Iterated random multiplications and invariance under randomly weighted averaging

Purpose Low residual noise (RN) levels are critically important when obtaining electrophysiological recordings of threshold auditory brainstem responses. In this study, we examine the effectiveness and efficiency of Kalman-weighted averaging (KWA) implemented on the Vivosonic Integrity System and artifact rejection (AR) implemented on the Intelligent Hearing Systems SmartEP system for obtaining low RN levels. Method Sixteen adults participated. Electrophysiological measures were obtained using simultaneous recordings by the Vivosonic and Intelligent Hearing Systems for subjects in 2 relaxed conditions and 4 active motor conditions. Three averaging times were used for the relaxed states (1, 1.5, and 3 min) and for the active states (1.5, 3, and 6 min). Repeated-measures analyses of variance were used to examine RN levels as a function of noise reduction strategy (i.e., KWA, AR) and averaging time. Results Lower RN levels were obtained using KWA than AR in both the relaxed and active motor states. Thus, KWA was more effective than was AR under the conditions examined in this study. Using KWA, approximately 3 min of averaging was needed in the relaxed condition to obtain an average RN level of 0.025 μV. In contrast, in the active motor conditions, approximately 6 min of averaging was required using KWA. Mean RN levels of 0.025 μV were not attained using AR. Conclusions When patients are not physiologically quiet, low RN levels are more likely to be obtained and more efficiently obtained using KWA than AR. However, even when using KWA, in active motor states, 6 min of averaging or more may be required to obtain threshold responses. Averaging time needed and whether a low RN level can be attained will depend on the level of motor activity exhibited by the patient.

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Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽

10.3390/electronics10101195 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1195

Author(s):

Priya Varshini A G ◽

Anitha Kumari K ◽

Vijayakumar Varadarajan

Keyword(s):

Deep Learning ◽

Random Forest ◽

Software Development ◽

Weighted Averaging ◽

Software Project ◽

Effort Estimation ◽

Software Effort Estimation ◽

Single Model ◽

Ensemble Techniques ◽

Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

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Radiator threat evaluation with missing data based on improved technique for order preference by similarity to ideal solution

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202245 ◽

2021 ◽

pp. 1-10

Author(s):

Yu-Heng Xu ◽

Si-Yi Cheng ◽

Hu-Biao Zhang

Keyword(s):

Missing Data ◽

Prior Knowledge ◽

Attribute Reduction ◽

Estimation Algorithm ◽

Weighted Averaging ◽

Ideal Solution ◽

Attribute Weights ◽

Order Preference ◽

Improved Technique ◽

Threat Evaluation

To solve the problem of the missing data of radiator during the aerial war, and to address the problem that traditional algorithms rely on prior knowledge and specialized systems too much, an algorithm for radiator threat evaluation with missing data based on improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been proposed. The null estimation algorithm based on Induced Ordered Weighted Averaging (IOWA) is adopted to calculate the aggregate value for predicting missing data. The attribute reduction is realized by using the Rough Sets (RS) theory, and the attribute weights are reasonably allocated with the theory of Shapley. Threat degrees can be achieved through quantization and ranking of radiators by constructing a TOPSIS decision space. Experiment results show that this algorithm can solve the incompleteness of radiator threat evaluation, and the ranking result is in line with the actual situation. Moreover, the proposed algorithm is highly automated and does not rely on prior knowledge and expert systems.

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Approach to Multi-Attribute Decision-Making Methods for Performance Evaluation Process Using Interval-Valued T-Spherical Fuzzy Hamacher Aggregation Information

Axioms ◽

10.3390/axioms10030145 ◽

2021 ◽

Vol 10 (3) ◽

pp. 145

Author(s):

Yun Jin ◽

Zareena Kousar ◽

Kifayat Ullah ◽

Tahir Mahmood ◽

Nimet Yapici Pehlivan ◽

...

