An Evaluation Study of Neural Network and Heuristic Approaches to Construction Resource Planning

2009 ◽  
Author(s):  
N.N. Dawood
Keyword(s):  
Neural Network ◽  
Resource Planning ◽  
Evaluation Study ◽  
Heuristic Approaches

scholarly journals Depth-Aware Arbitrary Style Transfer Using Instance Normalization

2020 ◽  
pp. paper2-1-paper2-11
Author(s):  
Victor Kitov ◽  
Konstantin Kozlovtsev ◽  
Margarita Mishustina
Keyword(s):  
Neural Network ◽  
Qualitative Analysis ◽  
Loss Function ◽  
Human Perception ◽  
Depth Map ◽  
Evaluation Study ◽  
User Evaluation ◽  
Style Transfer ◽  
Separate Model ◽  
Transfer Method

Style transfer is the process of rendering one image with some content in the style of another image, representing the style. Recent studies of Liu et al. (2017) show that traditional style transfer methods of Gatys et al. (2016) and Johnson et al.(2016) fail to reproduce the depth of the content image, which is critical for human perception. They suggest to preserve the depth map by additional regularizer in the optimized loss function, forcing preservation of the depth map. However these traditional methods are either computationally inefficient or require training a separate neural network for each style. AdaIN method of Huang et al. (2017) allows efficient transferring of arbitrary style without training a separate model but is not able to reproduce the depth map of the content image. We propose an extension to this method, allowing depth map preservation by applying variable stylization strength. Qualitative analysis and results of user evaluation study indicate that the proposed method provides better stylizations, compared to the original AdaIN style transfer method.

An Expert System for Predicting ERP Post-Implementation Benefits Using Artificial Neural Network

2018 ◽  
pp. 760-784
Author(s):  
Ahad Zare Ravasan ◽  
Saeed Rouhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Good Prediction ◽  
Industry Type ◽  
Artificial Neural ◽  
Expected Benefits ◽  
Artificial Neural Network Ann

Implementing Enterprise Resource Planning systems (ERPs) is a complex and costly project which usually delivers only a few of expected benefits. Obtaining the expected benefits of ERPs is impressed by a variety of factors and variables which is related to an organization or project environment. In this paper, the idea of predicting ERP post-implementation benefits based on the organizational profiles and factors has been discussed. Regarding the need to form the expectations of organizations about ERP, an expert system is developed by using Artificial Neural Network (ANN) method to articulate the relationships between some organizational factors and ERP's achieved benefits. The expert system's role is in the preparation to capture the data from the new enterprises wishes to implement ERP and predict likely benefits might be achieved from the system. For this end, factors of organizational profiles (such as industry type, size, structure, and so on) are recognized and a feed-forward architecture and Levenberg-Marquardt (trainlm) neural network model is designed, trained and validated with 171 surveyed data of Middle-East located enterprises experienced ERP. The trained ANN embedded in developed expert system predicts with the average correlation coefficients of 0.745, which is respectively high and proves the idea of dependency of ERP post-implementation benefits on the organizational profiles. Besides, total correct classification rate of 0.701 shows good prediction power which can help firms in predicting ERP benefits before system implementation.

An Expert System for Predicting ERP Post-Implementation Benefits Using Artificial Neural Network

2014 ◽  
Vol 10 (3) ◽  
pp. 24-45 ◽  
Author(s):  
Ahad Zare Ravasan ◽  
Saeed Rouhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Enterprise Resource Planning ◽  
Size Structure ◽  
Organizational Factors ◽  
Resource Planning ◽  
Good Prediction ◽  
Artificial Neural ◽  
Expected Benefits

Implementing Enterprise Resource Planning systems (ERPs) is a complex and costly project which usually delivers only a few of expected benefits. Obtaining the expected benefits of ERPs is impressed by a variety of factors and variables which is related to an organization or project environment. In this paper, the idea of predicting ERP post-implementation benefits based on the organizational profiles and factors has been discussed. Regarding the need to form the expectations of organizations about ERP, an expert system is developed by using Artificial Neural Network (ANN) method to articulate the relationships between some organizational factors and ERP's achieved benefits. The expert system's role is in the preparation to capture the data from the new enterprises wishes to implement ERP and predict likely benefits might be achieved from the system. For this end, factors of organizational profiles (such as industry type, size, structure, and so on) are recognized and a feed-forward architecture and Levenberg-Marquardt (trainlm) neural network model is designed, trained and validated with 171 surveyed data of Middle-East located enterprises experienced ERP. The trained ANN embedded in developed expert system predicts with the average correlation coefficients of 0.745, which is respectively high and proves the idea of dependency of ERP post-implementation benefits on the organizational profiles. Besides, total correct classification rate of 0.701 shows good prediction power which can help firms in predicting ERP benefits before system implementation.

scholarly journals Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiafeng Zheng ◽  
Ruijun Ma
Keyword(s):  
Neural Network ◽  
Human Resources ◽  
Prediction Model ◽  
Human Resource ◽  
Resource Planning ◽  
Strategic Decision ◽  
Demand Forecast ◽  
Human Resource Planning ◽  
Resource Demand ◽  
Som Neural Network

Human resource planning is the prerequisite of human resource management, and the basic work of human resource planning is to predict human resource demand. Scientific and reasonable human resource demand forecasting results can provide important data support for enterprise human resource planning and strategic decision-making so that human resources management can play a better role in the realization of corporate goals. Because human resource demand is affected by many factors, there is a high degree of nonlinearity and uncertainty between each factor and personnel demand, as well as the incompleteness and inaccuracy of corporate human resource data. In this paper, the self-organizing feature mapping (SOM) artificial neural network prediction model is selected as the prediction model, and the input and output process of sample data is converted into the optimal solution process of the nonlinear function. In the application of the model, the human resource demand prediction index system is used as the input of the SOM neural network and the total number of employees in the enterprise is used as the output so that the problem of nonlinear fitting between human resource demand-influencing factors and human resource demand can be solved. Finally, through the empirical analysis of the enterprise, the model forecasting process is explained and the human resource demand forecast is realized.

scholarly journals Cross-species cell-type assignment of single-cell RNA-seq by a heterogeneous graph neural network

2021 ◽  
Author(s):  
Xingyan Liu ◽  
Qunlun Shen ◽  
Shihua Zhang
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Cell Types ◽  
Evaluation Study ◽  
Systematic Evaluation ◽  
Homologous Gene ◽  
Cell Type ◽  
Evolutionary Mechanisms ◽  
Comparative Analyses ◽  
Type Assignment

Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of cellular diversity and the evolutionary mechanisms that shape cellular form and function. Here, we aimed to utilize a heterogeneous graph neural network to learn aligned and interpretable cell and gene embeddings for cross-species cell type assignment and gene module extraction (CAME) from scRNA-seq data. A systematic evaluation study on 649 pairs of cross-species datasets showed that CAME outperformed six benchmarking methods in terms of cell-type assignment and model robustness to insufficiency and inconsistency of sequencing depths. Comparative analyses of the major types of human and mouse brains by CAME revealed shared cell type-specific functions in homologous gene modules. Alignment of the trajectories of human and macaque spermatogenesis by CAME revealed conservative gene expression dynamics during spermatogenesis between humans and macaques. Owing to the utilization of non-one-to-one homologous gene mappings, CAME made a significant improvement on cell-type characterization cross zebrafish and other species. Overall, CAME can not only make an effective cross-species assignment of cell types on scRNA-seq data but also reveal evolutionary conservative and divergent features between species.

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