A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).

Computer Vision Based Virtual Sketch Using Detection

In todays world during this pandemic situation Online Learning is the only source where one could learn. Online learning makes students more curious about the knowledge and so they decide their learning path . But considering the academics as they have to pass the course or exam given, they need to take time to study, and have to be disciplined about their dedication. And there are many barriers for Online learning as well. Students are lowering their grasping power the reason for this is that each and every student was used to rely on their teacher and offline classes. Virtual writing and controlling system is challenging research areas in field of image processing and pattern recognition in the recent years. It contributes extremely to the advancement of an automation process and can improve the interface between man and machine in numerous applications. Several research works have been focusing on new techniques and methods that would reduce the processing time while providing higher recognition accuracy. Given the real time webcam data, this jambord like python application uses OpenCV library to track an object-of-interest (a human palm/finger in this case) and allows the user to draw bymoving the finger, which makes it both awesome and interesting to draw simple thing. Keyword: Detection, Handlandmark , Keypoints, Computer vision, OpenCV

Isotopic Composition of Glacier Ice and Meltwater in the Arid Parts of the Altai Mountains (Central Asia)

The objective of this study is to reveal the isotopic composition of ice and meltwater in glaciated regions of South-Eastern Altai. The paper depicts differences between the isotopic composition of glacier ice from several types of glaciers and from various locations. Detected differences between the isotopic composition of glacier ice in diversified parts of the study region are related to local climate patterns. Isotopic composition of meltwater and isotopic separation for glacier rivers runoff showed that in the Tavan-Bogd massif, seasonal snow participates more in the formation of glacier runoff due to better conditions for snow accumulation on the surface of glaciers. In other research areas pure glacier meltwater prevails in runoff.

Neurohypofyzární peptidy v lékařství z pražských a švédských laboratoří. Část I: Historie výzkumu a počátky produkce lékových forem

From the fifties to the seventies of the last century, the neurohypophyseal peptides oxytocin and vasopressin constituted one of the main research areas at the Institute of Organic Chemistry and Biochemistry in Prague (IOCB). A significant contribution to this area is associated with the names of František Šorm, director of the said institute, and Josef Rudinger, head of the institute's peptide laboratory. At that time, newly developed research tools enabled to synthesize structural analogues of these hormones in numerous laboratories worldwide and hence to investigate the structure-activity relationships within this peptide group. Contributions of single peptide-chain positions to the respective biological activities were identified which opened a possibility to rationalize a design of peptides with a combination of changes in several positions. Several clinically interesting peptides were synthesized in the late 1960s at the IOCB and employed as therapeutics: [(Gly)3-Cys1,Lys8]-vasopressin (Glypressin Ferring®, Terli­pressin INN), 1-deamino-8-ᴅ-arginine vasopressin (Desmopressin INN, dDAVP), and later the uterotonics carbetocin (INN), widely used in obstetrics to prevent postpartum haemorrhage. Since the industrial production of peptide therapeutics was scarcely possible under the conditions of socialist economy in Czechoslovakia as well as in other countries under the Soviet influence, F. Šorm agreed to use the already established scientific contacts of IOCB with the Swedish pharmaceutical company Ferring AB and to transfer the production licences to Sweden. The license agreements were signed in 1969 and led to a quick spread of dDAVP in the substitution therapy of the central form of diabetes insipidus and, moreover, contributed to a fast upsurge of the Ferring company. Somewhat later, Glypressin was produced as a therapeutic with a prolonged action in cases of cardiovascular collapse. Contacts between Prague peptide chemists and the Ferring company lasted on a rather informal base until the end of the 1980s. After the fall of the totalitarian regime in Czechoslovakia in 1990, Ferring started a joint-venture collaboration with the newly organized Czech company Léčiva st.p. Praha in a newly established group Prague Polypeptide Institute spol. s. r.o. (later Ferring-Léčiva A.S.). A substantial part of the peptide-production capacities was then transferred to new buildings in Prague.

