Econometrics: Econometric & Statistical Methods - Special Topics eJournal最新文献_第2页

Conceptual Framework of Agile Project Management, Affecting Project Performance, Key: Requirements and Challenges 敏捷项目管理的概念框架，影响项目绩效，关键字:需求和挑战

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-07-01 DOI: 10.21276/IJIREM.2021.8.4.3

Ahmed Muayad Younus, H. Younis

{"title":"Conceptual Framework of Agile Project Management, Affecting Project Performance, Key: Requirements and Challenges","authors":"Ahmed Muayad Younus, H. Younis","doi":"10.21276/IJIREM.2021.8.4.3","DOIUrl":"https://doi.org/10.21276/IJIREM.2021.8.4.3","url":null,"abstract":"There have been a few studies that have adequately compared the advantages and disadvantages of various types of agile techniques. This research study develops a conceptual model that enables top management team members, software developers, project managers, and researchers to gain insight and understanding of agile techniques and methods. Those involved in the project want to successfully complete projects on time and within budget while maintaining high quality standards and operating in a safe and environmentally conscious manner They also want to minimize the negative impact on the environment. When it comes to project execution, however, there are numerous constraints and risks that limit their ability to begin or progress operations, and which frequently have a significant negative impact on the overall performance of the project. After reviewing the literature, it was discovered that the Agile method is capable of accurately representing most factors. It is based on the findings of this research that this paper presents a conceptual model for the effect of agile project management on project performance in terms of timeliness, cost, and quality. factors are unpredictable and can have consequences that are difficult to undo without incurring significant costs in the process. As a result, it is more critical than ever to investigate their impact on project outcomes. The ability of countries to manage risks, control expenditures, exploit and benefit from opportunities is dependent on their ability to comprehend the impact of Agile methods on their respective organizations and cultures.","PeriodicalId":139983,"journal":{"name":"Econometrics: Econometric & Statistical Methods - Special Topics eJournal","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122012572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Convolutional Neural Networks for Modeling and Forecasting Nonlinear Nonstationary Processes 卷积神经网络在非线性非平稳过程建模与预测中的应用

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-06-30 DOI: 10.21303/2313-8416.2021.001924

A. Belas, P. Bidyuk

{"title":"Convolutional Neural Networks for Modeling and Forecasting Nonlinear Nonstationary Processes","authors":"A. Belas, P. Bidyuk","doi":"10.21303/2313-8416.2021.001924","DOIUrl":"https://doi.org/10.21303/2313-8416.2021.001924","url":null,"abstract":"The object of research: The object of research is modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Investigated problem: There are several popular approaches to solving the problems of adequate model constructing and forecasting nonlinear nonstationary processes, such as autoregressive models and recurrent neural networks. However, each of them has its advantages and drawbacks. Autoregressive models cannot deal with the nonlinear or combined influence of previous states or external factors. Recurrent neural networks are computationally expensive and cannot work with sequences of high length or frequency. The main scientific result: The model for forecasting nonlinear nonstationary processes presented in the form of the time series data was built using convolutional neural networks. The current study shows results in which convolutional networks are superior to recurrent ones in terms of both accuracy and complexity. It was possible to build a more accurate model with a much fewer number of parameters. It indicates that one-dimensional convolutional neural networks can be a quite reasonable choice for solving time series forecasting problems. The area of practical use of the research results: Forecasting dynamics of processes in economy, finances, ecology, healthcare, technical systems and other areas exhibiting the types of nonlinear nonstationary processes. Innovative technological product: Methodology of using convolutional neural networks for modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Scope of the innovative technological product: Nonlinear nonstationary processes presented in the form of time-series data.","PeriodicalId":139983,"journal":{"name":"Econometrics: Econometric & Statistical Methods - Special Topics eJournal","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126110109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of Product Complexity Index in 3D Models Using a Hybrid Feature Recognition Method with Rule-Based and Graph-Based Methods 基于规则和图的混合特征识别方法在三维模型中建立产品复杂性指数

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-06-10 DOI: 10.15587/1729-4061.2021.227848

