◽  
Common Bile Duct ◽  
Imaging Modality ◽  
False Positive Rate ◽  
Gallstone Disease ◽  
False Negative ◽  
False Negative Rate ◽  
Biliary Disease ◽  
Biliary Surgery ◽  
Operative Findings

Background: Biliary disease is one of the most common surgical problems encountered all over the world. Ultrasound is widely accepted for the diagnosis of biliary system disease. However, it is a highly operator dependent imaging modality and its diagnostic success is also influenced by the situation, such as non-fasting, obesity, intestinal gas. Objective: To compare the ultrasonographic findings with the peroperative findings in biliary surgery. Methods: This prospective study was conducted in General Hospital, comilla between the periods of July 2006 to June 2008 among 300 patients with biliary diseases for which operative treatment is planned. Comparison between sonographic findings with operative findings was performed. Results: Right hypochondriac pain and jaundice were two significant symptoms (93% and 15%). Right hypochondriac tenderness, jaundice and palpable gallbladder were most valuable physical findings (respectively, 40%, 15% and 5%). Out of 252 ultrasonically positive gallbladder, stone were confirmed in 249 cases preoperatively. Sensitivity of USG in diagnosis of gallstone disease was 100%. There was, however, 25% false positive rate detection. Specificity was, however, 75% in this case. USG could demonstrate stone in common bile duct in only 12 out of 30 cases. Sensitivity of the test in diagnosing common bile duct stone was 40%, false negative rate 60%. In the series, ultrasonography sensitivity was 100% in diagnosing stone in cystic duct. USG could detect with relatively good but less sensitivity the presence of chronic cholecystitis (92.3%) and worm inside gallbladder (50%). Conclusion: Ultrasonography is the most important investigation in the diagnosis of biliary disease and a useful test for patients undergoing operative management for planning and anticipating technical difficulties. Journal of Surgical Sciences (2018) Vol. 22 (1): 25-29

scholarly journals Preliminary Results of MyCog, a Brief Assessment for the Detection of Cognitive Impairment in Primary Care

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 259-260
Author(s):  
Laura Curtis ◽  
Lauren Opsasnick ◽  
Julia Yoshino Benavente ◽  
Cindy Nowinski ◽  
Rachel O’Conor ◽  
...  
Keyword(s):  
Primary Care ◽  
Cognitive Impairment ◽  
Cognitive Aging ◽  
False Positive ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Self Report ◽  
Card Sorting ◽  
Primary Care Settings

Abstract Early detection of Cognitive impairment (CI) is imperative to identify potentially treatable underlying conditions or provide supportive services when due to progressive conditions such as Alzheimer’s Disease. While primary care settings are ideal for identifying CI, it frequently goes undetected. We developed ‘MyCog’, a brief technology-enabled, 2-step assessment to detect CI and dementia in primary care settings. We piloted MyCog in 80 participants 65 and older recruited from an ongoing cognitive aging study. Cases were identified either by a documented diagnosis of dementia or mild cognitive impairment (MCI) or based on a comprehensive cognitive battery. Administered via an iPad, Step 1 consists of a single self-report item indicating concern about memory or other thinking problems and Step 2 includes two cognitive assessments from the NIH Toolbox: Picture Sequence Memory (PSM) and Dimensional Change Card Sorting (DCCS). 39%(31/80) participants were considered cognitively impaired. Those who expressed concern in Step 1 (n=52, 66%) resulted in a 37% false positive and 3% false negative rate. With the addition of the PSM and DCCS assessments in Step 2, the paradigm demonstrated 91% sensitivity, 75% specificity and an area under the ROC curve (AUC)=0.82. Steps 1 and 2 had an average administration time of <7 minutes. We continue to optimize MyCog by 1) examining additional items for Step 1 to reduce the false positive rate and 2) creating a self-administered version to optimize use in clinical settings. With further validation, MyCog offers a practical, scalable paradigm for the routine detection of cognitive impairment and dementia.

scholarly journals Ultrasound guided needle biopsy of axilla to evaluate nodal metastasis after preoperative systemic therapy in cohort of 106 breast cancers enriched with BRCA1/2 pathogenic variant carriers

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Baiba Līcīte ◽  
Arvīds Irmejs ◽  
Jeļena Maksimenko ◽  
Pēteris Loža ◽  
Genādijs Trofimovičs ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systemic Therapy ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Axillary Node ◽  
Pathogenic Variant ◽  
Axillary Lymph ◽  
Ultrasound Guided ◽  
Node Positive

Abstract Background Aim of the study is to evaluate the role of ultrasound guided fine needle aspiration cytology (FNAC) in the restaging of node positive breast cancer after preoperative systemic therapy (PST). Methods From January 2016 – October 2020 106 node positive stage IIA-IIIC breast cancer cases undergoing PST were included in the study. 18 (17 %) were carriers of pathogenic variant in BRCA1/2. After PST restaging of axilla was performed with ultrasound and FNAC of the marked and/or the most suspicious axillary node. In 72/106 cases axilla conserving surgery and in 34/106 cases axillary lymph node dissection (ALND) was performed. Results False Positive Rate (FPR) of FNAC after PST in whole cohort and BRCA1/2 positive subgroup is 8 and 0 % and False Negative Rate (FNR) – 43 and 18 % respectively. Overall Sensitivity − 55 %, specificity- 93 %, accuracy 70 %. Conclusion FNAC after PST has low FPR and is useful to predict residual axillary disease and to streamline surgical decision making regarding ALND both in BRCA1/2 positive and negative subgroups. FNR is high in overall cohort and FNAC alone are not able to predict ypCR and omission of further axillary surgery. However, FNAC performance in BRCA1/2 positive subgroup is more promising and further research with larger number of cases is necessary to confirm the results.

