scholarly journals Nonlinear Prediction Models in Data Analysis

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Željko Račić ◽  
Zoran Ž. Avramović ◽  
Đuro Mikić
Keyword(s):  
Neural Networks ◽  
Human Resources ◽  
Prediction Models ◽  
Historical Data ◽  
Knowledge Workers ◽  
Mean Value ◽  
Nonlinear Prediction ◽  
Systematic Analysis ◽  
Grouping Method ◽  
The Right

The modern entrepreneurial sensibility of the company’s business implies directing the right information to the appropriate parts of the company at the right time. That is why it is necessary to digitalize processes as much as possible and make the organization “intelligent”, and its human resources, to the greatest extent, the knowledge workers. The application of neural networks, i.e. nonlinear prediction models, enables systematic analysis of data in the function of evaluating the behavior of the system. Neural networks are a powerful tool, especially for forecasting trends and forecasting based on historical data. The grouping method, i.e., the k-mean value algorithm, is used as a precursor to neural networks.

Knowledge Management as Organizational Strategy

2011 ◽  
pp. 2343-2347 ◽  
Author(s):  
Cheryl D. Edwards-Buckingham
Keyword(s):  
Knowledge Management ◽  
Human Resources ◽  
Information Technologies ◽  
Knowledge Workers ◽  
Leadership Role ◽  
Collective Wisdom ◽  
Organizational Challenges ◽  
Knowledge Processes ◽  
Intellectual Assets ◽  
The Right

“More than ever before, the effectiveness of organizations depends on their ability to address issues such as knowledge management, change management, and capability building, all of which could fall into the domain of the HR function” (Lawler & Mohrman 2003, p. 7). In its leadership role, Human Resources (HR) has many tasks and responsibilities. According to Lawler and Mohrman (2003), there are several key organizational challenges faced by HR departments. These challenges include improving productivity, increasing quality, facilitating mergers and acquisitions, improving new product possibilities, and knowledge management. Knowledge management (KM) is defined as the tools, techniques, and processes for the most effective and efficient management of an organization’s intellectual assets (Davies, Studer, Sure, & Warren, 2005). Knowledge management consists of the combination of data and information processing capacity (i.e., information technologies), as well as the creative and innovative capacity of human resources. Knowledge management entails an organization viewing its processes as knowledge processes, in which these processes involve application of knowledge within the organization. Knowledge management focuses on the generation and application of knowledge, leveraging and sharing knowledge to increase the derived value, importing knowledge in the form of skilled employees, connecting knowledge workers, and motivating knowledge workers (Mohrman & Finegold, 2000). According to Robbins (2003) the process of knowledge management entails organizing and distributing an organization’s collective wisdom so that the right information gets to the right people at the right time. As knowledge management becomes increasingly important, organizations must strive to understand the dynamics of knowledge management. This article will discuss the elements of knowledge management, in addition to presenting a case on how organizations can use knowledge management as strategy, where knowledge management is valued more than funding as a strategic resource.

ANALISA TINGKAT KEPUASAN WISATAWAN TERHADAP SARANA TRANSPORTASI DALAM UPAYA MENCIPTAKAN KAWASAN WISATA TERINTEGRASI DI YOGYAKARTA (STUDI KASUS) TAMANSARI, KRATON, TITIK NOL KILOMETER DAN MALIOBORO

2019 ◽  
Vol 13 (02) ◽  
pp. 1-14
Author(s):  
Agung Sulistyo ◽  
Yerika Ayu Salindri
Keyword(s):  
Data Analysis ◽  
Local Government ◽  
Human Resources ◽  
Low Cost ◽  
Mean Value ◽  
Mean Values ◽  
The Mean ◽  
Ease Of Access ◽  
Tourism Service ◽  
The Right

The process of assessing the satisfaction of tourists must be done an organization or business. By knowing the attitude of tourists, Local Government can issue policies related to services for tourists and can find out how far the level of customer satisfaction with the service. Fishbein method is used to determine the attitude of tourists to a particular object or product (such as brand, service, etc.). Based on data analysis that has been done, it can be concluded that Si Thole tourism service is considered good based on tourist attitude. with attributes include: low cost, ease of access, precise purpose, quality human resources services and provide comfort for tourists. This can be seen through Fishbein analysis that has been done and obtained the mean value of 79.49. The mean values are at neutral answer intervals and agree or are on the right side indicating a positive value.Keywords: Transportation Performance Tourism, Si Thole, Fishbein Method

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

Perimeter of The Tricuspid Valve: A Cadaveric Human Heart Study

2020 ◽  
Vol 11 (3) ◽  
pp. 3424-3428
Author(s):  
Kirti Chaudhary ◽  
Amey Dhatrak ◽  
Brij Raj Singh ◽  
Ujwal Gajbe
Keyword(s):  
Tricuspid Valve ◽  
Tricuspid Regurgitation ◽  
Right Ventricular Dysfunction ◽  
Mean Value ◽  
Female Population ◽  
Functional Tricuspid Regurgitation ◽  
Lv Dysfunction ◽  
Heart Study ◽  
Males And Females ◽  
The Right

Historically, the research on the right ventricle (RV) has been neglected by his left equivalent because of the complexity of left ventricle (LV) dysfunction. Tricuspid regurgitation (TR) can be classified as linked to primary valve disease or functional in nature, but most are functional. Although it was historically assumed that such functional Tricuspid regurgitation, i.e. arising from leftsided disease, and it can be resolved after corrective surgery, but after successful surgery, on the aortic or mitral valve annular dilatation, the Tricuspid regurgitation and right ventricular dysfunction may persist.To study the circumference of tricuspid orifice and it’s the diameter in two perpendicular planes and its comparison among the male and female population. The material for the present study comprised of 50 formalin fixed human hearts (35 males and 15 females) which were obtained from the department of anatomy. In this study, it is observed that: The mean value of circumference of a tricuspid orifice is 11.01+/-0.63 cm. The diameter of tricuspid orifice along the frontal dimension is 3.06+/-0.38 cm, and the diameter along the sagittal dimension is 2.26+/-0.23 cm. The measurements of the circumference of tricuspid orifice reported for males and females in western countries were higher than the present study and the diameter along the frontal dimension is greater than the diameter along the sagittal dimension. The tricuspid valve diameter along the frontal dimension was more than the diameter along the sagittal dimension in both males and females.

A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

scholarly journals Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks

2021 ◽  
Vol 13 (4) ◽  
pp. 742
Author(s):  
Jian Peng ◽  
Xiaoming Mei ◽  
Wenbo Li ◽  
Liang Hong ◽  
Bingyu Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Fundamental Problem ◽  
Semantic Representation ◽  
Feature Learning ◽  
Essential Elements ◽  
Complex Scene ◽  
Feature Representations ◽  
The Right ◽  
The Relationship

Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.

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