A New Active Contours Image Segmentation Model Driven by Generalized Mean with Outlier Restoration Achievements

2020 ◽  
Vol 34 (11) ◽  
pp. 2054026
Author(s):  
Haider Ali ◽  
Amna Shujjahuddin ◽  
Lavdie Rada
Keyword(s):  
Optimization Problem ◽  
Active Contours ◽  
State Of The Art ◽  
Negative Effects ◽  
Two Phase ◽  
Model Driven ◽  
Proposed Model ◽  
Generalized Mean ◽  
Multi Phase ◽  
Vector Valued

In this paper, we propose a robust variational segmentation model capable of overcoming the problem of the negative effects of outliers. The proposed method is based on the combination of the characteristics of the generalized mean with the concept of the active contours. The optimization problem raising in this combination employs a power method technique. We demonstrate the performance of the proposed model on a series of sample images from diverse modalities and show an outperforming proposed model in comparison with the state-of-the-art methods. The proposed method shows better or equivalent performance in terms of accuracy and robustness than the conventional state-of-the-art models. The validation of the efficiency of the proposed two-phase algorithm is further extended to vector-valued images and multi-phase formulation.

A HIERARCHICAL MODEL FOR MULTI-PHASE FRACTAL MEDIA

Fractals ◽  
2010 ◽  
Vol 18 (01) ◽  
pp. 53-64 ◽  
Author(s):  
MAOFEI MEI ◽  
BOMING YU ◽  
JIANCHAO CAI ◽  
LIANG LUO
Keyword(s):  
Distribution Function ◽  
Pore Space ◽  
Fractal Dimensions ◽  
Solid Particles ◽  
Two Phase ◽  
Fractal Media ◽  
Proposed Model ◽  
Three Phase ◽  
Multi Phase ◽  
Good Agreement

The size distributions of solid particles and pores in porous media are approximately hierarchical and statistically fractals. In this paper, a model for single-phase fractal media is constructed, and the analytical expressions for area, fractal dimension and distribution function for solid particles are derived. The distribution function of solid particles obtained from the proposed model is in good agreement with available experimental data. Then, a model for approximate two-phase fractal media is developed. Good agreement is found between the predicted fractal dimensions for pore space from the two-phase fractal medium model and the existing measured data. A model for approximate three-phase fractal media is also presented by extending the obtained two-phase model.

scholarly journals Environmental protection and social development: an optimization perspective from two polarities

2021 ◽  
Vol 269 ◽  
pp. 01001
Author(s):  
Qianyue He ◽  
Zihao Lei ◽  
Ruoding An ◽  
Yiyun Yu ◽  
Xin Huang ◽  
...  
Keyword(s):  
Social Development ◽  
Environmental Protection ◽  
Optimization Problem ◽  
State Of The Art ◽  
Human Society ◽  
Proposed Model ◽  
Conflict Problem ◽  
The Relationship ◽  
Further Development ◽  
Single Objective

The development of human society is currently in a dilemma, where the further development of society needs to be considered, simultaneously, the environmental problems caused by development are becoming increasingly prominent. Therefore, how to weigh the relationship between environmental protection and social development is still a hot research topic. In consideration of such a bipolar conflict problem, a novel optimization model was proposed to study the relationship between environmental protection and social development. First, this problem is modeled as a single objective optimization problem. Secondly, a series of representative characteristics are selected from social development and environmental protection. Then, these features are preprocessed, decomposed and analyzed. Finally, the objective function is obtained through regression analysis. In order to verify the effectiveness of the proposed approach, the realistic case of China from 2000 to 2018 are employed and analyzed. The analysis result confirms that proposed model is able to achieve state-of-the-art performances.