Keyword(s):

Decision Making ◽

Fuzzy Set ◽

Evaluation Process ◽

Aggregation Operators ◽

Weighted Averaging ◽

Membership Degree ◽

Operational Laws ◽

Multi Attribute Decision Making ◽

Interval Valued ◽

Do So

Interval-valued T-spherical fuzzy set (IVTSFS) handles uncertain and vague information by discussing their membership degree (MD), abstinence degree (AD), non-membership degree (NMD), and refusal degree (RD). MD, AD, NMD, and RD are defined in terms of closed subintervals of that reduce information loss compared to the T-spherical fuzzy set (TSFS), which takes crisp values from intervals; hence, some information may be lost. The purpose of this manuscript is to develop some Hamacher aggregation operators (HAOs) in the environment of IVTSFSs. To do so, some Hamacher operational laws based on Hamacher t-norms (HTNs) and Hamacher t-conorms (HTCNs) are introduced. Using Hamacher operational laws, we develop some aggregation operators (AOs), including an interval-valued T-spherical fuzzy Hamacher (IVTSFH) weighted averaging (IVTSFHWA) operator, an IVTSFH-ordered weighted averaging (IVTSFHOWA) operator, an IVTSFH hybrid averaging (IVTSFHHA) operator, an IVTSFH-weighted geometric (IVTSFHWG) operator, an IVTSFH-ordered weighted geometric (IVTSFHOWG) operator, and an IVTSFH hybrid geometric (IVTSFHHG) operator. The validation of the newly developed HAOs is investigated, and their basic properties are examined. In view of some restrictions, the generalization and proposed HAOs are shown, and a multi-attribute decision-making (MADM) procedure is explored based on the HAOs, which are further exemplified. Finally, a comparative analysis of the proposed work is also discussed with previous literature to show the superiority of our work.

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A Note on Universal Bilinear Portfolios

International Journal of Financial Studies ◽

10.3390/ijfs9010011 ◽

2021 ◽

Vol 9 (1) ◽

pp. 11

Author(s):

Alex Garivaltis

Keyword(s):

Growth Factor ◽

Trading Strategy ◽

Weighted Averaging ◽

Asset Market ◽

Best Constant ◽

Capital Growth ◽

Universal Portfolios ◽

Universal Portfolio ◽

Technical Sense ◽

Constant Rebalanced Portfolios

This note provides a neat and enjoyable expansion and application of the magnificent Ordentlich-Cover theory of “universal portfolios”. I generalize Cover’s benchmark of the best constant-rebalanced portfolio (or 1-linear trading strategy) in hindsight by considering the best bilinear trading strategy determined in hindsight for the realized sequence of asset prices. A bilinear trading strategy is a mini two-period active strategy whose final capital growth factor is linear separately in each period’s gross return vector for the asset market. I apply Thomas Cover’s ingenious performance-weighted averaging technique to construct a universal bilinear portfolio that is guaranteed (uniformly for all possible market behavior) to compound its money at the same asymptotic rate as the best bilinear trading strategy in hindsight. Thus, the universal bilinear portfolio asymptotically dominates the original (1-linear) universal portfolio in the same technical sense that Cover’s universal portfolios asymptotically dominate all constant-rebalanced portfolios and all buy-and-hold strategies. In fact, like so many Russian dolls, one can get carried away and use these ideas to construct an endless hierarchy of ever more dominant H-linear universal portfolios.

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Extrapolating Parametric Survival Models in Health Technology Assessment Using Model Averaging: A Simulation Study

Medical Decision Making ◽

10.1177/0272989x21992297 ◽

2021 ◽

Vol 41 (4) ◽

pp. 476-484

Author(s):

Daniel Gallacher ◽

Peter Kimani ◽

Nigel Stallard

Keyword(s):

Survival Time ◽

Survival Data ◽

Mean Squared Error ◽

Model Averaging ◽

Information Criterion ◽

Health Technology ◽

Weighted Averaging ◽

Health Technologies ◽

Mean Survival Time ◽

Restricted Mean Survival Time

Previous work examined the suitability of relying on routine methods of model selection when extrapolating survival data in a health technology appraisal setting. Here we explore solutions to improve reliability of restricted mean survival time (RMST) estimates from trial data by assessing model plausibility and implementing model averaging. We compare our previous methods of selecting a model for extrapolation using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Our methods of model averaging include using equal weighting across models falling within established threshold ranges for AIC and BIC and using BIC-based weighted averages. We apply our plausibility assessment and implement model averaging to the output of our previous simulations, where 10,000 runs of 12 trial-based scenarios were examined. We demonstrate that removing implausible models from consideration reduces the mean squared error associated with the restricted mean survival time (RMST) estimate from each selection method and increases the percentage of RMST estimates that were within 10% of the RMST from the parameters of the sampling distribution. The methods of averaging were superior to selecting a single optimal extrapolation, aside from some of the exponential scenarios where BIC already selected the exponential model. The averaging methods with wide criterion-based thresholds outperformed BIC-weighted averaging in the majority of scenarios. We conclude that model averaging approaches should feature more widely in the appraisal of health technologies where extrapolation is influential and considerable uncertainty is present. Where data demonstrate complicated underlying hazard rates, funders should account for the additional uncertainty associated with these extrapolations in their decision making. Extended follow-up from trials should be encouraged and used to review prices of therapies to ensure a fair price is paid.

