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Article
Publication date: 1 July 1996

K.P. Soman and K.B. Misra

Describes a simple algorithm which has been developed for determining exact moments of top event failure probability from the moments of the basic events in a fault tree. This…

867

Abstract

Describes a simple algorithm which has been developed for determining exact moments of top event failure probability from the moments of the basic events in a fault tree. This method requires neither Taylor series expansion of top event failure probability function nor its partial derivates to find these moments. This method has subsequently extended to systems with multistate components. Describes a general algorithm for the purpose. Provides several illustrations to highlight its usefulness.

Details

International Journal of Quality & Reliability Management, vol. 13 no. 5
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 11 November 2020

Komal

In recent years, the application of robots in different industrial sectors such as nuclear power generation, construction, automobile, firefighting and medicine, etc. is…

217

Abstract

Purpose

In recent years, the application of robots in different industrial sectors such as nuclear power generation, construction, automobile, firefighting and medicine, etc. is increasing day by day. In large industrial plants generally humans and robots work together to accomplish several tasks and lead to the problem of safety and reliability because any malfunction event of robots may cause human injury or even death. To access the reliability of a robot, sufficient amount of failure data is required which is sometimes very difficult to collect due to rare events of any robot failures. Also, different types of their failure pattern increase the difficulty which finally leads to the problem of uncertainty. To overcome these difficulties, this paper presents a case study by assessing fuzzy fault tree analysis (FFTA) to control robot-related accidents to provide safe working environment to human beings in any industrial plant.

Design/methodology/approach

Presented FFTA method uses different fuzzy membership functions to quantify different uncertainty factors and applies alpha-cut coupled weakest t-norm (Tω) based approximate fuzzy arithmetic operations to obtain fuzzy failure probability of robot-human interaction fault event which is the main contribution of the paper.

Findings

The result obtained from presented FFTA method is compared with other listing approaches. Critical basic events are also ranked using V-index for making suitable action plan to control robot-related accidents. Study indicates that the presented FFTA is a good alternative method to analyze fault in robot-human interaction for providing safe working environment in an industrial plant.

Originality/value

Existing fuzzy reliability assessment techniques designed for robots mainly use triangular fuzzy numbers (TFNs), triangle vague sets (TVS) or triangle intuitionistic fuzzy sets (IFS) to quantify data uncertainty. Present study overcomes this shortcoming and generalizes the idea of fuzzy reliability assessment for robots by adopting different IFS to control robot-related accidents to provide safe working environment to human. This is the main contribution of the paper.

Details

International Journal of Quality & Reliability Management, vol. 38 no. 6
Type: Research Article
ISSN: 0265-671X

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Publication date: 29 May 2023

Divya Nair and Neeta Mhavan

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and…

Abstract

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

Details

Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy
Type: Book
ISBN: 978-1-80382-555-7

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Article
Publication date: 4 October 2018

Wangyu Liu, Dong Sun, Aimin Tang and Mingke Li

Hydrogel is an excellent material for the fabrication of porous scaffold by mask-prototyping method. Different from the common commercial resin, hydrogel is hydrophilic and…

138

Abstract

Purpose

Hydrogel is an excellent material for the fabrication of porous scaffold by mask-prototyping method. Different from the common commercial resin, hydrogel is hydrophilic and hyperelastic, so that it cannot bear the conventional post-curing process to improve its mechanical properties. The purpose of this paper is to put forward a method to improve the curing bonding strength at the weak juncture of the porous hydrogel scaffold.

Design/methodology/approach

The working curve of the resin was obtained through the single layer cure experiment, and the energy accumulation model has been set up by MATLAB. Aimed at the specificity of material, a new method of partial curing on different kind of structure has been proposed. Under the same condition, only the tn2 needs to be changed to fabricate different test specimens with different accumulated energy between two layers. The tensile test is carried out with the authors’ preferred equipment.

Findings

The analysis reveals that accumulated energy can be changed by adjusting the key parameters, and the tensile test shows that when the accumulated energy is bigger, the ultimate tensile strength is higher.

Research limitations/implications

Subject to the equipment accuracy and specificity of material, some errors coming from the experiment and test might exist, but the authors believe they will not change their findings and conclusions in this paper.

