Autism Spectrum Disorder Risk Gene Prediction Using Improved Salp Swarm Algorithm and Enhanced Convolution Neural Network Algorithm

2021 ◽  
Vol 19 (9) ◽  
pp. 97-109
Author(s):  
Dr.J. Anitha
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Gene Selection ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Convolution Neural Network ◽  
Salp Swarm Algorithm ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Swarm Algorithm

A group of Neuro developmental disorders is Autism Spectrum Disorder (ASD) which is characterized by communication skills and social interaction difficulties. In past few years, there is a rapid increase in ASD and its root symptom’s cause is not yet determined. Comparatively large effort is needed in the existing system using Bayes network and there is no universally accepted technique to construct a network from data. An Enhanced Convolution Neural Network (ECNN) and Improved Salp Swarm Algorithm (ISSA) is proposed to solve these issues and for effective ASD classification. The task corresponds to classifying a long non-coding RNA (lncRNA) gene would cause a disease or not. After class balancing, discretization is applied for converting continuous values into discrete values and for optimal gene selection, ISSA algorithm is used. From genomic data, candidate gene biomarkers are identified using this gene selection. Every possible feature subset is computed for minimizing irrelevant features and error rate in gene data. It is focused to enhance ASD classification model’s accuracy. The Enhanced Convolutional Neural Network algorithm is used for ASD classification. The autism microarray dataset from the benchmark public repository, Gene Expression Omnibus (GEO) (National Center for Biotechnology Information (NCBI) is used for analysis. The proposed work using ISSA and ECNN exhibits better performance in terms of precision, accuracy, specificity, sensitivity and time complexity as indicated in the results.

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

scholarly journals Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks

2021 ◽  
Vol 14 ◽  
Author(s):  
Jingjing Gao ◽  
Mingren Chen ◽  
Yuanyuan Li ◽  
Yachun Gao ◽  
Yanling Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Convolutional Neural Network ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Structural Covariance ◽  
Almost All ◽  
New Framework

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients’ families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.

scholarly journals Consecrate Recurrent Neural Network Classifier for Autism Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 2033-2041
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Sequence Data ◽  
Large Data ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Neural Network Classifier ◽  
Gene Sequences

Most recent discoveries in Autism Spectrum Disorder (ASD) detection and classification studies reveal that there is a substantial relationship between Autism disorders and gene sequences. This work is indented to classify the autism spectrum disorder groups and sub-groups based on the gene sequences. The gene sequences are large data and perplexed for handling with conventional data mining or classification procedures. The Consecrate Recurrent Neural Network Classifier for Autism Classification (CRNNC-AC) work is introduced in this work to classify autism disorders using gene sequence data. A dedicated Elman [1] type Recurrent Neural Network (RNN) is introduced along with a legacy Long Short-Term Memory (LSTM) [2] in this classifier. The LSTM model is contrived to achieve memory optimization to eliminate memory overflows without affecting the classification accuracy. The classification quality metrics [3] such as Accuracy, Sensitivity, Specificity and F1-Score are concerned for optimization. The processing time of the proposed method is also measured to evaluate the pertinency.

scholarly journals Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jinlong Hu ◽  
Lijie Cao ◽  
Tenghui Li ◽  
Bin Liao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Piecewise Linear ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Support Vector ◽  
Resting State Functional Connectivity

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

scholarly journals A review on neural network models of schizophrenia and autism spectrum disorder

2020 ◽  
Vol 122 ◽  
pp. 338-363 ◽  
Author(s):  
Pablo Lanillos ◽  
Daniel Oliva ◽  
Anja Philippsen ◽  
Yuichi Yamashita ◽  
Yukie Nagai ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Network Models ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Neural Network Models
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