An integrated framework for predicting the best financial performance of banks: evidence from Egypt

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamed El-Sayed Mousa ◽  
Mahmoud Abdelrahman Kamel
Keyword(s):  
Neural Networks ◽  
Financial Performance ◽  
Performance Improvement ◽  
Empirical Test ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Prediction Errors ◽  
Content Type ◽  
Proactive Management

Purpose This study aims to develop and test a framework for integration between data envelopment analysis (DEA) and artificial neural networks (ANN) to predict the best financial performance concerning return on assets and return on equity for banks listed on the Egyptian Exchange, to help managers generate what-if scenarios? For performance improvement and benchmarking. Design/methodology/approach The study empirically tested the three-stage DEA-ANN framework. First, DEA was used as a preprocessor of the banks’ efficiency scores. Second, a back-propagation neural network as a multi-layer perceptron-ANN’s model was designed using expected data sets from DEA to learn optimal performance patterns. Third, the superior performance of banks was forecasted. Findings The results indicated that banks are not operating under their most productive operations, and there is room for potential improvements to reach outperformance. Moreover, the neural networks’ empirical test results showed high correlations between the actual and expected values, with low prediction errors in both the test and prediction phases. Practical implications Based on best performance prediction, banks can generate alternative scenarios for future performance improvement and enabling managers to develop effective strategies for performance control under uncertainty and limited data. Besides, supporting the decision-making process and proactive management of performance. Originality/value Despite the growing research stream supporting DEA-ANN integration applications, these are still limited and scarce, especially in the Middle East and North Africa region. Therefore, the study trying to fill this gap to help bank managers predict the best financial performance.

scholarly journals Predicting the discharge coefficient of oblique cylindrical weir using neural network techniques

2021 ◽  
Vol 14 (16) ◽  
Author(s):  
Adnan A. Ismael ◽  
Saleh J. Suleiman ◽  
Raid Rafi Omar Al-Nima ◽  
Nadhir Al-Ansari
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Mean Square ◽  
The Third ◽  
Testing Stage ◽  
Inclination Angles

AbstractCylindrical weir shapes offer a steady-state overflow pattern, where the type of weirs can offer a simple design and provide the ease-to-pass floating debris. This study considers a coefficient of discharge (Cd) prediction for oblique cylindrical weir using three diameters, the first is of D1 = 0.11 m, the second is of D2 = 0.09 m, and the third is of D3 = 0.06.5 m, and three inclination angles with respect to channel axis, the first is of θ1 = 90 ͦ, the second is of θ2 = 45 ͦ, and the third is of θ3 = 30 ͦ. The Cd values for total of 56 experiments are estimated by using the radial basis function network (RBFN), in addition of comparing that with the back-propagation neural network (BPNN) and cascade-forward neural network (CFNN). Root mean square error (RMSE), mean square error (MSE), and correlation coefficient (CC) statics are used as metrics measurements. The RBFN attained superior performance comparing to the other neural networks of BPNN and CFNN. It is found that, for the training stage, the RBFN network benchmarked very small RMSE and MSE values of 1.35E-12 and 1.83E-24, respectively and for the testing stage, it also could benchmark very small RMSE and MSE values of 0.0082 and 6.80E-05, respectively.

scholarly journals ON SOME BOUNDS OF THE MINIMUM EDGE DOMINATING ENERGY OF A GRAPH

2021 ◽  
Vol 12 (4) ◽  
pp. 1285-1296
Author(s):  
Dr. Gauri Ghule , Et. al.
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Fuzzy Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Fuzzy Classifier ◽  
Manual Intervention ◽  
The Neural Network ◽  
Hidden Neurons

Number of hidden neurons is necessary constant for tuning the neural network to achieve superior performance. These parameters are set manually through experimentation. The performance of the network is evaluated repeatedly to choose the best input parameters.Random selection of hidden neurons may cause underfitting or overfitting of the network. We propose a novel fuzzy controller for finding the optimal value of hidden neurons automatically. The hybrid classifier helps to design competent neural network architecture, eliminating manual intervention for setting the input parameters. The effectiveness of tuning the number of hidden neurons automatically on the convergence of a back-propagation neural network, is verified on speech data. The experimental outcomes demonstrate that the proposed Neuro-Fuzzy classifier can be viably utilized for speech recognition with maximum classification accuracy.

