scholarly journals Tipos de big data y análisis sociológico: usos, críticas y problemas éticos

2022 ◽  
Author(s):  
Biagio Aragona
Keyword(s):  
Big Data ◽  
Expert Knowledge ◽  
Social Research ◽  
Digital Data ◽  
Ethical Guidelines ◽  
Ethical Problems ◽  
Evaluation Practice ◽  
Different Types

Solo con un conocimiento más consciente de los diferentes tipos de big data y sus posibles usos, límites y ventajas la sociología se beneficiará realmente de estas bases empíricas. En este artículo, a partir de una clasificación de los diversos tipos de big data, se describen algunas áreas de uso en la investigación social destacando cuestiones críticas y problemas éticos. Los límites se vinculados a cuestiones fundamentales relativas a la calidad de los big data. Otra cuestión clave se refiere al acceso. Otro aspecto metodológico a tener en cuenta es que los datos digitales en la web deben considerarse no intrusivos. Los métodos de investigación encubiertos ha desafiado la práctica de evaluación ética establecidas adoptadas en la mayoría de las instituciones de investigación: el consentimiento informado. Las pautas éticas digitales no pueden ser universales y estar establecidas de una vez por todas. Only through expert knowledge of the different types of big data and their possible uses, limits and advantages will sociology benefit from these empirical bases. In this article, based on a classification of the various types of big data, some areas of use in social research are described, highlighting critical questions and ethical problems. The limits are related to fundamental questions regarding the quality of big data. Another paramount issue concerns access. A further methodological aspect is that digital data on the web should be considered non-intrusive. Covert research methods have challenged the established ethical evaluation practice adopted in most research institutions: informed consent. Digital ethical guidelines cannot be universal and established once and for all.

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

Applications of Big Data and Green IoT-Enabling Technologies for Smart Cities

2019 ◽  
pp. 22-41
Author(s):  
Onur Dogan ◽  
Omer Faruk Gurcan
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Digital Data ◽  
Rapid Urbanization ◽  
Data Resource ◽  
Parallel Data ◽  
Information And Communication

In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.

scholarly journals Classification of options of the temporomandibular joint during muscular-articular dysfunctions

2019 ◽  
Vol 06-07 (1-2) ◽  
pp. 22-25
Author(s):  
O.Y. Stoyan ◽  
G.P. Ruzin ◽  
I.I. Sokolova
Keyword(s):  
Temporomandibular Joint ◽  
Treatment Method ◽  
Radiological Features ◽  
Quality Of Treatment ◽  
Temporomandibular Joints ◽  
Different Types ◽  
Choice Of Treatment

The article presents the rationale for a differentiated approach to diagnostics in order to improve the quality of treatment of patients with musculoskeletal dysfunction of the temporomandibular joints according to the degree of dysfunctional manifestations. Clinical and radiological features of different types of dysfunction are described, which makes it possible to improve the differentiated approach to the choice of treatment method for dysfunctional disorders.

scholarly journals Digital Transformation, Big Data and Research Landscape in Digital Communication

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Imam Syafganti
Keyword(s):  
Big Data ◽  
Digital Media ◽  
Research Method ◽  
Social Research ◽  
Digital Data ◽  
Communication Research ◽  
Research Directions ◽  
Critical Issues ◽  
Data Source ◽  
Media Research

The digitization of communications technology has led to an intense interaction between human and digital-based technology. A large number of digital data traces produced by humans as a result of that activity. Such data is commonly referred to Big Data. The availability of Big Data as a digital data source in turn, opens opportunities for communication scientists to be able to use that data to get the patterns and trends of human activities that have been done through social research. It is necessary to understand the basic concept of the Big Data, using appropriate tools and adequate access to the data, and appropriate research method in order to be able to conduct research by using such digital data. This paper aims to describe the potential of Big Data for the purposes of communication research, the use of appropriate tools, techniques and methods and to identify potential research directions in the digital realm. Some limitations and critical issues related to the research validity, population and sample, as well as ethics in digital media research method were also discussed.

scholarly journals Precision medicine and digital phenotyping: Digital medicine's way from more data to better health

