scholarly journals A common weights model for investigating efficiency-based leadership in the Russian banking industry

2020 ◽  
Author(s):  
Sajad Kazemi ◽  
Madjid Tavana ◽  
Mehdi Toloo ◽  
Nikolay A. Zenkevich
Keyword(s):  
High Efficiency ◽  
Banking Industry ◽  
Uncertain Data ◽  
Ideal Point ◽  
Evaluation Framework ◽  
Common Weights ◽  
Corporate Size ◽  
Environmental Criteria ◽  
Proposed Model ◽  
Measuring Efficiency

In this race for productivity, the most successful leaders in the banking industry are those with high-efficiency and a competitive edge.  Data envelopment analysis is one of the most widely used methods for measuring efficiency in organizations. In this study, we use the ideal point concept and propose a common weights model with fuzzy data and non-discretionary inputs. The proposed model considers environmental criteria with uncertain data to produce a full ranking of homogenous decision-making units. We use the proposed model to investigate the efficiency-based leaders in the Russian banking industry. The results show that the unidimensional and unilateral assessment of leading organizations solely according to corporate size is insufficient to characterize industry leaders effectively. In response, we recommend a multilevel, multicomponent, and multidisciplinary evaluation framework for a more reliable and realistic investigation of leadership at the network level of analysis. 

scholarly journals Evaluation of water loss in transit and surface runoff in a Brazilian semi-arid basin

2020 ◽  
Vol 15 (4) ◽  
pp. 1
Author(s):  
Cristian Epifanio Toledo ◽  
João Carlos Mohn Nogueira ◽  
Alexandre De Amorim Camargo
Keyword(s):  
Water Loss ◽  
Surface Runoff ◽  
High Efficiency ◽  
Heterogeneous Environments ◽  
Runoff Coefficient ◽  
Water Losses ◽  
Proposed Model ◽  
Reservoir Basin ◽  
In Transit ◽  
Semi Arid

The objective of this work was to propose and evaluate a model to estimate transit water losses and surface runoff in a Brazilian semi-arid basin, fundamental components in the hydrological studies of the region, such as in the verification of hydrological connectivity. The study area was the Orós Reservoir Basin, located in the state of Ceará. The modeling of transit water loss and surface runoff were developed based on the work of Araújo and Ribeiro (1996) and Peter et al. (2014). In the proposed model, the parameter of loss in transit (k) was estimated at 0.027 km-1 for a section of the river basin, and when simulated for other stretches it provided good flow results at the end of the stretch, obtaining an NSE of 82%. The value of the runoff coefficient was estimated at 3% and when evaluating a spatial variation of this coefficient in the basin, the values varied from 2% to 12%, and the use of specialized runoff coefficient (RC) values promoted a higher NSE in the discharge simulation in the basin. It is concluded that the proposed model to estimate transit water losses and surface runoff demonstrated a high efficiency in the simulation of hydrological processes. The basin of Orós reservoir presented a high variability of the coefficient of surface runoff, justifying the need for a greater spatiality of this coefficient in heterogeneous environments.

scholarly journals Second-order total generalized variation based model for restoring images with mixed Poisson — Gaussian noise

2021 ◽  
pp. 20-32
Author(s):  
Cong Pham ◽  
Thi Thu Thao Tran ◽  
Thanh Cong Nguyen ◽  
Duc Hoang Vo
Keyword(s):  
Gaussian Noise ◽  
High Efficiency ◽  
Gaussian Model ◽  
Second Order ◽  
Total Generalized Variation ◽  
Proposed Model ◽  
Total Variation Model ◽  
Alternating Minimization Algorithm ◽  
Generalized Variation ◽  
Noise Models

Introduction: A common problem in image restoration is image denoising. Among many noise models, the mixed Poisson-Gaussian model has recently aroused considerable interest. Purpose: Development of a model for denoising images corrupted by mixed Poisson-Gaussian noise, along with an algorithm for solving the resulting minimization problem. Results: We proposed a new total variation model for restoring an image with mixed Poisson-Gaussian noise, based on second-order total generalized variation. In order to solve this problem, an efficient alternating minimization algorithm is used. To illustrate its comparison with related methods, experimental results are presented, demonstrating the high efficiency of the proposed approach. Practical relevance: The proposed model allows you to remove mixed Poisson-Gaussian noise in digital images, preserving the edges. The presented numerical results demonstrate the competitive features of the proposed model.

scholarly journals Establishing a Sustainable Sports Tourism Evaluation Framework with a Hybrid Multi-Criteria Decision-Making Model to Explore Potential Sports Tourism Attractions in Taiwan

