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The bank is termed as an imperative part of the marketing economy. The failure or success of an institution relies on the ability of industries to compute the credit risk. The loan eligibility prediction model utilizes analysis method that adapts past and current information of credit user to make prediction. However, precise loan prediction with risk and assessment analysis is a major challenge in loan eligibility prediction.

This aim of the research technique is to present a new method, namely Social Border Collie Optimization (SBCO)-based deep neuro fuzzy network for loan eligibility prediction. In this method, box cox transformation is employed on input loan data to create the data apt for further processing. The transformed data utilize the wrapper-based feature selection to choose suitable features to boost the performance of loan eligibility calculation. Once the features are chosen, the naive Bayes (NB) is adapted for feature fusion. In NB training, the classifier builds probability index table with the help of input data features and groups values. Here, the testing of NB classifier is done using posterior probability ratio considering conditional probability of normalization constant with class evidence. Finally, the loan eligibility prediction is achieved by deep neuro fuzzy network, which is trained with designed SBCO. Here, the SBCO is devised by combining the social ski driver (SSD) algorithm and Border Collie Optimization (BCO) to produce the most precise result.

The analysis is achieved by accuracy, sensitivity and specificity parameter by. The designed method performs with the highest accuracy of 95%, sensitivity and specificity of 95.4 and 97.3%, when compared to the existing methods, such as fuzzy neural network (Fuzzy NN), multiple partial least squares regression model (Multi_PLS), instance-based entropy fuzzy support vector machine (IEFSVM), deep recurrent neural network (Deep RNN), whale social optimization algorithm-based deep RNN (WSOA-based Deep RNN).

This paper devises SBCO-based deep neuro fuzzy network for predicting loan eligibility. Here, the deep neuro fuzzy network is trained with proposed SBCO, which is devised by combining the SSD and BCO to produce most precise result for loan eligibility prediction.

With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.

This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.

Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.

The Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

Bicycle industry, emerging markets, competitor analysis, financial forecasting.

This case can be used as a teaching tool in the following courses: MBA/post-graduate programs in management in management accounting, management control systems and strategic cost management; executive training programs for middle and senior level employees; and under-graduate/post-graduate programs in entrepreneurship. It can be used to explain and test the concepts of SWOT analysis, Porter's five forces model and PEST analysis. It introduces the technique of breakeven analysis and its relationship with operating leverage. Moreover, it demonstrates the application and analyses of the Du Pont equation.

Hero Cycles Ltd was established by the four Munjal brothers in pre-independence India. It started off as a business of bicycle spare parts, but quickly expanded in post-independence India, with Ludhiana as its base. The company later joined with foreign firms like Honda Motors, Japan to become the largest manufacturers of bicycles in the world. It dominates domestic markets with a market share of around 40 percent. Ananth Munjal, a learned, ambitious and cautious individual, is the next generation, ready to take over the reins of the company. Being someone who believes in learning from past mistakes, he forms a team to critically examine the decisions made by his predecessors. This team is also directed to utilize forecasting techniques for determining the expected profitability given the existing state of affairs that prevail. Additionally, Du Pont analysis is to be performed for studying the efficiency of the company on the facets of operating performance, asset turnover and associated financial leverage. Also, Ananth's risk-averse nature compels him to study the past with regard to the relationship between operating leverage, breakeven sales and corresponding margin of safety. Furthermore, he wishes to inspect the historical cost structure of the firm, and its influence on company performance.

These include the use of: SWOT analysis to identify the strengths, weaknesses, opportunities and threats to a company; PEST analysis to identify the political, economic, social and technological factors that affect the operations of a company; Porter's five forces model to analyse an industry. The case also helps students: by identifying fixed costs and variable costs that are a part of operating expenditure of a business; in the use of forecasting the financials of a company for the sake of predicting the future outcomes of certain business strategies; by application of Du Pont analysis to examine the efficiency of the various processes and strategies; in determining quantitative terms like contribution margin, breakeven sales, operating leverage, margin of safety, their significance, and the relationship between these terms.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

Relief item distribution to victims is a key activity during disaster response. Currently many humanitarian organizations follow simple guidelines based on experience to assess need and distribute relief supplies. However, the interviews with practitioners suggest a problem in efficiency in relief distribution efforts. The purpose of this paper is to develop a model and solution methodology that can estimate relief center (RC) performance, measured by waiting time for victims and throughput, for any RC design and analyze the impact of key design decisions on these performance measures.

Interviews with practitioners and current practice guidelines are used to understand relief distribution and a queuing network model is used to represent the relief distribution. Finally, the model is applied to data from the 2015 Nepal earthquake.

The findings identify that dissipating congestion created by crowds, varying item assignment decisions to points of distribution, limiting the physical RC capacity to control congestion and using triage queue to balance distribution times, are effective strategies that can improve RC performance.

This research bases the RC designs on Federal Emergency Management Agency guidelines and assumes a certain area and volunteer availability.

This paper contributes to humanitarian logistics by discussing useful insights that can impact how relief agencies set up and operate RCs. It also contributes to the queuing literature by deriving analytic solutions for the steady state probabilities of finite capacity, state dependent queues with blocking.

The advancements of deep learning (DL) models demonstrate significant performance on accurate pancreatic tumor segmentation and classification.

