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World Congress on engineering 2016, WCE 2016

作者： Madhulika Bansal, Abhay Yadav, Divakar Amity University UP Amity School of Engineering and Technology Department of Computer Science and Engineering Noida201313 India Jaypee University Department of Computer Science and Information Technology Noida India

ISBN: (纸本)9789881925305

Random eye moment sleep is characterized by random and rapid movement of eyes when a person sleeps. Rapid eye moment sleep occurs normally close to morning and usually for longest period of sleep. An infant spends most of his sleep under REM phase. There are some neurons in the brain stem called REM sleep-on cells. REM sleep on cells contributes 25% of total sleep. In fact REM sleep is an incorporation of procedural memory and spatial memory. During REM, the brain activities are similar to that of during waking hours. During REM sleep there is repetition of ideas and thoughts in the form of dreams. During REM, the sleep signals originated in the brain stem can be interpreted as information. So in this work we detected neuron components *** image using Matlab. The main purpose of detecting these components is to show that one of neuron component called dendrite is responsible for storing, and receiving information. We have done preprocessing and extracted the features with the help using marker controlled watershed segmentation technique.

关键词： Sleep research

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Developing Efficient Data Mining Algorithms

Developing Efficient Data Mining Algorithms

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International Conference on Intelligent Sustainable Systems

作者： Avinash Pandey Anuradha Sharma Krishna Kant Agrawal Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University

ISBN: (纸本)9781538619605

Today in computer world huge amount of data is generated every second. This generated data is so huge that it is difficult to find the relevant data for a specific purpose on demand. Data mining is an approach to provide a way to extract the purpose specific data in the relevant time for the decision-making purpose. Data mining include the various process such as data cleaning, data selection, data integration, data transformation and pattern evaluation. Data mining also include several functionalities such as characterization, discrimination, association analysis, classification, prediction, clustering, evolution and deviation analysis. These all processes are depending greatly on mathematical functions and foundation. The mathematics can be seen as the tool for data mining. These mathematical functions can be combined, changed and refined to produce a more efficient algorithm for the classification of data sets. The data classification is done by the classifier also based on function on which the whole algorithmic efficiency is dependent.

关键词： Data Mining C4.5 K Means Support vector Machine Algorithm

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Preface

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Advances in Intelligent Systems and Computing 2018年 584卷 v-vi页

作者： Pant, Millie Ray, Kanad Sharma, Tarun K. Rawat, Sanyog Bandyopadhyay, Anirban Department of Applied Science and Engineering IIT Roorkee Saharanpur India Department of Physics Amity School of Applied Sciences Amity University Rajasthan JaipurRajasthan India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Rajasthan JaipurRajasthan India Department of Electronics and Communication Engineering SEEC Manipal University Jaipur JaipurRajasthan India Surface Characterization Group NIMS Nano Characterization Unit Advanced Key Technologies Division TsukubaIbaraki Japan

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Investigation of Multiple Models of Artificial Neural Networks

Investigation of Multiple Models of Artificial Neural Networ...

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International Conference on Intelligent Sustainable Systems

作者： Monika Verma Shikha Singh Krishna Kant Agrawal Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow

ISBN: (纸本)9781538619605

The Artificial neural networks are basically signified as a system of neurons that are form of facts or a communication processing of numerical models which is usually based on a genetically neurons. Artificial Neural Network is intricate structured that consists the pair of an interrelated neurons that provides very extreme possibilities for the multiplex problem solving and another applications which can usually plays an essential role in present computer science field so that the researchers from a variant disciplines are designed an artificial neural networks for solving the problems of a pattern recognitions, predictions, optimizations, an associative memory and an control.

关键词： Artificial Neural Network Neurons Pattern Recognitions The Learning Process

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Raspberry Pi voting system, a reliable technology for transparency in democracy

Raspberry Pi voting system, a reliable technology for transp...

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2016 IEEE International Conference on Advances in Electronics, Communication and computer technology, ICAECCT 2016

作者： Vamsikrishna, Patchava Kumar, Sonti Dinesh Bommisetty, Dinesh Tyagi, Akshat Department of Electronics and Communication Engineering RGUKT-Nuzvid Andhra Pradesh India Department of Computer Science and Engineering Amity School of Engineering and Technology Noida India

ISBN: (纸本)9781509036622

Voting process takes the significant part of democratic systems. In most of the developing countries, paper ballot method is still being used. Information technology also has offered significant advantages and facilities to improve the quality of voting process. Digital recording electronic systems are developed replacing paper ballots. Although this is a great advancement, this process is prone to tampering threats and electoral frauds. This paper presents a new voting process which makes use of Raspberry Pi which has improved reliability and transparency over currently used systems. This system employs biometric identifiers in pursuit of authenticity. Pre collected details of all the voters in the country will be maintained as a central database by the government. This data is rechecked at the time of voting to ensure the identity of voters. Usage of Raspberry Pi for web casting the complete voting process and time to time display of polling percentage etc., details will tremendously affect the reliability of the process. Transparency and minimum usage of personnel is achieved at a cheaper cost and simpler process with this Raspberry Pi and its peripherals. © 2016 IEEE.

