scholarly journals A New Framework for Matching Forensic Composite Sketches With Digital Images

2021 ◽  
Vol 13 (5) ◽  
pp. 1-19
Author(s):  
Chethana H. T. ◽  
Trisiladevi C. Nagavi
Keyword(s):  
Digital Images ◽  
State Of The Art ◽  
Threshold Value ◽  
Activation Function ◽  
Final Layer ◽  
Age Variations ◽  
Face Sketch Recognition ◽  
Challenging Tasks ◽  
Final Output ◽  
New Framework

Face sketch recognition is considered as a sub-problem of face recognition. Matching composite sketches with its corresponding digital image is one of the challenging tasks. A new convolution neural network (CNN) framework for matching composite sketches with digital images is proposed in this work. The framework consists of a base CNN model that uses swish activation function in the hidden layers. Both composite sketches and digital images are trained separately in the network by providing matching pairs and mismatching pairs. The final output resulted from the network's final layer is compared with the threshold value, and then the pair is assigned to the same or different class. The proposed framework is evaluated on two datasets, and it exhibits an accuracy of 78.26% with extended-PRIP (E-PRIP) and 69.57% with composite sketches with age variations (CSA) respectively. Experimental analysis shows the improved results compared to state-of-the-art composite sketch matching systems.

Breast cancer diagnosis using multiple activation deep neural network

2021 ◽  
pp. 1063293X2110251
Author(s):  
K Vijayakumar ◽  
Vinod J Kadam ◽  
Sudhir Kumar Sharma
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Neural Network ◽  
Breast Cancer Diagnosis ◽  
Activation Function ◽  
Linear Functions ◽  
Cancer Data ◽  
Final Layer ◽  
Improved Performance ◽  
Hidden Layer

Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so well. But it is clear from many studies that within DNN, the AF choice has a notable impact on the kinetics of training and the success of tasks. In the past few years, different AFs have been formulated. The choice of AF is still an area of active study. Hence, in this study, a novel deep Feed forward NN model with four AFs has been proposed for breast cancer classification: hidden layer 1: Swish, hidden layer, 2:-LeakyReLU, hidden layer 3: ReLU, and final output layer: naturally Sigmoidal. The purpose of the study is twofold. Firstly, this study is a step toward a more profound understanding of DNN with layer-wise different AFs. Secondly, research is also aimed to explore better DNN-based systems to build predictive models for breast cancer data with improved accuracy. Therefore, the benchmark UCI dataset WDBC was used for the validation of the framework and evaluated using a ten-fold CV method and various performance indicators. Multiple simulations and outcomes of the experimentations have shown that the proposed solution performs in a better way than the Sigmoid, ReLU, and LeakyReLU and Swish activation DNN in terms of different parameters. This analysis contributes to producing an expert and precise clinical dataset classification method for breast cancer. Furthermore, the model also achieved improved performance compared to many established state-of-the-art algorithms/models.

scholarly journals BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yikui Zhai ◽  
He Cao ◽  
Wenbo Deng ◽  
Junying Gan ◽  
Vincenzo Piuri ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
Learning Strategy ◽  
State Of The Art ◽  
Activation Function ◽  
Training Data ◽  
Fine Grained ◽  
Pattern Recognition Problem ◽  
Face Features ◽  
Facial Beauty

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet’s performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.

scholarly journals Layer contour characterization in additive manufacturing through image binarization

2021 ◽  
Vol 1193 (1) ◽  
pp. 012067
Author(s):  
D Blanco ◽  
A Fernández ◽  
P Fernández ◽  
B J Álvarez ◽  
F Peña
Keyword(s):  
Digital Images ◽  
Median Filter ◽  
Threshold Value ◽  
Image Binarization ◽  
Actual Contour ◽  
Filter Size ◽  
Characterization Procedure ◽  
Flat Bed Scanner ◽  
Selection Of ◽  
Excellent Coverage

Abstract On-Machine Measurement adoption will be key to dimensional and geometrical improvement of additively manufactured parts. One possible approach based on OMM aims at using digital images of manufactured layers to characterize actual contour deviations with respect to their theoretical profile. This strategy would also allow for in-process corrective actions. This work describes a layer-contour characterization procedure based on binarization of digital images acquired with a flat-bed scanner. This procedure has been tested off-line to evaluate the influence of two of the parameters for image treatment, the median filter size (S f ) and the threshold value (T), on the dimensional/geometrical reliability of the contour characterization. Results showed that an appropriate selection of configuration parameters allowed to characterize the proposed test-target with excellent coverage and reasonable accuracy.

scholarly journals A Novel Stacked Ensemble for Hate Speech Recognition

2021 ◽  
Vol 11 (24) ◽  
pp. 11684
Author(s):  
Mona Khalifa A. Aljero ◽  
Nazife Dimililer
Keyword(s):  
Machine Learning ◽  
Hate Speech ◽  
State Of The Art ◽  
Majority Voting ◽  
Support Vector ◽  
Timely Manner ◽  
Machine Learning Classifiers ◽  
Significant Challenge ◽  
Final Output ◽  
Harmful Content

Detecting harmful content or hate speech on social media is a significant challenge due to the high throughput and large volume of content production on these platforms. Identifying hate speech in a timely manner is crucial in preventing its dissemination. We propose a novel stacked ensemble approach for detecting hate speech in English tweets. The proposed architecture employs an ensemble of three classifiers, namely support vector machine (SVM), logistic regression (LR), and XGBoost classifier (XGB), trained using word2vec and universal encoding features. The meta classifier, LR, combines the outputs of the three base classifiers and the features employed by the base classifiers to produce the final output. It is shown that the proposed architecture improves the performance of the widely used single classifiers as well as the standard stacking and classifier ensemble using majority voting. We also present results on the use of various combinations of machine learning classifiers as base classifiers. The experimental results from the proposed architecture indicated an improvement in the performance on all four datasets compared with the standard stacking, base classifiers, and majority voting. Furthermore, on three of these datasets, the proposed architecture outperformed all state-of-the-art systems.

