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Suvodeep Mazumdar
Information School, University of Sheffield, Regent Court

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Article
Published: 29 July 2021
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Study Objective Tools proposed to triage patient acuity in COVID-19 infection have only been validated in hospital populations. We estimated the accuracy of five risk-stratification tools recommended to predict severe illness and compare accuracy to existing clinical decision-making in a pre-hospital setting. Methods An observational cohort study using linked ambulance service data for patients attended by EMS crews in the Yorkshire and Humber region of England between 18th March 2020 and 29th June 2020 was conducted to assess performance of the PRIEST tool, NEWS2, the WHO algorithm, CRB-65 and PMEWS in patients with suspected COVID-19 infection. The primary outcome was death or need for organ support. Results Of 7549 patients in our cohort, 17.6% (95% CI:16.8% to 18.5%) experienced the primary outcome. The NEWS2, PMEWS, PRIEST tool and WHO algorithm identified patients at risk of adverse outcomes with a high sensitivity (>0.95) and specificity ranging from 0.3 (NEWS2) to 0.41 (PRIEST tool). The high sensitivity of NEWS2 and PMEWS was achieved by using lower thresholds than previously recommended. On index assessment, 65% of patients were transported to hospital and EMS decision to transfer patients achieved a sensitivity of 0.84 (95% CI 0.83 to 0.85) and specificity of 0.39 (95% CI 0.39 to 0.40). Conclusion Use of NEWS2, PMEWS, PRIEST tool and WHO algorithm could improve sensitivity of EMS triage of patients with suspected COVID-19 infection. Use of the PRIEST tool would improve sensitivity of triage without increasing the number of patients conveyed to hospital.

ACS Style

Carl Marincowitz; Laura Sutton; Tony Stone; Richard Pilbery; Richard Campbell; Benjamin Thomas; Janette Turner; Peter A. Bath; Fiona Bell; Katie Biggs; Madina Hasan; Frank Hopfgartner; Suvodeep Mazumdar; Jennifer Petrie; Steve Goodacre. Prognostic accuracy of triage tools for adults with suspected COVID-19 in a pre-hospital setting: an observational cohort study. 2021, 1 .

AMA Style

Carl Marincowitz, Laura Sutton, Tony Stone, Richard Pilbery, Richard Campbell, Benjamin Thomas, Janette Turner, Peter A. Bath, Fiona Bell, Katie Biggs, Madina Hasan, Frank Hopfgartner, Suvodeep Mazumdar, Jennifer Petrie, Steve Goodacre. Prognostic accuracy of triage tools for adults with suspected COVID-19 in a pre-hospital setting: an observational cohort study. . 2021; ():1.

Chicago/Turabian Style

Carl Marincowitz; Laura Sutton; Tony Stone; Richard Pilbery; Richard Campbell; Benjamin Thomas; Janette Turner; Peter A. Bath; Fiona Bell; Katie Biggs; Madina Hasan; Frank Hopfgartner; Suvodeep Mazumdar; Jennifer Petrie; Steve Goodacre. 2021. "Prognostic accuracy of triage tools for adults with suspected COVID-19 in a pre-hospital setting: an observational cohort study." , no. : 1.

Preprint content
Published: 29 June 2021
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Objective: To assess accuracy of telephone triage in identifying patients who need emergency care amongst those with suspected COVID-19 infection and identify factors which affect triage accuracy. Design: Observational cohort study Setting: Community telephone triage in the Yorkshire and Humber, Bassetlaw, North Lincolnshire and North East Lincolnshire region. Participants: 40, 261 adults who contacted NHS 111 telephone triage services provided by Yorkshire Ambulance Service NHS Trust between the 18th March 2020 and 29th June 2020 with symptoms indicating possible COVID-19 infection were linked to Office for National Statistics death registration data, hospital and general practice electronic health care data collected by NHS Digital. Outcome: Accuracy of triage disposition (self-care/non-urgent clinical assessment versus ambulance dispatch/urgent clinical assessment) was assessed in terms of death or need for organ support at 30, 7 and 3 days from first contact with the telephone triage service. Results: Callers had a 3% (1, 200/40, 261) risk of adverse outcome. Telephone triage recommended self-care or non-urgent assessment for 60% (24, 335/40, 261), with a 1.3% (310/24, 335) risk of subsequent adverse outcome. Telephone triage had 74.2% sensitivity (95% CI: 71.6 to 76.6%) and 61.5% specificity (61% to 62%) for adverse outcomes at 30 days from first contact. Multivariable analysis suggested some co-morbidities (such as chronic respiratory disease) may be over-estimated as predictors of adverse outcome, while the association of diabetes with adverse outcome may be under-estimated. Repeat contact with the service appears to be an important under recognised predictor of adverse outcomes with both 2 contacts (OR 1.77 95% CI: 1.14 to 2.75) and 3 or more contacts (OR 4.02 95% CI: 1.68 to 9.65) associated with clinical deterioration when not provided with an ambulance or urgent clinical assessment. Conclusion: Patients advised to self-care or receive non-urgent clinical assessment had a small but non-negligible risk of serious clinical deterioration. The sensitivity and specificity of telephone triage was comparable to other tools used to triage patient acuity in emergency and urgent care. Repeat contact with telephone services needs recognition as an important predictor of subsequent adverse outcomes.

