VALAB: Expert System for Validation of Biochemical Data

1992 ◽  
Vol 38 (1) ◽  
pp. 83-87 ◽  
Author(s):  
P M Valdiguié ◽  
E Rogari ◽  
H Philippe
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Real Time ◽  
High Throughput ◽  
Medical Patients ◽  
Hospital Department ◽  
Laboratory Results ◽  
Aid Decision ◽  
Age And Sex

Abstract In large laboratories that use "high-throughput" equipment, it is now possible to use artificial intelligence techniques to aid decision making and validation of data. This paper describes an artificial intelligence project, VALAB, that has been carried out in our laboratory. VALAB, an expert system that permits real-time validation of data, is designed to be equivalent to validation by the laboratory director. The decision produced by the expert system is based on several factors, including correlation between repeated laboratory results, physiological association between different variables, the hospital department from which the test was ordered, and the patient's age and sex. In 200 abnormal chemistry profiles randomly selected, VALAB's ability to detect abnormal cases (i.e., sensitivity = 0.75) was exceeded by only one of seven laboratory experts. However, all seven experts outperformed VALAB's measured specificity of 0.63. The VALAB system incorporates greater than 4000 rules. Operational since November 1988, it has validated greater than 50,000 medical patients' reports in real time.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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.

Artificial Intelligence in Medical Science

2014 ◽  
pp. 11-23 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Decision Making ◽  
Signal Processing ◽  
Expert System ◽  
Knowledge Base ◽  
Medical Diagnosis ◽  
Medical Science ◽  
Inference Engine ◽  
Medical Decision

Applying Artificial Intelligence (AI) for increasing the reliability of medical decision making has been studied for some years, and many researchers have studied in this area. In this chapter, AI is defined and the reason of its importance in medical diagnosis is explained. Various applications of AI in medical diagnosis such as signal processing and image processing are provided. Expert system is defined and it is mentioned that the basic components of an expert system are a “knowledge base” or KB and an “inference engine”. The information in the KB is obtained by interviewing people who are experts in the area in question.

Emerging Application of Fuzzy Expert System in Medical Domain

2015 ◽  
pp. 1-20 ◽  
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Fuzzy Inference ◽  
Fuzzy Expert System ◽  
Inference Mechanism ◽  
Medical Practitioners ◽  
Fuzzy Operators ◽  
Decision Making Problem ◽  
Fuzzy Inference Mechanism

Fuzzy expert system is an artificial intelligence tool that helps to resolve the decision-making problem with the existence of uncertainty and plays an important role in medicine for symptomatic diagnostic remedies. In this chapter, construction of Fuzzy expert system is the focused, which helps to diagnosis disease. Fuzzy expert system is constructed by using the fuzzification to convert crisp values into fuzzy values. Fuzzy expert system consists of fuzzy inference, implication, and aggregation. The system contains a set of rules with fuzzy operators T-norm and of T-Conorm. By applying the fuzzy inference mechanism, diagnosis of disease becomes simple for medical practitioners and patients. Defuzzification method is adopted to convert the fuzzy values into crisp values. With crisp values, the knowledge regarding the disease is given to medical doctors and patients. Application of Fuzzy expert system to diagnosis of disease is mainly focused on in this chapter.

Artificial intelligence based commuter behaviour profiling framework using Internet of things for real-time decision-making

2020 ◽  
Vol 32 (20) ◽  
pp. 16057-16071 ◽  
Author(s):  
Tharindu Bandaragoda ◽  
Achini Adikari ◽  
Rashmika Nawaratne ◽  
Dinithi Nallaperuma ◽  
Ashish Kr. Luhach ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Internet Of Things ◽  
Real Time

Decision-making laboratory computer systems as essential tools for achievement of total quality

1997 ◽  
Vol 43 (5) ◽  
pp. 908-912 ◽  
Author(s):  
Kenneth E Blick
Keyword(s):  
Decision Making ◽  
Quality Assurance ◽  
Expert System ◽  
Real Time ◽  
Computer System ◽  
Laboratory Testing ◽  
Computer Systems ◽  
Total Quality ◽  
Laboratory Computer ◽  
The Past

Abstract Areas other than the analytical process should be the focus of concern about quality issues in the laboratory because nearly 95% of errors occur at the nonanalytical front and back ends of the testing process. Until now, computer systems have been designed to handle the more predictable aspects of laboratory testing, necessitating that the infrequent and unpredictable data events be handled by manual systems. The manual systems are termed “workarounds” and indeed, because they occur sporadically, they are frequently not handled predictably. Here, I describe and give examples of an expert laboratory computer system that can be designed to handle both predictable and unpredictable data events without the use of manual workarounds. This expert system works in concert with a dynamic database allowing such data events to be detected in real time and handled predictably, thus providing a tool to address quality assurance issues throughout the testing process. The system performs up to 31 separate actions or tasks based on data events that in the past were handled by human workarounds.

Application of an Expert System to the Design of Furnace of a Circulating Fluidized Bed Boiler

1994 ◽  
Vol 116 (3) ◽  
pp. 462-467 ◽  
Author(s):  
P. Basu ◽  
S. Mitra
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Fluidized Bed ◽  
Cross Section ◽  
Circulating Fluidized Bed ◽  
New Technology ◽  
Fluidized Bed Combustion ◽  
The Subject ◽  
Circulating Fluidized Bed Combustion

The design of a boiler using a new technology, i.e., circulating fluidized bed combustion, requires a considerable amount of expertise, which is a combination of experience, knowledge of the subject, and intuition. Boiler vendors, who are required to prepare a large number of proposals, rely heavily on the the skill and judgment of their senior (expert) designers. An artificial intelligence based expert system can greatly simplify this task. This system can assist expert designers to store their experience and decision-making skill through the code of a computer program, which remains intact and ready to apply their skill uniformly and rapidly to all designs when required. This may allow novice designers to carry out routine proposal designs, freeing the experts to improve current designs. The present paper gives an illustration of the use of expert systems to the design of only one aspect of the furnace, which is furnace cross section. It shows that in addition to the standard method of determining the furnace area from the fluidization, the design can take advantage of previous experience, which lists grate heat release rate and other relevant parameters. The expert system also modifies the calculated value to meet different concerns of the boiler purchaser and/or his consultants. Finally the expert develops a compromise of different considerations and requirements with importance attached to them. The paper also shows how the design will change when the importance attached to a particular constraint is relaxed.

Towards an Intelligent System for the Territorial Planning

2018 ◽  
pp. 158-178
Author(s):  
AMRI Benaouda ◽  
Francisco José García-Peñalvo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Information System ◽  
Decision Support ◽  
Forest Management ◽  
Intelligent System ◽  
Geographical Information ◽  
Territorial Planning ◽  
Ecology And Environment ◽  
Aid Decision

This chapter concerns the conceptualization of an intelligent system for the territorial planning, taking as an example the agriculture case as a tool in decision making. It is started by giving a comparison between the geographical information system (GIS) and the intelligent system (IS), demonstrating the limits of the GIS and the appeal to the artificial intelligence. Also, the chapter gives an overview of the application of decision support systems (DSSs), modeling and simulation applied in forest management, agriculture, ecology, and environment. Finally, the chapter proposes the methodology and the intelligent system proposed, setting up some indicators which help to aid decision making.

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