Implementing E-Commerce from Logistic Perspective: Literature Review and Methodological Framework

E-commerce is always more diffused as a selling channel around the whole world market, and its importance has increased and continues to increase with the COVID-19 pandemic emergency. It provides enterprises a lot of opportunities, as the importance of physical stores to sell goods is bypassed. However, it has also changed the role of logistics in the supply chain. For this reason, this work aims to identify the main logistics research areas related to e-commerce implementation and the factors and key performance indicators, which should be taken into account for each logistics research area, with particular attention to sustainable aspects. For doing this, a structured and comprehensive literature analysis is carried out. Keywords associated with e-commerce and logistics areas are matched to identify the most interesting works related to its implementation. From the analysis, five main research areas are identified: Supply Chain Network Design (SCND); Outbound Logistics (OL); Reverse Logistics (RL); Warehousing (WR); and IT and data management (E-IT). For each area, key factors, strategies and performance indicators have been identified. Finally, a methodological framework that summarizes the results of the analysis is presented; this is a useful tool for managers to implement or expand their e-commerce business. Many works are focused on one research area, carrying out critical factors, models, and methods to implement that topic. Instead, the methodological framework presented here summarizes multiple research areas from a logistic point of view, identifying for each one input and output variables and how they influence each other.

A profile of research on the parasitic trypanosomatids and the diseases they cause

The parasitic trypanosomatids cause lethal and debilitating diseases, the leishmaniases, Chagas disease, and the African trypanosomiases, with major impacts on human and animal health. Sustained research has borne fruit by assisting efforts to reduce the burden of disease and by improving our understanding of fundamental molecular and cell biology. But where has the research primarily been conducted, and which research areas have received the most attention? These questions are addressed below using publication and citation data from the past few decades.

EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain’s electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86 % using only 24 sensors with AR 20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

Business Models in the Industry 4.0 Environment—Results of Web of Science Bibliometric Analysis

The Fourth Industrial Revolution affects the operations of companies and results in new strategic thinking. The changes resulting from the requirements of Industry 4.0 force restructuring in many areas of management or the building of new business models. The aim of this article was to indicate the pillars that will form the basis for building business models of enterprises functioning in the era of Industry 4.0. The research methods used in this article were bibliometric analysis and analysis of the content of sophisticated publications. The results of this research are the analysis of the dynamics of publications in the area of business models in the era of Industry 4.0, an indication of the research areas undertaken in these publications and the identification of the pillars that will constitute the basis for building business models in the era of Industry 4.0. Business models in the era of Industry 4.0 are to be a method of increasing and using the company’s resources in order to achieve a competitive advantage through personalization of products and their new quality; their key competitive advantage will be a structure based on a network of cyber-physical cooperation. This article is dedicated to scientists and business practitioners looking for tips for building modern business models.

Damage to black locust leaves (Robinia pseudoacacia L.) by the black locust gall midge (Obolodiplosis robiniae) (Haldeman, 1847) in the Włoszczowa-Jędrzejów Protected Landscape Area - research on strengthening and developing knowledge and awareness in environmental education

The prepared article by the team of authors aims to show research in the field of strengthening and developing knowledge and awareness from environmental education in the community of nature conservation services and the academic community. This paper is the result of research conducted in 2014–2015, in the Włoszczowa-Jędrzejów Protected Landscape Area, in the Świętokrzyskie Province. The material for the study was acacia robinia (Robinia pseudoacacia L.) leaves collected on two research areas, differentiated due to the influence of anthropogenic factors. As a result of the study, 5,000 black locust leaves were collected, 65% of which were found to be damaged. Research stands under the influence of strong anthropopressure were characterised by a higher number of lesions on leaves.

Determining Research Priorities for Astronomy Using Machine Learning

We summarize the first exploratory investigation into whether Machine Learning techniques can augment science strategic planning. We find that an approach based on Latent Dirichlet Allocation using abstracts drawn from high-impact astronomy journals may provide a leading indicator of future interest in a research topic. We show two topic metrics that correlate well with the high-priority research areas identified by the 2010 National Academies’ Astronomy and Astrophysics Decadal Survey. One metric is based on a sum of the fractional contribution to each topic by all scientific papers (“counts”) while the other is the Compound Annual Growth Rate of counts. These same metrics also show the same degree of correlation with the whitepapers submitted to the same Decadal Survey. Our results suggest that the Decadal Survey may under-emphasize fast growing research. A preliminary version of our work was presented by Thronson et al.