An Improved Technique for Diabeties Prediction By Combining Feature Selection Techniques And BFGS Optimization Algorithm With Weight Constrained Neural Network

2021 ◽  
Author(s):  
Shahee Parveen ◽  
Pooja Patre ◽  
Jasmine Minj
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Optimization Algorithm ◽  
Improved Technique ◽  
Feature Selection Techniques

scholarly journals Feature Selection Based on BP Neural Network and Adaptive Particle Swarm Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bingsheng Chen ◽  
Huijie Chen ◽  
Mengshan Li
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Data Sets ◽  
Swarm Optimization

Feature selection can classify the data with irrelevant features and improve the accuracy of data classification in pattern classification. At present, back propagation (BP) neural network and particle swarm optimization algorithm can be well combined with feature selection. On this basis, this paper adds interference factors to BP neural network and particle swarm optimization algorithm to improve the accuracy and practicability of feature selection. This paper summarizes the basic methods and requirements for feature selection and combines the benefits of global optimization with the feedback mechanism of BP neural networks to feature based on backpropagation and particle swarm optimization (BP-PSO). Firstly, a chaotic model is introduced to increase the diversity of particles in the initial process of particle swarm optimization, and an adaptive factor is introduced to enhance the global search ability of the algorithm. Then, the number of features is optimized to reduce the number of features on the basis of ensuring the accuracy of feature selection. Finally, different data sets are introduced to test the accuracy of feature selection, and the evaluation mechanisms of encapsulation mode and filtering mode are used to verify the practicability of the model. The results show that the average accuracy of BP-PSO is 8.65% higher than the suboptimal NDFs model in different data sets, and the performance of BP-PSO is 2.31% to 18.62% higher than the benchmark method in all data sets. It shows that BP-PSO can select more distinguishing feature subsets, which verifies the accuracy and practicability of this model.

scholarly journals Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

2020 ◽  
Author(s):  
Surbhi Vijh ◽  
Prashant Gaurav ◽  
Hari Mohan Pandey
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Lung Tumor ◽  
Tumor Detection ◽  
Whale Optimization Algorithm ◽  
Swarm Optimization ◽  
Whale Optimization

Abstract In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction.

scholarly journals Automated Maintenance Data Classification Using Recurrent Neural Network: Enhancement by Spotted Hyena-Based Whale Optimization

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2008
Author(s):  
Mustufa Haider Abidi ◽  
Usama Umer ◽  
Muneer Khan Mohammed ◽  
Mohamed K. Aboudaif ◽  
Hisham Alkhalefah
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Feature Selection ◽  
Data Acquisition ◽  
Recurrent Neural Network ◽  
Optimization Algorithm ◽  
Data Classification ◽  
Whale Optimization Algorithm ◽  
Spotted Hyena ◽  
Whale Optimization

Data classification has been considered extensively in different fields, such as machine learning, artificial intelligence, pattern recognition, and data mining, and the expansion of classification has yielded immense achievements. The automatic classification of maintenance data has been investigated over the past few decades owing to its usefulness in construction and facility management. To utilize automated data classification in the maintenance field, a data classification model is implemented in this study based on the analysis of different mechanical maintenance data. The developed model involves four main steps: (a) data acquisition, (b) feature extraction, (c) feature selection, and (d) classification. During data acquisition, four types of dataset are collected from the benchmark Google datasets. The attributes of each dataset are further processed for classification. Principal component analysis and first-order and second-order statistical features are computed during the feature extraction process. To reduce the dimensions of the features for error-free classification, feature selection was performed. The hybridization of two algorithms, the Whale Optimization Algorithm (WOA) and Spotted Hyena Optimization (SHO), tends to produce a new algorithm—i.e., a Spotted Hyena-based Whale Optimization Algorithm (SH-WOA), which is adopted for performing feature selection. The selected features are subjected to a deep learning algorithm called Recurrent Neural Network (RNN). To enhance the efficiency of conventional RNNs, the number of hidden neurons in an RNN is optimized using the developed SH-WOA. Finally, the efficacy of the proposed model is verified utilizing the entire dataset. Experimental results show that the developed model can effectively solve uncertain data classification, which minimizes the execution time and enhances efficiency.

Incremental text categorization based on hybrid optimization-based deep belief neural network

2021 ◽  
pp. 1-20
Author(s):  
V. Srilakshmi ◽  
K. Anuradha ◽  
C. Shoba Bindu
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Optimization Algorithm ◽  
Incremental Learning ◽  
Text Categorization ◽  
Large Scale ◽  
Performance Metrics ◽  
Experimental Result ◽  
Text Document ◽  
Bounding Model

One of the effective text categorization methods for learning the large-scale data and the accumulated data is incremental learning. The major challenge in the incremental learning is improving the accuracy as the text document consists of numerous terms. In this research, a incremental text categorization method is developed using the proposed Spider Grasshopper Crow Optimization Algorithm based Deep Belief Neural network (SGrC-based DBN) for providing optimal text categorization results. The proposed text categorization method has four processes, such as are pre-processing, feature extraction, feature selection, text categorization, and incremental learning. Initially, the database is pre-processed and fed into vector space model for the extraction of features. Once the features are extracted, the feature selection is carried out based on mutual information. Then, the text categorization is performed using the proposed SGrC-based DBN method, which is developed by the integration of the spider monkey optimization (SMO) with the Grasshopper Crow Optimization Algorithm (GCOA) algorithm. Finally, the incremental text categorization is performed based on the hybrid weight bounding model that includes the SGrC and Range degree and particularly, the optimal weights of the Range degree model is selected based on SGrC. The experimental result of the proposed text categorization method is performed by considering the data from the Reuter database and 20 Newsgroups database. The comparative analysis of the text categorization method is based on the performance metrics, such as precision, recall and accuracy. The proposed SGrC algorithm obtained a maximum accuracy of 0.9626, maximum precision of 0.9681 and maximum recall of 0.9600, respectively when compared with the existing incremental text categorization methods.

scholarly journals Skin Cancer Detection Using Kernel Fuzzy C-Means and Improved Neural Network Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jia Huaping ◽  
Zhao Junlong ◽  
A. M. Norouzzadeh Gil Molk
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Skin Cancer ◽  
Optimization Algorithm ◽  
Network Optimization ◽  
Support Vector ◽  
Svm Classifier ◽  
Neural Network Optimization ◽  
Fuzzy C Means ◽  
Comparison Results

Early diagnosis of malignant skin cancer from images is a significant part of the cancer treatment process. One of the principal purposes of this research is to propose a pipeline methodology for an optimum computer-aided diagnosis of skin cancers. The method contains four main stages. The first stage is to perform a preprocessing based on noise reduction and contrast enhancement. The second stage is to segment the region of interest (ROI). This study uses kernel fuzzy C-means for ROI segmentation. Then, some features from the ROI are extracted, and then, a feature selection is used for selecting the best ones. The selected features are then injected into a support vector machine (SVM) for final identification. One important part of the contribution in this study is to propose a developed version of a new metaheuristic, named neural network optimization algorithm, to optimize both parts of feature selection and SVM classifier. Comparison results of the method with 5 state-of-the-art methods showed the approach’s higher superiority toward the others.

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