ACS Style

Carl Marincowitz; Tony Stone; Peter Bath; Richard Richard Campbell; Janette Turner; Madina Hussein; Richard Pilbery; Benjamin Thomas; Laura Sutton; Fiona Bell; Katie Biggs; Frank Hopfgartner; Suvodeep Mazumdar; Jennifer Petrie; Steve Goodacre. Accuracy of telephone triage for predicting adverse outcome in suspected COVID-19: An observational cohort study. 2021, 1 .

AMA Style

Carl Marincowitz, Tony Stone, Peter Bath, Richard Richard Campbell, Janette Turner, Madina Hussein, Richard Pilbery, Benjamin Thomas, Laura Sutton, Fiona Bell, Katie Biggs, Frank Hopfgartner, Suvodeep Mazumdar, Jennifer Petrie, Steve Goodacre. Accuracy of telephone triage for predicting adverse outcome in suspected COVID-19: An observational cohort study. . 2021; ():1.

Chicago/Turabian Style

Carl Marincowitz; Tony Stone; Peter Bath; Richard Richard Campbell; Janette Turner; Madina Hussein; Richard Pilbery; Benjamin Thomas; Laura Sutton; Fiona Bell; Katie Biggs; Frank Hopfgartner; Suvodeep Mazumdar; Jennifer Petrie; Steve Goodacre. 2021. "Accuracy of telephone triage for predicting adverse outcome in suspected COVID-19: An observational cohort study." , no. : 1.

Journal article
Published: 04 May 2021 in IoT
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A key aspect of the development of Smart Cities involves the efficient and effective management of resources to improve liveability. Achieving this requires large volumes of sensors strategically deployed across urban areas. In many cases, however, it is not feasible to install devices in remote and inaccessible areas, resulting in incomplete data coverage. In such situations, citizens can often play a crucial role in filling this data collection gap. A popular complimentary science to traditional sensor-based data collection is to design Citizen Science (CS) activities in collaboration with citizens and local communities. Such activities are also designed with a feedback loop where the Citizens benefit from their participation by gaining a greater sense of awareness of their local issues while also influencing how the activities can align best with their local contexts. The participation and engagement of citizens are vital and yet often a real challenge in ensuring the long-term continuity of CS projects. In this paper, we explore engagement factors, factors that help keeping engagement high, in technology-centric CS projects where technology is a key enabler to support CS activities. We outline a literature review of exploring and understanding various motivational and engagement factors that influence the participation of citizens in technology-driven CS activities. Based on this literature, we present a mobile-based flood monitoring citizen science application aimed at supporting data collection activities in a real-world CS project as part of an EU project. We discuss the results of a user evaluation of this app, and finally discuss our findings within the context of citizens’ engagement.

ACS Style

Muhammad Ali; Bhupesh Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Sydney Simpson. Using Citizen Science to Complement IoT Data Collection: A Survey of Motivational and Engagement Factors in Technology-Centric Citizen Science Projects. IoT 2021, 2, 275 -309.

AMA Style

Muhammad Ali, Bhupesh Mishra, Dhavalkumar Thakker, Suvodeep Mazumdar, Sydney Simpson. Using Citizen Science to Complement IoT Data Collection: A Survey of Motivational and Engagement Factors in Technology-Centric Citizen Science Projects. IoT. 2021; 2 (2):275-309.

