Improving performance evaluation based on balanced scorecard with grey relational analysis and data envelopment analysis approaches: Case study in water and wastewater companies

Sarraf, Fatemeh; Nejad, Shabnam Hashemi

doi:10.1016/j.evalprogplan.2019.101762

Cited by 86 publications

(67 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Then they used Artificial Neural Networks for adaptive training to obtain the optimized connection weights [4]. Sarraf and Nejad (2020) [5] ranked the importance of enterprise services through gray-level correlation analysis and obtained a reference sequence. Then they analyzed the data at all levels and measured the performance of the enterprise using the balanced scorecards [5].…”

Section: Introductionmentioning

confidence: 99%

“…Sarraf and Nejad (2020) [5] ranked the importance of enterprise services through gray-level correlation analysis and obtained a reference sequence. Then they analyzed the data at all levels and measured the performance of the enterprise using the balanced scorecards [5]. Based on the balanced scorecard model combined with questionnaires, Zhao (2020) [6] constructed an enterprise performance appraisal model from the dimensions of finance, customers, internal processes, learning and growth, and environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Luo¹,

Da²

2021

PLoS ONE

View full text Add to dashboard Cite

The purpose is to study the performance compensation of the bid purchased during the mergers and acquisitions (M&A) process. An intelligent model of enterprise performance appraisal is built to analyze the performances of the acquired enterprises. First, the evaluation indicators of enterprise performance are selected from both financial and non-financial aspects. An enterprise performance appraisal model is established based on the neural networks and optimized by the factor analysis method and Genetic Algorithm (GA). The principal factors affecting enterprise performance are analyzed. Then the M&A parties’ performances during the M&A commitment period under the earnings compensation mechanism are analyzed quantitatively. Corresponding hypotheses and evaluation indicators are established. Mean test results and regression analyses demonstrate that the hypotheses proposed are valid under particular circumstances. Introducing the earnings compensation mechanism during the M&A process can improve the enterprise performance effectively so that the earnings forecasted in the commitment period are significantly higher than the historical profitability. Hence, the earnings compensation mechanism plays a positive role in guiding enterprise performance. Comparison with models proposed in previous research reveals that the output error ratio of the designed corporate performance evaluation model is 1.16%, which can effectively evaluate corporate performance. The above results provide a reference for studying the impact of the earnings compensation mechanism on enterprise performance during the M&A process.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Luo¹,

Da²

2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In multilayer classifiers, supervised backpropagation or unsupervised competitive-based learning algorithms are used to train the multilayer neural networks or deep learning neural networks. However, these methods require the assignment of the network connecting weights in multi-hidden layers by using iteration computations, which will increase the rate of design cycle The GRA algorithm [26]- [29] was used to design a classifier to identify the multi label classes in the right lung (RL) and LL. The model consists of a radial Bayesian network (RBN) with Gaussian activation functions, gray relational pattern analysis with Euclidean distance (ED), and maximum and minimum operations, which are straightforward mathematical operations for nonlinear mapping of the EDs to the degrees of similarity between the training and untraining patterns; thus, regardless of the ED, the range of relational grade, as gray grade (GG), can be bounded in a specific closed range [0, 1].…”

Section: Methodsmentioning

confidence: 99%

“…Hence, after image enhancement and feature extraction with a specific bounding box (B-box), the new feature pattern can be reconstructed in the higher dimensional feature space and lead to separability in the feature space to improve the screening accuracy in nonlinear separable classification or in multilabel image classification problems. Then, a gray relational analysis (GRA) [26]- [29] based on a multilayer machine vision classifier is carried out to deal with nonlinear separable tasks for multiclass classification to distinguish the N control from those with lung diseases. In the validation stage, A-P CXR images from the National Institutes of Health (NIH) CXR database (NIH Clinical Center) are enrolled [30].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Chest X-Ray Images to Improve Screening Accuracy Rate Using Iterated Function System and Multilayer Fractional-Order Machine Learning Classifier

Lin

et al. 2020

IEEE Photonics J.

