Probability Of Correct Decision Research Articles

Goodness-of-fit tests for multinomial models with inverse sampling

This article proposes goodness-of-fit tests for multinomial models using an inverse sampling scheme. From the multiple decision-theoretic perspective, we devise a test statistic and stopping rule that satisfy a prespecified probability level P* and obtain corresponding optimal sample sizes. Incomplete Dirichlet type II distribution functions are used to develop the procedure and to express the probability of correct decisions for various cell configurations for multinomial models. For empirical studies, Monte Carlo experiments are conducted, and for illustrations, various cell configurations of a wheel of fortune are demonstrated.

Finding Feasible Systems for Subjective Constraints Using Recycled Observations

We consider the problem of finding a set of feasible or near-feasible systems among a finite number of simulated systems in the presence of stochastic constraints. When the constraints are subjective, a decision maker may want to test multiple threshold values for the constraints. Or the decision maker may simply want to determine how a set of feasible systems changes as constraints become more strict with the objective of pruning systems or finding the system with the best performance. When only the constraint thresholds change for the same set of underlying systems, it is natural to reuse observations collected from the feasibility check with a different threshold value. We present an indifference-zone procedure that recycles observations and provide an overall probability of correct decision for all threshold values. Our numerical experiments show that the proposed procedure performs well in reducing the required number of observations while providing a statistical guarantee on the probability of correct decision.Summary of Contribution: We consider the problem of determining the feasibility of a finite number of systems in the presence of subjective constraints on performance measures that can be estimated only by stochastic simulation. Specifically, our work focuses on the situation where the decision maker is willing to relax some constraint thresholds if necessary to achieve feasibility. Also, we discuss how our proposed procedures can help select the best system in the presence of multiple objectives. This is the first work that considers subjective constraints in the field of ranking and selection in simulation and it provides a practically useful decision-making tool with more flexibility on feasibility determination.History: Accepted by Bruno Tuffin, Area Editor for Simulation.Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2022.1227 .

Prognosis patients with COVID-19 using deep learning

BackgroundThe coronavirus (COVID-19) is a novel pandemic and recently we do not have enough knowledge about the virus behaviour and key performance indicators (KPIs) to assess the mortality risk forecast. However, using a lot of complex and expensive biomarkers could be impossible for many low budget hospitals. Timely identification of the risk of mortality of COVID-19 patients (RMCPs) is essential to improve hospitals' management systems and resource allocation standards.MethodsFor the mortality risk prediction, this research work proposes a COVID-19 mortality risk calculator based on a deep learning (DL) model and based on a dataset provided by the HM Hospitals Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. To evaluate the proposed methods, an over-sampling Synthetic Minority TEchnique (SMOTE) and data imputation approaches are introduced which is based on the K-nearest neighbour.ResultsA total of 1,503 seriously ill COVID-19 patients having a median age of 70 years old are comprised in the research work, with 927 (61.7%) males and 576 (38.3%) females. A total of 48 features are considered to evaluate the proposed method, and the following results are achieved. It includes the following values i.e., area under the curve (AUC) 0.93, F2 score 0.93, recall 1.00, accuracy, 0.95, precision 0.91, specificity 0.9279 and maximum probability of correct decision (MPCD) 0.93.ConclusionThe results show that the proposed method is significantly best for the mortality risk prediction of patients with COVID-19 infection. The MPCD score shows that the proposed DL outperforms on every dataset when evaluating even with an over-sampling technique. The benefits of the data imputation algorithm for unavailable biomarker data are also evaluated. Based on the results, the proposed scheme could be an appropriate tool for critically ill Covid-19 patients to assess the risk of mortality and prognosis.

RAN energy efficiency and failure rate through ANN traffic predictions processing

In this paper, we focus on the application of ML tools to resource management in a portion of a Radio Access Network (RAN) and, in particular, to Base Station (BS) activation and deactivation, aiming at reducing energy consumption while providing enough capacity to satisfy the variable traffic demand generated by end users. In order to properly decide on BS (de)activation, traffic predictions are needed, and Artificial Neural Networks (ANN) are used for this purpose. Since critical BS (de)activation decisions are not taken in proximity of minima and maxima of the traffic patterns, high accuracy in the traffic estimation is not required at those times, but only close to the times when a decision is taken. This calls for careful processing of the ANN traffic predictions to increase the probability of correct decision. Numerical performance results in terms of energy saving and traffic lost due to incorrect BS deactivations are obtained by simulating algorithms for traffic predictions processing, using real traffic as input. Results suggest that good performance trade-offs can be achieved even in presence of non-negligible traffic prediction errors, if these forecasts are properly processed. The impact of forecast processing for dynamic resource allocation on the BS failure rate is also investigated. Results reveal that conservative approaches better prevent BSs from hardware failure. Nevertheless, the deployment of newer devices, designed for fast dynamic networks, allows the adoption of approaches which frequently activate and deactivate BSs, thus achieving higher energy saving.