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A Study of OWA Operators Learned in Convolutional Neural Networks

Applied Sciences ◽

10.3390/app11167195 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7195

Author(s):

Iris Dominguez-Catena ◽

Daniel Paternain ◽

Mikel Galar

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Practical Method ◽

Local Information ◽

Weighted Averaging ◽

Global Information ◽

Owa Operator ◽

Owa Operators ◽

Ordered Weighted Averaging

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.

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An approach for supplier selection problem based on picture cubic fuzzy aggregation operators

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200194 ◽

2021 ◽

Vol 40 (5) ◽

pp. 10145-10162

Author(s):

Ahmad Bakr Khoshaim ◽

Muhammad Qiyas ◽

Saleem Abdullah ◽

Muhammad Naeem ◽

Muneeza

Keyword(s):

Supply Chain ◽

Supply Chain Management ◽

Aggregation Operators ◽

Weighted Averaging ◽

Numerical Application ◽

Chain Management ◽

Fuzzy Environment ◽

Picture Fuzzy Set ◽

Fuzzy Aggregation ◽

Fuzzy Order

This article is an advanced approach to picture fuzzy set through the application of cubic set theory. For instance, we establish the idea of the picture cubic fuzzy sets (PCFSs) theory and define several operations for PCFS. Also, presented some weighted aggregation operators under picture cubic fuzzy information, so called picture cubic fuzzy weighted averaging (PCFWA) operator, picture cubic fuzzy order weighted averaging (PCFOWA) operator, picture cubic fuzzy weighted geometric (PCFWG) operator, and picture cubic fuzzy order weighted geometric (PCFOWG) operator. Further, we study their fundamental properties and showed the relationship among these aggregation operators. In order to determine the feasibility and practicality of the mentioned new technique, we developed multi-attribute group decision -making algorithm with picture cubic fuzzy environment. Further, the developed method applied to supply chain management and for implementation, consider numerical application of supply chain management. Compared the developed approach with other preexisting aggregation operators, and we concluded that the defined technique is better, reliable and effective.

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Research on the Evaluation Model of Urban Tourism Management Efficiency with Uncertain Linguistic Information

Journal of Control Science and Engineering ◽

10.1155/2014/582454 ◽

2014 ◽

Vol 2014 ◽

pp. 1-4 ◽

Cited By ~ 1

Author(s):

Song-Mao Wang ◽

Liang-Yan Fang ◽

Feng Deng

Keyword(s):

Decision Making ◽

Evaluation Model ◽

Multiple Attribute Decision Making ◽

Urban Tourism ◽

Weighted Averaging ◽

Linguistic Information ◽

Management Efficiency ◽

Tourism Management ◽

Proposed Model ◽

Multiple Attribute

We investigate the multiple attribute decision making problems for evaluating the urban tourism management efficiency with uncertain linguistic information. We utilize the uncertain linguistic weighted averaging (ULWA) operator to aggregate the uncertain linguistic information corresponding to each alternative and get the overall value of the alternatives and, then rank the alternatives and select the most desirable one(s). Finally, a numerical example for evaluating the urban tourism management efficiency with uncertain linguistic information is used to illustrate the proposed model.

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Weighted averaging of steady-state responses

Clinical Neurophysiology ◽

10.1016/s1388-2457(01)00456-4 ◽

2001 ◽

Vol 112 (3) ◽

pp. 555-562 ◽

Cited By ~ 67

Author(s):

M.Sasha John ◽

Andrew Dimitrijevic ◽

Terence W Picton

Keyword(s):

Steady State ◽

Weighted Averaging ◽

Steady State Responses ◽

State Responses

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