Current Trends

2019 ◽  
pp. 181-198
Author(s):  
Jayanthi Jagannathan ◽  
Anitha Elavarasi S.
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Predictive Analytics ◽  
Process Industry ◽  
Healthcare Industry ◽  
Current Trends ◽  
Smart Campus ◽  
Industry Automation ◽  
The Internet Of Things

This chapter addresses the key role of machine learning and artificial intelligence for various applications of the internet of things. The following are the most significant applications of IoT: (1) manufacturing industry: automation of industries is on the rise; there is an urge for analyzing the energy in the process industry; (2) anomaly detection: to detect the existing fault and abnormality in functioning by using ML algorithms thereby avoiding the adverse effect during its operation; (3) smart campus: in-order to efficiently handle the energy in buildings, smart campus systems are developed; (4) improving product decisions: with the help of the predictive analytics system products are designed and developed based on the user's requirements and usability; (5) healthcare industry: IoT with machine learning provides numerous ways for the betterment of the human wellbeing. In this chapter, the most predominant approaches to machine learning that can be useful in the IoT applications to achieve a significant set of outcomes will be discussed.

Current Trends in Integrating the Internet of Things Into Software Engineering Practices

2021 ◽  
pp. 413-428
Author(s):  
S. Kavitha ◽  
J. V. Anchitaalagammai ◽  
S. Nirmala ◽  
S. Murali
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Internet Of Things ◽  
The Internet ◽  
Infrastructure Management ◽  
Integration Testing ◽  
Current Trends ◽  
Software Construction ◽  
Engineering Practices ◽  
The Internet Of Things

The chapter summarizes the concepts and challenges of DevOps in IoT, DevSecOps in IoT, integrating security into IoT, machine learning and AI in IoT of software engineering practices. DevOps is a software engineering culture and practice that aims at unifying software development (Dev) and software operation (Ops). The main characteristic of DevOps is the automation and monitoring at all steps of software construction, from integration, testing, releasing to deployment and infrastructure management. DevSecOps is a practice of integrating security into every aspect of an application lifecycle from design to development.

scholarly journals PREDICTIVE MACHINE LEARNING (ML) ALGORITHM USING IOT FRAMEWORK FOR NOVEL CORONA VIRUS (COVID-19)

2021 ◽  
Vol 57 (9) ◽  
pp. 6328-6336
Author(s):  
G. S. N. Murthy, M. V. Sangameswar, Venubabu Rachapudi, Mylavarapu Kalyan Ram
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Research Work ◽  
Healthcare Industry ◽  
Blood Oxygen ◽  
Nearest Neighbour ◽  
Blood Oxygen Level ◽  
Clinical Methods ◽  
The World ◽  
The Internet Of Things

During earlier months of the pandemic COVID-19 with no recommended cure or vaccine available only solution to destroy the chain is self-isolation which can be maintained by physical distancing. This is now understood that the world require much faster solution to accommodate and deal with the future COVID-19 spread over the world by non-clinical methods namely data mining, augmented intelligence and several Artificial Intelligence (AI) techniques. It has become a huge hindrance to mitigate for the healthcare industry to provide more potential involved for patient's diagnosis and also for effective prognosis of 2019-CoV pandemic. Therefore, the proposed framework is implemented with the Internet of Things (IoTs) in healthcare industry for collecting the symptom data of real-time that is beneficial in predicting whether the person gets infected with COVID-19 virus or not. This can be done through various signs namely body temperature, blood oxygen level, headache, coughing patterns, etc. Thus, the research work focused on faster identification of COVID-19 virus infection cases potentially using Machine Learning (ML) algorithm from the real-time symptom data. Moreover, the obtained results have illustrated that K-Nearest Neighbour (KNN) algorithm is highly efficient while compared with other ML algorithms such as Naive Bayes and Logistic Regression (LR) in predicting the possible recovery of the infected patients from pandemic COVID-19 with the accuracy of 96.85%.

Current Trends in Integrating the Internet of Things Into Software Engineering Practices

2019 ◽  
pp. 14-35 ◽  
Author(s):  
S. Kavitha ◽  
J. V. Anchitaalagammai ◽  
S. Nirmala ◽  
S. Murali
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Internet Of Things ◽  
The Internet ◽  
Infrastructure Management ◽  
Integration Testing ◽  
Current Trends ◽  
Software Construction ◽  
Engineering Practices ◽  
The Internet Of Things

The chapter summarizes the concepts and challenges of DevOps in IoT, DevSecOps in IoT, integrating security into IoT, machine learning and AI in IoT of software engineering practices. DevOps is a software engineering culture and practice that aims at unifying software development (Dev) and software operation (Ops). The main characteristic of DevOps is the automation and monitoring at all steps of software construction, from integration, testing, releasing to deployment and infrastructure management. DevSecOps is a practice of integrating security into every aspect of an application lifecycle from design to development.