Practical implications

The research provides a method which is different from the common methods but friendlier to improve the bonding strength of the hydrogel scaffold.

Social implications

This work can help to adjust the mechanical property of the scaffold used in tissue engineering.

Originality/value

This method can improve the bonding strength at weak juncture and give a direction for the design of porous scaffold.

Details

Rapid Prototyping Journal, vol. 24 no. 6
Type: Research Article
ISSN: 1355-2546

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Article
Publication date: 20 November 2017

Andreas Behr and Jurij Weinblat

The purpose of this paper is to do a performance comparison of three different data mining techniques.

835

Abstract

Purpose

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Design/methodology/approach

Logit model, decision tree and random forest are applied in this study on British, French, German, Italian, Portuguese and Spanish balance sheet data from 2006 to 2012, which covers 446,464 firms. Because of the strong imbalance with regard to the solvency status, classification trees and random forests are modified to adapt to this imbalance. All three model specifications are optimized extensively using resampling techniques, relying on the training sample only. Model performance is assessed, strictly, based on out-of-sample predictions.

Findings

Random forest is found to strongly outperform the classification tree and the logit model in almost all considered years and countries, according to the quality measure in this study.

Originality/value

Obtaining reliable estimates of default propensity scores is of immense importance for potential credit grantors, portfolio managers and regulatory authorities. As the overwhelming majority of firms are not listed on stock exchanges, annual balance sheets still provide the most important source of information. The obtained ranking of the three models according to their predictive performance is relatively robust, due to the consideration of several countries and a relatively long time period.

Details

The Journal of Risk Finance, vol. 18 no. 5
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 4 July 2016

M. Punniyamoorthy and P. Sridevi

Credit risk assessment has gained importance in recent years due to global financial crisis and credit crunch. Financial institutions therefore seek the support of credit rating…

1155

Abstract

Purpose

Credit risk assessment has gained importance in recent years due to global financial crisis and credit crunch. Financial institutions therefore seek the support of credit rating agencies to predict the ability of creditors to meet financial persuasions. The purpose of this paper is to construct neural network (NN) and fuzzy support vector machine (FSVM) classifiers to discriminate good creditors from bad ones and identify a best classifier for credit risk assessment.

Design/methodology/approach

This study uses artificial neural network, the most popular AI technique used in the field of financial applications for classification and prediction and the new machine learning classification algorithm, FSVM to differentiate good creditors from bad. As membership value on data points influence the classification problem, this paper presents the new FSVM model. The instances membership is computed using fuzzy c-means by evolving a new membership. The FSVM model is also tested on different kernels and compared and the classifier with highest classification accuracy for a kernel is identified.

Findings

The paper identifies a standard AI model by comparing the performances of the NN model and FSVM model for a credit risk data set. This work proves that that FSVM model performs better than back propagation-neural network.

Practical implications

The proposed model can be used by financial institutions to accurately assess the credit risk pattern of customers and make better decisions.

Originality/value

This paper has developed a new membership for data points and has proposed a new FCM-based FSVM model for more accurate predictions.

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Article
Publication date: 21 November 2023

Armin Mahmoodi, Leila Hashemi and Milad Jasemi

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid…

87

Abstract

Purpose

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Design/methodology/approach

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

Findings

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

Research limitations/implications

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

Originality/value

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Details

EuroMed Journal of Business, vol. 19 no. 4
Type: Research Article
ISSN: 1450-2194

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Article
Publication date: 30 March 2010

Komal, S.P. Sharma and Dinesh Kumar

The puprose of this paper is to analyse the stochastic behavior of an industrial system using a novel hybridized technique NGABLT. The forming unit of a paper mill situated in…

302

Abstract

Purpose

The puprose of this paper is to analyse the stochastic behavior of an industrial system using a novel hybridized technique NGABLT. The forming unit of a paper mill situated in north India producing approximately 200 tons of paper per day has been considered for analysis. The authors have made efforts to incorporate vague, ambiguous, imprecise and conflicting information quantified by fuzzy numbers.

