scholarly journals Signature Authentication using Deep Learning

2019 ◽  
Vol 8 (9S3) ◽  
pp. 531-535
Keyword(s):  
Deep Learning ◽  
Neural Systems ◽  
Feed Forward

This paper depicts the strategy to confirm marks utilizing profound learning. The paper covers the different modules and the design required to accomplish the reason. Convolutional neural systems are actualized to parse marks and feed forward neural systems are executed to investigate the attributes of the mark. Angle plummet is utilized to address mistakes with a technique got back to spread. This paper discusses how the mark on the check is contrasted with the mark in the database and how a last verification score is given to the client

Deep Learning

2019 ◽  
pp. 130-158 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Deep Learning ◽  
Neural Systems ◽  
Review Period ◽  
Remote Part

The vast majority of the examination on profound neural systems so far has been centered on acquiring higher exactness levels by building progressively vast and profound structures. Preparing and assessing these models is just practical when a lot of assets; for example, handling power and memory are easy run of the mill applications that could profit by these models. The system starts handling the compelled gadget and depends on the remote part when the neighborhood part does not give a sufficiently precise outcome. The falling system takes into account a new ceasing component amid the review period of the system. This chapter empowers an entire assortment of independent frameworks where sensors, actuators, and registering hubs can cooperate and demonstrate that the falling design takes into account a free change in assessment speed on obliged gadgets while the misfortune in precision is kept to a base.

Implementation of Deep Learning Neural Network for Retinal Images

2020 ◽  
pp. 77-95
Author(s):  
R. Murugan
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Neural System ◽  
Neural Systems ◽  
Retinal Images ◽  
Manual Segmentation ◽  
Convolutional Network ◽  
The World ◽  
Deep Learning Neural Network ◽  
Wide Assortment

The retinal parts segmentation has been recognized as a key component in both ophthalmological and cardiovascular sickness analysis. The parts of retinal pictures, vessels, optic disc, and macula segmentations, will add to the indicative outcome. In any case, the manual segmentation of retinal parts is tedious and dreary work, and it additionally requires proficient aptitudes. This chapter proposes a supervised method to segment blood vessel utilizing deep learning methods. All the more explicitly, the proposed part has connected the completely convolutional network, which is normally used to perform semantic segmentation undertaking with exchange learning. The convolutional neural system has turned out to be an amazing asset for a few computer vision assignments. As of late, restorative picture investigation bunches over the world are rapidly entering this field and applying convolutional neural systems and other deep learning philosophies to a wide assortment of uses, and uncommon outcomes are rising constantly.

scholarly journals DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains

2020 ◽  
Vol 14 ◽  
Author(s):  
Xiayu Chen ◽  
Ming Zhou ◽  
Zhengxin Gong ◽  
Wei Xu ◽  
Xingyu Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Neuroscience ◽  
Deep Neural Networks ◽  
Learning Strategy ◽  
Neural Systems ◽  
Levels Of Abstraction ◽  
Internal Representations ◽  
Internal Operations ◽  
High Level

Deep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks via an end-to-end deep learning strategy. Deep learning allows data representations that have multiple levels of abstraction; however, it does not explicitly provide any insights into the internal operations of DNNs. Deep learning's success is appealing to neuroscientists not only as a method for applying DNNs to model biological neural systems but also as a means of adopting concepts and methods from cognitive neuroscience to understand the internal representations of DNNs. Although general deep learning frameworks, such as PyTorch and TensorFlow, could be used to allow such cross-disciplinary investigations, the use of these frameworks typically requires high-level programming expertise and comprehensive mathematical knowledge. A toolbox specifically designed as a mechanism for cognitive neuroscientists to map both DNNs and brains is urgently needed. Here, we present DNNBrain, a Python-based toolbox designed for exploring the internal representations of DNNs as well as brains. Through the integration of DNN software packages and well-established brain imaging tools, DNNBrain provides application programming and command line interfaces for a variety of research scenarios. These include extracting DNN activation, probing and visualizing DNN representations, and mapping DNN representations onto the brain. We expect that our toolbox will accelerate scientific research by both applying DNNs to model biological neural systems and utilizing paradigms of cognitive neuroscience to unveil the black box of DNNs.

