Identification of Flash floods using Soil Flux and CO2: An Implementation of Neural Network with Less False Alarm Rate

2018 ◽  
Vol 10 (7) ◽  
Author(s):  
Talha Ahmed Khan ◽  
◽  
Kushsairy Kadir ◽  
Muhammad Alam ◽  
Zeeshan Shahid ◽  
...  
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Flash Floods ◽  
Soil Flux

scholarly journals Infrared small target detection based on region proposal and CNN classifier

2021 ◽  
Author(s):  
Mingming Fan ◽  
Shaoqing Tian ◽  
Kai Liu ◽  
Jiaxin Zhao ◽  
Yunsong Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Rate ◽  
Corner Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Potential Target ◽  
Infrared Small Target

AbstractInfrared small target detection has been a challenging task due to the weak radiation intensity of targets and the complexity of the background. Traditional methods using hand-designed features are usually effective for specific background and have the problems of low detection rate and high false alarm rate in complex infrared scene. In order to fully exploit the features of infrared image, this paper proposes an infrared small target detection method based on region proposal and convolution neural network. Firstly, the small target intensity is enhanced according to the local intensity characteristics. Then, potential target regions are proposed by corner detection to ensure high detection rate of the method. Finally, the potential target regions are fed into the classifier based on convolutional neural network to eliminate the non-target regions, which can effectively suppress the complex background clutter. Extensive experiments demonstrate that the proposed method can effectively reduce the false alarm rate, and outperform other state-of-the-art methods in terms of subjective visual impression and quantitative evaluation metrics.

scholarly journals LA-GRU: Building Combined Intrusion Detection Model Based on Imbalanced Learning and Gated Recurrent Unit Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Binghao Yan ◽  
Guodong Han
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Network Traffic ◽  
Detection Performance ◽  
Imbalanced Learning ◽  
Traffic Distribution ◽  
Proposed Model ◽  
Gated Recurrent Unit

The intrusion detection models (IDMs) based on machine learning play a vital role in the security protection of the network environment, and, by learning the characteristics of the network traffic, these IDMs can divide the network traffic into normal behavior or attack behavior automatically. However, existing IDMs cannot solve the imbalance of traffic distribution, while ignoring the temporal relationship within traffic, which result in the reduction of the detection performance of the IDM and increase the false alarm rate, especially for low-frequency attacks. So, in this paper, we propose a new combined IDM called LA-GRU based on a novel imbalanced learning method and gated recurrent unit (GRU) neural network. In the proposed model, a modified local adaptive synthetic minority oversampling technique (LA-SMOTE) algorithm is provided to handle imbalanced traffic, and then the GRU neural network based on deep learning theory is used to implement the anomaly detection of traffic. The experimental results evaluated on the NSL-KDD dataset confirm that, compared with the existing state-of-the-art IDMs, the proposed model not only obtains excellent overall detection performance with a low false alarm rate but also more effectively solves the learning problem of imbalanced traffic distribution.

An Intelligent Approach for Intrusion Detection using Convolutional Neural Network

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
P. Manoj Kumar ◽  
M. Parvathy ◽  
C. Abinaya Devi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
False Alarm Rate ◽  
Real Time ◽  
Detection Rate ◽  
Performance Metrics ◽  
Classification Model ◽  
Real Time Traffic

Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.

scholarly journals Fire detection with infrared images using cascaded neural network

2019 ◽  
Vol 13 ◽  
pp. 174830261989543
Author(s):  
Li Deng ◽  
Qian Chen ◽  
Yuanhua He ◽  
Xiubao Sui ◽  
Quanyi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Early Stage ◽  
Infrared Camera ◽  
Fire Detection ◽  
Image Texture ◽  
Infrared Images ◽  
Detection Technology ◽  
Civil Aircraft