scholarly journals A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

2021 ◽  
Vol 13 (6) ◽  
pp. 1211
Author(s):  
Pan Fan ◽  
Guodong Lang ◽  
Bin Yan ◽  
Xiaoyan Lei ◽  
Pengju Guo ◽  
...  
Keyword(s):  
Vision System ◽  
False Positive Rate ◽  
Clustering Algorithms ◽  
False Negative ◽  
Color Space ◽  
False Negative Rate ◽  
Feature Selection Method ◽  
Image Features ◽  
Accurate Identification ◽  
Rgb Color Space

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

scholarly journals A new criterion for the alarm point using a combination of waveform amplitude and onset latency in Br(E)-MsEP monitoring in spine surgery

2018 ◽  
Vol 29 (4) ◽  
pp. 435-441 ◽  
Author(s):  
Kazuyoshi Kobayashi ◽  
Kei Ando ◽  
Ryuichi Shinjo ◽  
Kenyu Ito ◽  
Mikito Tsushima ◽  
...  
Keyword(s):  
Spine Surgery ◽  
Predictive Value ◽  
False Positive ◽  
Roc Analysis ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
High Rate ◽  
Motor Deficit ◽  
Onset Latency

OBJECTIVEMonitoring of brain evoked muscle-action potentials (Br[E]-MsEPs) is a sensitive method that provides accurate periodic assessment of neurological status. However, occasionally this method gives a relatively high rate of false-positives, and thus hinders surgery. The alarm point is often defined based on a particular decrease in amplitude of a Br(E)-MsEP waveform, but waveform latency has not been widely examined. The purpose of this study was to evaluate onset latency in Br(E)-MsEP monitoring in spinal surgery and to examine the efficacy of an alarm point using a combination of amplitude and latency.METHODSA single-center, retrospective study was performed in 83 patients who underwent spine surgery using intraoperative Br(E)-MsEP monitoring. A total of 1726 muscles in extremities were chosen for monitoring, and acceptable baseline Br(E)-MsEP responses were obtained from 1640 (95%). Onset latency was defined as the period from stimulation until the waveform was detected. Relationships of postoperative motor deficit with onset latency alone and in combination with a decrease in amplitude of ≥ 70% from baseline were examined.RESULTSNine of the 83 patients had postoperative motor deficits. The delay of onset latency compared to the control waveform differed significantly between patients with and without these deficits (1.09% ± 0.06% vs 1.31% ± 0.14%, p < 0.01). In ROC analysis, an intraoperative 15% delay in latency from baseline had a sensitivity of 78% and a specificity of 96% for prediction of postoperative motor deficit. In further ROC analysis, a combination of a decrease in amplitude of ≥ 70% and delay of onset latency of ≥ 10% from baseline had sensitivity of 100%, specificity of 93%, a false positive rate of 7%, a false negative rate of 0%, a positive predictive value of 64%, and a negative predictive value of 100% for this prediction.CONCLUSIONSIn spinal cord monitoring with intraoperative Br(E)-MsEP, an alarm point using a decrease in amplitude of ≥ 70% and delay in onset latency of ≥ 10% from baseline has high specificity that reduces false positive results.

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).

Abstract TMP1: Predicting Scale Design and Derivation to Select Acute Ischemic Stroke Patients for Direct Angiosuite Intervention

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Zeguang Ren ◽  
Runqi Wangqin ◽  
Maxim Mokin ◽  
Anxin Wang ◽  
Liang Zhao ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Stroke ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Vascular Imaging ◽  
Operating Characteristics ◽  
Stroke Patients ◽  
Additional Time ◽  
Vessel Occlusion

Introduction: Vascular imaging after head CT to confirm large vessel occlusion (LVO) for acute stroke patients requires additional time and delays recanalization. We developed the T hrombectomy A cute M echanical re P erfusion A ssessment ( TAMPA ) scale for selecting patients with LVO for direct angiosuite transfer and intervention to improve recanalization time. Methods: The TAMPA scale was developed from our prospectively collected “Get with the Guidelines” database. We included all “stroke alert” patients between 1/2017 and 8/2018 with vascular imaging and National Institutes of Health Stroke Scale scores between 5 and 25. We excluded patients with immediately obvious non-stroke diagnoses, those lacking subsequent vascular imaging, or those with incomplete records. Different variables were collected. The TAMPA scale receiver operating characteristics curve (ROC) was compared with the ROCs of other commonly used scales. Results: 571 eligible patients from 2115 “acute stroke alerts” were identified for developing the TAMPA scale. The scale was established with a combination of 5 items with a total score of 9: CT hyperdense sign, parenchymal hypodensity, lateralizing hemiparalysis, gaze deviation and speech disturbance. A cutoff of ≥ 4 yielded a sensitivity of 68.98%, specificity of 72.91%, false positive rate of 27.09%, and false negative rate of 31.02%. Compared with other scales, such as total NIHSS, C-stat/CPSSS, RACE, FAST-ED and 3I-SS, the TAMPA scale had the best ROC for the selected group of patients. Conclusions: The TAMPA scale accurately predicts presence of clinically amenable LVO in patients with moderate to severe ischemic stroke. Use of the TAMPA scale to identify high probability mechanical embolectomy candidates for direct transfer to the angiosuite could potentially reduce revascularization times and increase treatment rates.

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.

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