A multimodal deep learning framework using local feature representations for face recognition

YesThe most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to address the face recognition problem in unconstrained conditions. Firstly, a novel multimodal local feature extraction approach based on merging the advantages of the Curvelet transform with Fractal dimension is proposed and termed the Curvelet–Fractal approach. The main motivation of this approach is that theCurvelet transform, a newanisotropic and multidirectional transform, can efficiently represent themain structure of the face (e.g., edges and curves), while the Fractal dimension is one of the most powerful texture descriptors for face images. Secondly, a novel framework is proposed, termed the multimodal deep face recognition (MDFR)framework, to add feature representations by training aDBNon top of the local feature representations instead of the pixel intensity representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary to those acquired by the Curvelet–Fractal approach. Finally, the performance of the proposed approaches has been evaluated by conducting a number of extensive experiments on four large-scale face datasets: the SDUMLA-HMT, FERET, CAS-PEAL-R1, and LFW databases. The results obtained from the proposed approaches outperform other state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by achieving new state-of-the-art results on all the employed datasets

Disease awareness campaigns in printed and online media in Latvia : Cross-sectional study on consistency with WHO ethical criteria for medicinal drug promotion and European standards

Funding Information: Teresa Leonardo Alves declares no conflicts of interest. She has worked in the past for not-for-profit organizations which have advocated against the relaxation of the direct-to-consumer advertising ban in the European Union, namely Prescrire (2012–2016) and Health Action International (2006–2011). Elita Poplavska is a board member of not-for-profit organizations - Health Projects for Latvia and Health Action International (which aim to promote rational use of medicines and reduce influence of pharmaceutical advertisement). Signe Mezinska is a board member of not-for-profit organizations - Health Projects for Latvia and Health Action International (which aim to promote rational use of medicines and reduce influence of pharmaceutical advertisement). Ieva Salmane-Kulikovska declares no conflicts of interest. Liga Andersone declares no conflicts of interest. Aukje Mantel-Teeuwisse is the Managing Director of the WHO Collaborating Centre for Pharmaceutical Policy & Regulation, which receives no direct funding or donations from private parties, including the pharmaceutical industry. Research funding from public-private partnerships, e.g. IMI, Lygature (https://www.lygature.org), is accepted under the condition that no company-specific product or company-related study is conducted. The Centre has received unrestricted research funding from public sources, e.g. Netherlands Organisation for Health Research and Development (ZonMW), Zorg Instituut Nederland (ZIN), the Dutch Medicines Evaluation Board (MEB), and the Dutch Ministry of Health. Barbara Mintzes has acted as an expert witness on behalf of plaintiffs in a Canadian class action suit on cardiovascular risks of testosterone therapy. Publisher Copyright: © 2018 The Author(s).Background: European legislation prohibits direct-to-consumer advertising of prescription medicines, but allows drug manufacturers to provide information to the public on health and diseases. Our aim was to measure the frequency of disease awareness campaigns in Latvian media and assess their compliance with international and European standards. Methods: Materials on health/disease and treatments were collected between April and September 2015 from 12 newspapers and magazines and six online portals. Disease awareness campaigns were assessed using a previously developed instrument based on the WHO Ethical Criteria for Medicinal Drug promotion and European standards (EU law and pharmaceutical industry self-regulatory guidelines). Collected materials were used to examine the information provided on medical conditions and their diagnosis and treatment. The inter-rater reliability was calculated. Results: We collected 263 materials from print (n = 149) and online media (n = 114); 94 were news items and 169 were disease-awareness advertisements. Cancer, cardiovascular problems, allergies and respiratory diseases were common topics. Of the 157 campaigns assessed, non-compliance was identified in 149 cases (inter-rater reliability 90%), mainly due to misleading or incomplete information, lack of balance and the absence of a listed author/sponsor. Six disease awareness campaigns directly mentioned a pharmaceutical product by brand name and other four included the logo or name of a manufacturer, referred to a condition and indirectly mentioned a treatment, all in contravention with European law. Conclusions: The compliance of disease awareness campaigns in Latvian media with international and European standards is low. This raises concerns about the nature of information being conveyed. Through lack of balance, missing sponsorship information, and misleading or incomplete information, these campaigns could contribute to inaccurate self-diagnosis and generate demand among those who might not need medical treatment.publishersversionPeer reviewe

Machine Learning based Employee Attrition Predicting

Now a day’s variety of reasons for job resignations due to this, we have to take different types of measurements for prediction of job seekers. They have different reasons for not doing jobs well and fell like pressure. Many employees suddenly come to an end of their service without any reason. Techniques of machine learning have full-grown in fame in the middle of researchers in current years. It is accomplished of propose answer to a broad range of problems. Help of machine learning, you may produce prediction concerning staff abrasion. So machine learning model we will be using TCS employee attrition a genuine time dataset to train our model. The aim of this study is to at hand a comparison of different machine learning algorithms for predict which employees are probable to go away their society. We propose two methods to crack the dataset into train and test data: the 75 percent train 25 percent test split and the K Fold methods. Three techniques are three methods that we employ to train our model for correctness comparison, and we will compare the exactness of the models generate using these three Boosting Algorithms