Effects of false-alarm vs. miss-prone automation and likelihood alarm technology on trust, reliance, and compliance in a miss-prone task

2010 ◽  
Author(s):  
Randy B. Davenport ◽  
Ernesto A. Bustamante
Keyword(s):  
False Alarm

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.

Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents

1998 ◽  
Vol 1634 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Sherif S. Ishak ◽  
Haitham M. Al-Deek
Keyword(s):  
Neural Networks ◽  
False Alarm ◽  
False Alarm Rate ◽  
Back Propagation ◽  
Incident Detection ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Detection Algorithms ◽  
Fuzzy Art ◽  
Freeway Incidents

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

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