Boosted Weighted Optimized Convolutional Neural Network Ensemble Classification for Lung Cancer Prediction

One of the major causes of cancer-related mortality worldwide is lung tumors. An earlier prediction of lung tumors is crucial since it may severely increase the death rates. For this reason, genomic profiles have been considered in many advanced microarray technology schemes. Amongst, an Improved Dragonfly optimization Algorithm (IDA) with Boosted Weighted Optimized Neural Network Ensemble Classification (BWONNEC) has been developed which extracts most suitable features and fine-tunes the weights related to the ensemble neural network classifiers. But, its major limitations are the number of learning factors in neural network and computational difficulty. Therefore in this article, a Boosted Weighted Optimized Convolutional Neural Network Ensemble Classification (BWOCNNEC) algorithm is proposed to lessen the number of learning factors and computation cost of neural network. In this algorithm, the boosting weights are combined into the CNN depending on the least square fitness value. Then, the novel weight values are assigned to the features extracted by the IDA. Moreover, these weight values and the chosen features are processed in different CNN structures within the boosted classifier. Further, the best CNN structure in each iteration i.e., CNNs having the least weighted loss is selected and ensemble to predict and diagnose the lung tumors effectively. Finally, the investigational outcomes exhibit that the IDA-BWOCNNEC achieves better prediction efficiency than the existing algorithms.

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Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

Remote Sensing ◽

10.3390/rs13020274 ◽

2021 ◽

Vol 13 (2) ◽

pp. 274

Author(s):

Guobiao Yao ◽

Alper Yilmaz ◽

Li Zhang ◽

Fei Meng ◽

Haibin Ai ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Stereo Matching ◽

Least Square ◽

Affine Invariant ◽

Stereo Images ◽

Distance Ratio ◽

Matching Algorithm ◽

End To End

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

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Short-Term Traffic Flow Forecasting Model Based on GA-TCN

Journal of Advanced Transportation ◽

10.1155/2021/1338607 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Rongji Zhang ◽

Feng Sun ◽

Ziwen Song ◽

Xiaolin Wang ◽

Yingcui Du ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Convolutional Neural Network ◽

Traffic Flow ◽

Absolute Error ◽

Forecasting Model ◽

Average Absolute Error ◽

Short Term ◽

Traffic Flow Forecasting ◽

Fitness Value

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

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