2020 ◽  
Vol 12 (4) ◽  
pp. 1673 ◽  
Author(s):  
Jen-Jen Yang ◽  
Huai-Wei Lo ◽  
Chen-Shen Chao ◽  
Chih-Chien Shen ◽  
Chin-Cheng Yang
Keyword(s):  
Decision Making ◽  
Evaluation Framework ◽  
Government Support ◽  
Multi Criteria Decision Making ◽  
Practical Applications ◽  
Proposed Model ◽  
Sports Tourism ◽  
Criteria Weights ◽  
Tourism Attractions ◽  
Multiple Experts

In recent years, the awareness of sustainable tourism has risen around the world. Many tourism industries combine sports to attract more customers to facilitate the development of the economy and the promotion of local culture. However, it is an important task to establish a comprehensive tourism evaluation framework for sustainable sports tourism. This study proposes a Multi-Criteria Decision-Making (MCDM) model to discuss the above issues, using the Bayesian Best Worst Method (Bayesian BWM) to integrate multiple experts’ judgments to generate the group optimal criteria weights. Next, the modified Visekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) technique is combined with the concept of aspiration level to determine the performance of sports attractions and their priority ranks. In addition, this study adds a perspective of institutional sustainability to emphasize the importance of government support and local marketing. The effectiveness and robustness of the proposed model is demonstrated through potential sports tourism attractions in Taiwan. A sensitivity analysis and models comparison were also performed in this study. The results show that the proposed model is feasible for practical applications and that it effectively provides some management implications to support decision-makers in formulating improvement strategies.

scholarly journals Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network

2019 ◽  
Vol 8 (9) ◽  
pp. 366 ◽  
Author(s):  
Yong Han ◽  
Cheng Wang ◽  
Yibin Ren ◽  
Shukang Wang ◽  
Huangcheng Zheng ◽  
...  
Keyword(s):  
High Efficiency ◽  
Short Term Memory ◽  
Spatial Scales ◽  
Optimization Procedure ◽  
Stochastic Gradient Descent ◽  
Short Term ◽  
Passenger Flow ◽  
Proposed Model ◽  
Gradient Descent Algorithm ◽  
Lstm Network

The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In recent years, an increasing number of researchers have sought to apply the LSTM model to passenger flow prediction. However, few of them pay attention to the optimization procedure during model training. In this article, we propose a hybrid, optimized LSTM network based on Nesterov accelerated adaptive moment estimation (Nadam) and the stochastic gradient descent algorithm (SGD). This method trains the model with high efficiency and accuracy, solving the problems of inefficient training and misconvergence that exist in complex models. We employ a hybrid optimized LSTM network to predict the actual passenger flow in Qingdao, China and compare the prediction results with those obtained by non-hybrid LSTM models and conventional methods. In particular, the proposed model brings about a 4%–20% extra performance improvements compared with those of non-hybrid LSTM models. We have also tried combinations of other optimization algorithms and applications in different models, finding that optimizing LSTM by switching Nadam to SGD is the best choice. The sensitivity of the model to its parameters is also explored, which provides guidance for applying this model to bus passenger flow data modelling. The good performance of the proposed model in different temporal and spatial scales shows that it is more robust and effective, which can provide insightful support and guidance for dynamic bus scheduling and regional coordination scheduling.

Design, Simulation, and Implementation of an Electric Hybrid Motorcycle

2010 ◽  
Author(s):  
Behzad Asaei ◽  
Seyed Hosein Seyed mohammadi ◽  
Aghil Yousefi koma ◽  
Mahdi Habibidoost ◽  
Roohollah Aghnoot ◽  
...  
Keyword(s):  
Control Strategy ◽  
High Efficiency ◽  
Power Transmission ◽  
Combustion Engine ◽  
Front Wheel ◽  
Bldc Motor ◽  
Brushless Dc ◽  
Operating Modes ◽  
Proposed Model ◽  
Variable Transmission

This paper presents a general integrated procedure of fabricating a Hybrid Electric Motorcycle (HEM). Firstly, a simple model designed and simulated using ADVISOR2002 and the proposed model is exported to MATLAB/SIMULINK. Secondly, the controller schematic and its optimized control strategy are described. In addition, the ratings of the components including the batteries, electric motor, and internal combustion engine (ICE) are calculated based on the design. A 125 cc ICE motorcycle is selected for conversion to HEM. A brushless DC (BLDC) motor assembled in front wheel as accessory propellant. The nominal powers are 8.2 kW at 8500 rpm and 500 W for the ICE and BLDC respectively. The original motorcycle has a Continues Variable Transmission (CVT) that is the best choice for the HEM power transmission because it can operate in automatic handling mode and has high efficiency. Moreover, by using CVT the ICE can be started while it is running at 15 km/h. Finally, the three operating modes of the HEM, the servo motors, and the LCD panel were explained.

