Tackling Worm Detection Speed and False Alarm in Virus Throttling

2006 ◽  
pp. 67-77
Author(s):  
Jangbok Kim ◽  
Jaehong Shim ◽  
Gihyun Jung ◽  
Kyunghee Choi
Keyword(s):  
False Alarm ◽  
Worm Detection ◽  
Detection Speed

scholarly journals Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2335 ◽  
Author(s):  
Yuelei Xu ◽  
Mingming Zhu ◽  
Peng Xin ◽  
Shuai Li ◽  
Min Qi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
False Alarm ◽  
Feature Fusion ◽  
Deep Layer ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Fully Convolutional Neural Networks ◽  
Aerial Remote Sensing ◽  
Detection Speed

To address the issues encountered when using traditional airplane detection methods, including the low accuracy rate, high false alarm rate, and low detection speed due to small object sizes in aerial remote sensing images, we propose a remote sensing image airplane detection method that uses multilayer feature fusion in fully convolutional neural networks. The shallow layer and deep layer features are fused at the same scale after sampling to overcome the problems of low dimensionality in the deep layer and the inadequate expression of small objects. The sizes of candidate regions are modified to fit the size of the actual airplanes in the remote sensing images. The fully connected layers are replaced with convolutional layers to reduce the network parameters and adapt to different input image sizes. The region proposal network shares convolutional layers with the detection network, which ensures high detection efficiency. The simulation results indicate that, when compared to typical airplane detection methods, the proposed method is more accurate and has a lower false alarm rate. Additionally, the detection speed is considerably faster and the method can accurately and rapidly complete airplane detection tasks in aerial remote sensing images.

scholarly journals Reducing False Alarm and Shortening Worm Detection Time in Virus Throttling

2005 ◽  
Vol 12C (6) ◽  
pp. 847-854
Author(s):  
Jae-Hong Shim ◽  
Jang-bok Kim ◽  
Hyung-Hee Choi ◽  
Gi-Hyun Jung
Keyword(s):  
False Alarm ◽  
Detection Time ◽  
Worm Detection

Hit and False-Alarm Rates of Selected ABR Indices in Differentiating Cochlear Disorders From Acoustic Tumors

1996 ◽  
Vol 5 (1) ◽  
pp. 90-96 ◽  
Author(s):  
Frank E. Musiek ◽  
Cynthia A. McCormick ◽  
Raymond M. Hurley
Keyword(s):  
False Alarm ◽  
Amplitude Ratio ◽  
High Specificity ◽  
Auditory Brainstem ◽  
Latency Difference ◽  
Cochlear Pathology ◽  
Brainstem Response ◽  
Absolute Latency ◽  
Two Indices ◽  
Low Sensitivity

We performed a retrospective study of 26 patients with acoustic tumors and 26 patients with otologically diagnosed cochlear pathology to determine the sensitivity (hit rate), specificity (false-alarm rate), and efficiency of six auditory brainstem response indices. In addition, a utility value was determined for each of these six indices. The I–V interwave interval, the interaural latency difference, and the absolute latency of wave V provided the highest hit rates, the best A’ values and good utility. The V/I amplitude ratio index provided high specificity but low sensitivity scores. In regard to sensitivity and specificity, using the combination of two indices provided little overall improvement over the best one-index measures.

The Fusion of False Alarm and False Hope

1985 ◽  
Vol 30 (3) ◽  
pp. 207-208
Author(s):  
Jeffrey Z. Rubin
Keyword(s):  
False Alarm

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

Chest pain in pregnancy: a false alarm

2019 ◽  
Vol 178 (3) ◽  
Author(s):  
Muhammad Azrai Abu ◽  
Kah T. Chew ◽  
Abdul G. Nur Azurah ◽  
Mohammad Shawal Faizal ◽  
Chai S. Ngiu ◽  
...  
Keyword(s):  
Chest Pain ◽  
False Alarm ◽  
In Pregnancy

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

2020 ◽  
Vol 38 (1B) ◽  
pp. 6-14
Author(s):  
ٍٍSarah M. Shareef ◽  
Soukaena H. Hashim
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Multinomial Logistic Regression ◽  
Network Intrusion Detection ◽  
Limiting Factor ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Normal Behavior

Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

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