关键词： Transparency

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An Automatic Brain Tumor Extraction System Using Different Segmentation Methods

An Automatic Brain Tumor Extraction System Using Different S...

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IEEE International Conference on Computational Intelligence & Communication technology (CICT)

作者： Arashdeep Kaur Computer Science and Engineering Department Amity School of Engineering & Technology Amity University Uttar Pradesh

ISBN: (纸本)9781509002115

This paper present algorithms for brain tumor extraction from Magnetic Resonance Image (MRI) using four different methods namely Otsu, K-means, Fuzzy-c-Means and thresholding. Brain tumor is inherently serious and life-threatening disease which can threat life of a human being. A robust automated brain tumor detection system with high accuracy is always preferable over the manual detection. The main objective of this paper is to develop a fully automated brain tumor detection system that can detect and extract tumor from MR Image of brain. In this paper a sophisticated automated tumor detection system is presented having very good accuracy with low computational time. This paper also gives the comparison between the algorithms presented. Experimental results validate that all the algorithms presented in this paper have accuracy more than 87.32% of detecting the brain tumor. A dataset of 25 MR Images of brain have been used for testing the system and recording the experimental results. It has been found that algorithm developed using Fuzzy c-means algorithm gives the highest accuracy i.e. 90.57% while thresholding algorithm takes the least time to compute the results i.e. 1.11 seconds.

关键词： Tumors Image segmentation Clustering algorithms Algorithm design and analysis Magnetic resonance imaging Feature extraction Image enhancement

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Big Data in Mobile and Pervasive Computing

Big Data in Mobile and Pervasive Computing

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International Conference on Intelligent Sustainable Systems

作者： Anuradha Sharma Omar Farooq Praveen Kumar Misra Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Lucknow Campus India Department of Statistics Dr. Shakuntala Misra National Rehab University Lucknow

ISBN: (纸本)9781538619605

The data from the pervasive devices/computing can be taken from the servers of the respective service providers or application oriented services. The data can be further processed by analytics using Big Data with different tools for the analytics like Bluemix, Hadoop and Matlab so, that we can get useful information in real time without further processing and delay. The data in pervasive computing is mostly unstructured. As the Data generated from sensor networks and data in mobile and smart devices is increasing day by day, Data Analytics in unstructured data like this is difficult with limited processing power. But the tools like Hadoop, Bluemix, and Matlab with good connectivity to cloud can provide us some important data analysis tools. Big Data can help in many ways which will ensure better services and responsiveness which is lacking in most of the devices right now. The inference from the captured data can be further processed for various applications like business, decisions making, forecasting etc.

关键词： Hadoop MapReduce HDFS Hive Pig Flume

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Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000-17: analysis for the Global Burden of Disease Study 2017 (vol 395, pg 1779, 2020)

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LANCET 2020年第10239期395卷 1762-1762页

作者： Reiner, R. C., Jr. Hay, S., I Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Global Health School of Medicine University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States College of Health and Medical Sciences Haramaya University Harar Ethiopia Department of Epidemiology and Biostatistics Haramaya University Harar Ethiopia Department of Medical Laboratory Sciences Haramaya University Harar Ethiopia School of Nursing and Midwifery Haramaya University Harar Ethiopia School of Pharmacy Haramaya University Harar Ethiopia School of Public Health Haramaya University Harar Ethiopia Haramaya University Harar Ethiopia Advanced Diagnostic and Interventional Radiology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Biology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Research Institute Tehran University of Medical Sciences Tehran Iran Department of Economics and Management Sciences for Health Tehran University of Medical Sciences Tehran Iran Department of Environmental Health Engineering Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran Department of Microbiology Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran Hematology-Oncology and Stem Cell Transplantation Research Center Tehran University of Medical Sciences Tehran Iran Iran National Institute of Health Research Tehran University of Medical Sciences Tehran Iran Metabolomics and

SummaryBackgroundAcross low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood diarrhoea. MethodsWe used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor estimates. FindingsThe greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54·0% (95% uncertainty interval [UI] 38·1–65·8), 17·4% (7·7–28·4), and 59·5% (34·2–86·9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy coverage. InterpretationBy co-analysing geospatial trends in dia

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General chair's message

Proceedings of the 2016 6th International Conference - Cloud...

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Proceedings of the 2016 6th International Conference - Cloud System and Big Data engineering, Confluence 2016 2016年 iv页

作者： Bansal, Abhay Amity School of Engineering and Technology India Department of Computer Science and Engineering Amity University Uttar Pradesh India

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