scholarly journals An Approach to Risk Assessment and Threat Prediction for Complex Object Security Based on a Predicative Self-Configuring Neural System

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 102
Author(s):  
Nikolai Vladimirovich Korneev ◽  
Julia Vasilievna Korneeva ◽  
Stasis Petrasovich Yurkevichyus ◽  
Gennady Ivanovich Bakhturin
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Fundamental Problem ◽  
Experimental Studies ◽  
Neural System ◽  
Assessment System ◽  
Classification Model ◽  
Complex Object ◽  
Assessment Problems ◽  
First Time

We identified a set of methods for solving risk assessment problems by forecasting an incident of complex object security based on incident monitoring. The solving problem approach includes the following steps: building and training a classification model using the C4.5 algorithm, a decision tree creation, risk assessment system development, and incident prediction. The last system is a predicative self-configuring neural system that includes a SCNN (self-configuring neural network), an RNN (recurrent neural network), and a predicative model that allows for determining the risk and forecasting the probability of an incident for an object. We proposed and developed: a mathematical model of a neural system; a SCNN architecture, where, for the first time, the fundamental problem of teaching a perceptron SCNN was solved without a teacher by adapting thresholds of activation functions of RNN neurons and a special learning algorithm; and a predicative model that includes a fuzzy output system with a membership function of current incidents of the considered object, which belongs to three fuzzy sets, namely “low risk”, “medium risk”, and “high risk”. For the first time, we gave the definition of the base class of an object’s prediction and SCNN, and the fundamental problem of teaching a perceptron SCNN was solved without a teacher. We propose an approach to neural system implementation for multiple incidents of complex object security. The results of experimental studies of the forecasting error at the level of 2.41% were obtained.

scholarly journals Risk Assessment System Based on Fuzzy Composite Evaluation and a Backpropagation Neural Network for a Shield Tunnel Crossing under a River

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiao Liang ◽  
Taiyue Qi ◽  
Zhiyi Jin ◽  
Shaojie Qin ◽  
Pengtao Chen
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Risk Assessment ◽  
Assessment System ◽  
Chamber Pressure ◽  
Shield Tunnel ◽  
Risk Level ◽  
Backpropagation Neural Network ◽  
Grouting Pressure ◽  
Simulation Results

Constructing a shield tunnel that crosses under a river poses considerable safety risks, and risk assessment is essential for guaranteeing the safety of tunnel construction. This paper studies a risk assessment system for a shield tunnel crossing under a river. Risk identification is performed for the shield tunnel, and the risk factors and indicators are determined. The relationship between the two is determined preliminarily by numerical simulation, the numerical simulation results are verified by field measurements, and a sample set is established based on the numerical simulation results. Fuzzy comprehensive evaluation and a backpropagation neural network are then used to evaluate and analyze the risk level. Finally, the risk assessment system is used to evaluate the risk for Line 5 of the Hangzhou Metro in China. Based on the evaluation results, adjustments to the slurry strength, grouting pressure, and soil chamber pressure are proposed, and the risk is mitigated effectively.

scholarly journals A CNN Classification Model For Diagnosis Covid19

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
hatem khater ◽  
gamal selim
Keyword(s):  
Neural Network ◽  
Virus Disease ◽  
Real Data ◽  
Classification Model ◽  
X Ray ◽  
Corona Virus ◽  
Chest X Ray ◽  
Neural Network Structure ◽  
Extraction Model ◽  
First Time

The paper demonstrates the analysis of Corona Virus Disease based on a CNN probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed Convolution neural network structure. The Study is validated on 2002 chest X-ray images with 60 confirmed positive covid19 cases and (650 bacterial – 412 viral -880 normal) x-ray images. The proposed CNN compared with traditional classifiers with proposed CHFS feature extraction model. The experimental study has done with real data demonstrates the feasibility and potential of the proposed approach for the said cause. The result of proposed CNN structure has been successfully done to achieve 98.20% accuracy of covid19 potential cases with comparable of traditional classifiers.

An Analytical and Experimental Study of SC-CNN-Based Simple Nonautonomous Chaotic Circuit

2019 ◽  
Vol 14 (12) ◽  
Author(s):  
H. Shameem Banu ◽  
P. S. Sheik Uduman ◽  
K. Thamilmaran
Keyword(s):  
Neural Network ◽  
Circuit Simulation ◽  
Experimental Studies ◽  
Power Spectra ◽  
Cellular Neural Network ◽  
Phase Portraits ◽  
Explicit Analytical Solution ◽  
Periodic Dynamics ◽  
First Time ◽  
Good Agreement