Design/methodology/approach

Field data for repairable industrial systems are in the form of failures and repair rates are vague, ambiguous, qualitative and imprecise in nature. Using the data, system stochastic behavior in terms of six well‐known reliability indices is analysed considering some desired degree of accuracy. A practical case of forming unit in a paper mill is considered to compute the reliability indices by using NGABLT technique. Sensitive of system behavior is analysed through surface plots by taking different combinations of reliability indices. The findings have been supplied to the nearby industry for future course of action in maintenance.

Findings

The behavior analysis results computed by NGABLT technique have reduced region of prediction in comparison of existing Lambda‐Tau technique region i.e. uncertainties involved in the analysis are reduced. It may be a more useful tool to assess the current system condition and to improve the system performance.

Originality/value

The authors have suggested a hybridized technique for analyzing the stochastic behavior of the repairable industrial system by computing its reliability indices.

Details

Journal of Quality in Maintenance Engineering, vol. 16 no. 1
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 19 May 2021

Anshul Sharma, Pardeep Kumar, Hemant Kumar Vinayak, Raj Kumar Patel and Suresh Kumar Walia

This study aims to perform the experimental work on a laboratory-constructed steel truss bridge model on which hammer blows are applied for excitation. The vibration response…

179

Abstract

Purpose

This study aims to perform the experimental work on a laboratory-constructed steel truss bridge model on which hammer blows are applied for excitation. The vibration response signals of the bridge structure are collected using sensors placed at different nodes. The different damaged states such as no damage, single damage, double damage and triple damage are introduced by cutting members of the bridge. The masked noise with recorded vibration responses generates challenge to properly analyze the health of bridge structure.

Design/methodology/approach

The analytical modal properties are obtained from finite element model (FEM) developed using SAP2000 software. The response signals are analyzed in frequency domain by power spectrum and in time-frequency domain using spectrogram and Stockwell transform. Various low pass signal-filtering techniques such as variational filter, lowpass sparse banded (AB) filter and Savitzky–Golay (SG) differentiator filter are also applied to refine vibration signals. The proposed methodology further comprises application of Hilbert transform in combination with MUSIC and ESPRIT techniques.

Findings

The outcomes of SG filter provided the denoised signals using appropriate polynomial degree with proper selected window length. However, certain unwanted frequency peaks still appeared in the outcomes of SG filter. The SG-filtered signals are further analyzed using fused methodology of Hilbert transform-ESPRIT, which shows high accuracy in identifying modal frequencies at different states of the steel truss bridge.

Originality/value

The sequence of proposed methodology for denoising vibration response signals using SG filter with Hilbert transform-ESPRIT is a novel approach. The outcomes of proposed methodology are much refined and take less computational time.

Details

Journal of Engineering, Design and Technology , vol. 20 no. 5
Type: Research Article
ISSN: 1726-0531

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Article
Publication date: 27 April 2022

Sachin Kashyap

This paper aims to analyze and give directions for advancing research in stock market volatility highlighting its features, structural breaks and emerging developments. This study…

446

Abstract

Purpose

This paper aims to analyze and give directions for advancing research in stock market volatility highlighting its features, structural breaks and emerging developments. This study offers a platform to research the benchmark studies to know the research gap and give directions for extending future research.

Design/methodology/approach

The author has performed the literature review, and, reference checking as per the snowballing approach. Firstly, the author has started with outlining and simplifying the significance of the subject area, the review illustrating the various elements along with the research gaps and emphasizing the finding.

Findings

This work summarizes the studies covering the volatility, its properties and structural breaks on various aspects such as techniques applied, subareas and the markets. From the review’s analysis, no study has clarified the supremacy of any model because of the different market conditions, nature of data and methodological aspects. The outcome of this research work has delivered further magnitude to research the benchmark studies for the upcoming work on stock market volatility. This paper has also proposed the hybrid volatility models combining artificial intelligence with econometric techniques to detect noise, sudden changes and chaotic information easily.

Research limitations/implications

The author has taken the research papers from the scholarly journal published in the English language only and the author may also consider other nonscholarly or other language journals.

Originality/value

To the best of the author’s knowledge, this research work highlights an updated and more comprehensive framework examining the properties and demonstrating the contemporary developments in the field of stock market volatility.

Details

Journal of Modelling in Management, vol. 18 no. 3
Type: Research Article
ISSN: 1746-5664

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