Determining Headache Diseases With Genetic Algorithm

2018 ◽  
pp. 249-262
Author(s):  
Gaffari Celik
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Biomedical Engineering ◽  
Medical Diagnosis ◽  
Input Data ◽  
Expert Knowledge ◽  
Effective Solution ◽  
Evolutionary Mechanisms ◽  
Medical Diagnoses ◽  
Strong Relation

Currently, medical diagnosis has a strong relation with the artificial-intelligence-oriented approaches. Because it is practical to employ intelligent mechanisms over some input data-expert knowledge and design effective solution ways, even the biomedical engineering field is interested in taking support from artificial intelligence. If applications in this manner are taken into consideration, we can see that medical diagnoses have a big percentage. In the sense of the explanations, the objective of this chapter is to use genetic algorithm (GA) for diagnosing headache diseases. As a popular and essential technique benefiting from evolutionary mechanisms, GA can deal with many different types of real-world problems. So, it has been chosen as the solution way/algorithm over the headache disease detection problem, which shapes the research framework of the study. The chapter content gives information about the performed diagnosis application and the results.

Determining Headache Diseases With Genetic Algorithm

2021 ◽  
pp. 642-655
Author(s):  
Gaffari Celik
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Biomedical Engineering ◽  
Medical Diagnosis ◽  
Input Data ◽  
Expert Knowledge ◽  
Effective Solution ◽  
Evolutionary Mechanisms ◽  
Medical Diagnoses ◽  
Strong Relation

Currently, medical diagnosis has a strong relation with the artificial-intelligence-oriented approaches. Because it is practical to employ intelligent mechanisms over some input data-expert knowledge and design effective solution ways, even the biomedical engineering field is interested in taking support from artificial intelligence. If applications in this manner are taken into consideration, we can see that medical diagnoses have a big percentage. In the sense of the explanations, the objective of this chapter is to use genetic algorithm (GA) for diagnosing headache diseases. As a popular and essential technique benefiting from evolutionary mechanisms, GA can deal with many different types of real-world problems. So, it has been chosen as the solution way/algorithm over the headache disease detection problem, which shapes the research framework of the study. The chapter content gives information about the performed diagnosis application and the results.

scholarly journals Hybrid Models for Efficient Control, Optimization, and Monitoring of Thermo-Chemical Processes and Plants

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 515
Author(s):  
Thomas Freudenmann ◽  
Hans-Joachim Gehrmann ◽  
Krasimir Aleksandrov ◽  
Mohanad El-Haji ◽  
Dieter Stapf
Keyword(s):  
Artificial Intelligence ◽  
Expert Knowledge ◽  
Numerical Models ◽  
Integrated Control ◽  
Nox Reduction ◽  
Hybrid Models ◽  
Control Optimization ◽  
Pulverized Fuel ◽  
Renewable Energy Law ◽  
Institute Of Technology

This paper describes a procedure and an IT product that combine numerical models, expert knowledge, and data-based models through artificial intelligence (AI)-based hybrid models to enable the integrated control, optimization, and monitoring of processes and plants. The working principle of the hybrid model is demonstrated by NOx reduction through guided oscillating combustion at the pulverized fuel boiler pilot incineration plant at the Institute for Technical Chemistry, Karlsruhe Institute of Technology. The presented example refers to coal firing, but the approach can be easily applied to any other type of nitrogen-containing solid fuel. The need for a reduction in operation and maintenance costs for biomass-fired plants is huge, especially in the frame of emission reductions and, in the case of Germany, the potential loss of funding as a result of the Renewable Energy Law (Erneuerbare-Energien-Gesetz) for plants older than 20 years. Other social aspects, such as the departure of experienced personnel may be another reason for the increasing demand for data mining and the use of artificial intelligence (AI).

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Forming Diagnostic Opinions

Rhetorik ◽  
2018 ◽  
Vol 37 (1) ◽  
pp. 68-93
Author(s):  
Markus H. Woerner ◽  
Ricca Edmondson
Keyword(s):  
Medical Diagnosis ◽  
Rhetorical Analysis ◽  
Voluntary Action ◽  
Medical Communication ◽  
Medical Diagnoses ◽  
Textual Evidence ◽  
Social And Emotional ◽  
Context Dependent ◽  
The Brain

Abstract Using an understanding of rhetoric as a method of communicative reasoning capable of providing grounds for conviction in those to whom it is addressed, this article argues that the formation of medical diagnoses shares a structure with Aristotle’s account of the rhetorical syllogism (the enthymeme). Here the argument itself (logos), together with characterological elements (ethos) and emotions (pathos), are welded together so that each affects the operation of the others. In the initial three sections of the paper, we contend, first, that diagnoses, as verdictive performatives, differ from scientific claims in being irreducibly personal and context-dependent; secondly, that they fit the structure of voluntary action as analysed by Aristotle and Aquinas; thirdly, that as practical syllogisms they differ from theoretical syllogisms, for example in taking effect in action, being ›addressed‹, and being intrinsically embedded in wider contexts of medical communication and practices. In the remaining sections we apply this account to textual evidence about diagnosis, drawing on work by the brain surgeon Henry Marsh. A rhetorical analysis of his observations on the formation of diagnostic opinions in situilluminates how moral, social and emotional features are fused with the cognitive aspects of medical judgement, making or marring how diagnoses and treatment are enacted. In other words, a philosophical- rhetorical account of diagnosis can help us to appreciate how medical diagnosis takes effect. We briefly conclude with some implications of our work for how diagnostic processes could in practice be better supported.

scholarly journals Review of Artificial Intelligence

2020 ◽  
pp. 143-171
Author(s):  
K. P. V. Sai Aakarsh ◽  
Adwin Manhar
Keyword(s):  
Artificial Intelligence ◽  
Computational Intelligence ◽  
Medical Diagnosis ◽  
Computer Application ◽  
Knowledge Based Systems ◽  
Human Race ◽  
Knowledge Based ◽  
Ordinary Person ◽  
Digital Computers

Over many centuries, tools of increasing sophistication have been developed to serve the human race Digital computers are, in many respects, just another tool. They can perform the same sort of numerical and symbolic manipulations that an ordinary person can, but faster and more reliably. This paper represents review of artificial intelligence algorithms applying in computer application and software. Include knowledge-based systems; computational intelligence, which leads to Artificial intelligence, is the science of mimicking human mental faculties in a computer. That assists Physician to make dissection in medical diagnosis.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

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.

Detection of Blood-Related Diseases Using Deep Neural Nets

2019 ◽  
pp. 1-15
Author(s):  
Rajithkumar B. K. ◽  
Shilpa D. R. ◽  
Uma B. V.
Keyword(s):  
Image Processing ◽  
Biomedical Engineering ◽  
Medical Diagnosis ◽  
Blood Smear ◽  
Neural Nets ◽  
Laboratory Methods ◽  
Intelligent Algorithms ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Traditional Image

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.

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