Chicago/Turabian Style

Muhammad Ali; Bhupesh Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Sydney Simpson. 2021. "Using Citizen Science to Complement IoT Data Collection: A Survey of Motivational and Engagement Factors in Technology-Centric Citizen Science Projects." IoT 2, no. 2: 275-309.

Journal article
Published: 26 November 2020 in Future Internet
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This paper presents a long-term study on how the public engage with discussions around citizen science and crowdsourcing topics. With progress in sensor technologies and IoT, our cities and neighbourhoods are increasingly sensed, measured and observed. While such data are often used to inform citizen science projects, it is still difficult to understand how citizens and communities discuss citizen science activities and engage with citizen science projects. Understanding these engagements in greater depth will provide citizen scientists, project owners, practitioners and the generic public with insights around how social media can be used to share citizen science related topics, particularly to help increase visibility, influence change and in general and raise awareness on topics. To the knowledge of the authors, this is the first large-scale study on understanding how such information is discussed on Twitter, particularly outside the scope of individual projects. The paper reports on the wide variety of topics (e.g., politics, news, ecological observations) being discussed on social media and a wide variety of network types and the varied roles played by users in sharing information in Twitter. Based on these findings, the paper highlights recommendations for stakeholders for engaging with citizen science topics.

ACS Style

Suvodeep Mazumdar; Dhavalkumar Thakker. Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks. Future Internet 2020, 12, 210 .

AMA Style

Suvodeep Mazumdar, Dhavalkumar Thakker. Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks. Future Internet. 2020; 12 (12):210.

Chicago/Turabian Style

Suvodeep Mazumdar; Dhavalkumar Thakker. 2020. "Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks." Future Internet 12, no. 12: 210.

Journal article
Published: 13 November 2020 in Smart Cities
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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

ACS Style

Dhavalkumar Thakker; Bhupesh Kumar Mishra; Amr Abdullatif; Suvodeep Mazumdar; Sydney Simpson. Explainable Artificial Intelligence for Developing Smart Cities Solutions. Smart Cities 2020, 3, 1353 -1382.

AMA Style

Dhavalkumar Thakker, Bhupesh Kumar Mishra, Amr Abdullatif, Suvodeep Mazumdar, Sydney Simpson. Explainable Artificial Intelligence for Developing Smart Cities Solutions. Smart Cities. 2020; 3 (4):1353-1382.

Chicago/Turabian Style

Dhavalkumar Thakker; Bhupesh Kumar Mishra; Amr Abdullatif; Suvodeep Mazumdar; Sydney Simpson. 2020. "Explainable Artificial Intelligence for Developing Smart Cities Solutions." Smart Cities 3, no. 4: 1353-1382.

Research article
Published: 22 May 2020 in British Journal of Occupational Therapy
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Introduction Home assessments are integral to the occupational therapy role, providing opportunities to personalise and integrate care. However, they are resource intensive and declining in number. A 3-month service development within one United Kingdom National Health Service acute hospital setting explored the concept of using digital technology to undertake remote home assessments. Methods Four work streams explored the concept’s feasibility and acceptability: real-world testing; user consultations; narrative case study collection; traditional visit resource use exploration. Project participants were occupational therapists and patient and public representatives recruited via snowball sampling or critical case sampling. Qualitative data were thematically analysed identifying key themes. Analysis of quantitative data provided descriptive statistics. Findings The remote home visit concept was feasible within four specific contexts. Qualitative themes suggest acceptability depends on visitor safety, visitor training, visitor induction and standardisation of practice. Consultees perceived the approach to have potential for resource savings, personalisation and integration of care. Barriers to acceptance included data security, data governance, technology failure and threat to occupational therapists’ role and skills. Conclusion Applying digital technology to occupational therapy home assessment appears feasible and acceptable within a specific context. Further research is recommended to develop the technology, and test and investigate perceived benefits within wider contexts and stakeholder groups.

ACS Style

Jennifer Read; Natalie Jones; Colette Fegan; Peter Cudd; Emma Simpson; Suvodeep Mazumdar; Fabio Ciravegna. Remote Home Visit: Exploring the feasibility, acceptability and potential benefits of using digital technology to undertake occupational therapy home assessments. British Journal of Occupational Therapy 2020, 83, 648 -658.

AMA Style

Jennifer Read, Natalie Jones, Colette Fegan, Peter Cudd, Emma Simpson, Suvodeep Mazumdar, Fabio Ciravegna. Remote Home Visit: Exploring the feasibility, acceptability and potential benefits of using digital technology to undertake occupational therapy home assessments. British Journal of Occupational Therapy. 2020; 83 (10):648-658.