Building a Fuzzy Logic-Based Artificial Neural Network to Uplift Recommendation Accuracy

2019 ◽  
Vol 63 (11) ◽  
pp. 1624-1632
Author(s):  
Bam Bahadur Sinha ◽  
R Dhanalakshmi
Keyword(s):  
Neural Network ◽  
Recommender System ◽  
State Of The Art ◽  
Absolute Error ◽  
Negative Effects ◽  
User Interest ◽  
Proposed Model ◽  
Recommendation Accuracy ◽  
Membership Value ◽  
Personalized Recommender System

Abstract With the advent of the internet, the recommender system escorts the users in a customized way to nominate items from a massive set of possible alternatives. The emergence of overspecification in recommender system has emphasized negative effects on the context of prediction. The drift of user interest over time is one of the challenging affairs in present personalized recommender system. In this paper, we present a neural network model to improve the recommendation performance along with usage of fuzzy-based clustering to decide membership value of users and matching imputation to cutback sparsity to some extent. We evaluate our model on the MovieLens dataset and show that our model not only elevates accuracy, but also considers the order in which recommendation should be given. We compare the proposed model with a number of state-of-the-art personalization methods and show the dominance of our model using accuracy metrics such as root-mean-square error and mean absolute error.

scholarly journals Lipschitz continuity of the eigenfunctions on optimal sets for functionals with variable coefficients

2020 ◽  
Vol 26 ◽  
pp. 89 ◽  
Author(s):  
Baptiste Trey
Keyword(s):  
Optimization Problem ◽  
Lipschitz Continuity ◽  
Variable Coefficients ◽  
Two Phase ◽  
Hölder Continuous ◽  
Open Set ◽  
Spectral Optimization ◽  
Vector Valued Functions ◽  
Optimal Set ◽  
Vector Valued

This paper is dedicated to the spectral optimization problem min{λ1(Ω)+⋯+λk(Ω)+Λ|Ω| : Ω⊂Dquasi-open} where D ⊂ ℝd is a bounded open set and 0 < λ1(Ω) ≤⋯ ≤ λk(Ω) are the first k eigenvalues on Ω of an operator in divergence form with Dirichlet boundary condition and Hölder continuous coefficients. We prove that the first k eigenfunctions on an optimal set for this problem are locally Lipschtiz continuous in D and, as a consequence, that the optimal sets are open sets. We also prove the Lipschitz continuity of vector-valued functions that are almost-minimizers of a two-phase functional with variable coefficients.

scholarly journals Time-Consistent Investment-Reinsurance Strategies for the Insurer and the Reinsurer under the Generalized Mean-Variance Criteria

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 857 ◽  
Author(s):  
Helu Xiao ◽  
Tiantian Ren ◽  
Yanfei Bai ◽  
Zhongbao Zhou
Keyword(s):  
Optimization Problem ◽  
Continuous Process ◽  
Optimal Investment ◽  
Decision Makers ◽  
Weighted Sum ◽  
Proposed Model ◽  
Generalized Mean ◽  
Continuous Trading ◽  
The Impact ◽  
Mean Variance

Most of the existing literature on optimal investment-reinsurance only studies from the perspective of insurers and also treats the investment-reinsurance decision as a continuous process. However, in practice, the benefits of reinsurers cannot be ignored, nor can decision-makers engage in continuous trading. Under the discrete-time framework, we first propose a multi-period investment-reinsurance optimization problem considering the joint interests of the insurer and the reinsurer, among which their performance is measured by two generalized mean-variance criteria. We derive the time-consistent investment-reinsurance strategies for the proposed model by maximizing the weighted sum of the insurer’s and the reinsurer’s mean-variance objectives. We discuss the time-consistent investment-reinsurance strategies under two special premium principles. Finally, we provide some numerical simulations to show the impact of the intertemporal restrictions on the time-consistent investment-reinsurance strategies. These results indicate that the intertemporal restrictions will urge the insurer and the reinsurer to shrink the position invested in the risky asset; however, for the time-consistent reinsurance strategy, the impact of the intertemporal restrictions depends on who is the leader in the proposed model.