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110664
Author(s):  
Renate Baumgartner
Keyword(s):  
Big Data ◽  
Precision Medicine ◽  
Treatment Options ◽  
Digital Data ◽  
Private Companies ◽  
Digital Devices ◽  
Digital Phenotyping ◽  
Digital Medicine ◽  
Different Types ◽  
Distribution Of Power

Precision medicine and digital phenotyping are two prominent data-based approaches within digital medicine. While precision medicine historically used primarily genetic data to find targeted treatment options, digital phenotyping relies on the usage of big data deriving from digital devices such as smartphones, wearables and other connected devices. This paper first focusses on the aspect of data type to explore differences and similarities between precision medicine and digital phenotyping. It outlines different ways of data collection and production and the consequences thereof. Second, it shows how these sorts of data influence dominant beliefs in the field: The field of precision medicine relying on the dominant understanding of ‘genetic determinism’ imported from genetics, digital phenotyping building on the logic of ‘data fundamentalism’. In the end, the analysis shows how digital data informs potentials as well as challenges of precision medicine and digital phenotyping: a better health care for (some) individuals connected with individualisation and responsibilisation for all, with a prognosed shift from reactive to preventive medicine. Additionally, data-based approaches might facilitate epistemological and ontological redirections for the whole field of medicine that will also affect knowledge production and a reassessment of the value of different types of knowledge (quantifiable vs. non-quantifiable) with all its consequences. Institutionally, it might lead to shifts in distribution of power to experts in big data related technologies, i.e. private companies.

Applications of Big Data and Green IoT-Enabling Technologies for Smart Cities

2022 ◽  
pp. 1090-1109
Author(s):  
Onur Dogan ◽  
Omer Faruk Gurcan
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Digital Data ◽  
Rapid Urbanization ◽  
Data Resource ◽  
Parallel Data ◽  
Information And Communication

In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.

scholarly journals Classification of Watermelon using Sound Processing.

2020 ◽  
Vol 9 (4) ◽  
pp. 2489-2492
Keyword(s):  
Naive Bayes ◽  
Random Tree ◽  
Human Beings ◽  
Sound Processing ◽  
Sound File ◽  
Average Accuracy ◽  
Different Types ◽  
Sound Files

In a country like India, wide variety of fruits are available. Fruits plays an important role in the health of human beings and naturally health improves, if the quality of the fruit is good. Grading of the watermelon quality helps the consumers and vendors. The proposed work is to classify the watermelons based on the sound. Sound file dataset is created manually by tapping the watermelon and recording the sound. Dataset consist of different types of watermelon. For this, different size, colour and shape of the watermelons are used. Features are extracted from the sound files. Naïve Bayes, SMO and Random Tree classifiers are used for classification. The proposed work has achieved average accuracy of 78.8 %.

scholarly journals Big Data: las implicancias de la democratización de los métodos. Oportunidades y desafíos que los dispositivos y datos digitales plantean para la investigación social

2016 ◽  
pp. 237
Author(s):  
Andrés Asenjo Morosetti ◽  
Christian Riveros Pino
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Research ◽  
Social Practices ◽  
Digital Data ◽  
Technology Research ◽  
Social Production ◽  
Social Scientists ◽  
Challenges And Opportunities ◽  
The Subject