Combining BP with PSO algorithms in weights optimisation and ANNs training for mass appraisal of properties

2018 ◽  
Vol 11 (2) ◽  
pp. 290-314 ◽  
Author(s):  
Joseph Awoamim Yacim ◽  
Douw Gert Brand Boshoff
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Accuracy ◽  
Back Propagation ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Content Type ◽  
Attribute Weights ◽  
Artificial Neural ◽  
And Training

Purpose The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models. Design/methodology/approach The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics. Findings The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes. Research limitations/implications A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values. Originality/value This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.

Constructing Expert System to Automatic Translation for Software development

2018 ◽  
Vol 2 (2) ◽  
pp. 231-247
Author(s):  
Safwan Hasoon ◽  
Fatima Younis
Keyword(s):  
Neural Networks ◽  
Expert System ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Transition Process ◽  
Back Propagation Neural Network ◽  
Knowledge Bases ◽  
Translation Process ◽  
Automatic Translation ◽  
The Cost

the development in computer fields, especially in the software engineering, emerged the need to construct intelligence tool for automatic translation from design phase to coding phase, for producing the source code from the algorithm model represented in pseudo code, and execute it depending on the constructing expert system which reduces the cost, time and errors that may occur during the translation process, which has been built the knowledge base, inference engine, and the user interface. The knowledge bases consist of the facts and the rules for the automatic transition. The results are compared with a set of neural networks, which are Back propagation neural network, Cascade-Forward network, and Radial Basis Function network. The results showed the superiority of the expert system in automatic transition process speed, as well as easy to add, delete or modify process for rules or data of the pseudo code compared with previously mentioned neural networks.

Knowledge-driven business model innovation through the introduction of equity investment: evidence from China’s primary market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiali Zheng ◽  
Han Qiao ◽  
Xiumei Zhu ◽  
Shouyang Wang
Keyword(s):  
Business Model ◽  
Business Models ◽  
Learning Algorithm ◽  
Empirical Studies ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Business Model Innovation ◽  
Data Set ◽  
Equity Investment ◽  
Content Type

Purpose This study aims to explore the role of equity investment in knowledge-driven business model innovation (BMI) in context of open modes according to the evidence from China’s primary market. Design/methodology/approach Based on the database of China’s private market and data set of news clouds, the statistic approach is applied to explore and explain whether equity investment promotes knowledge-driven BMI. Machine learning method is also used to prove and predict the performance of such open innovation. Findings The results of logistic regression show that explanatory variables are significant, providing evidence that knowledge management (KM) promotes BMI through equity investment. By further using back propagation neural network, the classification learning algorithm estimates the possibility of BMI, which can be regarded as a score to quantify the performance of knowledge-driven BMI Research limitations/implications The quality of secondhand big data is not very ideal, and future empirical studies should use first-hand survey data. Practical implications This study provides new insights into the link between KM and BMI by highlighting the important roles of external investments in open modes. Social implications From the perspective of investment, the findings of this study suggest the importance for stakeholders to share knowledge and strategies for entrepreneurs to manage innovation. Originality/value The concepts and indicators related to business models are difficult to quantify currently, while this study provides feasible and practical methods to estimate knowledge-driven BMI with secondhand data from the primary market. The mechanism of knowledge and innovation bridged by the experience from investors is introduced and analyzed.

