scholarly journals A Machine Learning SDN-Enabled Big Data Model for IoMT Systems

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2228
Author(s):  
Khalid Haseeb ◽  
Irshad Ahmad ◽  
Israr Iqbal Awan ◽  
Jaime Lloret ◽  
Ignacio Bosch
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Smart Cities ◽  
Network Resources ◽  
Data Accessibility ◽  
Efficient Management ◽  
Machine Learning Model ◽  
Medical Sensors ◽  
The Stability

In recent times, health applications have been gaining rapid popularity in smart cities using the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both patients and professionals for remote data accessibility and suitable actions. However, timely medical decisions and efficient management of big data using IoT-based resources are the burning research challenges. Additionally, the distributed nature of data processing in many proposed solutions explicitly increases the threats of information leakages and damages the network integrity. Such solutions impose overhead on medical sensors and decrease the stability of the real-time transmission systems. Therefore, this paper presents a machine-learning model with SDN-enabled security to predict the consumption of network resources and improve the delivery of sensors data. Additionally, it offers centralized-based software define network (SDN) architecture to overcome the network threats among deployed sensors with nominal management cost. Firstly, it offers an unsupervised machine learning technique and decreases the communication overheads for IoT networks. Secondly, it predicts the link status using dynamic metrics and refines its strategies using SDN architecture. In the end, a security algorithm is utilized by the SDN controller that efficiently manages the consumption of the IoT nodes and protects it from unidentified occurrences. The proposed model is verified using simulations and improves system performance in terms of network throughput by 13%, data drop ratio by 39%, data delay by 11%, and faulty packets by 46% compared to HUNA and CMMA schemes.

scholarly journals COVID-19 Prognosis and Mortality Risk Predictions from Symptoms: A Cloud-Based Smartphone Application

BioMed ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 114-125
Author(s):  
Ocean Monjur ◽  
Rahat Bin Preo ◽  
Abdullah Bin Shams ◽  
Md. Mohsin Sarker Raihan ◽  
Fariha Fairoz
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Mortality Risk ◽  
Smartphone Application ◽  
Time Analysis ◽  
The Real ◽  
Real Time Analysis ◽  
Efficient Management ◽  
Machine Learning Model ◽  
Critical Patients

The coronavirus pandemic overwhelmed many countries and their healthcare systems. Shortage of testing kits and Intensive-Care-Unit (ICU) beds for critical patients have become a norm in most developing countries. This has prompted the need to rapidly identify the COVID-19 patients to stop the spread of the virus and also to find critical patients. The latter is imperative for determining the state of critically ill patients as quickly as possible. This will lower the number of deaths from the infection. In this paper, we propose a cloud-based smartphone application for the early prognosis of COVID-19 infected patients and also predict their mortality risk using their symptoms. Moreover, we heuristically identified the most important symptoms necessary for making such predictions. We have successfully reduced the number of features by almost half for the prognosis and by more than a third for forecasting the mortality risk, compared to the contemporary studies. The application makes the real-time analysis using machine learning models, designed and stored in the cloud. Our machine learning model demonstrates an accuracy, precision, recall, and F1 score of 97.72%, 100%, 95.55%, and 97.70%, respectively, in identifying the COVID-19 infected patients and with an accuracy, precision, recall, and F1 score of 90.83%, 88.47%, 92.94%, and 90.65%, respectively, in forecasting the mortality risk from the infection. The real-time cloud-based approach yields faster responses, which is critical in the time of pandemic for mitigating the infection spread and aiding in the efficient management of the limited ICU resources.

