Prediction of DNA sequences using adaptative neuro-fuzzy inference system

2018 ◽  
Vol 11 (04) ◽  
pp. 1850047
Author(s):  
Assia Mihi ◽  
Nourredine Boucenna ◽  
Kheir Benmahammmed
Keyword(s):  
Dna Sequences ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Wavelet Coefficient ◽  
Genomic Sequences ◽  
Computational Techniques ◽  
Inference System ◽  
Training Performance ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann

Accurate prediction and detection of the DNA regions or their underlying structural patterns are constant difficulties for researchers. Feature extraction and functional classification of genomic sequences is an interesting area of research. Many computational techniques have already been applied including the artificial neural network (ANN), nonlinear model, spectrogram and statistical techniques. In this paper, some features are extracted from the wavelet coefficient and second set of features are extracted from the frequency of transition of nucleotides. These two features sets are examined. The purpose was to investigate the abilities of these parameters to predict critical segment in the DNA sequence. The neuro-fuzzy system was used for prediction. The performance of the neuro-fuzzy system was evaluated in terms of training performance and prediction accuracies. Two genomic sequences of the classes: prokaryotic and eukaryotic were used, as an example, (Escherichia coli) and (Caenorhabditis elegans) sequences were selected.

Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors

2021 ◽  
pp. 181-189
Author(s):  
Wayan Firdaus Mahmudy ◽  
Aji Prasetya Wibawa ◽  
Nadia Roosmalita Sari ◽  
H. Haviluddin ◽  
P. Purnawansyah
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Online Journal ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
Network Weight

Artificial Neural Network (ANN) is recognized as one of effective forecasting engines for various business fields. This approach fits well with non-linear data. In fact, it is a black box system with random weighting, which is hard to train. One way to improve its performance is by hybridizing ANN with other methods. In this paper, a hybrid approach, Genetic Algorithm-Neural Fuzzy System (GA-NFS) is proposed to predict the number of unique visitors of an online journal website. The neural network weight is precisely determined using GA. Afterwards, the best weight has been used for testing data and processed using Sugeno Fuzzy Inference System (FIS) for time-series forecasting. Based on experiment, GA-NFS have been produced accuracy with 0.989 of root mean square error (RMSE) that is lower than the RMSE of a common NFS (2,004). This may indicate that the GA based weighting is able to improve the NFS performance on forecasting the number of journal unique visitors.

scholarly journals Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 286 ◽  
Author(s):  
Athanasios Bogiatzis ◽  
Basil Papadopoulos
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Initial Step ◽  
Local Area ◽  
Mathematical Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Complex Procedure ◽  
Artificial Neural Network Ann

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri
Keyword(s):  
Pancreatic Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Patient Survival ◽  
Data Partitioning ◽  
Structure Identification ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions

2014 ◽  
Vol 9 (12) ◽  
pp. 1226-1234
Author(s):  
Kadir Temizel ◽  
Mehmet Odabas ◽  
Nurettin Senyer ◽  
Gokhan Kayhan ◽  
Sreekala Bajwa ◽  
...  
Keyword(s):  
High Performance ◽  
Fuzzy Inference ◽  
High Water ◽  
Stress Conditions ◽  
Ann Model ◽  
Inference System ◽  
Leaf Reflectance ◽  
Neuro Fuzzy ◽  
Salinity Levels ◽  
Artificial Neural Network Ann

AbstractLack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.

Biometric Identification System Using Neuro and Fuzzy Computational Approaches

2016 ◽  
pp. 335-366
Author(s):  
Tripti Rani Borah ◽  
Kandarpa Kumar Sarma ◽  
Pranhari Talukdar
Keyword(s):  
Fuzzy System ◽  
Fuzzy Inference ◽  
Biometric Identification ◽  
Identification System ◽  
Inference System ◽  
Computational Approaches ◽  
Neuro Fuzzy ◽  
Actual Design ◽  
Authentication System ◽  
Numeric Data

In all authentication systems, biometric samples are regarded to be the most reliable one. Biometric samples like fingerprint, retina etc. is unique. Most commonly available biometric system prefers these samples as reliable inputs. In a biometric authentication system, the design of decision support system is critical and it determines success or failure. Here, we propose such a system based on neuro and fuzzy system. Neuro systems formulated using Artificial Neural Network learn from numeric data while fuzzy based approaches can track finite variations in the environment. Thus NFS systems formed using ANN and fuzzy system demonstrate adaptive, numeric and qualitative processing based learning. These attributes have motivated the formulation of an adaptive neuro fuzzy inference system which is used as a DSS of a biometric authenticable system. The experimental results show that the system is reliable and can be considered to be a part of an actual design.

Application of Neuro-Fuzzy Inference System on Wood Identification

2014 ◽  
Vol 590 ◽  
pp. 667-671
Author(s):  
Fábio Henrique Antunes Vieira ◽  
Carlos Affonso ◽  
Manoel Cléber de Sampaio Alves
Keyword(s):  
Process Improvement ◽  
Fuzzy Inference ◽  
Autonomous Systems ◽  
Computational Techniques ◽  
Wood Industry ◽  
Noise Elimination ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Specific Goal ◽  
Human Operators

Searching for systems with intelligent, flexible, and self-adjusting solutions on imaging, which could provide the contraction of the human operators’ presence, a range of techniques is found. Each one of them can control the process through the assistance of autonomous systems, either software or hardware. Therefore, modeling by traditional computational techniques is quite difficult, considering the complexity and non-linearity of image systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) behaves well as noise elimination and non-linear data treatment. Consequently, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, therefore, add value to the final product. Additionally, the Artificial Intelligence techniques, such as Neuro-Fuzzy Networks (NFN), have shown efficient, since they combine the ability to learn from examples and to generalize the learned information from the ANNs with the capacity of Fuzzy Logic, in order to transform linguistic variables in rules. Then, ANFIS plays active roles in an effort to reach a specific goal.

Investigation of trap efficiency of retention dams

2017 ◽  
Vol 18 (2) ◽  
pp. 450-459 ◽  
Author(s):  
Abbas Parsaie ◽  
Samad Ememgholizadeh ◽  
Amir Hamzeh Haghiabi ◽  
Amir Moradinejad
Keyword(s):  
Fuzzy Inference ◽  
Kernel Functions ◽  
Support Vector ◽  
Inference System ◽  
Trap Efficiency ◽  
Neuro Fuzzy ◽  
Management Plans ◽  
Longitudinal Slope ◽  
The Mean ◽  
Artificial Neural Network Ann

Abstract In this paper, the trap efficiency (TE) of retention dams was investigated using laboratory experiments. To map the relation between TE and involved parameters, artificial intelligence (AI) methods including artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were utilized. Results of experiments indicated that the range of TE varies between 30 and 98%; hence, this structure can be recommended to control sediment transport in watershed management plans. Experimental results showed that by increasing the longitudinal slope of streams, TE decreases. This finding was observed for Vf/Vs parameter, as well. By increasing the mean diameter grain size (D50) and specific gravity of sediments (Gs), TE increases. Results of all applied AI models demonstrated that all of them have suitable performance; however, the minimum data dispersivity was observed in SVM outcomes. It is notable that the best performance of transfer, membership and kernel functions were related to tansig, gaussmf and radial basis function (RBF) for ANN, SVM and ANFIS, respectively.

Sign in / Sign up

Export Citation Format

Share Document