COVID-19 epidemic has swiftly disrupted our day-to-day lives affecting the international trade and movements. Wearing a face mask to protect one's face has become the new normal. In the near future, many public service providers will expect the clients to wear masks appropriately to partake of their services. Therefore, face mask detection has become a critical duty to aid worldwide civilization. This paper provides a simple way to achieve this objective utilising some fundamental Machine Learning tools as TensorFlow, Keras, OpenCV and Scikit-Learn. The suggested technique successfully recognises the face in the image or video and then determines whether or not it has a mask on it. As a surveillance job performer, it can also recognise a face together with a mask in motion as well as in a video. The technique attains excellent accuracy. We investigate optimal parameter values for the Convolutional Neural Network model (CNN) in order to identify the existence of masks accurately without generating over-fitting.

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Concerning the increasing emphasis on risk management in this uncertain global environment, there is an urgent demand for practical decision support tools that support supply chain risk communication and management. This research proposes an integrated framework that takes explicit account of multiple types of risk in aiding decision-making, and compares and ranks alternative risk mitigation strategies individually and collectively in indicator basis using fuzzy set theory and multiple criteria decision analysis (MCDA) methods. Through an illustrative case, the research demonstrates that the proposed framework provides a holistic view of supply chain risks and enables firms to foresee, spot and respond to the exposed risks in an effective and efficient manner.

The iso, hypo or hyper intensity, similarity of shape, size and location complicates the identification of brain tumors. Therefore, an adequate Computer Aided Diagnosis (CAD) system is designed for classification of brain tumor for assisting inexperience radiologists in diagnosis process. A multifarious database of real post contrast T1-weighted MR images from 10 patients has been taken. This database consists of primary brain tumors namely Meningioma (MENI-class 1), Astrocytoma (AST-class 2), and Normal brain regions (NORM-class 3). The region of interest(s) (ROIs) of size 20 x 20 is extracted by the radiologists from each image in the database. A total of 371 texture and intensity features are extracted from these ROI(s). An Artificial Neural Network (ANN) is used to classify these three classes as it shows better classification results on multivariate non-linear, complicated, rule based domains, and decision making domains. It is being observed that ANN provides much accurate results in terms of individual classification accuracy and overall classification accuracy. The four discrete experiments have been performed. Initially, the experiment was performed by extracting 263 features and an overall classification accuracy 78.10% is achieved, however, it was noticed that MENI (class-1) was highly misclassified with AST (class-2). Further, to improve the overall classification accuracy and individual classification accuracy specifically for MENI (class-1), LAWs textural energy measures (LTEM) are added in the feature bank (263+108=371). An individual class accuracy of 91.40% is obtained for MENI (class-1), 91.43% for AST (class-2), 94.29% for NORM (class-3) and an overall classification accuracy of 92.43% is achieved. The results are calculated with and without addition of LTEM feature with Principle component analysis (PCA)-ANN. LTEM-PCA-ANN approach improved results with an overall accuracy of 93.34%. The texture patterns obtained were clear enough to differentiate between MENI (class-1) and AST (class-2) despite of necrotic and cystic component and location and size of tumor. LTEM detected fundamental texture properties such as level, edge, spot, wave and ripple in both horizontal and vertical directions which boosted the texture energy.

Plagiarism is a serious problem identified amongst research community. Plagiarism has been around for centuries, but with the use of Internet and easy access to material in electronic format has made it easier to plagiarize materials of others. On the other hand plagiarism detection is now as easy as plagiarizing a document. There are a number of anti-plagiarism software available either freely or commercially, which can be used for plagiarism detection. But the commercial softwares are too expensive. So it is not affordable for an individual to purchase those softwares. This paper highlights the plagiarism detection softwares which are freely available online, that can be downloaded free of cost. It is suggested that faculty members and research scholars can use these anti plagiarism softwares in checking their thesis or research papers before submitting to universities or conferences.Identifying the plagiarized content has become one of the major concerns of journals publishers, research center and conferences organisers. The software that are mentioned and referred to in this paper are all valuable resources to discover plagiarized materials. By employing these softwares one can ascertain that none of the articles, documents or research work in any form and capacity, is not plagiarized and thereby the copyright of the publisher and the authors are not violated. Plagiarism can destroy any one's career and this paper creates awareness of plagiarism softwares among research scholars, faculty members and helps them to have successful academic careers in future by avoiding plagiarism.

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