Chicago/Turabian Style

Jennifer Read; Natalie Jones; Colette Fegan; Peter Cudd; Emma Simpson; Suvodeep Mazumdar; Fabio Ciravegna. 2020. "Remote Home Visit: Exploring the feasibility, acceptability and potential benefits of using digital technology to undertake occupational therapy home assessments." British Journal of Occupational Therapy 83, no. 10: 648-658.

Original article
Published: 24 February 2020 in Journal of Reliable Intelligent Environments
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Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms.

ACS Style

Bhupesh Kumar Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Daniel Neagu; Marian Gheorghe; Sydney Simpson. A novel application of deep learning with image cropping: a smart city use case for flood monitoring. Journal of Reliable Intelligent Environments 2020, 6, 51 -61.

AMA Style

Bhupesh Kumar Mishra, Dhavalkumar Thakker, Suvodeep Mazumdar, Daniel Neagu, Marian Gheorghe, Sydney Simpson. A novel application of deep learning with image cropping: a smart city use case for flood monitoring. Journal of Reliable Intelligent Environments. 2020; 6 (1):51-61.

Chicago/Turabian Style

Bhupesh Kumar Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Daniel Neagu; Marian Gheorghe; Sydney Simpson. 2020. "A novel application of deep learning with image cropping: a smart city use case for flood monitoring." Journal of Reliable Intelligent Environments 6, no. 1: 51-61.

Chapter
Published: 01 January 2019 in Emergency and Disaster Management
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Availability and access to information is critical for a highly effective response to an ongoing event however, information reported by citizens is based on their context, bias and subjective interpretation, and the channel of communication may be too narrow to provide clear, accurate reporting. This can often lead to inadequate response to an emergency, which can in turn result in loss of property or even lives. Excessive response to an emergency can also result in a waste of highly resources. The authors' solution to address this problem is to make the citizen act as a camera for the control room by exploiting the user's mobile camera. The system is designed to provide a live view of the citizen's immediate surroundings, while control room personnel can provide instructions. In this paper, the authors introduce their approach and share initial insights from a focus group validation session and then four evaluations with users within a separate but closely related domain. They discuss their observations, evaluation results and provide a set of recommendations for the Emergency Response domain.

ACS Style

Suvodeep Mazumdar; Fabio Ciravegna; Neil Ireson; Jennifer Read; Emma Simpson; Peter Cudd. Communicating With Citizens on the Ground. Emergency and Disaster Management 2019, 464 -485.

AMA Style

Suvodeep Mazumdar, Fabio Ciravegna, Neil Ireson, Jennifer Read, Emma Simpson, Peter Cudd. Communicating With Citizens on the Ground. Emergency and Disaster Management. 2019; ():464-485.

Chicago/Turabian Style

Suvodeep Mazumdar; Fabio Ciravegna; Neil Ireson; Jennifer Read; Emma Simpson; Peter Cudd. 2019. "Communicating With Citizens on the Ground." Emergency and Disaster Management , no. : 464-485.

Book chapter
Published: 15 October 2018 in Citzen Science
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ACS Style

Suvodeep Mazumdar; Luigi Ceccaroni; Jaume Piera; Franz Hölker; Arne J. Berre; Robert Arlinghaus; Anne Bowser. Citizen science technologies and new opportunities for participation. Citzen Science 2018, 303 -320.

AMA Style

Suvodeep Mazumdar, Luigi Ceccaroni, Jaume Piera, Franz Hölker, Arne J. Berre, Robert Arlinghaus, Anne Bowser. Citizen science technologies and new opportunities for participation. Citzen Science. 2018; ():303-320.

Chicago/Turabian Style

Suvodeep Mazumdar; Luigi Ceccaroni; Jaume Piera; Franz Hölker; Arne J. Berre; Robert Arlinghaus; Anne Bowser. 2018. "Citizen science technologies and new opportunities for participation." Citzen Science , no. : 303-320.

Chapter
Published: 05 September 2018 in Computer Communications and Networks
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With organisations and governments significantly investing in cyber defenses, there is an urgent need to develop tools and technologies to help security professionals understand cyber security within their application domains. A critical aspect of this is to develop and maintain situation awareness of security aspects within cyber infrastructures. Visual analytics provide support to security professionals to help understand evolving situations and the overall status of systems, particularly when dealing with large volumes of data. This chapter explores situation awareness in cyber security in more detail, aligning design recommendations for visual analytics to assist security professionals with progressive levels of situation awareness.

