scholarly journals Machine Learning Approach to Analyze the Different Optical Properties of FBGs

2021 ◽  
Author(s):  
Koustav Dey ◽  
V. Nikhil ◽  
Sourabh Roy
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Optical Properties ◽  
Fiber Bragg Gratings ◽  
Learning Approach ◽  
Complex Behaviour ◽  
Fast Training ◽  
Proposed Model ◽  
Machine Learning Approach

Abstract A generalized machine learning (ML) approach is proposed and demonstrated to analyse the various optical properties such as effective refractive index, bandwidth, reflectivity and wavelength of the Fiber Bragg gratings (FBGs). For this purpose, three commonly used FBG variants namely conventional, π phase-shifted and chirped FBG have been taken into consideration. Furthermore, the reflected spectra of those types of FBGs were predicted using a common tool. An exact spectrum was able to reproduce using this proposed model. This simple and fast-training feed-forward artificial neural network can predict the output for unknown device parameters along with the non-linear and complex behaviour of the spectrum minutely.

Machine learning approach for accurate backmapping of coarse-grained models to all-atom models

2020 ◽  
Vol 56 (65) ◽  
pp. 9312-9315 ◽  
Author(s):  
Yaxin An ◽  
Sanket A. Deshmukh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Regression Models ◽  
Gaussian Process Regression ◽  
Nearest Neighbors ◽  
Coarse Grained ◽  
Learning Approach ◽  
Machine Learning Approach

Four different machine learning (ML) regression models: artificial neural network, k-nearest neighbors, Gaussian process regression and random forest were built to backmap coarse-grained models to all-atom models.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

scholarly journals Generating Interactive Worlds with Text

2020 ◽  
Vol 34 (02) ◽  
pp. 1693-1700 ◽  
Author(s):  
Angela Fan ◽  
Jack Urbanek ◽  
Pratik Ringshia ◽  
Emily Dinan ◽  
Emma Qian ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Common Sense ◽  
Learning Approach ◽  
Adventure Game ◽  
Machine Learning Approach ◽  
Game Environment ◽  
Game Elements ◽  
Construction Algorithms ◽  
Game Environments

Procedurally generating cohesive and interesting game environments is challenging and time-consuming. In order for the relationships between the game elements to be natural, common-sense has to be encoded into arrangement of the elements. In this work, we investigate a machine learning approach for world creation using content from the multi-player text adventure game environment LIGHT (Urbanek et al. 2019). We introduce neural network based models to compositionally arrange locations, characters, and objects into a coherent whole. In addition to creating worlds based on existing elements, our models can generate new game content. Humans can also leverage our models to interactively aid in worldbuilding. We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.

Detection of Abnormal Activity to Alert the Nearby Persons via M-DNN Based Surveillance System

2021 ◽  
Vol 16 ◽  
pp. 668-685
Author(s):  
Shankargoud Patil ◽  
Kappargaon S. Prabhushetty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Normal Behavior ◽  
Machine Learning Approach ◽  
Artificial Neural Network Ann ◽  
Static Conditions ◽  
Advanced System

In today's environment, video surveillance is critical. When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far. Different methods are in place using the above combinations to help distinguish various wary activities from the live tracking of footages. Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough. In a theoretical setting, a deep learning approach is utilized to detect suspicious or normal behavior and sends an alarm to the nearby people if suspicious activity is predicted. In this paper, data fusion technique is used for feature extraction which gives an accurate outcome. Moreover, the classes are classified by the well effective machine learning approach of modified deep neural network (M-DNN), that predicts the classes very well. The proposed method gains 95% accuracy, as well the advanced system is contrast with previous methods like artificial neural network (ANN), random forest (RF) and support vector machine (SVM). This approach is well fitted for dynamic and static conditions.

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