Does guanxi in China always produce value? The contingency effects of contract enforcement and market turbulence

2016 ◽  
Vol 31 (7) ◽  
pp. 861-876 ◽  
Author(s):  
Liping Qian ◽  
Pianpian Yang ◽  
Yao Li
Keyword(s):  
Financial Performance ◽  
Empirical Test ◽  
Contract Enforcement ◽  
Behavioral Risk ◽  
Negative Effects ◽  
Dyadic Relationship ◽  
Firm Level ◽  
Content Type ◽  
Strategic Performance ◽  
Market Turbulence

Purpose The purpose of this study is to reconcile the positive, non-significant and even negative effects of guanxi on firm performance from two aspects. First, it explores the linear and curvilinear relationships between guanxi and distinct performance dimensions. Second, it examines the moderating effects of both exchange-related behavioral risk (reflected by contract enforcement in this study) and market-related environmental risk (reflected by market turbulence in this study) on the above relationship. Design/methodology/approach Based on data for 206 samples collected from distributors of house furnishings, computers and their components, a moderated regression is used to test the hypotheses. Findings The empirical test generally supports the conceptual model and demonstrates three findings. First, guanxi has a linear, positive effect on financial performance and an inverted U-shaped effect on strategic performance. Second, contract enforcement decreases the effect of guanxi on financial performance and enhances its effect on strategic performance. Third, market turbulence enhances the effect of guanxi on financial performance and weakens its effect on strategic performance. Research limitations/implications First, this study collects data only from China. Future studies should collect data from other emerging markets to allow for either model validation or cross-country comparisons. Second, the data come only from buyers, and suppliers’ viewpoints are not included. Third, in addition to contract enforcement and market turbulence, other important contingencies should be considered in the guanxi–performance link. Practical implications The results provide important implications for managers to manage guanxi in an emerging economy. Managers should be very clear about their primary goal (i.e. pursuing short-term financial revenue or long-term strategic targets); next, they should understand how to match guanxi with various levels of contract enforcement and market turbulence to achieve that goal. Originality/value First, prior research has documented guanxi’s role in channel relationships, but it has not achieved consistent conclusions. Second, although existing studies have analyzed the contingencies of guanxi at the firm level, market level and institutional level, another important contingency “the dyadic relationship condition” is rarely considered. Third, although the extant research has realized the value of guanxi contingent on various market conditions, conflicting views exist. This study contributes by addressing these issues.

scholarly journals Automatical Knowledge Representation of Logical Relations by Dynamical Neural Network

2017 ◽  
Vol 26 (4) ◽  
pp. 625-639 ◽  
Author(s):  
Gang Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Knowledge Representation ◽  
Network Structure ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ann Model ◽  
Functional Approximation ◽  
Dynamical Neural Network

AbstractCurrently, most artificial neural networks (ANNs) represent relations, such as back-propagation neural network, in the manner of functional approximation. This kind of ANN is good at representing the numeric relations or ratios between things. However, for representing logical relations, these ANNs have disadvantages because their representation is in the form of ratio. Therefore, to represent logical relations directly, we propose a novel ANN model called probabilistic logical dynamical neural network (PLDNN). Inhibitory links are introduced to connect exciting links rather than neurons so as to inhibit the connected exciting links conditionally to make them represent logical relations correctly. The probabilities are assigned to the weights of links to indicate the belief degree in logical relations under uncertain situations. Moreover, the network structure of PLDNN is less limited in topology than traditional ANNs, and it is dynamically built completely according to the data to make it adaptive. PLDNN uses both the weights of links and the interconnection structure to memorize more information. The model could be applied to represent logical relations as the complement to numeric ANNs.