Abstract In this study, we report an explicit analytical solution of state-controlled cellular neural network (SC-CNN) based second-order nonautonomous system. The proposed system is modeled with an aid of a generalized two-state-controlled cellular neural network (CNN) equations and experimentally realized by imposing a suitable connection of simple two-state-controlled generalized CNN cells following the report of Swathi et al. [2014]. The chaotic and quasi-periodic dynamics observed from this system have been investigated through an analytical approach for the first time. The intriguing dynamics observed from the system where further substantiated by phase portraits, Poincaré map, power spectra, and “0−1 test.” We trace the transition of the system from periodic to chaos through analytical solutions, which are in good agreement with hardware experiments. Additionally, we show PSpice circuit simulation results for validating our analytical and experimental studies.

p-EMA (II): evaluating ecological risks of pesticides for a farm-level risk assessment system

Agronomie ◽  
2003 ◽  
Vol 23 (1) ◽  
pp. 75-84 ◽  
Author(s):  
Andy Hart ◽  
Colin D. Brown ◽  
Kathy A. Lewis ◽  
John Tzilivakis
Keyword(s):  
Risk Assessment ◽  
Assessment System ◽  
Ecological Risks ◽  
Farm Level

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

IRISK METHOD FOR SECURITY ESTIMATION OF THE SIMULATION POLYGON FOR THE PROTECTION OF CRITICAL INFORMATION RESOURCES

2019 ◽  
Author(s):  
Bogdan Korniyenko ◽  
Lilia Galata
Keyword(s):  
Risk Assessment ◽  
Information System ◽  
Information Security ◽  
Risk Analysis ◽  
Information Resources ◽  
Security System ◽  
Assessment System ◽  
Automated System ◽  
Information Risk ◽  
Critical Information

In this article, the research of information system protection by ana­ ly­ zing the risks for identifying threats for information security is considered. Information risk analysis is periodically conducted to identify information security threats and test the information security system. Currently, various information risk analysis techni­ ques exist and are being used, the main difference being the quantitative or qualitative risk assessment scales. On the basis of the existing methods of testing and evaluation of the vulnerabilities for the automated system, their advantages and disadvantages, for the possibility of further comparison of the spent resources and the security of the information system, the conclusion was made regarding the deter­ mi­ nation of the optimal method of testing the information security system in the context of the simulated polygon for the protection of critical information resources. A simula­ tion ground for the protection of critical information resources based on GNS3 application software has been developed and implemented. Among the considered methods of testing and risk analysis of the automated system, the optimal iRisk methodology was identified for testing the information security system on the basis of the simulated. The quantitative method Risk for security estimation is considered. Generalized iRisk risk assessment is calculated taking into account the following parameters: Vulnerabili­ ty  — vulnerability assessment, Threat — threat assessment, Control — assessment of security measures. The methodology includes a common CVSS vul­ nerability assessment system, which allows you to use constantly relevant coefficients for the calculation of vulnerabilities, as well as have a list of all major vulnerabilities that are associated with all modern software products that can be used in the automated system. The known software and hardware vulnerabilities of the ground are considered and the resistance of the built network to specific threats by the iRisk method is calculated.

scholarly journals CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chuandong Song ◽  
Haifeng Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Classification Model ◽  
Peptide Sequence ◽  
Sequence Information ◽  
Post Translational Modification ◽  
Fuzzy Neural ◽  
Human Complex ◽  
Better Than

Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. Then, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. The experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

scholarly journals CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines

2020 ◽  
Vol 34 (03) ◽  
pp. 2594-2601
Author(s):  
Arjun Akula ◽  
Shuai Wang ◽  
Song-Chun Zhu
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Input Image ◽  
Classification Model ◽  
Learning Models ◽  
Fault Line ◽  
Semantic Level ◽  
Explainable Ai ◽  
Fault Lines ◽  
Classification Category

We present CoCoX (short for Conceptual and Counterfactual Explanations), a model for explaining decisions made by a deep convolutional neural network (CNN). In Cognitive Psychology, the factors (or semantic-level features) that humans zoom in on when they imagine an alternative to a model prediction are often referred to as fault-lines. Motivated by this, our CoCoX model explains decisions made by a CNN using fault-lines. Specifically, given an input image I for which a CNN classification model M predicts class cpred, our fault-line based explanation identifies the minimal semantic-level features (e.g., stripes on zebra, pointed ears of dog), referred to as explainable concepts, that need to be added to or deleted from I in order to alter the classification category of I by M to another specified class calt. We argue that, due to the conceptual and counterfactual nature of fault-lines, our CoCoX explanations are practical and more natural for both expert and non-expert users to understand the internal workings of complex deep learning models. Extensive quantitative and qualitative experiments verify our hypotheses, showing that CoCoX significantly outperforms the state-of-the-art explainable AI models. Our implementation is available at https://github.com/arjunakula/CoCoX

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