scholarly journals Investigating Explanatory Factors of Machine Learning Models for Plant Classification

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2674
Author(s):  
Wilfried Wöber ◽  
Lars Mehnen ◽  
Peter Sykacek ◽  
Harald Meimberg
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Gaussian Process ◽  
Beta Vulgaris ◽  
Neuronal Network ◽  
Latent Variable ◽  
Neuronal Networks ◽  
Learning Models ◽  
Feed Forward ◽  
Explanatory Factors

Recent progress in machine learning and deep learning has enabled the implementation of plant and crop detection using systematic inspection of the leaf shapes and other morphological characters for identification systems for precision farming. However, the models used for this approach tend to become black-box models, in the sense that it is difficult to trace characters that are the base for the classification. The interpretability is therefore limited and the explanatory factors may not be based on reasonable visible characters. We investigate the explanatory factors of recent machine learning and deep learning models for plant classification tasks. Based on a Daucus carota and a Beta vulgaris image data set, we implement plant classification models and compare those models by their predictive performance as well as explainability. For comparison we implemented a feed forward convolutional neuronal network as a default model. To evaluate the performance, we trained an unsupervised Bayesian Gaussian process latent variable model as well as a convolutional autoencoder for feature extraction and rely on a support vector machine for classification. The explanatory factors of all models were extracted and analyzed. The experiments show, that feed forward convolutional neuronal networks (98.24% and 96.10% mean accuracy) outperforms the Bayesian Gaussian process latent variable pipeline (92.08% and 94.31% mean accuracy) as well as the convolutional autoenceoder pipeline (92.38% and 93.28% mean accuracy) based approaches in terms of classification accuracy, even though not significant for Beta vulgaris images. Additionally, we found that the neuronal network used biological uninterpretable image regions for the plant classification task. In contrast to that, the unsupervised learning models rely on explainable visual characters. We conclude that supervised convolutional neuronal networks must be used carefully to ensure biological interpretability. We recommend unsupervised machine learning, careful feature investigation, and statistical feature analysis for biological applications.

scholarly journals Generalizable Machine Learning in Neuroscience Using Graph Neural Networks

2021 ◽  
Vol 4 ◽  
Author(s):  
Paul Y. Wang ◽  
Sandalika Sapra ◽  
Vivek Kurien George ◽  
Gabriel A. Silva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Neural Systems ◽  
Graph Structure ◽  
Imaging Data ◽  
C Elegans ◽  
State Classification ◽  
Graph Neural Networks ◽  
Emergent Behaviors

Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.

Sentiment Mining and Analysis over Text Corpora via Complex Deep Learning Naural Architectures

2021 ◽  
Vol 2 (4) ◽  
pp. 448-461
Author(s):  
Teresa Alcamo ◽  
Alfredo Cuzzocrea ◽  
Giovanni Pilato ◽  
Daniele Schicchi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Deep Learning ◽  
Neural Systems ◽  
Italian Language ◽  
Text Corpora ◽  
Sentiment Mining ◽  
Computational Advantage ◽  
Computing Infrastructure

We analyze and compare five deep-learning neural architectures to manage the problem of irony and sarcasm detection for the Italian language. We briefly analyze the model architectures to choose the best compromise between performances and complexity. The obtained results show the effectiveness of such systems to handle the problem by achieving 93\% of F1-Score in the best case. As a case study, we also illustrate a possible embedding of the neural systems in a cloud computing infrastructure to exploit the computational advantage of using such an approach in tackling big data.

scholarly journals Approximation of phenol concentration using novel hybrid computational intelligence methods

2014 ◽  
Vol 24 (1) ◽  
pp. 165-181 ◽  
Author(s):  
Pawel Plawiak ◽  
Ryszard Tadeusiewicz
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Computational Intelligence ◽  
Recurrent Neural Networks ◽  
Phenol Concentration ◽  
Neural Systems ◽  
Feed Forward ◽  
Computational Intelligence Methods ◽  
Lm Algorithm ◽  
Selection Of

Abstract This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg-Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.

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