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A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

The purpose of this paper is to apply signal detection theory (SDT) to the problem of detecting management fraud. The use of SDT methodology significantly strengthens understanding of the relationships among audit technology, base rates of management fraud, costs of Type I and Type II errors, extensions of audit procedures, and risk assessments prior and during the audit. The analysis suggests that the auditor must accept disproportionate false alarm rates in order to maintain audit effectiveness in the presence of management fraud. This condition becomes even stronger as the costs of Type II errors increase compared to costs of Type I errors. The study also provides policy implications for auditor practice and standard‐setters.

The purpose of this paper is to explore and analyze the effectiveness of long period supplementary zone rules that can simultaneously increase chart sensitivity to small process drift and not significantly increase the false alarm rate.

A stable, on‐target process was simulated and drift induced into the process. The rates of drift varied from 0.03σ to .0003σ per subgroup measurement. A total of 613 different supplementary zone rules were implemented in conjunction with the three‐sigma limiting rule. For each combination, 100,000 observations were simulated and the effect on the false alarm rate and increase in chart sensitivity estimated. An effectiveness measure was developed to relate false alarm rate to chart sensitivity.

A total of 87 rules were uncovered which effectively detected a wide range of process drifts. When the increase in chart sensitivity is discounted by the false alarm rate, 13 rules increased chart sensitivity by over 10 percent. These rules were based on longer rather than shorter rule length.

The effective rules discovered form a nonlinear pattern in the space the examined rules define. This indicates a direction for future research outside the scope of this study. These rules are also easy to implement in existing Shewhart chart applications where the process drifts at an unknown rate.

While supplementary trend rules have been studied in the past, the extension to zone rules has not been made. This study begins to fill that void and indicates the direction for future efforts in the area.

The purpose of this paper is to assess whether firefighters display different decision‐making biases: either a liberal bias to accepting information as true or a conservative bias to rejecting information, with the former carrying risk of “false alarm” errors and the latter of “misses”.

Situation awareness (SA) and decision‐making biases were examined in Fire and Rescue (FRS) “table‐top” and Breathing Apparatus (BA) training exercises. The former involved showing 50 operational FRS personnel a powerpoint presentation representing the drive‐to, views and information related to the incident. The BA study involved 16 operational FRS personnel entering a smoke‐filled training building in a search‐and‐rescue exercise. True/False answers to statements about the incidents were analysed by a signal‐detection‐type tool (QASA) to give measures of SA and bias.

In both studies, there were two groups showing different bias patterns (either conservative with risk of “miss” errors, or liberal with risk of “false alarms”) (p≤0.001), but not different SA (p>0.05).

Future work will involve more realistic training exercises and explore the consistency of individual bias tendencies over different contexts.

Risk in fireground decision making may be minimised by increasing awareness of individual tendencies to either conservative or liberal bias patterns and the associated risk of respectively making “miss” or “false alarm” errors.

The results may help to minimise fireground risk.

This is the first evidence to show firefighter decision bias in two different exercises.

The expertise of a system administrator is believed to be important for effective use of intrusion detection systems (IDS). This paper examines two hypotheses concerning the system administrators' ability to filter alarms produced by an IDS by comparing the performance of an IDS to the performance of a system administrator using the IDS.

An experiment was constructed where five computer networks are attacked during four days. The experiment assessed difference made between the output of a system administrator using an IDS and the output of the IDS alone. The administrator's analysis process was also investigated through interviews.

The experiment shows that the system administrator analysing the output from the IDS significantly improves the portion of alarms corresponding to attacks, without decreasing the probability that an attack is detected significantly. In addition, an analysis is made of the types of expertise that is used when output from the IDS is processed by the administrator.

Previous work, based on interviews with system administrators, has suggested that competent system administrators are important in order to achieve effective IDS solutions. This paper presents a quantitative test of the value system administrators add to the intrusion detection solution.

This paper aims to propose a multi-dimensional hierarchical K-means clustering algorithm for the purpose of intrusion detection. Initially, the clustering set of rules is proposed to shape some of clusters in the network and then the most beneficial clusters are decided on by the use of Cuckoo search optimization set of rules. Finally, an Artificial Bee Colony primarily based selection tree (ABC-DT) classifier is rented to classify the regular and unusual instances present in the network with the aid of the extracted features.

Intrusion detection system (IDS) is crucial for the network system; the intruder can take sensitive details about the network. IDS are said to be more effective when it has both high intrusion detection rate and low false alarm rate. Numerous strategies including gadget mastering, records mining and statistical techniques were tested for IDS mission. Recent study reveals that combining multiple classifiers, i.e. classifiers ensemble, can also own better performance than unmarried classifier. In this paper, a comparative study is conducted of the overall performance of four classifiers, i.e. hybrid ABC-DT particle swarm optimization-based K-means clustering (PSO-KM), help vector device (SVM) and K-Nearest neighbour (KNN). All the four classifiers are tested with exceptional packet sizes 1470, 1024, 512 and 256. The experiment is carried out for the speed ranging from turned into done for the velocity ranging from 250Mbps, 500Mbps, 750Mbps, 1.0Gpbs, 1.5Gbps, and 2.0Gbps in terms of accuracy, detection charge, specificity, false alarm charge and computational time. The experimental results reveals that the hybridization of classifiers performs better than the base classifiers in all scenarios.