The existing equipment of civil aircraft cargo fire detection mainly uses photoelectric smoke detectors, which has a high false alarm rate. According to Federal Aviation Agency’s (FAA) statistics, the false alarm rate is as high as 99%. 1 In the cargo of civil aircraft, the traditional photoelectric detection technology cannot effectively distinguish interference particles from smoke particles. Since the video smoke detection technology has proven to be reliable in many large scenarios, a deep learning method of image processing for fire detection is proposed. The proposed convolutional neural network is constructed of front end network and back end network cascaded with the capsule network and the circularity computation for the dynamic infrared fire image texture extraction. In order to accurately identify whether there is a fire in the area and give the kind of burning substances, a series of fuels are selected, such as n-heptane, cyclohexane, and carton for combustion reaction, and infrared camera is used to take infrared images of all fuel combustion. Experimental results show that the proposed method can effectively detect fire at the early stage of fire which is applicable for fire detection in civil aircraft cargoes.

scholarly journals Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images

2019 ◽  
Vol 11 (23) ◽  
pp. 2862 ◽  
Author(s):  
Weiwei Fan ◽  
Feng Zhou ◽  
Xueru Bai ◽  
Mingliang Tao ◽  
Tian Tian
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
False Alarm Rate ◽  
Synthetic Aperture ◽  
Deep Convolutional Neural Networks ◽  
Ship Detection ◽  
Sensing Applications ◽  
Aperture Radar

Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship detection method for PolSAR images based on modified faster region-based convolutional neural network (Faster R-CNN). The main improvements include proposal generation by adopting multi-level features produced by the convolution layers, which fits ships with different sizes, and the addition of a Deep Convolutional Neural Network (DCNN)-based classifier for training sample generation and coast mitigation. The proposed method has been validated by four measured datasets of NASA/JPL airborne synthetic aperture radar (AIRSAR) and uninhabited aerial vehicle synthetic aperture radar (UAVSAR). Performance comparison with the modified constant false alarm rate (CFAR) detector and the Faster R-CNN has demonstrated that the proposed method can improve the detection probability while reducing the false alarm rate and missed detections.

scholarly journals A neural network framework for binary classification of radar detections

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jabran Akhtar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
False Alarm ◽  
False Alarm Rate ◽  
High Probability ◽  
Binary Classification ◽  
Guard Cells ◽  
Probability Of Detection ◽  
Constant False Alarm Rate

AbstractA desired objective in radar target detection is to satisfy two very contradictory requirements: offer a high probability of detection with a low false alarm rate. In this paper, we propose the utilization of artificial neural networks for binary classification of targets detected by a depreciated detection process. It is shown that trained neural networks are capable of identifying false detections with considerable accuracy and can to this extent utilize information present in guard cells and Doppler profiles. This allows for a reduction in the false alarm rate with only moderate loss in the probability of detection. With an appropriately designed neural network, an overall improved system performance can be achieved when compared against traditional constant false alarm rate detectors for the specific trained scenarios.

Improving the Accuracy of an Artificial Neural Network Using Multiple Differently Trained Networks

1992 ◽  
Vol 4 (5) ◽  
pp. 772-780 ◽  
Author(s):  
William G. Baxt
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Artificial Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Rate Sensitivity ◽  
The Other ◽  
Risk Patients ◽  
Artificial Neural

When either detection rate (sensitivity) or false alarm rate (specificity) is optimized in an artificial neural network trained to identify myocardial infarction, the increase in the accuracy of one is always done at the expense of the accuracy of the other. To overcome this loss, two networks that were separately trained on populations of patients with different likelihoods of myocardial infarction were used in concert. One network was trained on clinical pattern sets derived from patients who had a low likelihood of myocardial infarction, while the other was trained on pattern sets derived from patients with a high likelihood of myocardial infarction. Unknown patterns were analyzed by both networks. If the output generated by the network trained on the low risk patients was below an empirically set threshold, this output was chosen as the diagnostic output. If the output was above that threshold, the output of the network trained on the high risk patients was used as the diagnostic output. The dual network correctly identified 39 of the 40 patients who had sustained a myocardial infarction and 301 of 306 patients who did not have a myocardial infarction for a detection rate (sensitivity) and false alarm rate (1-specificity) of 97.50 and 1.63%, respectively. A parallel control experiment using a single network but identical training information correctly identified 39 of 40 patients who had sustained a myocardial infarction and 287 of 306 patients who had not sustained a myocardial infarction (p = 0.003).

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