9,050 research outputs found
Business Case and Technology Analysis for 5G Low Latency Applications
A large number of new consumer and industrial applications are likely to
change the classic operator's business models and provide a wide range of new
markets to enter. This article analyses the most relevant 5G use cases that
require ultra-low latency, from both technical and business perspectives. Low
latency services pose challenging requirements to the network, and to fulfill
them operators need to invest in costly changes in their network. In this
sense, it is not clear whether such investments are going to be amortized with
these new business models. In light of this, specific applications and
requirements are described and the potential market benefits for operators are
analysed. Conclusions show that operators have clear opportunities to add value
and position themselves strongly with the increasing number of services to be
provided by 5G.Comment: 18 pages, 5 figure
Modern Information Systems
The development of modern information systems is a demanding task. New technologies and tools are designed, implemented and presented in the market on a daily bases. User needs change dramatically fast and the IT industry copes to reach the level of efficiency and adaptability for its systems in order to be competitive and up-to-date. Thus, the realization of modern information systems with great characteristics and functionalities implemented for specific areas of interest is a fact of our modern and demanding digital society and this is the main scope of this book. Therefore, this book aims to present a number of innovative and recently developed information systems. It is titled "Modern Information Systems" and includes 8 chapters. This book may assist researchers on studying the innovative functions of modern systems in various areas like health, telematics, knowledge management, etc. It can also assist young students in capturing the new research tendencies of the information systems' development
Big Data Analytics for Complex Systems
The evolution of technology in all fields led to the generation of vast amounts of data by modern systems. Using data to extract information, make predictions, and make decisions is the current trend in artificial intelligence. The advancement of big data analytics tools made accessing and storing data easier and faster than ever, and machine learning algorithms help to identify patterns in and extract information from data. The current tools and machines in health, computer technologies, and manufacturing can generate massive raw data about their products or samples. The author of this work proposes a modern integrative system that can utilize big data analytics, machine learning, super-computer resources, and industrial health machines’ measurements to build a smart system that can mimic the human intelligence skills of observations, detection, prediction, and decision-making. The applications of the proposed smart systems are included as case studies to highlight the contributions of each system. The first contribution is the ability to utilize big data revolutionary and deep learning technologies on production lines to diagnose incidents and take proper action. In the current digital transformational industrial era, Industry 4.0 has been receiving researcher attention because it can be used to automate production-line decisions. Reconfigurable manufacturing systems (RMS) have been widely used to reduce the setup cost of restructuring production lines. However, the current RMS modules are not linked to the cloud for online decision-making to take the proper decision; these modules must connect to an online server (super-computer) that has big data analytics and machine learning capabilities. The online means that data is centralized on cloud (supercomputer) and accessible in real-time. In this study, deep neural networks are utilized to detect the decisive features of a product and build a prediction model in which the iFactory will make the necessary decision for the defective products. The Spark ecosystem is used to manage the access, processing, and storing of the big data streaming. This contribution is implemented as a closed cycle, which for the best of our knowledge, no one in the literature has introduced big data analysis using deep learning on real-time applications in the manufacturing system. The code shows a high accuracy of 97% for classifying the normal versus defective items. The second contribution, which is in Bioinformatics, is the ability to build supervised machine learning approaches based on the gene expression of patients to predict proper treatment for breast cancer. In the trial, to personalize treatment, the machine learns the genes that are active in the patient cohort with a five-year survival period. The initial condition here is that each group must only undergo one specific treatment. After learning about each group (or class), the machine can personalize the treatment of a new patient by diagnosing the patients’ gene expression. The proposed model will help in the diagnosis and treatment of the patient. The future work in this area involves building a protein-protein interaction network with the selected genes for each treatment to first analyze the motives of the genes and target them with the proper drug molecules. In the learning phase, a couple of feature-selection techniques and supervised standard classifiers are used to build the prediction model. Most of the nodes show a high-performance measurement where accuracy, sensitivity, specificity, and F-measure ranges around 100%. The third contribution is the ability to build semi-supervised learning for the breast cancer survival treatment that advances the second contribution. By understanding the relations between the classes, we can design the machine learning phase based on the similarities between classes. In the proposed research, the researcher used the Euclidean matrix distance among each survival treatment class to build the hierarchical learning model. The distance information that is learned through a non-supervised approach can help the prediction model to select the classes that are away from each other to maximize the distance between classes and gain wider class groups. The performance measurement of this approach shows a slight improvement from the second model. However, this model reduced the number of discriminative genes from 47 to 37. The model in the second contribution studies each class individually while this model focuses on the relationships between the classes and uses this information in the learning phase. Hierarchical clustering is completed to draw the borders between groups of classes before building the classification models. Several distance measurements are tested to identify the best linkages between classes. Most of the nodes show a high-performance measurement where accuracy, sensitivity, specificity, and F-measure ranges from 90% to 100%. All the case study models showed high-performance measurements in the prediction phase. These modern models can be replicated for different problems within different domains. The comprehensive models of the newer technologies are reconfigurable and modular; any newer learning phase can be plugged-in at both ends of the learning phase. Therefore, the output of the system can be an input for another learning system, and a newer feature can be added to the input to be considered for the learning phase
SMACovid-19 — autonomous monitoring system for Covid-19
Mestrado em Engenharia Industrial ESTG-IPBAtualmente, existe uma pandemia global, COVID-19, que pode provocar consequências
devastadoras para a saúde humana. Além disso, pessoas idosas têm uma probabilidade
maior de sofrerem os efeitos mais severos da doença. wearable devices podem monitorizar
parâmetros biológicos/fÃsicos, e uma análise de previsão aos dados resultantes deve
permitir a identificação mais rápida possÃvel de pessoas que possam estar infetadas com
o vÃrus.
