scholarly journals Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

2017 ◽  
Vol 13 (3) ◽  
pp. 342 ◽  
Author(s):  
Alaá Rateb Mahmoud Al-shamasneh ◽  
Unaizah Hanum Binti Obaidellah
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Human Cancer ◽  
Imaging Techniques ◽  
Detection Methods ◽  
Support Vector ◽  
Inference System ◽  
Mortality And Morbidity ◽  
Artificial Intelligence Techniques

Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer.

scholarly journals Development of a new empirical correlation for predicting formation volume factor of reservoir oil using artificial intelligence

2021 ◽  
Vol 44 (4) ◽  
pp. 408-416
Author(s):  
E. V. Shakirova ◽  
A. A. Aleksandrov ◽  
M. V. Semykin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Fuzzy Inference ◽  
Empirical Correlation ◽  
Support Vector ◽  
Volume Factor ◽  
Inference System ◽  
Study Results ◽  
Artificial Neural

It is known that oil in reservoir conditions is characterized by the content of a certain amount of dissolved gas. As reservoir pressure decreases this gas is released from oil significantly changing its physical properties, primarily its density and viscosity. In addition, the oil volume also reduces, sometimes by 50–60 %. In this regard, when calculating reserves, it is necessary to justify the reduction amount of the reservoir oil volume when oil is extracted to the surface. For this purpose, the concept of formation volume factor of reservoir oil has been introduced. The formation volume factor of oil is considered one of the main characterizing parameters of crude oil. It is also required for modeling and predicting the characteristics of an oil reservoir. The purpose of the present work is to develop a new empirical correlation for predicting the formation volume factor of reservoir oil using artificial intelligence methods based on MATLAB software, such as: an artificial neural network, an adaptive neuro-fuzzy inference system, and a support vector machine. The article presents a new empirical correlation extracted from the artificial neural network based on 503 experimental data points for oils from the Eastern Siberia field, which was able to predict the formation volume factor of oil with the correlation coefficient of 0.969 and average absolute error of less than 1 %. The conducted study shows that the prediction accuracy of the desired parameter in the developed artificial intelligence model exceeds the accuracy of study results obtained by conventional statistical methods. Moreover, the model can be useful in the prospect of process optimization in field planning and development.

Thermal Resistance Modeling of Oscillating Heat Pipes for Nanofluids by Artificial Intelligence Approach

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
M. Malekan ◽  
A. Khosravi ◽  
H. R. Goshayeshi ◽  
M. E. H. Assad ◽  
J. J. Garcia Pabon
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Thermal Resistance ◽  
Heat Pipe ◽  
Fuzzy Inference ◽  
Working Fluid ◽  
Group Method ◽  
Feed Forward Neural Network ◽  
Inference System ◽  
Intelligent Models

In this study, thermal resistance of a closed-loop oscillating heat pipe (OHP) is investigated using experimental tests and artificial intelligence methods. For this target, γFe2O3 and Fe3O4 nanoparticles are mixed with the base fluid. Also, intelligent models are developed to predict the thermal resistance of the OHP. These models are developed based on the heat input into evaporator section, the thermal conductivity of working fluids, and the ratio of the inner diameter to length of OHP. The intelligent methods are multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) type neural network. Thermal resistance of the heat pipe (as a measure of thermal performance) is considered as the target. The results showed that using the nanofluids as working fluid in the OHP decreased the thermal resistance, where this decrease for Fe3O4/water nanofluid was more than that of γFe2O3/water. The intelligent models also predicted successfully the thermal resistance of OHP with a correlation coefficient close to 1. The root-mean-square error (RMSE) for MLFFNN, ANFIS, and GMDH models was obtained as 0.0508, 0.0556, and 0.0569 (°C/W) (for the test data), respectively.

scholarly journals Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt

2019 ◽  
Vol 9 (15) ◽  
pp. 3172 ◽  
Author(s):  
Hoang-Long Nguyen ◽  
Thanh-Hai Le ◽  
Cao-Thang Pham ◽  
Tien-Thinh Le ◽  
Lanh Si Ho ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Support Vector ◽  
Inference System ◽  
Stone Matrix Asphalt ◽  
Hybrid Artificial Intelligence ◽  
Stone Matrix

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.

scholarly journals Skin Cancer Recognition by Using a Neuro-Fuzzy System

2011 ◽  
Vol 10 ◽  
pp. CIN.S5950 ◽  
Author(s):  
Bareqa Salah ◽  
Mohammad Alshraideh ◽  
Rasha Beidas ◽  
Ferial Hayajneh
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Cancer Type ◽  
Inference System ◽  
Mortality And Morbidity ◽  
Neuro Fuzzy ◽  
Neural Network System ◽  
Diagnostic Technologies

Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer. The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type. The sensitivity of NN in diagnosing skin cancer was 95%, while the specificity was 88%. Skin cancer diagnosis by neuro-fuzzy system achieved sensitivity of 98% and a specificity of 89%.

Redesign of morphing UAV's winglet using DS algorithm based ANFIS model

2019 ◽  
Vol 91 (9) ◽  
pp. 1214-1222 ◽  
Author(s):  
Mehmet Konar
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Twist Angle ◽  
Inference System ◽  
Content Type ◽  
Artificial Intelligence Techniques ◽  
Maximum Value ◽  
Novel Approach ◽  
Input Parameters ◽  
Drag Ratio

Purpose The purpose of this paper is to present a novel approach based on the differential search (DS) algorithm integrated with the adaptive network-based fuzzy inference system (ANFIS) for unmanned aerial vehicle (UAV) winglet design. Design/methodology/approach The winglet design of UAV, which was produced at Faculty of Aeronautics and Astronautics in Erciyes University, was redesigned using artificial intelligence techniques. This approach proposed for winglet redesign is based on the integration of ANFIS into the DS algorithm. For this purpose, the cant angle (c), the twist angle (t) and taper ratio (λ) of winglet are selected as input parameters; the maximum value of lift/drag ratio (Emax) is selected as the output parameter for ANFIS. For the selected input and output parameters, the optimum ANFIS parameters are determined by the DS algorithm. Then the objective function based on optimum ANFIS structure is integrated with the DS algorithm. With this integration, the input parameters for the Emax value are obtained by the DS algorithm. That is, the DS algorithm is used to obtain both the optimization of the ANFIS structure and the necessary parameters for the winglet design. Thus, the UAV was reshaped and the maximum value of lift/drag ratio was calculated based on new design. Findings Considerable improvements on the max E are obtained through winglet redesign on morphing UAVs with artificial intelligence techniques. Research limitations/implications It takes a long time to obtain the optimum Emax value by the computational fluid dynamics method. Practical implications Using artificial intelligence techniques saves time and reduces cost in maximizing Emax value. The simulation results showed that satisfactory Emax values were obtained, and an optimum winglet design was achieved. Thus, the presented method based on ANFIS and DS algorithm is faster and simpler. Social implications The application of artificial intelligence methods could be used in designing more efficient aircrafts. Originality/value The study provides a new and efficient method that saves time and reduces cost in redesigning UAV winglets.

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