scholarly journals Diagnoses of Neisseria Bacteria via Neural Networks Techniques

2021 ◽  
Vol 12 (3) ◽  
pp. 587-593
Author(s):  
Kalthom Ibrahim ◽  
Mohammed Abdallah Almaleeh ◽  
Moaawia Mohamed Ahmed ◽  
Dalia Mahmoud Adam
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cerebrospinal Fluid ◽  
High Precision ◽  
Simple Approach ◽  
Color Spaces

This paper presented simple approach that automatically detects Neisseria Bacteria cell in the cerebrospinal fluid smear images. The proposed methodology mainly consists of cerebrospinal fluid smear images acquisition, transformation form red, green, blue smear images in to other color spaces. This step followed by subbing images and segmenting the images to extracting the images features then validation and classifying the Bacteria images based in features extracted using neural networks. The proposed diagnosis for Neisseria Bacteria through neural network techniques has performed high-precision performance in some suggested groups.

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

scholarly journals Energy-efficient memcapacitor devices for neuromorphic computing

2021 ◽  
Author(s):  
Kai-Uwe Demasius ◽  
Aron Kirschen ◽  
Stuart Parkin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Energy Efficiency ◽  
High Precision ◽  
Energy Efficient ◽  
Lateral Size ◽  
Data Intensive Computing ◽  
Data Intensive ◽  
Specialized Hardware

AbstractData-intensive computing operations, such as training neural networks, are essential for applications in artificial intelligence but are energy intensive. One solution is to develop specialized hardware onto which neural networks can be directly mapped, and arrays of memristive devices can, for example, be trained to enable parallel multiply–accumulate operations. Here we show that memcapacitive devices that exploit the principle of charge shielding can offer a highly energy-efficient approach for implementing parallel multiply–accumulate operations. We fabricate a crossbar array of 156 microscale memcapacitor devices and use it to train a neural network that could distinguish the letters ‘M’, ‘P’ and ‘I’. Modelling these arrays suggests that this approach could offer an energy efficiency of 29,600 tera-operations per second per watt, while ensuring high precision (6–8 bits). Simulations also show that the devices could potentially be scaled down to a lateral size of around 45 nm.

HPM: High-Precision Modeling of a Low-Power Inverter-Based Memristive Neural Network

2021 ◽  
pp. 2150274
Author(s):  
Negin Mohajeri ◽  
Behzad Ebrahimi ◽  
Massoud Dousti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Precision ◽  
Complementary Metal Oxide Semiconductor ◽  
Process Variations ◽  
Oxide Semiconductor ◽  
Ex Situ ◽  
Training Phase ◽  
Cmos Inverter ◽  
Memristive Neural Network

In this paper, we propose a high-precision memristive neural network with neurons implemented by complementary metal oxide semiconductor (CMOS) inverters. Regarding the process variations in the memristors and the sensitivity of the memristive crossbar structure to these fluctuations, the read operation with repetitive pulses and feedback-based write in the memristors are used to implement the neural networks trained by the ex-situ method. Moreover, accurate modeling of the neuron circuit (CMOS inverter) and decreasing the mismatch between trained weights and the limited memristances fill the gap between simulation and implementation. To employ physical constraints based on the memristor framework during the training phase, a linear function is utilized to map the trained weights to the acceptable range of memristances after the training phase. To solve the vanishing gradient problem due to the use of the tanh function as an activation function and for better learning of the network, some measures are taken. Moreover, fin field-effect transistor (FinFET) technology is used to prevent the reduction of the accuracy of the inverter-based memristive neural networks due to the process variations. Overall, our implementation improves the speed, area, power-delay product (PDP), and mean square error (MSE) of the training stage by 91.43%, 95.06%, 48.29% and 81.64%, respectively.

Human Skin Color Detection Using Neural Networks

2015 ◽  
Vol 24 (4) ◽  
pp. 425-436 ◽  
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Human Skin ◽  
Skin Color ◽  
Skin Detection ◽  
Color Spaces ◽  
Skin Color Detection ◽  
The Neural Network ◽  
Color Detection ◽  
Different Color

AbstractHuman skin detection is an essential phase in face detection and face recognition when using color images. Skin detection is very challenging because of the differences in illumination, differences in photos taken using an assortment of cameras with their own characteristics, range of skin colors due to different ethnicities, and other variations. Numerous methods have been used for human skin color detection, including the Gaussian model, rule-based methods, and artificial neural networks. In this article, we introduce a novel technique of using the neural network to enhance the capabilities of skin detection. Several different entities were used as inputs of a neural network, and the pros and cons of different color spaces are discussed. Also, a vector was used as the input to the neural network that contains information from three different color spaces. The comparison of the proposed technique with existing methods in this domain illustrates the effectiveness and accuracy of the proposed approach. Tests were done on two databases, and the results show that the neural network has better precision and accuracy rate, as well as comparable recall and specificity, compared with other methods.