The presented model involves different stages of operations, namely preprocessing, image segmentation, feature extraction and image classification. Primarily, bilateral filtering (BF) technique is applied for image preprocessing to eradicate the noise present in the CT pancreatic image. Besides, noninteractive GrabCut (NIGC) algorithm is applied for the image segmentation process. Subsequently, residual network 152 (ResNet152) model is utilized as a feature extractor to originate a valuable set of feature vectors. At last, the red deer optimization algorithm (RDA) tuned backpropagation neural network (BPNN), called RDA-BPNN model, is employed as a classification model to determine the existence of pancreatic tumor.

The experimental results are validated in terms of different performance measures and a detailed comparative results analysis ensured the betterment of the RDA-BPNN model with the sensitivity of 98.54%, specificity of 98.46%, accuracy of 98.51% and F-score of 98.23%.

The study also identifies several novel automated deep learning based approaches used by researchers to assess the performance of the RDA-BPNN model on benchmark dataset and analyze the results in terms of several measures.

The purpose of this paper is to focus on MHD unsteady flow of Carreau fluid over a variable thickness melting surface in the presence of chemical reaction and non-uniform heat sink/source.

The flow governing partial differential equations are transformed into ordinary ones with the help of similarity transformations. The set of ODEs are solved by a shooting technique together with the R.K.–Fehlberg method. Further, the graphs are depicted to scrutinize the velocity, concentration and temperature fields of the Carreau fluid flow. The numerical values of friction factor, heat and mass transfer rates are tabulated.

The results are presented for both Newtonian and non-Newtonian fluid flow cases. The authors conclude that the nature of three typical fields and the physical quantities are alike in both cases. An increase in melting parameter slows down the velocity field and enhances the temperature and concentration fields. But an opposite outcome is noticed with thermal relaxation parameter. Also the elevating values of thermal relaxation parameter/ wall thickness parameter/Prandtl number inflate the mass and heat transfer rates.

This is a new research article in the field of heat and mass transfer in fluid flows. Cattaneo–Christov heat flux model is used. The surface of the flow is assumed to be melting.

There has been substantial interest in US cesarean rates, which increased from 5% of deliveries in the 1970s to nearly one-third of births by the mid-2000s. Explanations typically emphasize individual risk factors (e.g., advanced maternal age, increased BMI, and greater desire for control over delivery) of women giving birth, or address institutional factors, such as the medicalization of childbirth and the culture of liability leading physicians to practice defensive medicine. We focus here on another non-medical explanation – childbirth education (CBE). CBE is an important, underexplored mechanism that can shape women’s expectations about labor and birth and potentially lead them to expect, or desire, a cesarean delivery as a normalized outcome. We analyze data from three waves (2002, 2006, 2013) of the Listening to Mothers national survey on US women’s childbearing experiences (n = 3,985). Using logistic regression analysis, we examined both mode of delivery (vaginal versus cesarean), and attitudes about future request for elective cesarean among both primiparous and multiparous women. Despite previous research suggesting that CBE increased the likelihood of vaginal delivery, we find that CBE attendance was not associated with likelihood of vaginal delivery among either primiparous or multiparous women. However, both primiparous and multiparous women who attended CBE classes were significantly more likely to say they would request a future, elective cesarean. Furthermore, these effects were in the opposite direction of effects for natural birth attitudes. Our findings suggest that contemporary CBE classes may be a form of “anticipatory socialization”, potentially priming women’s acceptance of medicalized childbirth.

Organizational scholars have long been interested in how jazz musicians manage tensions between structure and freedom, plans and action, and familiarity and novelty. Although improvisation has been conceptualized as a way of managing such paradoxes, the process of improvisation itself contains paradoxes. In this essay, we return to jazz improvisation to identify a new paradox of interest to organizational scholars: the paradox of intentionality. To improvise creatively, jazz musicians report that they must “try not to try,” or risk undermining the very spontaneity that is prized in jazz. Jazz improvisers must therefore control their ability to relinquish deliberate control of their actions. To accomplish this, they engage in three interdependent practices. Jazz musicians intentionally surrender their sense of active control (“letting go”) while creating a passive externalized role for this sense of active control (using a “third ear”). Letting go allows new and unexpected ideas to emerge, while the metaphorical third ear can identify promising ideas or problematic execution and, in doing so, re-engage active agency (“grabbing hold”). Examining the practices within creative improvisation reveals the complexity of the lived experience of the paradox, which we argue suggests further integration among organizational research on improvisation, creativity, and paradox.

Serial crowdfunding is becoming a common phenomenon as entrepreneurs repeatedly return to online crowdfunding to raise capital. In this study, the authors focus attention on serial crowdfunders, that is, entrepreneurs who experience launching more than one crowdfunding project. The purpose of this paper is to investigate the role of past experience on subsequent crowdfunding performance. This study also examines whether initial success vs initial failure leads serial crowdfunders to engage in more explorative behaviors (i.e. switching industry) and to take exploitative actions (i.e. adjusting campaign strategies in terms of goal setting and funding option).

Data on serial crowdfunding projects was retrieved from Indiegogo platform. The logistic regression models are estimated to assess the impact of past entrepreneurial experience on subsequent crowdfunding decisions, and to estimate the effects of the three strategies on subsequent funding performance.

The results show that serial creators who experienced successful initial crowdfunding are more likely to explore a new industry or product category in the crowdfunding market and to set a higher target capital for the subsequent campaign when they change a project category.

Despite the fact that there are a considerably large number of serial crowdfunders in crowdfunding market, relatively little research has been conducted to investigate the presence of learning benefits from a previous to a subsequent crowdfunding project. Two competing hypotheses, drawn from the attribution theory and hubris theory of entrepreneurship, were tested in this study to determine the impact of prior success vs failure experience on both subsequent crowdfunding decisions and funding performance.