Design of expert active knn classifier algorithm using flow stroop colour word test to assess flow state

Flow state assessment is essential to understand the involvement of an individual in a particular task assigned. If there is no involvement in the task assigned then the individual in due course of time gets affected either by psychological or physiological illnesses. The National Crime Records Bureau (NCRB) statistics show that non-involvement in the task drive the individual to a depression state and subsequently attempt for suicide. Therefore, it is essential to determine the decrease in flow level at an earlier stage and take remedial steps to recover them. There are many invasive methods to determine the flow state, which is not preferred and the commonly used non-invasive method is the questionnaire and interview method, which is the subjective and retroactive method, and hence chance to fake the result is more. Hence, the main objective of our work is to design an efficient flow level measurement system that measures flow in an objective method and also determines real-time flow classification. The accuracy of classification is achieved by designing an Expert Active k-Nearest Neighbour (EAkNN) which can classify the individual flow state towards the task assigned into nine states using non-invasive physiological Electrocardiogram (ECG) signals. The ECG parameters are obtained during the performance of FSCWT. Thus this work is a combination of psychological theory, physiological signals and machine learning concepts. The classifier is designed with a modified voting rule instead of the default majority voting rule, in which the contribution probability of nearest points to new data is considered. The dataset is divided into two sets, training dataset 75%and testing dataset 25%. The classifier is trained and tested with the dataset and the classification efficiency is 95%.

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Combined classifier based on feature space partitioning

International Journal of Applied Mathematics and Computer Science ◽

10.2478/v10006-012-0063-0 ◽

2012 ◽

Vol 22 (4) ◽

pp. 855-866 ◽

Cited By ~ 13

Author(s):

Michał Woźniak ◽

Bartosz Krawczyk

Keyword(s):

Computer Experiments ◽

Feature Space ◽

Majority Voting ◽

Space Partitioning ◽

Voting Rule ◽

Benchmark Datasets ◽

Proposed Modification ◽

The Individual ◽

Combined Classifier ◽

Feature Space Partitioning

This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier, which makes a decision based on a weighted combination of the discriminant functions of the individual classifiers selected for the committee. The weights mentioned above are dependent not only on the classifier identifier, but also on the class number. The proposed approach is based on the results of previous works, where it was proven that such a combined classifier method could achieve significantly better results than simple voting systems. The proposed modification was evaluated through computer experiments, carried out on diverse benchmark datasets. The results are very promising in that they show that, for most of the datasets, the proposed method outperforms similar techniques based on the clustering and selection approach.

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Voting Rules in Sequential Search by Committees: Theory and Experiments

Management Science ◽

10.1287/mnsc.2018.3146 ◽

2019 ◽

Vol 65 (9) ◽

pp. 4349-4364

Author(s):

Vincent Mak ◽

Darryl A. Seale ◽

Amnon Rapoport ◽

Eyran J. Gisches

Keyword(s):

Decision Makers ◽

Majority Voting ◽

Secretary Problem ◽

Sequential Search ◽

Voting Rules ◽

Unanimity Rule ◽

Game Theoretic Analysis ◽

Voting Rule ◽

Game Theoretic ◽

The Individual

We propose a committee extension of the individual sequential search model called the “secretary problem,” where collective decisions on when to stop the search are reached via a prespecified voting rule. We offer a game-theoretic analysis of our model and then report two experiments on three-person committees with either uncorrelated or perfectly correlated preferences under three different voting rules followed by a third experiment on single decision makers. Relative to equilibrium predictions, committees with uncorrelated preferences oversearched under minority and majority voting rules but, otherwise, undersearched or approximated equilibrium play. Individually, committee members were often less strategic when their preferences were uncorrelated than when they were perfectly correlated. Collectively, committees’ decisions were more strategic than single decision makers’ only under the unanimity rule, although still not significantly better in terms of the decision makers’ welfare. Finally, across our experiments that involved committee search, the unanimity rule always optimized committee welfare. This paper was accepted by Yan Chen, behavioral economics.