ACS Style

Suvodeep Mazumdar; Jing Wang. Big Data and Cyber Security: A Visual Analytics Perspective. Computer Communications and Networks 2018, 367 -381.

AMA Style

Suvodeep Mazumdar, Jing Wang. Big Data and Cyber Security: A Visual Analytics Perspective. Computer Communications and Networks. 2018; ():367-381.

Chicago/Turabian Style

Suvodeep Mazumdar; Jing Wang. 2018. "Big Data and Cyber Security: A Visual Analytics Perspective." Computer Communications and Networks , no. : 367-381.

Chapter
Published: 24 January 2018 in Earth Observation Open Science and Innovation
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Citizen Science, or the participation of non-professional scientists in a scientific project, has a long history—in many ways, the modern scientific revolution is thanks to the effort of citizen scientists. Like science itself, citizen science is influenced by technological and societal advances, such as the rapid increase in levels of education during the latter part of the twentieth century, or the very recent growth of the bidirectional social web (Web 2.0), cloud services and smartphones. These transitions have ushered in, over the past decade, a rapid growth in the involvement of many millions of people in data collection and analysis of information as part of scientific projects. This chapter provides an overview of the field of citizen science and its contribution to the observation of the Earth, often not through remote sensing but a much closer relationship with the local environment. The chapter suggests that, together with remote Earth Observations, citizen science can play a critical role in understanding and addressing local and global challenges.

ACS Style

Mordechai (Muki) Haklay; Suvodeep Mazumdar; Jessica Wardlaw. Citizen Science for Observing and Understanding the Earth. Earth Observation Open Science and Innovation 2018, 69 -88.

AMA Style

Mordechai (Muki) Haklay, Suvodeep Mazumdar, Jessica Wardlaw. Citizen Science for Observing and Understanding the Earth. Earth Observation Open Science and Innovation. 2018; ():69-88.

Chicago/Turabian Style

Mordechai (Muki) Haklay; Suvodeep Mazumdar; Jessica Wardlaw. 2018. "Citizen Science for Observing and Understanding the Earth." Earth Observation Open Science and Innovation , no. : 69-88.

Proceedings
Published: 01 January 2018 in Proceedings
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Accurate, long-term data are needed in order to determine trends in active travel, to examine the effectiveness of any interventions and to quantify the health, social and economic consequences of active travel. However, most studies of individual travel behaviour have either used self-report (which is limited in detail and open to bias), or provided logging devices for short periods, so lack the ability to monitor long-term trends. We have developed apps using participants’ own smartphones (Android or iOS) that monitor and feed-back individual user’s physical activity whilst the phone is carried or worn. The nature, time and location of any physical activity are uploaded to a secure survey and allow researchers to identify large scale behaviour. Pilot data from almost 2000 users have been logged and are reported. This constitutes a natural experiment, collecting long-term physical activity, transport mode and route choice information across a large cross-section of users.

ACS Style

Ben W. Heller; Suvodeep Mazumdar; Fabio Ciravegna. Large Scale, Long-Term, High Granularity Measurement of Active Travel Using Smartphones Apps. Proceedings 2018, 2, 293 .

AMA Style

Ben W. Heller, Suvodeep Mazumdar, Fabio Ciravegna. Large Scale, Long-Term, High Granularity Measurement of Active Travel Using Smartphones Apps. Proceedings. 2018; 2 (6):293.

Chicago/Turabian Style

Ben W. Heller; Suvodeep Mazumdar; Fabio Ciravegna. 2018. "Large Scale, Long-Term, High Granularity Measurement of Active Travel Using Smartphones Apps." Proceedings 2, no. 6: 293.