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

Equivariant Adversarial Network for Image-to-image Translation

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-14
Author(s):  
Masoumeh Zareapoor ◽  
Jie Yang
Keyword(s):  
State Of The Art ◽  
Generative Models ◽  
Generative Model ◽  
Target Domain ◽  
Adversarial Network ◽  
Proposed Model ◽  
Image Translation ◽  
Great Performance ◽  
Representative Model ◽  
The Ideal

Image-to-Image translation aims to learn an image from a source domain to a target domain. However, there are three main challenges, such as lack of paired datasets, multimodality, and diversity, that are associated with these problems and need to be dealt with. Convolutional neural networks (CNNs), despite of having great performance in many computer vision tasks, they fail to detect the hierarchy of spatial relationships between different parts of an object and thus do not form the ideal representative model we look for. This article presents a new variation of generative models that aims to remedy this problem. We use a trainable transformer, which explicitly allows the spatial manipulation of data within training. This differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation. To reap the benefits of proposed module into generative model, our architecture incorporates a new loss function to facilitate an effective end-to-end generative learning for image-to-image translation. The proposed model is evaluated through comprehensive experiments on image synthesizing and image-to-image translation, along with comparisons with several state-of-the-art algorithms.

scholarly journals Two-phase CFD simulation of the monodyspersed suspension hydraulic behaviour in the tank apparatus from a circulatory pipe

2008 ◽  
Vol 10 (1) ◽  
pp. 22-27 ◽  
Author(s):  
Roch Plewik ◽  
Piotr Synowiec ◽  
Janusz Wójcik
Keyword(s):  
Cfd Simulation ◽  
Fluidised Bed ◽  
Cfd Simulations ◽  
Two Phase ◽  
Hydraulic Behaviour ◽  
Hydraulic Conditions ◽  
The One ◽  
Cfd Method ◽  
Multi Phase ◽  
The Ideal

Two-phase CFD simulation of the monodyspersed suspension hydraulic behaviour in the tank apparatus from a circulatory pipe The hydrodynamics in fluidized-bed crystallizers is studied by CFD method. The simulations were performed by a commercial packet of computational fluid dynamics Fluent 6.x. For the one-phase modelling (15), a standard k-ε model was applied. In the case of the two-phase flows the Eulerian multi-phase model with a standard k-ε method, aided by the k-ε dispersed model for viscosity, has been used respectively. The collected data put a new light on the suspension flow behaviour in the annular zone of the fluidised bed crystallizer. From the presented here CFD simulations, it clearly issues that the real hydraulic conditions in the fluidised bed crystallizers are far from the ideal ones.

scholarly journals Robust CNN Compression Framework for Security-Sensitive Embedded Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 1093
Author(s):  
Jeonghyun Lee ◽  
Sangkyun Lee
Keyword(s):  
Embedded Systems ◽  
Optimization Problem ◽  
State Of The Art ◽  
Classification Problems ◽  
Proximal Gradient Method ◽  
Knowledge Distillation ◽  
New Type ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
Memory Efficient

Convolutional neural networks (CNNs) have achieved tremendous success in solving complex classification problems. Motivated by this success, there have been proposed various compression methods for downsizing the CNNs to deploy them on resource-constrained embedded systems. However, a new type of vulnerability of compressed CNNs known as the adversarial examples has been discovered recently, which is critical for security-sensitive systems because the adversarial examples can cause malfunction of CNNs and can be crafted easily in many cases. In this paper, we proposed a compression framework to produce compressed CNNs robust against such adversarial examples. To achieve the goal, our framework uses both pruning and knowledge distillation with adversarial training. We formulate our framework as an optimization problem and provide a solution algorithm based on the proximal gradient method, which is more memory-efficient than the popular ADMM-based compression approaches. In experiments, we show that our framework can improve the trade-off between adversarial robustness and compression rate compared to the existing state-of-the-art adversarial pruning approach.

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