The Role of Machine Learning in Game Development Domain - A Review of Current Trends and Future Directions

2021 ◽  
Author(s):  
Gemma Edwards ◽  
Nicholas Subianto ◽  
David Englund ◽  
Jun Wei Goh ◽  
Nathan Coughran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Game Development ◽  
Future Directions ◽  
Current Trends

scholarly journals Why the Data Train Needs Semantic Rails

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Krzysztof Janowicz ◽  
Frank Van Harmelen ◽  
James A. Hendler ◽  
Pascal Hitzler
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Statistical Techniques ◽  
Common Theme ◽  
Data Intensive ◽  
Making Sense ◽  
Smart Dust ◽  
Thematic Resolution ◽  
Smart Data

While catchphrases such as big data, smart data, data-intensive science, or smart dust highlight different aspects, they share a common theme: Namely, a shift towards a data-centric perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promises new insights, while, at the same time, reducing the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, that is, statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today’s chaotic information universe, how one would understand which datasets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The semantic web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights works best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.

Cloud Manufacturing: Current Trends and Future Implementations

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Ben Buckholtz ◽  
Ihab Ragai ◽  
Lihui Wang
Keyword(s):  
World Economy ◽  
Manufacturing Industry ◽  
Small And Medium Enterprises ◽  
Cloud Manufacturing ◽  
Current Trends ◽  
Technology Changes ◽  
Resource Virtualization ◽  
The Status ◽  
Medium Enterprises

Manufacturing technology changes with the needs of consumers. The globalization of the world economy has helped to create the concept of cloud manufacturing (CM). The purpose of this paper is to provide both an overview and an update on the status of CM and define the key technologies that are being developed to make CM a dependable configuration in today's manufacturing industry. Topics covered include: cloud computing (CC), the role of small and medium enterprises (SMEs), pay-as-you-go, resource virtualization, interoperability, security, equipment control, and the future outlook of the development of CM.

scholarly journals Role of Machine Learning in Manufacturing Sector

2019 ◽  
Vol 8 (4) ◽  
pp. 2320-2328
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Manufacturing Sector ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Applications Of Machine Learning ◽  
Relationship Of ◽  
The Impact ◽  
Maintenance Process

This paper discusses the basic concept Machine learning and its techniques, algorithms as well the impact of Machine Learning in Manufacturing processes and Industrial Production. There has been an unprecedented increase in the data available in the last couple of decades. This has enabled machine learning to be applied in various fields. Machine learning is field of study which enables the computer system to learn automatically as well as improve from experience and perform various tasks without explicit instructions. The primary aim of machine learning is to allow the computers learn automatically without human assistance or intervention and adjust actions accordingly It is being employed in many fields. Manufacturing one area where machine learning is very useful. This paper discusses the various areas where machine learning can improve the process of manufacturing like predictive maintenance, process optimisation, quality control, scheduling of resources among others. This can be done by employing various machine learning techniques and algorithms using concepts such as deep learning, neural networks, supervised, unsupervised and reinforcement learning. The relationship of how machines and humans can co-exist and work together to improve the efficiency of production is also discussed. Industry 4.0 or fourth revolution that has occurred in manufacturing which deals with advent of automation in manufacturing industry and its significance is discussed. We take a look at the various benefits and applications of machine learning in the field of manufacturing engineering. This paper also discusses the various challenges and future scope of employing machine learning in the manufacturing

scholarly journals Data Mining and Analytics in the Process Industry: The Role of Machine Learning

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 20590-20616 ◽  
Author(s):  
Zhiqiang Ge ◽  
Zhihuan Song ◽  
Steven X. Ding ◽  
Biao Huang
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Process Industry

scholarly journals Streaming Data Fusion for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1955 ◽  
Author(s):  
Klemen Kenda ◽  
Blaž Kažič ◽  
Erik Novak ◽  
Dunja Mladenić
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Data Fusion ◽  
Predictive Analytics ◽  
Heterogeneous Data ◽  
Streaming Data ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
The Internet ◽  
The Internet Of Things

To achieve the full analytical potential of the streaming data from the internet of things, the interconnection of various data sources is needed. By definition, those sources are heterogeneous and their integration is not a trivial task. A common approach to exploit streaming sensor data potential is to use machine learning techniques for predictive analytics in a way that is agnostic to the domain knowledge. Such an approach can be easily integrated in various use cases. In this paper, we propose a novel framework for data fusion of a set of heterogeneous data streams. The proposed framework enriches streaming sensor data with the contextual and historical information relevant for describing the underlying processes. The final result of the framework is a feature vector, ready to be used in a machine learning algorithm. The framework has been applied to a cloud and to an edge device. In the latter case, incremental learning capabilities have been demonstrated. The reported results illustrate a significant improvement of data-driven models, applied to sensor streams. Beside higher accuracy of the models the platform offers easy setup and thus fast prototyping capabilities in real-world applications.

The Role of Scheduling in the Healthcare Industry

2011 ◽  
Author(s):  
Christie L. Kelley ◽  
Kristin Charles
Keyword(s):  
Healthcare Industry
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