Prediction of the Consistency of Concrete by Means of the Use of Artificial Neural Networks

2011 ◽  
pp. 1484-1493
Author(s):  
Bele´n Gonzalez ◽  
Ma Isabel Martinez ◽  
Diego Carro
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Fresh Concrete ◽  
Rheological Behaviour ◽  
Simple Approach ◽  
Artificial Neural

This chapter displays an example of application of the ANN in civil engineering. Concretely, it is applied to the prediction of the consistency of the fresh concrete through the results that slump test provides, a simple approach to the rheological behaviour of the mixtures. From the previously done tests, an artificial neural network trained by means of genetic algorithms adjusts to the situation, and has the variable value of the cone as an output, and as an input, diverse variables related to the composition of each type of concrete. The final discussion is based on the quality of the results and its possible application.

scholarly journals Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)

2019 ◽  
Author(s):  
Panagiotis-Christos Kotsias ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Ola Engkvist ◽  
Christian Tyrchan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Molecular Descriptors ◽  
De Novo ◽  
Simple Approach ◽  
Regions Of Interest ◽  
Generative Process ◽  
The Past

<p>Deep learning has acquired considerable momentum over the past couple of years in the domain of <i>de-novo</i> drug design. Particularly, transfer and reinforcement learning have demonstrated the capability of steering the generative process towards chemical regions of interest. In this work, we propose a simple approach to the focused generative task by constructing a conditional recurrent neural network (cRNN). For this purpose, we aggregate selected molecular descriptors along with a QSAR-based bioactivity label and transform them into initial LSTM states before starting the generation of SMILES strings that are focused towards the aspired properties. We thus tackle the inverse QSAR problem directly by training on molecular descriptors, instead of iteratively optimizing around a set of candidate molecules. The trained cRNNs are able to generate molecules near multiple specified conditions, while maintaining an output that is more focused than traditional RNNs yet less focused than autoencoders. The method shows promise for applications in both scaffold hoping and ligand series generation, depending on whether the cRNN is trained on calculated scalar molecular properties or structural fingerprints. This also demonstrates that fingerprint-to-molecule decoding is feasible, leading to molecules that are similar – if not identical – to the ones the fingerprints originated from. Additionally, the cRNN is able to generate a larger fraction of predicted active compounds against the DRD2 receptor when compared to an RNN trained with the transfer learning model. </p>

scholarly journals Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)

2019 ◽  
Author(s):  
Panagiotis-Christos Kotsias ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Ola Engkvist ◽  
Christian Tyrchan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Molecular Descriptors ◽  
De Novo ◽  
Simple Approach ◽  
Regions Of Interest ◽  
Generative Process ◽  
The Past

<p>Deep learning has acquired considerable momentum over the past couple of years in the domain of <i>de-novo</i> drug design. Particularly, transfer and reinforcement learning have demonstrated the capability of steering the generative process towards chemical regions of interest. In this work, we propose a simple approach to the focused generative task by constructing a conditional recurrent neural network (cRNN). For this purpose, we aggregate selected molecular descriptors along with a QSAR-based bioactivity label and transform them into initial LSTM states before starting the generation of SMILES strings that are focused towards the aspired properties. We thus tackle the inverse QSAR problem directly by training on molecular descriptors, instead of iteratively optimizing around a set of candidate molecules. The trained cRNNs are able to generate molecules near multiple specified conditions, while maintaining an output that is more focused than traditional RNNs yet less focused than autoencoders. The method shows promise for applications in both scaffold hoping and ligand series generation, depending on whether the cRNN is trained on calculated scalar molecular properties or structural fingerprints. This also demonstrates that fingerprint-to-molecule decoding is feasible, leading to molecules that are similar – if not identical – to the ones the fingerprints originated from. Additionally, the cRNN is able to generate a larger fraction of predicted active compounds against the DRD2 receptor when compared to an RNN trained with the transfer learning model. </p>

scholarly journals Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)

2019 ◽  
Author(s):  
Panagiotis-Christos Kotsias ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Ola Engkvist ◽  
Christian Tyrchan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Molecular Descriptors ◽  
De Novo ◽  
Simple Approach ◽  
Regions Of Interest ◽  
Generative Process ◽  
The Past

<p>Deep learning has acquired considerable momentum over the past couple of years in the domain of <i>de-novo</i> drug design. Particularly, transfer and reinforcement learning have demonstrated the capability of steering the generative process towards chemical regions of interest. In this work, we propose a simple approach to the focused generative task by constructing a conditional recurrent neural network (cRNN). For this purpose, we aggregate selected molecular descriptors along with a QSAR-based bioactivity label and transform them into initial LSTM states before starting the generation of SMILES strings that are focused towards the aspired properties. We thus tackle the inverse QSAR problem directly by training on molecular descriptors, instead of iteratively optimizing around a set of candidate molecules. The trained cRNNs are able to generate molecules near multiple specified conditions, while maintaining an output that is more focused than traditional RNNs yet less focused than autoencoders. The method shows promise for applications in both scaffold hoping and ligand series generation, depending on whether the cRNN is trained on calculated scalar molecular properties or structural fingerprints. This also demonstrates that fingerprint-to-molecule decoding is feasible, leading to molecules that are similar – if not identical – to the ones the fingerprints originated from. Additionally, the cRNN is able to generate a larger fraction of predicted active compounds against the DRD2 receptor when compared to an RNN trained with the transfer learning model. </p>

Prediction of the Consistency of Concrete by Means of the Use of Artificial Neural Networks

2006 ◽  
pp. 188-201
Author(s):  
Belén Gonzalez ◽  
M. Isabel Martinez ◽  
Diego Carro
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Fresh Concrete ◽  
Rheological Behaviour ◽  
Simple Approach ◽  
Artificial Neural

This chapter displays an example of application of the ANN in civil engineering. Concretely, it is applied to the prediction of the consistency of the fresh concrete through the results that slump test provides, a simple approach to the rheological behaviour of the mixtures. From the previously done tests, an artificial neural network trained by means of genetic algorithms adjusts to the situation, and has the variable value of the cone as an output, and as an input, diverse variables related to the composition of each type of concrete. The final discussion is based on the quality of the results and its possible application.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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