This study analyses the performance of hybrid ABC-DT classifier and compares the performance against three well-known classifiers such as PSO-KM, SVM and K-NN. The performances of all the four classifiers are tested with Discovery in Data Mining (KDD) CUP 99 dataset with different packet sizes 1470, 1024, 512 and 256. The results show the classifier performance variations with different speed ranges. From the experimental results and analysis, the hybridization of classifiers such as ABC-DT outperforms the base classifiers in all scenarios.

The novel approach in this paper is used to study the hybrid ABC-DT classifier and compare the performance against three well-known classifiers such as PSO-KM, SVM and K-NN. The discussed concept is used within the network to monitor the traffic to and from all the devices connected in that network.

The main purpose of this chapter is to estimate a model for hedge fund returns that will endogenously generate failure probabilities using panel data where sample attrition due to fund failures is a dominant feature. We use the Lipper (TASS) hedge fund database, which includes all live and defunct hedge funds over the period January 1994 through March 2009, to estimate failure probabilities for hedge funds. Our results show that hedge fund failure prediction can be substantially improved by accounting for selectivity bias caused by censoring in the sample. After controlling for failure risk, we find that capital flow, lockup period, redemption notice period, and fund age are significant factors in explaining hedge fund returns. We also show that for an average hedge fund, failure risk increases substantially with age. Surprisingly, a 5-year-old fund on average has only a 65% survival rate.

Cognitive radio (CR) plays a very important role in enabling spectral efficiency in wireless communication networks, where the secondary user (SU) allows the licensed primary users (PUs). The purpose of this paper is to develop a prediction model for spectrum sensing in CR.

This paper proposes a hybrid prediction model, called krill-herd whale optimization-based actor critic neural network and hidden Markov model (KHWO-ACNN-HMM). The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs. For better sensing, the eigenvalue based on cooperative sensing used in CR. Finally, a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing. The predicted results of KHWO-ACNN and HMM are combined by a fusion model, for which a weighted entropy fusion is employed to determine the free spectrum available in CRs.

The performance of the prediction model is evaluated based on metrics, such as probability of detection, probability of false alarm, throughput and sensing time. The proposed spectrum sensing method achieves maximum probability of detection of 0.9696, minimum probability of false alarm rate as 0.78, minimum throughput of 0.0303 and the maximum sensing time of 650.08 s.

The proposed method is useful in various applications, including authentication applications, wireless medical networks and so on.

A hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models. The proposed hybrid model outperformed the other techniques.

An interactive processing scheme is proposed to improve the target detection probability as well as the tracking performance of the radar system.

Firstly, with the spatial-correlated features extracted from the foreground and background statistical models, the thresholds were adapted to distinguish the dim small targets from clutters in the complex incoherent radar images. Then, the target trajectories were constructed with the target tracking algorithm. According to the temporal correlation with the target life cycle, the thresholding values were modified in the neighbourhood of the predicted positions to improve the detection sensitivity in these areas during the tracking process. Finally, the temporal-correlated features of the remained clutters were used to further reduce the false alarm rate.

The proposed algorithm was applied on the simulated data, as well as the image sequences obtained with the incoherent marine radars. The detection results demonstrated that the interactive algorithm could detect and track the dim small targets with relatively low false alarm rate.

The interactive processing scheme could be applied for low-altitude airspace surveillance with incoherent marine radar.

The proposed scheme outperforms the classical radar target detection algorithms and the state-of-the-art image processing algorithms for video-based surveillance.

The purpose of this paper is to examine existing radar sensor results, techniques for through‐wall radar and current applications for the technology.

The paper provides information on sensing through a high‐attenuation obstacle and the associated pitfalls and considerations. Results from ultra‐wide‐band (UWB) impulse radar, micro‐Doppler radar, and synthetic aperture radar (SAR) targeted at this area are presented. Discussion of radar clutter classification is given and also observations on presenting a system with a non‐zero false alarm rate to a user to give best confidence and maximum decision capability.

There are significant new requirements for through‐wall radar which a combination of UWB, continuous wave, and SAR techniques with recent signal processing advances and the advent of low‐cost radio and image processing can meet in distributed markets. Risk of a poor user level decision in a non‐zero‐false‐alarm‐rate system can be mitigated by increasing the number of inputs into the decision.

The paper lists challenges that have been overcome in the area of through‐wall sensing and presents results from novel radar sensors.