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Decision Tree Integration Using Dynamic Regions of Competence

Entropy ◽

10.3390/e22101129 ◽

2020 ◽

Vol 22 (10) ◽

pp. 1129

Author(s):

Jędrzej Biedrzycki ◽

Robert Burduk

Keyword(s):

Decision Tree ◽

Decision Rules ◽

Feature Space ◽

Majority Voting ◽

Classification Model ◽

Training Dataset ◽

Multiple Classifier Systems ◽

Classifier Systems ◽

Voting Rule ◽

Multiple Classifier

A vital aspect of the Multiple Classifier Systems construction process is the base model integration. For example, the Random Forest approach used the majority voting rule to fuse the base classifiers obtained by bagging the training dataset. In this paper we propose the algorithm that uses partitioning the feature space whose split is determined by the decision rules of each decision tree node which is the base classification model. After dividing the feature space, the centroid of each new subspace is determined. This centroids are used in order to determine the weights needed in the integration phase based on the weighted majority voting rule. The proposal was compared with other Multiple Classifier Systems approaches. The experiments regarding multiple open-source benchmarking datasets demonstrate the effectiveness of our method. To discuss the results of our experiments, we use micro and macro-average classification performance measures.

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An Efficient and Accurate Depth-Wise Separable Convolutional Neural Network for Cybersecurity Vulnerability Assessment Based on CAPTCHA Breaking

Electronics ◽

10.3390/electronics10040480 ◽

2021 ◽

Vol 10 (4) ◽

pp. 480

Author(s):

Stephen Dankwa ◽

Lu Yang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Private Information ◽

Turing Test ◽

End Users ◽

Fine Tuning ◽

Training Dataset ◽

Testing Dataset ◽

Proposed Model ◽

The Individual

Cybersecurity practitioners generate a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) as a form of security mechanism in website applications, in order to differentiate between human end-users and machine bots. They tend to use standard security to implement CAPTCHAs in order to prevent hackers from writing malicious automated programs to make false website registrations and to restrict them from stealing end-users’ private information. Among the categories of CAPTCHAs, the text-based CAPTCHA is the most widely used. However, with the evolution of deep learning, it has been so dramatic that tasks previously thought not easily addressable by computers and used as CAPTCHA to prevent spam are now possible to break. The workflow of CAPTCHA breaking is a combination of efforts, approaches, and the development of the computation-efficient Convolutional Neural Network (CNN) model that attempts to increase accuracy. In this study, in contrast to breaking the whole CAPTCHA images simultaneously, this study split four-character CAPTCHA images for the individual characters with a 2-pixel margin around the edges of a new training dataset, and then proposed an efficient and accurate Depth-wise Separable Convolutional Neural Network for breaking text-based CAPTCHAs. Most importantly, to the best of our knowledge, this is the first CAPTCHA breaking study to use the Depth-wise Separable Convolution layer to build an efficient CNN model to break text-based CAPTCHAs. We have evaluated and compared the performance of our proposed model to that of fine-tuning other popular CNN image recognition architectures on the generated CAPTCHA image dataset. In real-time, our proposed model used less time to break the text-based CAPTCHAs with an accuracy of more than 99% on the testing dataset. We observed that our proposed CNN model has efficiently improved the CAPTCHA breaking accuracy and streamlined the structure of the CAPTCHA breaking network as compared to other CAPTCHA breaking techniques.

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Language in mental retardation: Individual and syndromic differences, and neurogenetic variation 1Based on a keynote presentation at the Third European Conference on Psychological Theory and Research in Mental Retardation, Geneva, September 1st, 2000.

Swiss Journal of Psychology ◽

10.1024//1421-0185.60.3.161 ◽

2001 ◽

Vol 60 (3) ◽

pp. 161-178 ◽

Cited By ~ 23

Author(s):

Jean A. Rondal

Keyword(s):

Mental Retardation ◽

Language Development ◽

Oral Language ◽

Psychological Theory ◽

Genetic Syndromes ◽

Explanatory Variables ◽

Keynote Presentation ◽

Genetic Origins ◽

The Sixties ◽

The Individual

Predominantly non-etiological conceptions have dominated the field of mental retardation (MR) since the discovery of the genetic etiology of Down syndrome (DS) in the sixties. However, contemporary approaches are becoming more etiologically oriented. Important differences across MR syndromes of genetic origin are being documented, particularly in the cognition and language domains, differences not explicable in terms of psychometric level, motivation, or other dimensions. This paper highlights the major difficulties observed in the oral language development of individuals with genetic syndromes of mental retardation. The extent of inter- and within-syndrome variability are evaluated. Possible brain underpinnings of the behavioural differences are envisaged. Cases of atypically favourable language development in MR individuals are also summarized and explanatory variables discussed. It is suggested that differences in brain architectures, originating in neurological development and having genetic origins, may largely explain the syndromic as well as the individual within-syndrome variability documented. Lastly, the major implications of the above points for current debates about modularity and developmental connectionism are spelt out.