Testing for the equivalence of several sets of time series and its multiple comparison procedure

We propose test statistics for testing for the equivalence of the mean functions with respect to time for two sets of time series and that for several sets of time series. We also propose multiple comparison procedures for testing for the equivalence of the mean functions with respect to time under several sets of time series. Multiple comparison procedure 1 was compared with some of the previously studied methods by simulation and proposed method 1 gave 1 for empirical power and almost 1 for empirical probability of correct decision when the standard deviation of pseudo normal random numbers was small whereas the other methods gave almost 0 for both empirical power and empirical probability of correct decision whatever the standard deviation of pseudo normal random numbers. On the proposed method for testing for the equivalence of all the mean functions for several sets of time series against that at least one of the mean functions is different from the others, we found that empirical powers of the proposed method were almost 1 when the standard deviation of pseudo normal random numbers was small by simulation.

A novel integrated framework for fault diagnosis with application to process safety

A novel integrated framework for Fault Detection and Isolation(FDI) is proposed, with applications to process safety, by sequentially integrating model-free(MFA) and model-based(MBA) approaches. The MFA includes Limit Checking/Visual and Plausibility analysis, Artificial Neural Network and Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System. The MBA uses a Linear Parameter-Varying model to handle a wide class of generally-nonlinear physical systems. The adaptive Kalman filter(KF) residuals are used for FDI in the MBA. Novel emulators, cascaded with the system during off-line data acquisition, system identification and Bayes’measure of belief computation for each FDI scheme, have their parameters perturbed at each operating point, to mimic unforeseen operational scenarios, thus covering all operating regions. Critical information about the presence/absence of a fault is quickly gained via the faster FDI scheme. A more accurate subsystem’s status is unfolded sequentially by the slower FDI scheme. The final decision on the fault status is obtained using a weighted Bayes classifier fusion scheme meeting the critical requirements of high(low) probability of correct decision (false alarm). Implications of FDI in process safety/environment protection are discussed. This framework is successfully evaluated on simulated and physical systems, including benchmarked laboratory-scale two-tank system, by detecting and isolating sensor, actuator and leakage faults.

Research on Manhattan Distance Based Trust Management in Vehicular Ad Hoc Network

In recent years, Vehicular Ad Hoc Network (VANET) has developed significantly. Coordination between vehicles can enhance driving safety and improve traffic efficiency. Due to the high dynamic characteristic of VANET, security has become one of the challenging problems. Trust of the message is a key element of security in VANET. This paper proposes a Manhattan Distance Based Trust Management model (MDBTM) in VANET environment which solves the problem in existing trust management research that considers the distance between the sending vehicle and event location. In this model, the Manhattan distance and the number of building obstacles are calculated by considering the movement relationship between the sending vehicle and event location. The Dijkstra algorithm is used to predict the path with the maximum probability, when the vehicle is driving toward the event location. The message scores are then calculated based on the Manhattan distance and the number of building obstacles. Finally, the scores are fused to determine whether to trust the message. The experimental results show that the proposed method has better performance than similar methods in terms of correct decision probability under different proportions of malicious vehicles, different numbers of vehicles, and different reference ranges.

Presence-Absence Sampling Plans for Stink Bugs (Hemiptera: Pentatomidae) in the Midwest Region of the United States.