scholarly journals Developing Common Set of Weights with Considering Nondiscretionary Inputs and Using Ideal Point Method

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Reza Kiani Mavi ◽  
Sajad Kazemi ◽  
Jay M. Jahangiri
Keyword(s):  
Decision Making ◽  
Ideal Point ◽  
Main Idea ◽  
Efficiency Score ◽  
Point Method ◽  
Bank Branches ◽  
Proposed Model ◽  
Decision Making Unit ◽  
Common Set Of Weights ◽  
Nondiscretionary Inputs

Data envelopment analysis (DEA) is used to evaluate the performance of decision making units (DMUs) with multiple inputs and outputs in a homogeneous group. In this way, the acquired relative efficiency score for each decision making unit lies between zero and one where a number of them may have an equal efficiency score of one. DEA successfully divides them into two categories of efficient DMUs and inefficient DMUs. A ranking for inefficient DMUs is given but DEA does not provide further information about the efficient DMUs. One of the popular methods for evaluating and ranking DMUs is the common set of weights (CSW) method. We generate a CSW model with considering nondiscretionary inputs that are beyond the control of DMUs and using ideal point method. The main idea of this approach is to minimize the distance between the evaluated decision making unit and the ideal decision making unit (ideal point). Using an empirical example we put our proposed model to test by applying it to the data of some 20 bank branches and rank their efficient units.

scholarly journals Breast tumor segmentation in mammography image via Chan-Vese technique

2021 ◽  
Vol 22 (2) ◽  
pp. 809
Author(s):  
Mohammed Y. Kamil ◽  
Eman A. Radhi
Keyword(s):  
High Efficiency ◽  
Noise Suppression ◽  
Low Frequency ◽  
Surrounding Tissue ◽  
Tumor Segmentation ◽  
Proposed Model ◽  
Abnormal Mammogram ◽  
Frequency Components ◽  
Mammography Image ◽  
Mammogram Images

The accurate segmentation of tumours is a crucial stage of diagnosis and treatment, reducing the damage that breast cancer causes, which is the most common type of cancer among women, especially after the age of forty. The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with small tumours that are not clear boundaries and hidden under the tissues. As algorithms often lose an automatic path toward the boundaries of the tumour at try to determine the site of this type of tumour. The study aims to create a clear contrast between the tumour and the healthy breast area. For this purpose, we used a Gaussian filter as a pre-processing as it works to intensify the low-frequency components while reducing the high-frequency components as the breast structure is enhanced and noise suppression. Then, CLAHE was used to improve the contrast of the image, which increases the contrast between the tumour and the surrounding tissue and sharpens the edges of the tumour. Next, the tumour was segmented by using the Chan-Vese method with appropriate parameters defined. The proposed method was applied to all abnormal mammogram images taken from a publicly available mini-MIAS database. The proposed model was tested in two ways, the first is statistical that got results (90.1, 94.8, 95.5, 92.1, 99.5) for Jaccard, Dice, PF-Score, precision, and sensitivity respectively. And the other is based on the segmented region's characteristics that results showed the algorithm could identify the tumour with high efficiency.

scholarly journals Providing a New Model for Discovering Cloud Services Based on Ontology

2017 ◽  
Vol 7 (6) ◽  
pp. 2268-2272
Author(s):  
B. Heydari ◽  
M. Aajami
Keyword(s):  
Cloud Computing ◽  
Semantic Web ◽  
Service Management ◽  
High Efficiency ◽  
Quality Parameters ◽  
Parameters Estimation ◽  
Cloud Services ◽  
User Needs ◽  
New Model ◽  
Proposed Model

Due to its efficient, flexible, and dynamic substructure in information technology and service quality parameters estimation, cloud computing has become one of the most important issues in computer world. Discovering cloud services has been posed as a fundamental issue in reaching out high efficiency. In order to do one’s own operations in cloud space, any user needs to request several various services either simultaneously or according to a working routine. These services can be presented by different cloud producers or different decision-making policies. Therefore, service management is one of the important and challenging issues in cloud computing. With the advent of semantic web and practical services accordingly in cloud computing space, access to different kinds of applications has become possible. Ontology is the core of semantic web and can be used to ease the process of discovering services. A new model based on ontology has been proposed in this paper. The results indicate that the proposed model has explored cloud services based on user search results in lesser time compared to other models.

scholarly journals Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

2021 ◽  
Vol 40 (1) ◽  
pp. 813-832
Author(s):  
Sajad Kazemi ◽  
Reza Kiani Mavi ◽  
Ali Emrouznejad ◽  
Neda Kiani Mavi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Ideal Point ◽  
Weight Vector ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Complex Organizations ◽  
Measuring Efficiency ◽  
Common Set Of Weights ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster.

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