H. D. Budiono, Finno Ariandiyudha Hadiwardoyo

{"title":"Development of Product Complexity Index in 3D Models Using a Hybrid Feature Recognition Method with Rule-Based and Graph-Based Methods","authors":"H. D. Budiono, Finno Ariandiyudha Hadiwardoyo","doi":"10.15587/1729-4061.2021.227848","DOIUrl":"https://doi.org/10.15587/1729-4061.2021.227848","url":null,"abstract":"A machining process is very dependent on the model created. The more complicated the model, the greater the design difficulty and the greater the machining process. Reduced production costs can help a company increase profits. A focus on production cost can be achieved in a number of ways, the first of which is by replacing materials or changing the design. It is better to reduce product costs during the design stage than during the manufacturing stage. The main objective of this research is to develop an application that can recognize features in a CAD program and calculate the complexity index of shapes in real time. In this study, the prismatic features and slab features classified by Jong-Yun Jung were used. The feature recognition method applied in this study is a hybrid of the rule-based and graph-based methods, which uses the STL file developed by Sunil and Pande to obtain all the information needed. Then, the results are extracted from feature recognition data and are used to calculate the product complexity index of the model being studied. This study applied the product complexity index, following the model developed earlier by El Maraghy. Validation is performed by comparing the software count with the complexity index calculated with the STEP method by Hendri and Sholeh et al. This research develops a program that recognizes features in CAD software and calculates the index complexity of shapes in real time. This will allow designers to calculate the expected complexity value during the design process. As a result, the estimated production cost can be seen early on. Finally, this software is tested for calculating the index values for the complexity of a combined features model. The use of eight slots and eight pockets as a benchmark scoring for shape produces a more accurate product complexity index","PeriodicalId":139983,"journal":{"name":"Econometrics: Econometric & Statistical Methods - Special Topics eJournal","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121863267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Prediction and Analysis of Recurrent Depression Disorder: Deep Learning Approach 复发性抑郁症的预测与分析:深度学习方法

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-05-31 DOI: 10.2139/ssrn.3857061

Anagha Pasalkar, D. Kalbande

引用次数: 0

A note on nonlinear least squares estimation of multivariate stochastic unit root models 多元随机单位根模型的非线性最小二乘估计

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-05-26 DOI: 10.2139/ssrn.3854968

Timofey Ginker

引用次数: 0

Optimal Price Targeting 最优价格目标

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-04-29 DOI: 10.2139/ssrn.3822459

Adam Smith, Stephan Seiler, Ishant Aggarwal

引用次数: 10

Topological Features of Multivariate Distributions: Dependency on the Covariance Matrix 多元分布的拓扑特征:对协方差矩阵的依赖

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-04-24 DOI: 10.2139/ssrn.3833492

Lloyd L. Aromi, Yuri A. Katz, J. Vives

引用次数: 6

Combining Dimensionality Reduction with Neural Networks for Realized Volatility Forecasting 结合降维和神经网络实现波动率预测

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-04-11 DOI: 10.2139/ssrn.3824136

Lidan He, Andrea Bucci, Zhi Liu

引用次数: 1

Self-Pruning based Probabilistic Approach to Minimize Redundancy Overhead for Performance Improvement in MANET 基于自修剪的最小化冗余开销的概率方法用于MANET的性能改进

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-03-31 DOI: 10.5121/IJCNC.2021.13202

G. K. Pallai, M. Sankaran, A. Rath

{"title":"Self-Pruning based Probabilistic Approach to Minimize Redundancy Overhead for Performance Improvement in MANET","authors":"G. K. Pallai, M. Sankaran, A. Rath","doi":"10.5121/IJCNC.2021.13202","DOIUrl":"https://doi.org/10.5121/IJCNC.2021.13202","url":null,"abstract":"The Broadcast storm problem causes severe interference, intense collision and channel contention, which greatly degrades the QoS performance metrics of the routing protocols. So, we suggest a neighbourhood coverage knowledge probabilistic broadcasting model (NCKPB) integrating with AODV protocol with knowledge on 2-hop neighbourhood coverage; a connectivity function to control a node’s forwarding probability of retransmission to alleviate significant overhead redundancy. Our objective is to minimize the broadcast RREQ overhead while ensuring fair retransmission bandwidth. We considered two more important measures called Saved Rebroadcast and Reachability. The outcomes of NCKPB, Fixed probability (FP) and Flooding (FL) routing schemes are examined under three major operating conditions, such as node density, mobility and traffic load. The NS-2 results demonstrate the efficacy of the proposed NCKPB model by illustrating its performance superiority over all key metrics such as redundancy overhead, end to end latency, throughput, reachability, saved rebroadcast and collision contrast to FP and FL.","PeriodicalId":139983,"journal":{"name":"Econometrics: Econometric & Statistical Methods - Special Topics eJournal","volume":"120 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124292319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Measuring the Benefits of Diversification Across Portfolios 衡量投资组合多样化的收益

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-03-26 DOI: 10.2139/ssrn.3813203

D. Madan, King Wang

引用次数: 0