Para criar um sistema com estas capacidades, desenvolveram-se os módulos health
data provider e data analysis para recolher os dados e fazer as previsões, com base nesses
dados, respetivamente. A plataforma FIWARE foi usada para implementar o backend do
sistema, através de módulos open source ready-to-use para gerir os dados no sistema.
Os médicos do Hospital da Terra Quente definiram os parâmetros biológicos/fÃsicos
de interesse e, considerando os diferentes tipos de variáveis, implementaram-se três tipos
distintos de previsões: última observação, estimador linear de tendência e método aditivo
Holt-Winters. As previsões obtidas são usadas para classificar o estado de uma pessoa,
para um cenário de pior caso possÃvel: provável que esteja infetado com o vÃrus ou provável
que não esteja infetado com o vÃrus. As regras de classificação foram definidas pelos
médicos do Hospital da Terra Quente.
Os resultados obtidos são promissores e trabalho futuro pode ser feito para melhorar
o sistema na inclusão de mais fontes de dados, ajuste dos métodos de previsão e na
introdução de novas metodologias de classificação final.Nowadays, there exists a global pandemic, COVID-19, that may provoke devastating
consequences to human health. Moreover, elder people have a higher probability to suffer
the most harsh effects of the disease. By monitoring biological/physical parameters with
the aid of wearable devices and applying forecast methods to the collected data, it should
be feasible to identify, as soon as possible, people that may be infected with the virus.
To create a system with such capabilities, a health data provider and a data analysis
modules were implemented, to fetch the biological/physical data and to generate forecasts
based on that data, respectively. The backend of the system was implemented through the
FIWARE platform, using open source ready-to-use modules of this platform to manage
the data in the system.
The biological/physical parameters of interest were defined by the medical personnel
of Hospital da Terra Quente and, considering the different type of variables in analysis,
three types of forecasts were implemented: last observation, linear trend estimator and
additive Holt-Winters method. The obtained forecasts are used, combined with a worst
case scenario approach to classify the state of a person: likely to have been infected by
the virus or not likely to have been infected by the virus. The classification rules were
devised by the Hospital da Terra Quente medical staff.
The obtained results are promising and future work can be done to improve the system
in terms of providing extra data sources, fine tuning of the forecast methods and by
introducing new final classification methodologies
The Elements of Big Data Value
This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation
A Social Informatics Account of the Assimilation of an Enterprise-Wide Health Information System
This paper presents findings from a case study of the assimilation process of an enterprise-wide information system in radiology practices in Australia. Interviews with radiologists, radiographers and administrative staff are used to explore the impact of institutional structures on the assimilation process. The paper develops an argument that culture within and outside the practice, institutional structures within the practice and organizational-level management mandates impacted on the assimilation process. The study explores a theoretical framework that integrates elements of social actor theory (Lamb & Kling, 2003) providing a more fine-grained analysis concentrating on the relationships among the radiology practitioners, the technology (an enterprise-wide HIS), and a larger social milieu surrounding its use. Findings are reported that demonstrate both the enablers and inhibitors to the assimilation process based on in-depth interviews with radiology practitioners
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
Sixth-generation (6G) networks anticipate intelligently supporting a wide
range of smart services and innovative applications. Such a context urges a
heavy usage of Machine Learning (ML) techniques, particularly Deep Learning
(DL), to foster innovation and ease the deployment of intelligent network
functions/operations, which are able to fulfill the various requirements of the
envisioned 6G services. Specifically, collaborative ML/DL consists of deploying
a set of distributed agents that collaboratively train learning models without
sharing their data, thus improving data privacy and reducing the
time/communication overhead. This work provides a comprehensive study on how
collaborative learning can be effectively deployed over 6G wireless networks.
In particular, our study focuses on Split Federated Learning (SFL), a technique
recently emerged promising better performance compared with existing
collaborative learning approaches. We first provide an overview of three
emerging collaborative learning paradigms, including federated learning, split
learning, and split federated learning, as well as of 6G networks along with
their main vision and timeline of key developments. We then highlight the need
for split federated learning towards the upcoming 6G networks in every aspect,
including 6G technologies (e.g., intelligent physical layer, intelligent edge
computing, zero-touch network management, intelligent resource management) and
6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous
systems). Furthermore, we review existing datasets along with frameworks that
can help in implementing SFL for 6G networks. We finally identify key technical
challenges, open issues, and future research directions related to SFL-enabled
6G networks
The Digitalisation of African Agriculture Report 2018-2019
An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains
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