BIOMETRIC AUTHENTICATION SYSTEM USING RPI

A biometric authentication system acquires biometric sample such as fingerprint. The fingerprint signifies physiological features of an individual.This is a system which maintains the attendance records of students automatically. In this designing of an efficient module that comprises of a fingerprint sensor to manage the attendance records of students. This module enrolls the student’s as well as staff’s fingerprints. This enrolling is a onetime process and their fingerprints will be stored in the fingerprint sensor. During enrolling of fingerprints alone requires a system since it is a onetime process. After enrolling process gets completed disconnect the module from the system and insert a battery into the module. This will provide power when the module is not connected with the system. The presence of each students will be updated in a database

UML Artefacts for a Blockchain-enabled Platform for Fairtrade

Fairtrade-certified products have successfully entered the mainstream distribution channels, mostly in developed countries, and these products are now sold in famous supermarket chains. Nonetheless, the packaging and labeling of products as “Fairtrade” command premium pricing in the marketplace. How much of this, however, is valid and justified? Despite the reputable certification mechanisms for quality assurance, mass media reports suggest that much of the “surplus value” goes to the accreditation agencies themselves instead of the producers. This article proposes an agenda to set this right with a blockchain platform that provides “trust-free” assurances of verifiable labeling. Using an Action Design Research methodology, we have specified a research prototype of a Blockchain-enabled Fair-Trade platform Unified Modelling Language artifacts. We believe this will set the direction for social inclusion as part of information systems scholars’ aspiration to promote “tech for good.

Trustworthy Cloud Computing

Trustworthy cloud computing has been a central tenet of the European Union cloud strategy for nearly a decade. This chapter discusses the origins of trustworthy computing and specifically how the goals of trustworthy computing—security and privacy, reliability, and business integrity—are represented in computer science research. We call for further inter- and multi-disciplinary research on trustworthy cloud computing that reflect a more holistic view of trust

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

UML Artefacts for a Blockchain-enabled Platform for Fairtrade

Fairtrade-certified products have successfully entered the mainstream distribution channels mostly in developed countries, and these products are now sold in famous supermarket chains. Nonetheless, the packaging and labeling of products as “Fairtrade” commands premium pricing in the marketplace. How much of this, however, is valid and justified? Despite the reputable certification mechanisms for quality assurance, mass media reports suggest that much of the “surplus value” goes to the accreditation agencies themselves instead of the producers. This article proposes an agenda to set this right with a blockchain platform that provides “trust-free” assurances of verifiable labeling. Using an Action Design Research methodology, we have specified a research prototype of a Blockchain-enabled Fair-Trade platform Unified Modelling Language artefacts. We believe this will set the direction for social inclusion as part of information systems scholars’ aspiration to promote “tech for good”.peerReviewe

AN ARCHITECTURAL FRAMEWORK FOR HETEROGENEOUS NETWORKING

Abstract: The growth over the last decade in the use of wireless networking devices has been explosive. Soon many devices will have multiple network interfaces, each with very different characteristics. We believe that a framework that encapsulates the key challenges of heterogeneous networking is required. Like a map clearly helps one to plan a journey, a framework is needed to help us move forward in this unexplored area. The approach taken here is similar to the OSI model in which tightly defined layers are used to specify functionality, allowing a modular approach to the extension of systems and the interchange of their components, whilst providing a model that is more oriented to heterogeneity and mobility.

Applications of Big Data Analytics to Control COVID-19 Pandemic

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications

Learning trends in customer churn with rule-based and kernel methods

In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program