Analyzing of process capability indices based on neutrosophic sets

Yalcin, Selin; Kaya, İ̇hsan

doi:10.1007/s40314-022-01973-5

Cited by 10 publications

(4 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neutrosophic sets, one of the fuzzy set extensions, have the ability to deliver more successful results when modeling uncertainty since they contain the membership functions of truth, indeterminacy, and falsity definitions, rather than only a membership function, thus providing a fair estimate of the reliability of the information. The independence of truth-membership (TM) and indeterminacy-membership (IM) functions from each other and expression of the fact that an individual does not have full control of the issue with the IM function have an essential role in modeling uncertainty problems [40]. The left-right non-skip HMM topology is often used to simulate posture signals.…”

Section: Methodsmentioning

confidence: 99%

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

View full text Add to dashboard Cite

In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

show abstract

Section: Methodsmentioning

confidence: 99%

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

View full text Add to dashboard Cite

show abstract

“…[5][6][7][8][9][10][11][12][13] More recent studies are considered by the references. [14][15][16][17] To model PCIs with uncertainty, Yalçin and Kaya 14 incorporated the fuzzy set theory. Haktanır and Kahraman 15 developed PCI models using penthagorean fuzzy sets, which is a new extension of intuitionistic fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

“…For more details, see the references 5–13 . More recent studies are considered by the references 14–17 . To model PCIs with uncertainty, Yalçin and Kaya 14 incorporated the fuzzy set theory.…”

Section: Introductionmentioning

confidence: 99%

Confidence intervals of the process capability index Cpc$C_{pc}$ revisited via modified bootstrap technique and ROC curves

Ouyang

Dey²,

Byun

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

We consider the process capability index (PCI), a widely used quality‐related statistic used to assess the quality of products and performance of monitored processes in various industries. It is widely known that the conventional PCIs perform well when the quality process being monitored has a normal distribution. Unfortunately, using the indices to evaluate a non‐normally distributed process often leads to inaccurate results. In this article, we consider a new PCI, , that can be used in both normal and non‐normal scenarios. The objective of this article is threefold: (i) We provide a corrected form of the confidence interval for . (ii) We compare the performance of three nonparametric bootstrap confidence intervals (BCIs) for . Specifically, the standard bootstrap, percentile bootstrap, and bias‐corrected percentile bootstrap. Under various distributional assumptions such as the normal, chi‐square, Student t, Laplace, and two‐parameter exponential distributions, the estimated coverage probabilities and average width of the confidence intervals and BCIs for are compared. (iii) The power of the respective bootstrap approaches is evaluated by using the equivalence relation between confidence interval construction and two‐sided hypothesis testing. We also provide the receiver operating characteristic curves to evaluate their performance. Finally, as an illustrative example, an actual data set related to groove dimensions (in inches) measured from a manufacturing process of ignition keys is re‐analyzed to illustrate the utility of the proposed methods.

show abstract

“…Process Capability Indices (PCIs) are measurements frequently adopted by the industry for process quality evaluation [1][2][3]. They are not just communication tools between sales and customers; for process engineers, they are useful means of evaluating, analyzing, and improving processes [4][5][6]. Industries that often employ process capability indices include various machine tools and machining industries, semiconductor manufacturing processes, and packaging processes [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Monitoring, Evaluation, and Improvement Model for Process Precision and Accuracy

Tsai,

Chiou,

Chen

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Process Capability Indices (PCIs) are devices widely used in the industry to evaluate process quality. The commonly used process capability indices all contain accuracy indices and precision indices. As the accuracy index is closer to zero, the process accuracy is higher. The precision index mainly represents the extent of process variation. As the value is smaller, the process variation is smaller, that is, the precision is higher. In fact, process capability indices are the functions of accuracy indices and precision indices. Obviously, as long as accuracy indices and precision indices are controlled, the process capability indices can be controlled as well. Therefore, this study first derived accuracy and precision control charts to observe not only process accuracy but also process precision. Then, this study adopted in-control data to acquire a 100 (1 − α)% confidence region of an accuracy index and a precision index, with which statistical tests were performed. Subsequently, according to the definition of the six sigma quality level, both indices were examined. Furthermore, based on the testing results, suggestions for process improvement were proposed, including correcting the direction of process deviation and deciding whether to reduce process variation. Finally, this study demonstrated the applicability of the proposed model using an empirical example.

show abstract

Analyzing of process capability indices based on neutrosophic sets

Cited by 10 publications

References 51 publications

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Confidence intervals of the process capability index Cpc$C_{pc}$ revisited via modified bootstrap technique and ROC curves

Monitoring, Evaluation, and Improvement Model for Process Precision and Accuracy

Contact Info

Product

Resources

About