Stink bugs represent an increasing risk to soybean production in the Midwest region of the United States. The current sampling protocol for stink bugs in this region is tailored for population density estimation and thus is more relevant to research purposes. A practical decision-making framework with more efficient sampling effort for management of herbivorous stink bugs is needed. Therefore, a binomial sequential sampling plan was developed for herbivorous stink bugs in the Midwest region. A total of 146 soybean fields were sampled across 11 states using sweep nets in 2016, 2017, and 2018. The binomial sequential sampling plans were developed using combinations of five tally thresholds at two proportion infested action thresholds to identify those that provided the best sampling outcomes. Final assessment of the operating characteristic curves for each plan indicated that a tally threshold of 3 stink bugs per 25 sweeps, and proportion infested action thresholds of 0.75 and 0.95 corresponding to the action thresholds of 5 and 10 stink bugs per 25 sweeps, provided the optimal balance between highest probability of correct decisions (≥ 99%) and lowest probability of incorrect decisions (≤ 1%). In addition, the average sample size for both plans (18 and 12 sets of 25 sweeps, respectively) was lower than that for the other proposed plans. The binomial sequential sampling plan can reduce the number of sample units required to achieve a management decision, which is important because it can potentially reduce risk/cost of management for stink bugs in soybean in this region.

Recall accuracy of notifications about incidental findings from an MRI examination: results from a population-based study

BackgroundSelf-reports of medical findings are indispensable in clinical practice and research but subject to recall bias. We analysed the recall accuracy of notifications about incidental findings (IFs) from a whole-body...

Optimal Stopping of Adaptive Dose-Finding Trials

The primary objective of this paper is to develop computationally efficient methods for optimal stopping of an adaptive Phase II dose-finding clinical trial, where the decision maker may terminate the trial for efficacy or abandon it as a result of futility. We develop two solution methods and compare them in terms of computational time and several performance metrics such as the probability of correct stopping decision. One proposed method is an application of the one-step look-ahead policy to this problem. The second proposal builds a diffusion approximation to the state variable in the continuous regime and approximates the trial’s stopping time by optimal stopping of a diffusion process. The secondary objective of the paper is to compare these methods on different dose-response curves, particularly when the true dose-response curve has no significant advantage over a placebo. Our results, which include a real clinical trial case study, show that look-ahead policies perform poorly in terms of the probability of correct decision in this setting, whereas our diffusion approximation method provides robust solutions.

PHYSICAL LAYER SECURITY FOR WIRELESS NETWORKS BASED ON COSET CONVOLUTIONAL CODING

This paper presents a new physical layer security for wireless networks using non-linear convolutional cryptosystem. Relevant performance metrics such as secret channel capacity and throughput are considered in the implementation. Secret channel capacity is implemented using confusion bits generated from coset convolutional coding while throughput is enhanced due to the forward error correction capability of convolutional codes. The paper establishes a method to determine wireless channel parameters for secure communication.It is shown that, the probability of correct decision of an eavesdropper is zero when appropriate values of M-PAM constellations, the number of transmitted bits, k and the signal-to-noise ratio (SNR) per bit in dB are chosen. In addition, it is shown that, the convolutional cryptosystem enhances security for the case where the eavesdropper probability of correct decision is not zero. The entire scheme applied to CDMA is ported to a Virtex 5 FPGA chip to circumvent poor key management due to additional keys used in the convolutional cryptosystem.

Optimal Stochastic Signaling Under Average Power and Bit Rate Constraints

The optimal stochastic signaling based on the joint design of prior distribution and signal constellation is investigated under an average bit rate and power constraints. First, an optimization problem is formulated to maximize the average probability of correct decision over the set of joint distribution functions for prior probabilities and the corresponding constellation symbols. Next, an alternative problem formulation, for which the optimal joint distribution is characterized by a randomization among at most three mass points, is provided, and it is shown that both formulations share the same solution. Three special cases of the problem are investigated in detail. First, in the absence of randomization, the optimal prior probability distribution is analyzed for a given signal constellation and a closed-form solution is provided. Second, the optimal deterministic pair of prior probabilities and the corresponding signal levels are considered. Third, a binary communication system with scalar observations is investigated in the presence of a zero-mean additive white Gaussian noise, and the optimal solution is obtained under practical assumptions. Finally, numerical examples are presented to illustrate the theoretical results. It is observed that the proposed approach can provide improvements in terms of average symbol error rate over the classical scheme for certain scenarios.