ResumenEl presente ensayo monográfico intenta brindar una panorámica globaldel fenómeno del Big Data, entendido como un sistema de producción,procesamiento, sistematización y análisis de datos sociales a gran escala.Actualmente, una multiplicidad de entes se han posicionado como agentesrecopiladores de datos sobre conductas y prácticas sociales, gracias aavances tecnológicos que han incrementado las capacidades sistémicas parala obtención y análisis de grandes volúmenes de información. Esto suponeuna suerte de democratización de los métodos, lo cual plantea una serie dedesafíos y oportunidades para los cientistas sociales, otrora agentes exclusivosde la producción de lo social. Ante esto, el presente documento ofrece unmarco general de análisis, esbozando reflexiones temáticas y estratégicaspara la comunidad científica en general y para la sociología en particular. Elensayo se estructura a partir de tres secciones: una introducción, que ofreceun acercamiento global al tema; una sección de discusión, en la que se revisaa grandes rasgos las principales problemáticas y desafíos que presenta elBig Data para la investigación social y los científicos sociales, y una secciónde conclusiones, en la que se esbozan posibles alternativas de integraciónmetodológica entre las nuevas formas de investigación social y los métodostradicionales.Palabras clave: Big Data, tecnologías de la información, métodos deinvestigación, investigación social, empirismo.Big Data: Implications of the democratization ofmethods.Opportunities and challenges for social researchprovided by digital data and devicesAbstractThis monographic essay attempts to provide a comprehensive overviewof the phenomenon of Big Data, understood as a system of production,processing, systematization and analysis of large-scale social data. Currently,a multiplicity of entities have positioned themselves as agents collectingdata, behaviors and social practices, thanks to technological advancesthat have increased systemic capacities for collection and analysis of largevolumes of information. This is a sort of democratization of methods, whichposes a number of challenges and opportunities for social scientists, onceexclusive agents of social production. In this way, the present documentprovides a general framework for analysis outlining thematic and strategicconsiderations for the scientific community in general and sociology inparticular. The essay is structured in three sections: an introduction, whichprovides a comprehensive approach to the subject; a section of discussion,which reviews in broad terms the main problems and challenges imposed byBig Data for social research and social scientists; and a section of conclusionswith possible alternatives for methodological integration among new formsof social research and the traditional methods.Keywords: Big Data, information technology, research methods, socialresearch, empiricism.Big Data: as implicações da democratização dosmétodos.Oportunidades e desafios que os dispositivos edados digitais projetam para a pesquisa socialResumoO presente ensaio monográfico tenta oferecer uma visão panorâmicado fenómeno do Big Data, entendido como um sistema de produção,processamento, sistematização e análise dos dados sociais em grandeescala. Atualmente, uma multiplicidade de entidades têm se posicionadocomo agentes coletores de dados sobre comportamentos e práticas sociais,graças aos avanços tecnológicos que aumentaram as capacidades sistêmicaspara a obtenção e análise de grandes volumes de informação. Esta é umaespécie de democratização dos métodos, o que coloca uma série de desafiose oportunidades para os cientistas sociais, pois em outros tempos foram osagentes exclusivos de produção social. Diante disso, o presente documentoapresenta um quadro geral de análise, delineando reflexões temáticase estratégicas para a comunidade científica em geral e para a sociologiaem particular. O ensaio é estruturado em três seções: uma introdução,que oferece uma abordagem global para o tema; uma seção de discussão, onde é revistada em termos gerais, os principais problemas e desafios queapresenta a Big Data para a investigação social e os cientistas sociais, e umaseção de conclusões onde se esboçam possíveis alternativas de integraçãometodológica entre as novas formas de investigação social e os métodostradicionais.Palavras-chave: Big Data, tecnologias da informação, métodos de pesquisa,pesquisa social, empirismo.

Big Data Research in the Tourism Industry

2020 ◽  
Vol 11 (4) ◽  
pp. 26-41
Author(s):  
Imadeddine Mountasser ◽  
Brahim Ouhbi ◽  
Bouchra Frikh ◽  
Ferdaous Hdioud
Keyword(s):  
Big Data ◽  
State Of The Art ◽  
Tourism Industry ◽  
Data Sources ◽  
Digital Data ◽  
Current State ◽  
Wide Range ◽  
The World ◽  
Data Acquisition Process

Nowadays, people and things are becoming permanently interconnected. This interaction overloaded the world with an incredible digital data deluge—termed big data—generated from a wide range of data sources. Indeed, big data has invaded the domain of tourism as a source of innovation that serves to better understand tourists' behavior and enhance tourism destination management and marketing. Thus, tourism stakeholders have increasingly leveraging tourism-related big data sources to gather abundant information concerning all tourism industry axes. However, big data has several complexity aspects and brings commensurate challenges that go along with its exploitation. It has specifically changed the way data is acquired and managed, which may influence the nature and the quality of the conducted analyses and the made decisions. Thus, this article investigates the big data acquisition process and thoroughly identifies its challenges and requirements. It also reveals its current state-of-the-art protocols and frameworks.

scholarly journals Big Data: las implicancias de la democratización de los métodos. Oportunidades y desafíos que los dispositivos y datos digitales plantean para la investigación social