Sparse Autoencoder-Based Multi-Head Deep Neural Networks for Machinery Fault Diagnostics With Novelty Detection

2020 ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jian-Yu Long ◽  
Yan-Yang Zi ◽  
Shao-Hui Zhang ◽  
...  
Keyword(s):  
Supervised Classification ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Novelty Detection ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Sparse Autoencoder

Abstract Novelty detection is a challenging task for the machinery fault diagnosis. A novel fault diagnostic method is developed for dealing with not only diagnosing the known type of defect, but also detecting novelties, i.e. the occurrence of new types of defects which have never been recorded. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that it is able to accurately diagnose known types of defects, as well as to detect unknown defects, outperforming other state-of-the-art methods.

scholarly journals Joint Entity and Event Extraction with Generative Adversarial Imitation Learning

2019 ◽  
Vol 1 (2) ◽  
pp. 99-120 ◽  
Author(s):  
Tongtao Zhang ◽  
Heng Ji ◽  
Avirup Sil
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Event Extraction ◽  
Imitation Learning ◽  
Learning Method ◽  
Inverse Reinforcement Learning ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
The Difference ◽  
New Framework

We propose a new framework for entity and event extraction based on generative adversarial imitation learning—an inverse reinforcement learning method using a generative adversarial network (GAN). We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse. We utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth (expert) and the extractor (agent). Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods.

Additive Manufacturing Viewed from Material Science: State of the Art & Fundamentals

2014 ◽  
Vol 783-786 ◽  
pp. 2284-2289 ◽  
Author(s):  
J.Y. Hascoet ◽  
K.P. Karunakaran ◽  
S. Marya
Keyword(s):  
Additive Manufacturing ◽  
Flexible Manufacturing ◽  
State Of The Art ◽  
Fusion Welding ◽  
Heat Transfer Fluid ◽  
Cnc Machine ◽  
Conservation Of Energy ◽  
Challenging Tasks ◽  
Articulated Robot ◽  
Physical Phenomena

Additive Manufacturing (AM), also designated as 3D Printing (3DP), is one of the most visionary and friendly approaches for flexible manufacturing with conservation of energy and material resources. It is a factory in a box that can generate multiple objects. It requires little manpower to bring virtual innovations into the real world. AM for metals can be mechanistically associated with welding. The technique employs a variety of energy sources (laser, electron beam, electric Arc, ...), feed stocks (powder, wire and ribbon) and motion kinematics & control (articulated robot and 3-5 axes CNC machine ). From the materials perspectives, akin to fusion welding in many respects, AM involves a multitude of complex and interacting physical phenomena such as heat transfer, fluid flow, discrete and continuum mechanics, sintering, melting, solidification, solid state transformations, grain growth, diffusion, textures etc. The desired process performance can be achieved by controlling the parameters of energy, feed stock and motion. The effect of successive thermal cycles along with the epitaxial relations between substratum and deposits constitute some of the challenging tasks for developing optimized parts. This paper reviews the state of the art and presents some challenges facing metal product development for service applications.

scholarly journals MOHE-NET: MONOCULAR OBJECT HEIGHT ESTIMATION NETWORK USING DEEP LEARNING AND SCENE GEOMETRY

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 557-564
Author(s):  
J. Wei ◽  
J. Jiang ◽  
A. Yilmaz
Keyword(s):  
Lower Cost ◽  
State Of The Art ◽  
Activation Function ◽  
Detection Task ◽  
Field Of View ◽  
Laser Ranging ◽  
Multi Layer Perceptron ◽  
Height Estimation ◽  
Set Up ◽  
Mlp Model

Abstract. Estimating the heights of objects in the field of view has applications in many tasks such as robotics, autonomous platforms and video surveillance. Object height is a concrete and indispensable characteristic people or machine could learn and capture. Many actions such as vehicle avoiding obstacles will be taken based on it. Traditionally, object height can be estimated using laser ranging, radar or stereo camera. Depending on the application, cost of these techniques may inhibit their use, especially in autonomous platforms. Use of available sensors with lower cost would make the adoption of such techniques at higher rates. Our approach to height estimation requires only a single 2D image. To solve this problem we introduce the Monocular Object Height Estimation Network (MOHE-Net) that includes a cascade of two networks. The first network performs the object detection task. This network detects the bounding box of objects of interest. This information is then input to a second network to estimate the object height and is a linear Multi-layer Perceptron (MLP). The linear MLP model models the camera-scene geometry and does not require training or contain activation function as normal MLP did. The developed approach works for static camera set up as well as moving platform. The proposed approach performs state-of-the-art and can be deployed for obstacle avoidance on autonomous platforms. Our code is available at https://github.com/OSUPCVLab/Ford2019/tree/master/Moving%20Object%20Height% 20Estimation%20Network

Sign in / Sign up

Export Citation Format

Share Document