Chapter
Published: 22 December 2017 in Earth Systems Data and Models
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Insights from satellite observations are increasingly being used to enhance a range of domains from highly specialised scientific research through to everyday applications directly benefiting members of the public. A particular category of satellite observations—Earth Observations (EO)—is concerned with capturing information regarding the Earth’s atmospheric and environmental conditions and observing human activity and its impact on the Earth’s surface. A growing number of technologies and services heavily rely on EO data and the rapidly improving fidelity, coverage, timeliness and accessibility of such observations are providing significant opportunities for new applications of economic and societal benefit. With the increasing importance, relevance and size of EO data sets, it is critical to understand how the value of such data can be maximised by complementing EO with other sources of data and efficiently making complex interpretations and decisions. The wide adoption and availability of smartphones, Internet devices and increased accessibility to information has paved the way for large numbers of citizens and communities to participate in scientific, technological, societal and decision-making activities. This chapter discusses the experience of the European Space Agency funded Crowd4Sat project led by the University of Sheffield that investigated different facets of how crowdsourcing and citizen science impact upon the validation, use and enhancement of Observations from Satellites products and services.

ACS Style

Suvodeep Mazumdar; Stuart Wrigley; Fabio Ciravegna; Camille Pelloquin; Sam Chapman; Laura De Vendictis; Domenico Grandoni; Michele Ferri; Luca Bolognini. Crowdsourcing to Enhance Insights from Satellite Observations. Earth Systems Data and Models 2017, 35 -52.

AMA Style

Suvodeep Mazumdar, Stuart Wrigley, Fabio Ciravegna, Camille Pelloquin, Sam Chapman, Laura De Vendictis, Domenico Grandoni, Michele Ferri, Luca Bolognini. Crowdsourcing to Enhance Insights from Satellite Observations. Earth Systems Data and Models. 2017; ():35-52.

Chicago/Turabian Style

Suvodeep Mazumdar; Stuart Wrigley; Fabio Ciravegna; Camille Pelloquin; Sam Chapman; Laura De Vendictis; Domenico Grandoni; Michele Ferri; Luca Bolognini. 2017. "Crowdsourcing to Enhance Insights from Satellite Observations." Earth Systems Data and Models , no. : 35-52.

Conference paper
Published: 11 September 2017 in Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
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ACS Style

Lu Bai; Neil Ireson; Suvodeep Mazumdar; Fabio Ciravegna. Lessons learned using wi-fi and Bluetooth as means to monitor public service usage. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers 2017, 432 -440.

AMA Style

Lu Bai, Neil Ireson, Suvodeep Mazumdar, Fabio Ciravegna. Lessons learned using wi-fi and Bluetooth as means to monitor public service usage. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. 2017; ():432-440.

Chicago/Turabian Style

Lu Bai; Neil Ireson; Suvodeep Mazumdar; Fabio Ciravegna. 2017. "Lessons learned using wi-fi and Bluetooth as means to monitor public service usage." Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers , no. : 432-440.

Journal article
Published: 19 January 2017 in Remote Sensing
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The impact of Crowdsourcing and citizen science activities on academia, businesses, governance and society has been enormous. This is more prevalent today with citizens and communities collaborating with organizations, businesses and authorities to contribute in a variety of manners, starting from mere data providers to being key stakeholders in various decision-making processes. The “Crowdsourcing for observations from Satellites” project is a recently concluded study supported by demonstration projects funded by European Space Agency (ESA). The objective of the project was to investigate the different facets of how crowdsourcing and citizen science impact upon the validation, use and enhancement of Observations from Satellites (OS) products and services. This paper presents our findings in a stakeholder analysis activity involving participants who are experts in crowdsourcing, citizen science for Earth Observations. The activity identified three critical areas that needs attention by the community as well as provides suggestions to potentially help in addressing some of the challenges identified.

ACS Style

Suvodeep Mazumdar; Stuart Wrigley; Fabio Ciravegna. Citizen Science and Crowdsourcing for Earth Observations: An Analysis of Stakeholder Opinions on the Present and Future. Remote Sensing 2017, 9, 87 .

AMA Style

Suvodeep Mazumdar, Stuart Wrigley, Fabio Ciravegna. Citizen Science and Crowdsourcing for Earth Observations: An Analysis of Stakeholder Opinions on the Present and Future. Remote Sensing. 2017; 9 (1):87.

Chicago/Turabian Style

Suvodeep Mazumdar; Stuart Wrigley; Fabio Ciravegna. 2017. "Citizen Science and Crowdsourcing for Earth Observations: An Analysis of Stakeholder Opinions on the Present and Future." Remote Sensing 9, no. 1: 87.

Journal article
Published: 01 January 2017 in Studies in health technology and informatics
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Suvodeep Mazumdar; Fabio Ciravegna; Neil Ireson; Jennifer Read; Emma Simpson; Peter Cudd. Access Visits Using Video Communication. Studies in health technology and informatics 2017, 242, 102 -110.

