Machine Learning for Industrial IoT Systems

2021 ◽  
pp. 336-358
Author(s):  
Mona Bakri Hassan ◽  
Elmustafa Sayed Ali Ahmed ◽  
Rashid A. Saeed
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Operations Management ◽  
Industrial Applications ◽  
Smart Devices ◽  
Iot Applications ◽  
Industrial Iot ◽  
Effective Decision ◽  
Effective Decision Making

The use of AI algorithms in the IoT enhances the ability to analyse big data and various platforms for a number of IoT applications, including industrial applications. AI provides unique solutions in support of managing each of the different types of data for the IoT in terms of identification, classification, and decision making. In industrial IoT (IIoT), sensors, and other intelligence can be added to new or existing plants in order to monitor exterior parameters like energy consumption and other industrial parameters levels. In addition, smart devices designed as factory robots, specialized decision-making systems, and other online auxiliary systems are used in the industries IoT. Industrial IoT systems need smart operations management methods. The use of machine learning achieves methods that analyse big data developed for decision-making purposes. Machine learning drives efficient and effective decision making, particularly in the field of data flow and real-time analytics associated with advanced industrial computing networks.

scholarly journals Decision-Making Enhancement in a Big Data Environment: Application of the K-Means Algorithm to Mixed Data

2019 ◽  
Vol 9 (4) ◽  
pp. 293-302
Author(s):  
Oded Koren ◽  
Carina Antonia Hallin ◽  
Nir Perel ◽  
Dror Bendet
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Mixed Data ◽  
Systems Research ◽  
Information Systems Research ◽  
Effective Decision ◽  
Data Ecosystem ◽  
Effective Decision Making ◽  
Business Requirements

Abstract Big data research has become an important discipline in information systems research. However, the flood of data being generated on the Internet is increasingly unstructured and non-numeric in the form of images and texts. Thus, research indicates that there is an increasing need to develop more efficient algorithms for treating mixed data in big data for effective decision making. In this paper, we apply the classical K-means algorithm to both numeric and categorical attributes in big data platforms. We first present an algorithm that handles the problem of mixed data. We then use big data platforms to implement the algorithm, demonstrating its functionalities by applying the algorithm in a detailed case study. This provides us with a solid basis for performing more targeted profiling for decision making and research using big data. Consequently, the decision makers will be able to treat mixed data, numerical and categorical data, to explain and predict phenomena in the big data ecosystem. Our research includes a detailed end-to-end case study that presents an implementation of the suggested procedure. This demonstrates its capabilities and the advantages that allow it to improve the decision-making process by targeting organizations’ business requirements to a specific cluster[s]/profiles[s] based on the enhancement outcomes.

Parliamentarism and the Legislative Branch

2017 ◽  
Author(s):  
András Sajó ◽  
Renáta Uitz
Keyword(s):  
Decision Making ◽  
Political Parties ◽  
Legislative Branch ◽  
Effective Decision ◽  
The Law ◽  
Internal Operations ◽  
Effective Decision Making ◽  
The Relationship ◽  
Law Making

This chapter examines the relationship between parliamentarism and the legislative branch. It explores the evolution of the legislative branch, leading to disillusionment with the rationalized law-making factory, a venture run by political parties beyond the reach of constitutional rules. The rise of democratically bred party rule is positioned between the forces favouring free debate versus effective decision-making in the legislature. The chapter analyses the institutional make-up and internal operations of the legislature, the role of the opposition in the legislative assembly, and explores the benefits of bicameralism for boosting the powers of the legislative branch. Finally, it looks at the law-making process and its outsourcing via delegating legislative powers to the executive.

Computing an effective decision making group of a society using social network analysis

2013 ◽  
Vol 28 (3) ◽  
pp. 577-587 ◽  
Author(s):  
Donghyun Kim ◽  
Deying Li ◽  
Omid Asgari ◽  
Yingshu Li ◽  
Alade O. Tokuta ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Effective Decision ◽  
Effective Decision Making

Effective Decision Making in a Life Company

2005 ◽  
Vol 11 (1) ◽  
pp. 133-166
Author(s):  
M. Iqbal
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Systematic Approach ◽  
Decision Making Process ◽  
Recent Past ◽  
Past Life ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Value Measurement

ABSTRACTIn the recent past life companies have made many decisions which they have had cause to deeply regret. This paper looks at the range of decision making theories available. It then examines recent examples of decisions that had unfavourable consequences and explores why they were taken, and goes on to describe a systematic approach to decision making which can help management assess more objectively the difficult choices confronting them today. The approach does not require espousal of any specific decision theory or method of value measurement. The focus is on the decision making process and the organisation's capacity to handle change. The paper identifies the three requirements for effective decision making.

Application of Unascertained Measure-Entropy Evaluation Model for Program Selection of Shaft Construction under Complex Conditions

2011 ◽  
Vol 225-226 ◽  
pp. 407-410 ◽  
Author(s):  
Wan Qing Li ◽  
Mu Jie Chen ◽  
Wen Qing Meng
Keyword(s):  
Decision Making ◽  
Evaluation Model ◽  
Entropy Weight ◽  
Program Selection ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Measure Entropy ◽  
Selection Of

An unascertained measure-entropy evaluation model for the program selection of shaft construction under complex conditions is established so that a scientific and effective decision making method is provided in this paper, the evaluation model of shaft construction is established based on unascertained measure and entropy weight theory, then, the model proposed in this paper is applied to evaluate three shaft construction program comprehensively, and the evaluation results show validity and applicability of the model.

Similarity-Based Decision Support for Internet Enabled Supply-Web Interactions

1998 ◽  
Author(s):  
Raj Veeramani ◽  
Narayanan Viswanathan ◽  
Shailesh M. Joshi
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Manufacturing Industry ◽  
Job Shop ◽  
The Internet ◽  
Agent Based ◽  
New Approaches ◽  
Effective Decision ◽  
Use Of The Internet ◽  
Effective Decision Making

Abstract New approaches for decision making are emerging to support the use of the Internet for supply-web interactions in the manufacturing industry. In this paper, we discuss one such paradigm, namely similarity-based decision support. It recognizes that knowledge of similar experiences can support rapid and effective decision making in various forms of supply-web interactions. We illustrate this approach using two prototype systems, WebScout (an agent-based system for customer–supplier matchmaking in the job-shop machining industry context) and TOME (Treasury of Manufacturing Experiences — an Intranet application to aid manufacturability assessment in foundries).

Role of machine learning and deep learning in securing 5G-driven industrial IoT applications

2021 ◽  
pp. 102685
Author(s):  
Parjanay Sharma ◽  
Siddhant Jain ◽  
Shashank Gupta ◽  
Vinay Chamola
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Iot Applications ◽  
Industrial Iot
Sign in / Sign up

Export Citation Format

Share Document