Decision Based Model for Real-Time IoT Analysis Using Big Data and Machine Learning

2021 ◽  
Author(s):  
Hina Jamil ◽  
Tariq Umer ◽  
Celal Ceken ◽  
Fadi Al-Turjman
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time

Cloud-Based Big Data Analysis Tools and Techniques Towards Sustainable Smart City Services

2021 ◽  
pp. 63-90
Author(s):  
Suresh P. ◽  
Keerthika P. ◽  
Sathiyamoorthi V. ◽  
Logeswaran K. ◽  
Manjula Devi R. ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Smart City ◽  
Traffic Management ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Streaming Data ◽  
City Development ◽  
Time Data ◽  
Public And Private

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

scholarly journals Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Simon Elias Bibri ◽  
John Krogstie
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Real Time ◽  
Environmental Sustainability ◽  
Smart Cities ◽  
Data Driven ◽  
Urban Metabolism ◽  
Pollution Reduction ◽  
Illustrative Case ◽  
Sustainable Cities

AbstractThe IoT and big data technologies have become essential to the functioning of both smart cities and sustainable cities, and thus, urban operational functioning and planning are becoming highly responsive to a form of data-driven urbanism. This offers the prospect of building models of smart sustainable cities functioning in real time from routinely sensed data. This in turn allows to monitor, understand, analyze, and plan such cities to improve their energy efficiency and environmental health in real time thanks to new urban intelligence functions as an advanced form of decision support. However, prior studies tend to deal largely with data-driven technologies and solutions in the realm of smart cities, mostly in relation to economic and social aspects, leaving important questions involving the underlying substantive and synergistic effects on environmental sustainability barely explored to date. These issues also apply to sustainable cities, especially eco-cities. Therefore, this paper investigates the potential and role of data-driven smart solutions in improving and advancing environmental sustainability in the context of smart cities as well as sustainable cities, under what can be labeled “environmentally data-driven smart sustainable cities.” To illuminate this emerging urban phenomenon, a descriptive/illustrative case study is adopted as a qualitative research methodology§ to examine and compare Stockholm and Barcelona as the ecologically and technologically leading cities in Europe respectively. The results show that smart grids, smart meters, smart buildings, smart environmental monitoring, and smart urban metabolism are the main data-driven smart solutions applied for improving and advancing environmental sustainability in both eco-cities and smart cities. There is a clear synergy between such solutions in terms of their interaction or cooperation to produce combined effects greater than the sum of their separate effects—with respect to the environment. This involves energy efficiency improvement, environmental pollution reduction, renewable energy adoption, and real-time feedback on energy flows, with high temporal and spatial resolutions. Stockholm takes the lead over Barcelona as regards the best practices for environmental sustainability given its long history of environmental work, strong environmental policy, progressive environmental performance, high environmental standards, and ambitious goals. It also has, like Barcelona, a high level of the implementation of applied data-driven technology solutions in the areas of energy and environment. However, the two cities differ in the nature of such implementation. We conclude that city governments do not have a unified agenda as a form of strategic planning, and data-driven decisions are unique to each city, so are environmental challenges. Big data are the answer, but each city sets its own questions based on what characterize it in terms of visions, policies, strategies, pathways, and priorities.

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries

2020 ◽  
Vol 223 (3) ◽  
pp. 437.e1-437.e15
Author(s):  
Joshua Guedalia ◽  
Michal Lipschuetz ◽  
Michal Novoselsky-Persky ◽  
Sarah M. Cohen ◽  
Amihai Rottenstreich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real Time ◽  
Learning Model ◽  
Time Data ◽  
Machine Learning Model ◽  
Vaginal Deliveries ◽  
Real Time Data

scholarly journals Streaming feature selection algorithms for big data: A survey

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Noura AlNuaimi ◽  
Mohammad Mehedy Masud ◽  
Mohamed Adel Serhani ◽  
Nazar Zaki
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Big Data ◽  
Real Time ◽  
Relevant Literature ◽  
Heterogeneous Data ◽  
Proper Solution ◽  
Exact Figure ◽  
Selection Algorithms ◽  
Over Time

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

scholarly journals Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction

2020 ◽  
pp. 193229682092262
Author(s):  
Darpit Dave ◽  
Daniel J. DeSalvo ◽  
Balakrishna Haridas ◽  
Siripoom McKay ◽  
Akhil Shenoy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Model ◽  
Machine Learning Model ◽  
Feature Based
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