AMA Style

Suvodeep Mazumdar, Fabio Ciravegna, Neil Ireson, Jennifer Read, Emma Simpson, Peter Cudd. Access Visits Using Video Communication. Studies in health technology and informatics. 2017; 242 ():102-110.

Chicago/Turabian Style

Suvodeep Mazumdar; Fabio Ciravegna; Neil Ireson; Jennifer Read; Emma Simpson; Peter Cudd. 2017. "Access Visits Using Video Communication." Studies in health technology and informatics 242, no. : 102-110.

Conference paper
Published: 15 July 2015 in University of Sheffield Engineering Symposium
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Suvodeep Mazumdar. Visualisation of Large Semantic Datasets: A Scalable, Generic and Aesthetic Approach. University of Sheffield Engineering Symposium 2015, 1 .

AMA Style

Suvodeep Mazumdar. Visualisation of Large Semantic Datasets: A Scalable, Generic and Aesthetic Approach. University of Sheffield Engineering Symposium. 2015; ():1.

Chicago/Turabian Style

Suvodeep Mazumdar. 2015. "Visualisation of Large Semantic Datasets: A Scalable, Generic and Aesthetic Approach." University of Sheffield Engineering Symposium , no. : 1.

Conference paper
Published: 21 April 2015 in Computer Vision
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VISUAL 2014 addressed the challenges in providing knowledge engineers and data analysts with visualizations and well-designed user interfaces to support the understanding of the concepts, data instances and relationships in different domains. The workshop was organized around two tracks: one focused on visualizations and user interfaces for Knowledge Engineering, and the other on Visual Analytics for dynamic and large-scale data. Six contributions were presented at the workshop, which also included an interactive tool demonstration session.

ACS Style

Valentina Ivanova; Tomi Kauppinen; Steffen Lohmann; Suvodeep Mazumdar; Catia Pesquita; Kai Xu. Introduction to VISUAL 2014 - Workshop on Visualizations and User Interfaces for Knowledge Engineering and Linked Data Analytics. Computer Vision 2015, 80 -82.

AMA Style

Valentina Ivanova, Tomi Kauppinen, Steffen Lohmann, Suvodeep Mazumdar, Catia Pesquita, Kai Xu. Introduction to VISUAL 2014 - Workshop on Visualizations and User Interfaces for Knowledge Engineering and Linked Data Analytics. Computer Vision. 2015; ():80-82.

Chicago/Turabian Style

Valentina Ivanova; Tomi Kauppinen; Steffen Lohmann; Suvodeep Mazumdar; Catia Pesquita; Kai Xu. 2015. "Introduction to VISUAL 2014 - Workshop on Visualizations and User Interfaces for Knowledge Engineering and Linked Data Analytics." Computer Vision , no. : 80-82.

Conference paper
Published: 01 January 2015 in Communications in Computer and Information Science
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Jie Gao; Suvodeep Mazumdar. Exploiting Linked Open Data to Uncover Entity Types. Communications in Computer and Information Science 2015, 51 -62.

AMA Style

Jie Gao, Suvodeep Mazumdar. Exploiting Linked Open Data to Uncover Entity Types. Communications in Computer and Information Science. 2015; ():51-62.

Chicago/Turabian Style

Jie Gao; Suvodeep Mazumdar. 2015. "Exploiting Linked Open Data to Uncover Entity Types." Communications in Computer and Information Science , no. : 51-62.

Journal article
Published: 01 January 2015 in Semantic Web
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Suvodeep Mazumdar; Daniela Petrelli; Khadija Elbedweihy; Vitaveska Lanfranchi; Fabio Ciravegna. Affective graphs: The visual appeal of Linked Data. Semantic Web 2015, 6, 277 -312.

AMA Style

Suvodeep Mazumdar, Daniela Petrelli, Khadija Elbedweihy, Vitaveska Lanfranchi, Fabio Ciravegna. Affective graphs: The visual appeal of Linked Data. Semantic Web. 2015; 6 (3):277-312.

Chicago/Turabian Style

Suvodeep Mazumdar; Daniela Petrelli; Khadija Elbedweihy; Vitaveska Lanfranchi; Fabio Ciravegna. 2015. "Affective graphs: The visual appeal of Linked Data." Semantic Web 6, no. 3: 277-312.