Analyzing Healthcare Data Using Water Wave Optimization-Based Clustering Technique

2021 ◽  
Vol 10 (4) ◽  
pp. 38-57
Author(s):  
Arvinder Kaur ◽  
Yugal Kumar
Keyword(s):  
Medical Informatics ◽  
Performance Indicators ◽  
Water Wave ◽  
Disease Diagnosis ◽  
Diagnostic Model ◽  
Mutation Operator ◽  
Healthcare Data ◽  
Proposed Model ◽  
And Performance ◽  
Computational Intelligence Methods

The medical informatics field gets wide attention among the research community while developing a disease diagnosis expert system for useful and accurate predictions. However, accuracy is one of the major medical informatics concerns, especially for disease diagnosis. Many researchers focused on the disease diagnosis system through computational intelligence methods. Hence, this paper describes a new diagnostic model for analyzing healthcare data. The proposed diagnostic model consists of preprocessing, diagnosis, and performance evaluation phases. This model implements the water wave optimization (WWO) algorithm to analyze the healthcare data. Before integrating the WWO algorithm in the proposed model, two modifications are inculcated in WWO to make it more robust and efficient. These modifications are described as global information component and mutation operator. Several performance indicators are applied to assess the diagnostic model. The proposed model achieves better results than existing models and algorithms.

scholarly journals Healthcare Data Analysis Using Water Wave Optimization-Based Diagnostic Model

2021 ◽  
Vol 20 (No.4) ◽  
pp. 457-488
Author(s):  
Yugal Kumar ◽  
Arvinder Kaur
Keyword(s):  
Performance Indicators ◽  
Water Wave ◽  
Optimal Solution ◽  
Premature Convergence ◽  
Diagnostic Model ◽  
Accuracy Rate ◽  
Convergence Problem ◽  
Healthcare Data ◽  
Diagnosis Accuracy ◽  
Convergence Problems

This paper presents a new diagnostic model for various diseases. In the proposed diagnostic model, a water wave optimization (WWO) algorithm was implemented for improving the diagnosis accuracy. It was observed that the WWO algorithm suffered from the absence of global best information and premature convergence problems. Therefore in this work, some improvements were proposed to formulate the WWO algorithm as more promising and efficient. The global best information issue was addressed by using an improved solution search equation and the aim of this was to explore the global best optimal solution. Furthermore, a premature convergence problem was rectified by using a decay operator. These improvements were incorporated in the propagation and refraction phases of the WWO algorithm. The proposed algorithm was integrated into a diagnostic model for the analysis of healthcare data. The proposed algorithm aimed to improve the diagnosis accuracy of various diseases. The diverse disease datasets were considered for implementing the performance of the proposed diagnostic model based on accuracy and F-score performance indicators, while the existing techniques were regarded to compare the simulation results. The results confirmed that the WWO-based diagnostic model achieved a higher accuracy rate as compared to existing models/techniques with most disease/healthcare datasets. Therefore, it stated that the proposed diagnostic model is more promising and efficient for the diagnosis of different diseases.

The Influence of Cognitive and Affective Reactions to Feedback on Subsequent Goals

2010 ◽  
Vol 15 (2) ◽  
pp. 121-131 ◽  
Author(s):  
Remus Ilies ◽  
Timothy A. Judge ◽  
David T. Wagner
Keyword(s):  
Performance Feedback ◽  
Business School ◽  
Structural Equation ◽  
Emotional Reactions ◽  
Equation Model ◽  
Proposed Model ◽  
And Performance ◽  
Dispositional Measure ◽  
The Relationship ◽  
Reactions To Feedback

This paper focuses on explaining how individuals set goals on multiple performance episodes, in the context of performance feedback comparing their performance on each episode with their respective goal. The proposed model was tested through a longitudinal study of 493 university students’ actual goals and performance on business school exams. Results of a structural equation model supported the proposed conceptual model in which self-efficacy and emotional reactions to feedback mediate the relationship between feedback and subsequent goals. In addition, as expected, participants’ standing on a dispositional measure of behavioral inhibition influenced the strength of their emotional reactions to negative feedback.