View full text Add to dashboard Cite

Chest X-ray (CXR) images are usually used to identify the causes of patients' symptoms, including the classes of lung or heart disorders. In visualization examination, CXR imaging in anterior-posterior (A-P) views is a preliminary screening method used by clinicians or radiologists to diagnose possible lung abnormalities, such as pneumothorax (Pt), emphysema (E), infiltration (In), lung cancer (M), pneumonia (P), pulmonary fibrosis (F), and pleural effusion (Ef). However, the identification of the causes of multiple abnormalities associated with coexisting conditions presents a challenge. In ruling out a suspected lung disease, the signs and symptoms of physical conditions need to be identified to arrive at a definitive diagnosis. In addition, low contrast CXR images and manual inspection restrict automated screening applications. Hence, this study aims to propose an iterated function system (IFS) and a multilayer fractional-order machine learning classifier to rapidly screen the possible classes of lung diseases within regions of interest on CXR images and to improve screening accuracy. For digital image processes, a two-dimensional (2D) fractional-order convolution is used to enhance symptomatic features. The IFS with nonlinear interpolation functions is then used to reconstruct the 2D feature patterns. These reconstructed patterns are self-affine in the same class and thus help distinguish normal subjects from those with lung diseases. The accuracy rate is thus improved. Pooling is performed to reduce the dimensions of the feature patterns and speed up complex computations. A gray relational analysis-based classifier is used to identify the possible classes of the signs and symptoms of lung diseases. For digital CXR images in A-P view, the proposed multilayer machine learning classifier with k-fold cross-validation presents promising results in screening lung diseases and improving screening accuracy rate relative to traditional methods. The proposed classifier is evaluated in terms of recall (99.6%), precision (87.78%), accuracy (88.88%), and F1 score (0.9334).

show abstract

“…The structure of this combination is shown in Figure 2(a). A multilayer fully connecting network with a radial Bayesian network (RBN) [26][27] and gray relational analysis (GRA) as an adaptive algorithm [28][29][30][31][32] is employed to rapidly screen typical lung diseases. In experimental validations, anterior-posterior chest X-ray images from the National Institutes of Health (NIH) chest X-ray database (NIH Clinical Center) are enrolled [33].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Fractional-Order Machine Vision Classifier for Rapid Typical Lung Diseases Screening on Digital Chest X-Ray Images

Chen

Li³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Lung diseases can result in acute breathing problems and prevent the human body from acquiring enough oxygen. These diseases, such as pneumonia (P), pleural effusion (Ef), lung cancer, pneumothorax (Pt), pulmonary fibrosis (F), infiltration (In) and emphysema (E), adversely affect airways, alveoli, blood vessels, pleura and other parts of the respiratory system. The death rates of P and lung cancer are higher than those of other typical lung diseases. In visualization examination, chest radiography, such as anterior-posterior or lateral image viewing, is a straightforward approach used by clinicians/radiologists to diagnose and locate possible lung abnormalities rapidly. However, a chest X-ray image of patients may show multiple abnormalities associated with coexisting conditions, such as P, E, F, Pt, atelectasis, lung cancer or surgical interventions, which further complicate diagnosis. In addition, poor-quality X-ray images and manual inspection have limitations in digital image-automated classification. Hence, this study intends to propose a multilayer machine vision classifier to automatically identify the possible class of lung diseases within a bounding region of interest (ROI) on a chest X-ray image. For digital image texture analysis, a two-dimensional (2D) fractional-order convolution (FOC) operation with a fractional-order parameter, v = 0.3-0.5, is used to enhance the symptomatic feature and remove unwanted noises. Then, maximum pooling is performed to reduce the dimensions of feature patterns and accelerate complex computations. A multilayer machine vision classifier with radial Bayesian network and gray relational analysis is used to screen subjects with typical lung diseases. Anterior-posterior chest X-ray images from the NIH chest X-ray database (NIH Clinical Center) are enrolled. For digital chest X-ray images, with K-fold cross-validation, the proposed multilayer machine vision classifier is applied to facilitate the diagnosis of typical lung diseases on specific bounding ROIs, as promising results with mean recall (%), mean precision (%), mean accuracy (%) and mean F1 score of 98.68%, 82.42%, 83.57% and 0.8981, respectively, for assessing the performance of proposed multilayer classifier for rapidly screening lung lesions on digital chest X-ray images.

show abstract

Improving performance evaluation based on balanced scorecard with grey relational analysis and data envelopment analysis approaches: Case study in water and wastewater companies

Cited by 86 publications

References 29 publications

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Enhancement of Chest X-Ray Images to Improve Screening Accuracy Rate Using Iterated Function System and Multilayer Fractional-Order Machine Learning Classifier

Multilayer Fractional-Order Machine Vision Classifier for Rapid Typical Lung Diseases Screening on Digital Chest X-Ray Images

Contact Info

Product

Resources

About