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The alienation of the individual from society: A social-psychological theory and cross-cultural comparison

10.31274/rtd-180813-8581 ◽

1983 ◽

Author(s):

Sidney Joe Jackson

Keyword(s):

Psychological Theory ◽

Cross Cultural ◽

Cultural Comparison ◽

Social Psychological ◽

Cross Cultural Comparison ◽

The Individual ◽

Social Psychological Theory

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Urinary Cytokines Reflect Renal Inflammation in Acute Tubulointerstitial Nephritis: A Multiplex Bead-Based Assay Assessment

Journal of Clinical Medicine ◽

10.3390/jcm10132986 ◽

2021 ◽

Vol 10 (13) ◽

pp. 2986

Author(s):

Laura Martinez Valenzuela ◽

Juliana Draibe ◽

Oriol Bestard ◽

Xavier Fulladosa ◽

Francisco Gómez-Preciado ◽

...

Keyword(s):

Tubulointerstitial Nephritis ◽

Urinary Concentration ◽

Blood Eosinophil ◽

Acute Tubulointerstitial Nephritis ◽

Luminex Assay ◽

Non Invasive ◽

Urinary Levels ◽

Histological Data ◽

The Individual ◽

Multiplex Bead

Background: Acute tubulointerstitial nephritis (ATIN) diagnosis lays on histological assessment through a kidney biopsy, given the absence of accurate non-invasive biomarkers. The aim of this study was to evaluate the accuracy of different urinary inflammation-related cytokines for the diagnostic of ATIN and its distinction from acute tubular necrosis (ATN). Methods: We included 33 patients (ATIN (n = 21), ATN (n = 12)), and 6 healthy controls (HC). We determined the urinary levels of 10 inflammation-related cytokines using a multiplex bead-based Luminex assay at the time of biopsy and after therapy, and registered main clinical, analytical and histological data. Results: At the time of biopsy, urinary levels of I-TAC/CXCL11, CXCL10, IL-6, TNFα and MCP-1 were significantly higher in ATIN compared to HC. A positive correlation between the extent of the tubulointerstitial cellular infiltrates in kidney biopsies and the urinary concentration of I-TAC/CXCL11, MIG/CXCL9, CXCL10, IL17, IFNα, MCP1 and EGF was observed. Notably, I-TAC/CXCL11, IL-6 and MCP-1 were significantly higher in ATIN than in ATN, with I-TAC/CXCL11 as the best discriminative classifier AUC (0.77, 95% CI 0.57–0.95, p = 0.02). A combinatory model of these three urinary cytokines increased the accuracy in the distinction of ATIN/ATN compared to the individual biomarkers. The best model resulted when combining the three cytokines with blood eosinophil and urinary leukocyte counts (LR = 9.76). Follow-up samples from 11ATIN patients showed a significant decrease in I-TAC/CXCL11, MIG/CXCL9 and CXCL10 levels. Conclusions: Urinary I-TAC/CXCL11, CXCL10, IL6 and MCP-1 levels accurately distinguish patients developing ATIN from ATN and healthy individuals and may serve as novel non-invasive biomarkers in this disease.

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Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN

Open Life Sciences ◽

10.1515/biol-2020-0055 ◽

2020 ◽

Vol 15 (1) ◽

pp. 588-596 ◽

Cited By ~ 1

Author(s):

Jie Meng ◽

Linyan Xue ◽

Ying Chang ◽

Jianguang Zhang ◽

Shilong Chang ◽

...

Keyword(s):

Deep Neural Networks ◽

Alimentary Tract ◽

Multiple Scale ◽

Colorectal Polyps ◽

Training Dataset ◽

Cad System ◽

Multicenter Trials ◽

Testing Dataset ◽

Adenoma Detection ◽

Aided Diagnosis

AbstractColorectal cancer (CRC) is one of the main alimentary tract system malignancies affecting people worldwide. Adenomatous polyps are precursors of CRC, and therefore, preventing the development of these lesions may also prevent subsequent malignancy. However, the adenoma detection rate (ADR), a measure of the ability of a colonoscopist to identify and remove precancerous colorectal polyps, varies significantly among endoscopists. Here, we attempt to use a convolutional neural network (CNN) to generate a unique computer-aided diagnosis (CAD) system by exploring in detail the multiple-scale performance of deep neural networks. We applied this system to 3,375 hand-labeled images from the screening colonoscopies of 1,197 patients; of whom, 3,045 were assigned to the training dataset and 330 to the testing dataset. The images were diagnosed simply as either an adenomatous or non-adenomatous polyp. When applied to the testing dataset, our CNN-CAD system achieved a mean average precision of 89.5%. We conclude that the proposed framework could increase the ADR and decrease the incidence of interval CRCs, although further validation through large multicenter trials is required.

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