An Analytical Transient Tire Model

2001 ◽  
Vol 29 (2) ◽  
pp. 108-132 ◽  
Author(s):  
A. Ghazi Zadeh ◽  
A. Fahim
Keyword(s):  
Transient Behavior ◽  
Stability Control ◽  
Vehicle Stability ◽  
Tire Model ◽  
Steering Angle ◽  
First Order ◽  
Vehicle Stability Control ◽  
Proposed Model ◽  
Depth Study ◽  
And Performance

Abstract The dynamics of a vehicle's tires is a major contributor to the vehicle stability, control, and performance. A better understanding of the handling performance and lateral stability of the vehicle can be achieved by an in-depth study of the transient behavior of the tire. In this article, the transient response of the tire to a steering angle input is examined and an analytical second order tire model is proposed. This model provides a means for a better understanding of the transient behavior of the tire. The proposed model is also applied to a vehicle model and its performance is compared with a first order tire model.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

scholarly journals Comparative analysis of models and performance indicators for optimal service facility location

2021 ◽  
Vol 145 ◽  
pp. 102174
Author(s):  
Edoardo Fadda ◽  
Daniele Manerba ◽  
Gianpiero Cabodi ◽  
Paolo Enrico Camurati ◽  
Roberto Tadei
Keyword(s):  
Comparative Analysis ◽  
Facility Location ◽  
Performance Indicators ◽  
Service Facility ◽  
Optimal Service ◽  
And Performance

scholarly journals Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 651
Author(s):  
Shengyi Zhao ◽  
Yun Peng ◽  
Jizhan Liu ◽  
Shuo Wu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Model Comparison ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Identification Accuracy ◽  
Main Research ◽  
Proposed Model ◽  
Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

Leader–follower narcissism and subgroup formation in teams: a conceptual model

2019 ◽  
Vol 34 (6) ◽  
pp. 429-442 ◽  
Author(s):  
Manuel London
Keyword(s):  
Team Performance ◽  
Team Identification ◽  
Goal Congruence ◽  
Content Type ◽  
Team Outcomes ◽  
Performance Pressure ◽  
Proposed Model ◽  
Leaders And Followers ◽  
Trait Activation ◽  
And Performance

Purpose Drawing on existing theory, a model is developed to illustrate how the interaction between leaders and followers similarity in narcissism and goal congruence may influence subgroup formation in teams, and how this interaction influences team identification and team performance. Design/methodology/approach The proposed model draws on dominance complementary, similarity attraction, faultline formation and trait activation theories. Findings Leader–follower similarity in narcissism and goal congruence may stimulate subgroup formation, possibly resulting in conformers, conspirators, outsiders and victims, especially when performance pressure on a team is high. Followers who are low in narcissism and share goals with a leader who is narcissistic are likely to become conformers. Followers who are high in narcissism and share goals with a narcissistic leader are likely to become confederates. Followers who do not share goals with a narcissistic leader will be treated by the leader and other members as outsiders if they are high in narcissism, and victimized if they are low in narcissism. In addition, the emergence of these subgroups leads to reduced team identification and lower team performance. Practical implications Higher level managers, coaches and human resource professions can assess and, if necessary, counteract low team identification and performance resulting from the narcissistic personality characteristics of leaders and followers. Originality/value The model addresses how and under what conditions narcissistic leaders and followers may influence subgroup formation and team outcomes.

Twenty Years After the Hype

2015 ◽  
Vol 2 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Alois Paulin
Keyword(s):  
Performance Indicators ◽  
The State ◽  
Government Performance ◽  
Before And After ◽  
To Come ◽  
And Performance ◽  
Government Reforms

In this study the authors analyze the effects of e-government reforms that began in mid-90ies by confronting the promises which these reforms made to government performance in the period before and after the reforms took place. The authors use fiscal and performance indicators of the Slovenian government and courts to argue that e-government did not yield any notable effects on the state performance. Finally, the authors analyze the reasons why e-government technology cannot be regarded as sustainable and suggest a different approach towards researching how to sustainably improve governance for generations to come.

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