MIA–QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA–ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives

Goodarzi, Mohammad; Freitas, Matheus P.

doi:10.1016/j.ejmech.2009.12.028

Cited by 37 publications

(18 citation statements)

References 30 publications

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“…Compared to other fuzzy logic-based systems, one of the advantages of ANFIS is that no conversion of certain data into fuzzy data is required [6,16]. Moreover, parameters do not need to be initialized.…”

Section: Methodsmentioning

confidence: 99%

“…Having the linguistic strength of a fuzzy system and the numerical strength of a neural network, ANFIS has proven to be particularly efficient in modeling complex processes [6,7,8].Accordingly, the present study aimed at proposing an intelligent model to estimate depth of Anesthesia using features extracted from EEG signals. Sample features obtained from wavelet coefficients as well as the spectral analysis of EEG signals were used as parameters in this study.…”

Section: Introductionmentioning

confidence: 99%

“…They are used widely and quite efficiently in investigations targeting different biological and non-biological phenomena [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks

Esmaeilpour¹,

Mohammadi²

2016

IJIMAI

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-Estimating depth of Anesthesia in patients with the objective to administer the right dosage of drug has always attracted the attention of specialists. To study Anesthesia, researchers analyze brain waves since this is the place which is directly affected by the drug. This study aimed to estimate the depth of Anesthesia using electroencephalogram (EGG) signals, wavelet transform, and adaptive Neuro Fuzzy inference system (ANFIS). ANFIS can estimate the depth of Anesthesia with high accuracy. A set of EEG signals regarding consciousness, moderate Anesthesia, deep Anesthesia, and iso-electric point were collected from the American Society of Anesthesiologists (ASA) and PhysioNet. First, the extracted features were combined using wavelet and spectral analysis after which the target features were selected. Later, the features were classified into four categories. The results obtained revealed that the accuracy of the proposed method was 98.45%. Since the visual analysis of EEG signals is difficult, the proposed method can significantly help anesthesiologists estimate the depth of Anesthesia. Further, the results showed that ANFIS could significantly increase the accuracy of Anesthesia depth estimation. Finally, the system was deemed to be advantageous since it was also capable of updating in real-time situations as well.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks

Esmaeilpour¹,

Mohammadi²

2016

IJIMAI

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“…Among these models, the adaptive-network-based fuzzy inference System (ANFIS), comprising both fuzzy and neural network systems, has attracted researchers in different fields of study due to its ability to model and predict the phenomena [4], biomaterial surface [5] and manufacturing process [6,7]. For example: (1) Faster convergence than typical feed forward neural network, (2) Smaller size training set, (3) Model compactness (smaller rules than using labels), (4) Fuzzy logic controller (FLC) tuning.…”

Section: Introductionmentioning

confidence: 99%

Prediction and characterization of surface roughness using sandblasting and acid etching process on new non-toxic titanium biomaterial: adaptive-network-based fuzzy inference System

Khanlou

Ang

Barzani

et al. 2015

Neural Comput & Applic

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An adaptive neuro-fuzzy system (ANFIS) model was employed to predict the surface roughness. Surface roughening of titanium biomaterials has a crucial effect on increasing the biocompatibility. For this purpose, sandblasted, large-grit, acid-etched (SLA) has been introduced as an effective method to change the surface texturing and roughness. Subsequent processes-polishing, sandblasting and acid etching or SLA-were employed to modify the surface. Alumina particles for surface blasting and Kroll's etchant (3 ml HF ? 6 ml HNO 3 ? 100 ml H 2 O) for acid etching were utilized in this experiment. This was performed for three different periods of time (10, 20 and 30 s) and temperatures (25, 45 and 60 centigrade). Correspondingly, the Ti-13Zr-13Nb surfaces were evaluated using a field emission scanning electron microscope for texturing, contact mode profile meter for the average surface roughness (Ra) (nm) and atomic force microscopy for surface texturing at the nano-scale. In addition, the surface roughness was reduced in each condition, particularly in extremely high conditions. Significantly, the ANFIS model predicted the Ra amount of textured surface with an error band of 10 %. This research presents an idea to use the ANFIS model to obtain proper biological signs on the roughened surface in terms of surface roughness.

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“…It has been used widely to investigate phenomena with nonlinear equations (Goodarzi & Freitas, 2010;Nikolic et al, 2016). Therefore, the hybrid of fuzzy systems based on logical rules, and artificial neural networks which are able to extract knowledge from numerical information, enables us to use the available information to develop a model in addition to benefiting from the human knowledge.…”

Section: Introductionmentioning

confidence: 99%

Business risk evaluation and management of Iranian commercial insurance companies

Hessami¹

2018

10.5267/j.msl

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Nowadays, with the expansion of economic businesses and also the dependency of economic activists on the insurance industry to provide the capitals security, there is now a growing need to identify and evaluate risks of the insurance industry. Therefore, in this study, a comprehensive model was developed to evaluate and manage business risk by reviewing the research literature, extensively. For this purpose, an adaptive neuro-fuzzy inference system (ANFIS) was developed for every business risk after identifying the relevant dimensions and the criteria and collecting the necessary data from the central insurance databases. Finally, a general model was presented to evaluate and manage risks of the insurance industry. Four major problems were also considered: optimal and efficient normalization, optimal training for testing ratio for every neural network, model validation, and the easiness of user communication with the system. The results show that the model can provide an accurate estimation for risk evaluation and management. Thus, this system can be considered as an appropriate tool for business risk evaluation and management of insurance companies. Furthermore, the effectiveness of this method in evaluating and managing the risk at insurance companies can be turned into a neural network and such a neural network can be used as an appropriate decision-making support tool.

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MIA–QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA–ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives

Cited by 37 publications

References 30 publications

Analyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks

Analyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks

Prediction and characterization of surface roughness using sandblasting and acid etching process on new non-toxic titanium biomaterial: adaptive-network-based fuzzy inference System

Business risk evaluation and management of Iranian commercial insurance companies

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