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Despite the profusion of research about knowledge management within larger organizations, fewer studies tried to analyze knowledge management in small and medium enterprises. The study contributes to research by providing a more nuanced classification of knowledge management approaches and guides managers about the types of knowledge management approaches that should be adopted based on the size, geographical dispersion, and task nature of the organization. A purposive sample of 34 companies was selected for this study along with a survey that focused on the objective of investigating awareness and implementing strategies of knowledge management. The various phases and processes of knowledge management were accounted for. Organizations were bifurcated on the criteria like the core area of the company, the size of the company, the type of company, etc. Knowledge management implementation was judged through each dimension. Different statistical tests were carried out to test a set hypothesis. Having established that wide variation in overall adoption of knowledge management practices exists across the software engineering organizations, the different characteristics associated with knowledge management adoption were tested: organization size in terms of employee strength, the domain of the software engineering, team distribution, and type of organization. To a surprise, most of the organizational characteristics are not found in the significantly associated with knowledge management adoption except knowledge management adoption level in full and partial agile organizations and the relationship between the organization KM level and the number of software developers in organization for only product development companies is found significant. Opposite to the claims of many researchers, this study does not find any significant difference between knowledge management adoptions between distributed and co-located agile teams. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "A convolution deep architecture for gender classification of Urdu handwritten characters" published in the journal Multimedia Tools & Applications. The correction addresses missing references and missing entries in Table 2 column "Data Batch." The original article has been corrected. The correction notice is authored by Syed Tufael Nabi, Munish Kumar, and Paramjeet Singh. Springer Nature, the publisher, remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

In this paper, we have proposed a feature extraction technique for recognition of segmented handwritten characters of Gurmukhi script. The experiments have been performed with 7000 specimens of segmented offine handwritten Gurmukhi characters collected from 200 different writers. We have considered the set of 35 basic characters of the Gurmukhi script and have proposed the feature extraction technique based on boundary extents of the character image. PCA based feature selection technique has also been implemented in this work to reduce the dimension of data. We have used k-NN, SVM and MLP classifiers. SVM has been used with four different kernels. In this work, we have achieved maximum recognition accuracy of 93.8% for the 35-class problem when SVM with RBF kernel and 5-fold cross validation technique were employed. [ABSTRACT FROM AUTHOR]

• This paper imparts a road map for the researchers of biometric recognition system. • Highlights both Unimodal and multimodal biometric system. • Covering diagrammatic view and major characteristics of all the biometric traits. • Focus is given to the feature extraction methods, selection of the classifier. • Datasets used and the accuracy achieved to explore the main ideas of the authors. Biometrics is the branch of science that deals with the identification and verification of an individual based on the physiological and behavioral traits. These traits or identifiers are permanent, unique and can separate one individual from another. Biometric recognition systems integrate complex definitional, technological and operational selection under various contexts. The systems are not going to replace the authentication tools and technologies, but the combination of biometric approaches and authentication methods to help in improving the security aspects of the applications where user cooperation can be inferred. Biometric based recognition methods and tools have become popular for the development of many useful, challenging and widely accepted applications such as security issues, surveillance, forensic investigations, fraudulent technologies, identity access management and access control. These systems also help to identify an individual in group of industrial networks, home/office building and control system. For the successful implementation of the biometric systems, deep artificial neural networks are in great demand. These systems can be built up either on the single modality or multiple modalities. This article explicates the comprehensive and deep survey that compactly and systematically summarizes the literature work done on unimodal and multimodal biometric systems and analyzes the feature extraction techniques, classifiers, datasets, results, efficiency and reliability of the system with high and multi-dimensional perspectives. This article also justifies in detail the classical methods, influential methods and taxonomy based on the biometric attributes. The goal is to aware the researchers of this area regarding various dimensions for the development of biometric systems to enhance the security aspects. The article begins with the fundamentals, types, need of system, challenges, uncertainties, motivations and then to the survey work. The tabular representation prepares for each biometric trait shows the author, year, major findings and results achieved with the synthesis analysis and the evaluation. The article finally ends up with the 3D biometric, a future perspective and concluding remarks. [ABSTRACT FROM AUTHOR]

• A pre-trained model based on a depth-wise separable convolution layer is employed. • The proposed model has low computation cost and better generalizability. • A comparative analysis of the various learning models has been presented. • The results are promising and show excellent authentication accuracy on a small dataset. Finger vein recognition received special attention among all other biometric traits due to its high security. Adequate recognition and classification accuracy ensure the security of personal authentication. Many convolutional neural networks (CNNs) have been proposed with a promising performance in biometric finger vein recognition. However, their architectures have several problems, such as high complexity, extraction of robust features, degraded performance, etc. Considering the issues of CNNs, the authors present a pre-trained CNN network named Xception model based on depth-wise separable CNNs with residual connection, which is considered to be a more effective, less complex neural network to extract robust features. Our work can be seen as a three-stage process. Initially, the concept of data pre-processing is applied to convert the raw input samples into the standard format. Afterward, data augmentation using different geometrical techniques is incorporated to overcome the lack of training samples required for training the deep learning model. Finally, the feature extraction and classification task is performed through the pre-trained Xception architecture to verify the person's identity. SDUMLA and THU-FVFDT2 datasets are utilized to test and evaluate the proposed multi-layered CNN model performance with existing arts. Our proposed method for the SDUMLA database achieved an accuracy of 99% with an F1-score of 98%. While on THU-FVFDT2, the proposed method obtained an accuracy of 90% with an F1-score of 88%. Experimental results conclude that the proposed work obtained excellent performance compared to existing methods. [ABSTRACT FROM AUTHOR]