scholarly journals Application of Artificial Intelligence Technology in Decision-Making of Mechanical Manufacturing Process

2021 ◽  
Vol 2143 (1) ◽  
pp. 012008
Author(s):  
Zhanfeng Li
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Manufacturing Process ◽  
Social Life ◽  
Raw Materials ◽  
Decision Making Process ◽  
Method Performance ◽  
Deformation Amount ◽  
Artificial Intelligence Technology ◽  
Basic Production

Abstract The traditional mechanical manufacturing process is to transform all raw materials into the final materials and products and directly into the international market all the production process, in this process we involved a lot of problems about decision-making methods, decision-making process is a most basic production technology activity, it is widely exists in the whole social life and each link of enterprise production. This paper studies the decision-making method of mechanical manufacturing process based on artificial intelligence, optimizes the process parameters of plastic integrated mechanical manufacturing process, and compares it with the traditional decision-making method. Finally, the experimental results are obtained that the traditional decision-based method is reduced by more than 10% in size error. But several experiments, the AI decision-making method appeared deviation, the error results are higher than the traditional decision-making method, which may be objective factors, but also reflects the possibility of instability, in the result of deformation. AI-based decision method performance is higher than the traditional decision-making method, reduce the deformation amount by 3.5%

Knowledge-Integration Model for Networked Manufacturing

2011 ◽  
Vol 314-316 ◽  
pp. 2027-2032
Author(s):  
Jiao Jian Liu ◽  
Wen He Liao ◽  
Yu Guo ◽  
Wen Bin Wang
Keyword(s):  
Decision Making ◽  
Correlation Analysis ◽  
Knowledge Sharing ◽  
Manufacturing Process ◽  
Knowledge Integration ◽  
Decision Making Process ◽  
Networked Manufacturing ◽  
Optimum Distribution ◽  
Integration Model ◽  
Key Technologies

In order to maximize knowledge sharing and reuse in networked manufacturing process and improve the rapidity and reliability of decision-making, a knowledge-integration model and its implementation methods are proposed in this paper. First, the requirement for knowledge integration in networked manufacturing is analyzed. On this basis, a knowledge-integration model is built, and then three key technologies are studied, namely knowledge representation and organization based on ontology, knowledge correlation analysis based on complex network and knowledge supply based on decision-making context. This model provides an effective way to realize the optimum distribution of knowledge in networked manufacturing process and to improve the efficiency of decision-making process.

scholarly journals Expert System for Diagnosis of Uterine Myomas using the Certainty Factor Method

2021 ◽  
Vol 1 (4) ◽  
pp. 103-108
Author(s):  
Syahrizal Dwi Putra ◽  
M Bahrul Ulum ◽  
Diah Aryani
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Computer System ◽  
Decision Making Process ◽  
Certainty Factor ◽  
Uterine Myomas ◽  
Factor Method ◽  
Two Factors

An expert system which is part of artificial intelligence is a computer system that is able to imitate the reasoning of an expert with certain expertise. An expert system in the form of software can replace the role of an expert (human) in the decision-making process based on the symptoms given to a certain level of certainty. This study raises the problem that many women experience, namely not understanding that they have uterine myomas. Many women do not understand and are not aware that there are already symptoms that are felt and these symptoms are symptoms of the presence of uterine myomas in their bodies. Therefore, it is necessary for women to be able to diagnose independently so that they can take treatment as quickly as possible. In this study, the expert will first provide the expert CF values. Then the user / respondent gives an assessment of his condition with the CF User values. In the end, the values obtained from these two factors will be processed using the certainty factor formula. Users must provide answers to all questions given by the system in accordance with their current conditions. After all the conditions asked are answered, the system will display the results to identify that the user is suffering from uterine myoma disease or not. The Expert System with the certainty factor method was tested with a patient who entered the symptoms experienced and got the percentage of confidence in uterine myomas/fibroids of 98.70%. These results indicate that an expert system with the certainty factor method can be used to assist in diagnosing uterine myomas as early as possible.

Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence

2021 ◽  
Author(s):  
Ekaterina Jussupow ◽  
Kai Spohrer ◽  
Armin Heinzl ◽  
Joshua Gawlitza
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Errors ◽  
Decision Makers ◽  
Medical Decision ◽  
Decision Making Process ◽  
Self Monitoring ◽  
Rule Based ◽  
Human Actors

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Artificial Intelligence, Human Intelligence, and Orientism Management Framework

2021 ◽  
pp. 1-30
Author(s):  
Luisa Dall'Acqua
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Makers ◽  
Human Intelligence ◽  
Decision Making Process ◽  
Knowledge Process ◽  
Management Framework ◽  
Theoretical Contribution ◽  
Practical Guideline

The chapter intends to be a theoretical contribution for developers in the field of artificial intelligence. It also means a practical guideline for leaders, as decision-makers, to manage tasks and optimize performance. The proposed approach interprets the fluid nature of the decision-making process looking at knowledge and knowledge activities as dynamic, adaptive, and self-regulative, based not only on well-known explicit curricular goals but also on unpredictable interactions and relationships between players. The knowledge process is emerging in human and biological, social, and cultural environments.

scholarly journals Whose morality? Which rationality? Challenging artificial intelligence as a remedy for the lack of moral enhancement

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Silviya Serafimova
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Intelligent Agents ◽  
Machine Ethics ◽  
Decision Making Process ◽  
Human Beings ◽  
First Order ◽  
Moral Agents ◽  
Act Utilitarianism ◽  
Implicit And Explicit

Abstract Moral implications of the decision-making process based on algorithms require special attention within the field of machine ethics. Specifically, research focuses on clarifying why even if one assumes the existence of well-working ethical intelligent agents in epistemic terms, it does not necessarily mean that they meet the requirements of autonomous moral agents, such as human beings. For the purposes of exemplifying some of the difficulties in arguing for implicit and explicit ethical agents in Moor’s sense, three first-order normative theories in the field of machine ethics are put to test. Those are Powers’ prospect for a Kantian machine, Anderson and Anderson’s reinterpretation of act utilitarianism and Howard and Muntean’s prospect for a moral machine based on a virtue ethical approach. By comparing and contrasting the three first-order normative theories, and by clarifying the gist of the differences between the processes of calculation and moral estimation, the possibility for building what—one might call strong “moral” AI scenarios—is questioned. The possibility of weak “moral” AI scenarios is likewise discussed critically.

Organizational Data Mining (ODM)

2008 ◽  
pp. 2289-2295 ◽  
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

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