Ant Colony Technique for Optimizing the Order of Cascaded SVM Classifier for Sunflower Seed Classification

In Seed Testing Laboratory (STL), test for other distinguishable variety (ODV) is carried out for the seed sample received from the seed producer after harvesting. This is done to obtain foundation/certified tag from the seed certification department before marketing the product. This is currently done manually by observing the morphological characteristics of the seeds through the naked eye. This is a time-consuming process for the STL and there is a chance for human error in identifying ODV. Hence, there is the need for the machine vision technique to automate the process of identifying ODV in the seed testing laboratory. Various techniques for varietal identification of sunflower seeds are explored in this paper. Experiments are performed on a dataset that contains ten varieties of sunflower seeds and the success rate achieved using various techniques is reported in this paper. In cascaded support vector machine (SVM), the order in which the classifier blocks are arranged plays an important role in improving the classification rate. The main contribution of this paper lies in the manipulation of ant colony optimization technique for obtaining the order of cascaded SVM by maximizing the total probability of correct decision. When the SVM is cascaded in optimum order, classification rate has been increased from $88.32\%$ (obtained using the actual order) to $98.82\%$ for kernel linear discriminant analysis based boundary descriptors. The closed form expression for computing the total probability of correct detection of the constructed cascaded SVM classifier is also reported in this paper.

Evaluating persistence of shape information using a matching protocol.

Many laboratories have studied persistence of shape information, the goal being to better understand how the visual system mediates recognition of objects. Most have asked for recognition of known shapes, e.g., letters of the alphabet, or recall from an array. Recognition of known shapes requires access to long-term memory, so it is not possible to know whether the experiment is assessing short-term encoding and working memory mechanisms, or has encountered limitations on retrieval from memory stores. Here we have used an inventory of unknown shapes, wherein a string of discrete dots forms the boundary of each shape. Each was displayed as a target only once to a given respondent, with recognition being tested using a matching task. Analysis based on signal detection theory was used to provide an unbiased estimate of the probability of correct decisions about whether comparison shapes matched target shapes. Four experiments were conducted, which found the following: a) Shapes were identified with a high probability of being correct with dot densities ranging from 20% to 4%. Performance dropped only about 10% across this density range. b) Shape identification levels remained very high with up to 500 milliseconds of target and comparison shape separation. c) With one-at-a-time display of target dots, varying the total time for a given display, the proportion of correct decisions dropped only about 10% even with a total display time of 500 milliseconds. d) With display of two complementary target subsets, also varying the total time of each display, there was a dramatic decline of proportion correct that reached chance levels by 500 milliseconds. The greater rate of decline for the two-pulse condition may be due to a mechanism that registers when the number of dots is sufficient to create a shape summary. Once a summary is produced, the temporal window that allows shape information to be added may be more limited.

Process-monitoring-for-quality — A model selection criterion

The new big data driven manufacturing quality philosophy, Process Monitoring for Quality (PMQ), proposes Big Data — Big Models, a new modeling paradigm that includes a big data-driven learning process that requires many models to be created to find the final one. Since many candidates are created, one of the main challenges is to select the model that efficiently solves the tradeoff between complexity and prediction. Most mature manufacturing organizations generate only a few Defects Per Million of Opportunities (DPMO); therefore, manufacturing-derived data sets for classification of quality tend to be highly unbalanced. The Penalized Maximum Probability of Correct Decision (PMPCD) is developed to solve the posed tradeoff. According to simulation and experimental results, the model selection criterion induces parsimony by selecting the model with the minimum number of features needed for an effective/efficient defect detection.

A Cooperative Wireless Sensor Network for Indoor Industrial Monitoring

Industrial wireless sensor networks are getting popular for indoor monitoring of heavy machinery and large factories to make a reliable decision on the state of machines in a certain area of interest. However, the indoor wireless communication channel is not always reliable, and observations of some sensors cannot be reported successfully to the base station. In order to deal with this problem, we propose a cooperative WSN scheme by introducing a novel cooperation mechanism and a medium access control protocol. The proposed scheme effectively increases the probability of correct decision about the state of the machine, reduces the probability of false alarms at a given signal level, and reduces the overall energy consumption as compared to noncooperative schemes. We also present a closed-form expression for the symbol-error rate analysis of the proposed scheme, which shows that our proposed scheme achieves full diversity order offered by the cooperation scheme.

