scholarly journals Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3104
Author(s):  
Nesrine Amor ◽  
Muhammad Tayyab Noman ◽  
Michal Petru
Keyword(s):  
Neural Network ◽  
Methylene Blue ◽  
High Performance ◽  
Deep Neural Network ◽  
Dye Removal ◽  
Titanium Dioxide Nanoparticles ◽  
Morphological Properties ◽  
Learning Tools ◽  
Tio2 Nps ◽  
Input Variables

This paper deals with the prediction of methylene blue (MB) dye removal under the influence of titanium dioxide nanoparticles (TiO2 NPs) through deep neural network (DNN). In the first step, TiO2 NPs were prepared and their morphological properties were analysed by scanning electron microscopy. Later, the influence of as synthesized TiO2 NPs was tested against MB dye removal and in the final step, DNN was used for the prediction. DNN is an efficient machine learning tools and widely used model for the prediction of highly complex problems. However, it has never been used for the prediction of MB dye removal. Therefore, this paper investigates the prediction accuracy of MB dye removal under the influence of TiO2 NPs using DNN. Furthermore, the proposed DNN model was used to map out the complex input-output conditions for the prediction of optimal results. The amount of chemicals, i.e., amount of TiO2 NPs, amount of ehylene glycol and reaction time were chosen as input variables and MB dye removal percentage was evaluated as a response. DNN model provides significantly high performance accuracy for the prediction of MB dye removal and can be used as a powerful tool for the prediction of other functional properties of nanocomposites.

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

scholarly journals The Effect of High Performance Computer on Deep Neural Network

2021 ◽  
Author(s):  
Xin Zhang ◽  
◽  
Ting Zhang ◽  
Jiang Lu ◽  
Xingang Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Deep Neural Network ◽  
High Performance Computer

scholarly journals Deep Learning for Computational Mode Decomposition in Optical Fibers

2020 ◽  
Vol 10 (4) ◽  
pp. 1367
Author(s):  
Stefan Rothe ◽  
Qian Zhang ◽  
Nektarios Koukourakis ◽  
Jürgen W. Czarske
Keyword(s):  
Neural Network ◽  
Optical Fibers ◽  
High Performance ◽  
Deep Neural Network ◽  
Light Propagation ◽  
Training Data ◽  
Steady Increase ◽  
Cyberphysical Systems ◽  
Multimode Fibers ◽  
Propagation Of Light

Multimode fibers are regarded as the key technology for the steady increase in data rates in optical communication. However, light propagation in multimode fibers is complex and can lead to distortions in the transmission of information. Therefore, strategies to control the propagation of light should be developed. These strategies include the measurement of the amplitude and phase of the light field after propagation through the fiber. This is usually done with holographic approaches. In this paper, we discuss the use of a deep neural network to determine the amplitude and phase information from simple intensity-only camera images. A new type of training was developed, which is much more robust and precise than conventional training data designs. We show that the performance of the deep neural network is comparable to digital holography, but requires significantly smaller efforts. The fast characterization of multimode fibers is particularly suitable for high-performance applications like cyberphysical systems in the internet of things.

scholarly journals AptaNet as a deep learning approach for aptamer–protein interaction prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Neda Emami ◽  
Reza Ferdousi
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Interaction ◽  
High Performance ◽  
Deep Neural Network ◽  
High Specificity ◽  
Benchmark Dataset ◽  
Training Dataset

AbstractAptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet—a new deep neural network—to predict the aptamer–protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer–protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet.

scholarly journals A Multichannel Deep Neural Network for Retina Vessel Segmentation via a Fusion Mechanism

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiaqi Ding ◽  
Zehua Zhang ◽  
Jijun Tang ◽  
Fei Guo
Keyword(s):  
Neural Network ◽  
Blood Vessels ◽  
High Performance ◽  
Deep Neural Network ◽  
High Efficiency ◽  
Vessel Segmentation ◽  
Prediction Probability ◽  
Binary Segmentation ◽  
Public Datasets ◽  
Retina Vessel

Changes in fundus blood vessels reflect the occurrence of eye diseases, and from this, we can explore other physical diseases that cause fundus lesions, such as diabetes and hypertension complication. However, the existing computational methods lack high efficiency and precision segmentation for the vascular ends and thin retina vessels. It is important to construct a reliable and quantitative automatic diagnostic method for improving the diagnosis efficiency. In this study, we propose a multichannel deep neural network for retina vessel segmentation. First, we apply U-net on original and thin (or thick) vessels for multi-objective optimization for purposively training thick and thin vessels. Then, we design a specific fusion mechanism for combining three kinds of prediction probability maps into a final binary segmentation map. Experiments show that our method can effectively improve the segmentation performances of thin blood vessels and vascular ends. It outperforms many current excellent vessel segmentation methods on three public datasets. In particular, it is pretty impressive that we achieve the best F1-score of 0.8247 on the DRIVE dataset and 0.8239 on the STARE dataset. The findings of this study have the potential for the application in an automated retinal image analysis, and it may provide a new, general, and high-performance computing framework for image segmentation.

scholarly journals The use of adversaries for optimal neural network training

2019 ◽  
Vol 214 ◽  
pp. 06013
Author(s):  
Anton Hawthorne-Gonzalvez ◽  
Martin Sevior
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Net ◽  
Network Algorithms ◽  
Network Training ◽  
Commercial Package ◽  
Boosted Decision Tree ◽  
Input Variables ◽  
Hidden Layer ◽  
Better Than

B-decay data from the Belle experiment at the KEKB collider have a substantial background from e+e- -h> qq¯ events. To suppress this we employ deep neural network algorithms. These provide improved signal from background discrimination. However, the deep neural network develops a substantial correlation with the ∆E kinematic variable used to distinguish signal from background in the final fit due to its relationship with input variables. The effect of this correlation is reduced by deploying an adversarial neural network. Over-all the adversarial deep neural network performs better than a Boosted Decision Tree algorithimn and a commercial package, NeuroBayes, which employs a neural net with a single hidden layer.

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