Prediction of Human Responses to Dairy Odor Using an Electronic Nose and Neural Networks

2018 ◽  
Vol 61 (2) ◽  
pp. 399-409 ◽  
Author(s):  
Fangle Chang ◽  
Paul Heinemann
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Electronic Nose ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Sources Of Information ◽  
Independent Validation ◽  
Quantitative Measurements ◽  
Resilient Back Propagation ◽  
Human Assessment

Abstract. Odor emitted from dairy operations may cause negative reactions by farm neighbors. Identification and evaluation of such malodors is vital for better understanding of human response and methods for mitigating effects of odors. The human nose is a valuable tool for odor assessment, but using human panels can be costly and time-consuming, and human evaluation of odor is subjective. Sensing devices, such as an electronic nose, have been widely used to measure volatile emissions from different materials. The challenge, though, is connecting human assessment of odors with the quantitative measurements from instruments. In this work, a prediction system was designed and developed to use instruments to predict human assessment of odors from common dairy operations. The model targets are the human responses to odor samples evaluated using a general pleasantness scale ranging from -11 (extremely unpleasant) to +11 (extremely pleasant). The model inputs were the electronic nose measurements. Three different neural networks, a Levenberg-Marquardt back-propagation neural network (LMBNN), a scaled conjugate gradient back-propagation neural network (CGBNN), and a resilient back-propagation neural network (RPBNN), were applied to connect these two sources of information (human assessments and instrument measurements). The results showed that the LMBNN model can predict human assessments with accuracy as high as 78% within a 10% range and as high as 63% within a 5% range of the targets in independent validation. In addition, the LMBNN model performed with the best stability in both training and independent validation. Keywords: Animal production, Hedonic tone, Olfactometric models.

scholarly journals DEVELOPMENT OF BIOMETRIC METHODS AND INFORMATION SECURITY TOOLS

2021 ◽  
Vol 2 (336) ◽  
pp. 121-124
Author(s):  
T. Zh. Mazakov ◽  
D. N. Narynbekovna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Future Perspective ◽  
Data Set

Now a day’s security is a big issue, the whole world has been working on the face recognition techniques as face is used for the extraction of facial features. An analysis has been done of the commonly used face recognition techniques. This paper presents a system for the recognition of face for identification and verification purposes by using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) and the implementation of face recognition system is done by using neural network. The use of neural network is to produce an output pattern from input pattern. This system for facial recognition is implemented in MATLAB using neural networks toolbox. Back propagation Neural Network is multi-layered network in which weights are fixed but adjustment of weights can be done on the basis of sigmoidal function. This algorithm is a learning algorithm to train input and output data set. It also calculates how the error changes when weights are increased or decreased. This paper consists of background and future perspective of face recognition techniques and how these techniques can be improved.

Deploying artificial neural networks for modeling energy demand: international evidence

2019 ◽  
Vol 14 (2) ◽  
pp. 285-315 ◽  
Author(s):  
Emmanuel Bannor B. ◽  
Alex O. Acheampong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Demand ◽  
Trade Openness ◽  
Back Propagation ◽  
Rank Correlation ◽  
Back Propagation Algorithm ◽  
Content Type ◽  
The Usa ◽  
Artificial Neural

Purpose This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA. Design/methodology/approach The study used quarterly data that span over the period of 1980Q1-2015Q4 to develop and validate the models. Eight input parameters were used for modeling the demand for energy. Hyperparameter optimization was performed to determine the ideal parameters for configuring each country’s model. To ensure stable forecasts, a repeated evaluation approach was used. After several iterations, the optimal models for each country were selected based on predefined criteria. A multi-layer perceptron with a back-propagation algorithm was used for building each model. Findings The results suggest that the validated models have developed high generalizing capabilities with insignificant forecasting deviations. The model for Australia, China, France, India and the USA attained high coefficients of determination of 0.981, 0.9837, 0.9425, 0.9137 and 0.9756, respectively. The results from the partial rank correlation coefficient further reveal that economic growth has the highest sensitivity weight on energy demand in Australia, France and the USA while industrialization has the highest sensitivity weight on energy demand in China. Trade openness has the highest sensitivity weight on energy demand in India. Originality/value This study incorporates other variables such as financial development, foreign direct investment, trade openness, industrialization and urbanization, which are found to have an important effect on energy demand in the model to prevent underestimation of the actual energy demand. Sensitivity analysis is conducted to determine the most influential variables. The study further deploys the models for hands-on predictions of energy demand.

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