2016 ◽  
pp. 237
Author(s):  
Andrés Asenjo Morosetti ◽  
Christian Riveros Pino
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Research ◽  
Social Practices ◽  
Digital Data ◽  
Technology Research ◽  
Social Production ◽  
Social Scientists ◽  
Challenges And Opportunities ◽  
The Subject

ResumenEl presente ensayo monográfico intenta brindar una panorámica globaldel fenómeno del Big Data, entendido como un sistema de producción,procesamiento, sistematización y análisis de datos sociales a gran escala.Actualmente, una multiplicidad de entes se han posicionado como agentesrecopiladores de datos sobre conductas y prácticas sociales, gracias aavances tecnológicos que han incrementado las capacidades sistémicas parala obtención y análisis de grandes volúmenes de información. Esto suponeuna suerte de democratización de los métodos, lo cual plantea una serie dedesafíos y oportunidades para los cientistas sociales, otrora agentes exclusivosde la producción de lo social. Ante esto, el presente documento ofrece unmarco general de análisis, esbozando reflexiones temáticas y estratégicaspara la comunidad científica en general y para la sociología en particular. Elensayo se estructura a partir de tres secciones: una introducción, que ofreceun acercamiento global al tema; una sección de discusión, en la que se revisaa grandes rasgos las principales problemáticas y desafíos que presenta elBig Data para la investigación social y los científicos sociales, y una secciónde conclusiones, en la que se esbozan posibles alternativas de integraciónmetodológica entre las nuevas formas de investigación social y los métodostradicionales.Palabras clave: Big Data, tecnologías de la información, métodos deinvestigación, investigación social, empirismo.Big Data: Implications of the democratization ofmethods.Opportunities and challenges for social researchprovided by digital data and devicesAbstractThis monographic essay attempts to provide a comprehensive overviewof the phenomenon of Big Data, understood as a system of production,processing, systematization and analysis of large-scale social data. Currently,a multiplicity of entities have positioned themselves as agents collectingdata, behaviors and social practices, thanks to technological advancesthat have increased systemic capacities for collection and analysis of largevolumes of information. This is a sort of democratization of methods, whichposes a number of challenges and opportunities for social scientists, onceexclusive agents of social production. In this way, the present documentprovides a general framework for analysis outlining thematic and strategicconsiderations for the scientific community in general and sociology inparticular. The essay is structured in three sections: an introduction, whichprovides a comprehensive approach to the subject; a section of discussion,which reviews in broad terms the main problems and challenges imposed byBig Data for social research and social scientists; and a section of conclusionswith possible alternatives for methodological integration among new formsof social research and the traditional methods.Keywords: Big Data, information technology, research methods, socialresearch, empiricism.Big Data: as implicações da democratização dosmétodos.Oportunidades e desafios que os dispositivos edados digitais projetam para a pesquisa socialResumoO presente ensaio monográfico tenta oferecer uma visão panorâmicado fenómeno do Big Data, entendido como um sistema de produção,processamento, sistematização e análise dos dados sociais em grandeescala. Atualmente, uma multiplicidade de entidades têm se posicionadocomo agentes coletores de dados sobre comportamentos e práticas sociais,graças aos avanços tecnológicos que aumentaram as capacidades sistêmicaspara a obtenção e análise de grandes volumes de informação. Esta é umaespécie de democratização dos métodos, o que coloca uma série de desafiose oportunidades para os cientistas sociais, pois em outros tempos foram osagentes exclusivos de produção social. Diante disso, o presente documentoapresenta um quadro geral de análise, delineando reflexões temáticase estratégicas para a comunidade científica em geral e para a sociologiaem particular. O ensaio é estruturado em três seções: uma introdução,que oferece uma abordagem global para o tema; uma seção de discussão, onde é revistada em termos gerais, os principais problemas e desafios queapresenta a Big Data para a investigação social e os cientistas sociais, e umaseção de conclusões onde se esboçam possíveis alternativas de integraçãometodológica entre as novas formas de investigação social e os métodostradicionais.Palavras-chave: Big Data, tecnologias da informação, métodos de pesquisa,pesquisa social, empirismo.

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