PurposeThe purpose of this study is to investigate how digital platforms capability, improvisational capability and organizational readiness directly affect innovation performance. This study also explores how organizational readiness acts as mediator.Design/methodology/approachThis empirical study is based on quantitative research design. Data were collected from 647 managers of small and medium enterprises (SMEs) working in Pakistan. Correlations and regression techniques were used for analyses. The Preacher and Hayes technique, the Sobel test and Bootstrap techniques were used to test mediation effect.FindingsThe results reveal a significant and positive relationship of digital platforms capability, improvisational capability and organizational readiness with innovation performance. Organizational readiness fully mediates the relationships between digital platforms capability and innovation performance link as well as between improvisational capability and innovation performance link.Originality/valueIn the age of digital economy the achievement of innovation performance is very important for SMEs. Businesses are shifting from traditional operational activities to digitalization. This study is imperative to offer new realm of modern technologies by exploring the role of digital platform capability, improvisational capability and organizational readiness for achieving innovation performance in digital economy.

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ObjectiveMeibomian gland dysfunction (MGD) is a primary cause of dry eye disease. Analysis of MGD, its severity, shapes and variation in the acini of the meibomian glands (MGs) is receiving much attention in ophthalmology clinics. Existing methods for diagnosing, detection and analysing meibomianitis are not capable to quantify the irregularities to IR (infrared) images of MG area such as light reflection, interglands and intraglands boundaries, the improper focus of the light and positioning, and eyelid eversion.Methods and analysisWe proposed a model that is based on adversarial learning that is, conditional generative adversarial network that can overcome these blatant challenges. The generator of the model learns the mapping from the IR images of the MG to a confidence map specifying the probabilities of being a pixel of MG. The discriminative part of the model is responsible to penalise the mismatch between the IR images of the MG and confidence map. Furthermore, the adversarial learning assists the generator to produce a qualitative confidence map which is transformed into binary images with the help of fixed thresholding to fulfil the segmentation of MG. We identified MGs and interglands boundaries from IR images.ResultsThis method is evaluated by meiboscoring, grading, Pearson correlation and Bland-Altman analysis. We also judged the quality of our method through average Pompeiu-Hausdorff distance, and Aggregated Jaccard Index.ConclusionsThis technique provides a significant improvement in the quantification of the irregularities to IR. This technique has outperformed the state-of-art results for the detection and analysis of the dropout area of MGD.

Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. e proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.