A multistage approach to decision fusion using a distributed network of non-identical nodes

A distributed sensor network deployed to detect a binary event using a hard decision fusion scheme is considered. Earlier works show that the K-out-of-N counting rule is optimum in minimizing the total error rate when the sensor nodes participating in the event detection have identical performance indexes (detection and false-alarm probability pairs). In most scenarios, this optimum rule turns out to be the half-voting or majority rule. However, when the sensor nodes have non-identical performance indexes, the optimum value of K that gives the maximum correct decision probability may be anything from 1 to N and no general expression for it is available. Trying all possibilities before choosing the best value of K is computationally very demanding and the complexity grows rapidly as the number of sensor nodes increases, making it an impractical approach for a large sensor network. We propose a multistage decision fusion scheme for such networks using unanimity fusion rules and show that it gives an improved performance over unanimity, half-voting, majority, and some soft fusion rules and a comparable performance to the best K-out-of-N fusion rule in many cases with considerably reduced computational complexity in determining the global detection and false-alarm probabilities.

Optimum Access Analysis of Collaborative Spectrum Sensing in Cognitive Radio Network using MRC

The performance of cognitive radio network mainly depends on the finest sensing of the presence or absence of Primary User (PU). The throughput of a Secondary User (SU) can be reduced because of the false detection of PU which causes an SU from its transmission opportunity. The factorization of the probability of correct decision is a really hard job when the special false alarm is incorporated into it. Previous works focus on collaborative sensing on the normal environment. In this paper, we have proposed a collaborative sensing method in Cognitive radio network for optimal access of PU licensed band by SU. It is shown performance analysis of energy detection through different cognitive users and conducts a clear comparison between local and collaborative sensing. In this paper, the maximal ratio combining diversity technique with energy detection has been employed to reduce the false alarm probability in the collaborative environment. The simulation result shows significant reduction of the probability of misdetection with increasing in the number of collaborative users. We also analyze that MRC scheme exhibits the best detection performance in collaborative environment.

A novel multistage decision fusion for cognitive sensor networks using AND and OR rules

We propose a centralized radix-2 multistage decision fusion strategy comprising simple AND and OR rules for cooperative spectrum sensing in cognitive sensor networks. Earlier works on centralized decision fusion show the half-voting and majority rules to be optimum in many spectrum sensing scenarios in terms of minimizing the decision error (or equivalently maximizing the probability of correct decision). We consider a commonly occurring case in spectrum sensing in which the detection probability of a cognitive radio enabled sensor node is greater than its false-alarm probability. For this case, we consider five scenarios and demonstrate that the proposed method either performs better than half-voting and majority rules or exhibits a comparable performance. In this context, we also establish a criterion to make a choice between the AND and OR rules and compute the optimum number of nodes participating in cooperative spectrum sensing for these rules to maximize the correct decision probability.

ERDT: Energy-Efficient Reliable Decision Transmission for Intelligent Cooperative Spectrum Sensing in Industrial IoT

Due to harsh environment, large number of sensors, limited energy, and spectrum scarcity, intelligent sensing becomes a key issue to enable many practical applications in industrial Internet of Things (IoT). In such an industrial environment with noise and interference, an efficient cooperative spectrum sensing (CSS) scheme can achieve spectrum sharing between primary users (PUs) and secondary users (SUs), and effectively solve the spectrum scarcity and reduce energy consumption to make the IoT smarter. As a vital part of CSS, decision transmission (DT) between SUs and fusion center (FC) plays a crucial role. In traditional DT, each SU will transmit its local decision to FC with orthogonal channel in each sensing, which does not consider the packet error and packet loss due to noise during transmission, and aggravates spectrum scarcity and energy consumption. An energy-efficient reliable DT (ERDT) scheme is proposed to enhance CSS in industrial IoT, which considers both packet error and packet loss. First, the CSS mathematical model based on DT is formulated. Second, with rigorous mathematical deduction, the correct decision probability and the energy consumption are analyzed for both ERDT and DT based on logic OR-rule and AND-rule under three cases, respectively: 1) bit error only; 2) packet loss only; and 3) both bit error and packet loss. Detailed simulation results show that, compared with DT, the proposed ERDT can increase correct decision probability and reduce energy consumption for CSS under three different cases. When the existence probability of PU is 50%, the energy consumption of ERDT is only half of that of DT in CSS. Furthermore, when there are 30 SUs in CSS, the existence probability of PU is 50%, both pocket loss rate and bit error rate are 0.05, and the correct decision probability of ERDT is approaching to 1 for CSS in industrial IoT.