IETI Transactions on Data Analysis and Forecasting (iTDAF)最新文献

Forecasting and Directions Regarding Sustainable Public Procurement 关于可持续公共采购的预测和方向

IETI Transactions on Data Analysis and Forecasting (iTDAF) Pub Date : 2023-07-06 DOI: 10.3991/itdaf.v1i2.39987

E. Rusu, Otilia Manta

引用次数: 0

The Applications of Platinum Catalysts in PEM Fuel Cells: Process and Data Analysis 铂催化剂在PEM燃料电池中的应用:过程与数据分析

IETI Transactions on Data Analysis and Forecasting (iTDAF) Pub Date : 2023-07-06 DOI: 10.3991/itdaf.v1i2.41671

Hailing Ma

引用次数: 0

Early Prediction of Monkeypox Virus Outbreak Using Machine Learning 利用机器学习对猴痘病毒爆发进行早期预测

IETI Transactions on Data Analysis and Forecasting (iTDAF) Pub Date : 2023-07-06 DOI: 10.3991/itdaf.v1i2.40175

S. Akinola, Qing-Guo Wang, Peter O. Olukanmi, T. Marwala

{"title":"Early Prediction of Monkeypox Virus Outbreak Using Machine Learning","authors":"S. Akinola, Qing-Guo Wang, Peter O. Olukanmi, T. Marwala","doi":"10.3991/itdaf.v1i2.40175","DOIUrl":"https://doi.org/10.3991/itdaf.v1i2.40175","url":null,"abstract":"At the onset of an infectious disease, such as the monkeypox virus (MPXV), surveillance data is crucial in keeping track of the outbreak’s progression. The surveillance data for MPXV received considerable attention after multiple European countries recorded cases. Historical data obtained from May 9, 2022, to August 10, 2022, were used to model the cumulative case trajectories of MPXV in five countries. Our study employed autoregressive integrated moving averages (ARIMA), neural network autoregression (NNETAR), exponential smoothing (ETS), and seasonal naïve regression (SNAÏVE) for training and evaluation. The paper makes the following contributions: (1) enhanced model stability with the Box-Cox transformation as a preprocessing step, (2) experimentation with both linear and non-linear models, and (3) simulation of the top five countries during the impulsive rise in cases of MPXV. The results were evaluated using three metrics: root mean square error (RMSE), mean square error (MAE), and mean absolute percentage error (MAPE). The ARIMA (0,1,3) (1,0,0)[7] model yielded the lowest percentage error of 5.16 in the holdout set for MAPE in France observations. The ETS (A, A, A) model, the lowest percentage error in the holdout set for MAE was 7.35 in Germany. Regarding the NNETAR (1,1,2) [7] model, the lowest percentage error in the holdout observations for RMSE was 8.33 in Spain, 2.75 in the United Kingdom (UK), and 8.05 in the United States of America (USA) in that order. Based on these findings, we can conclude that while the transformation proved crucial for model performance, it was not necessary for all experiments, as ARIMA remained dominant in France and the ETS model in Germany. At the same time, NNETAR model outperformed in cumulative case counts in Spain, the UK, and the USA. Our experimentation allows for early identification and contributes to a better understanding of forecasting MPXV cases using combinations of both linear and nonlinear models.","PeriodicalId":222021,"journal":{"name":"IETI Transactions on Data Analysis and Forecasting (iTDAF)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134061423","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

Link Prediction in Human Complex Network Based on Random Walk with Global Topological Features 基于全局拓扑特征随机行走的人类复杂网络链接预测

IETI Transactions on Data Analysis and Forecasting (iTDAF) Pub Date : 2023-07-06 DOI: 10.3991/itdaf.v1i2.39675

Syed Shah Hussain, Muhammad Arif, O. Inayat, Haji Gul

{"title":"Link Prediction in Human Complex Network Based on Random Walk with Global Topological Features","authors":"Syed Shah Hussain, Muhammad Arif, O. Inayat, Haji Gul","doi":"10.3991/itdaf.v1i2.39675","DOIUrl":"https://doi.org/10.3991/itdaf.v1i2.39675","url":null,"abstract":"Link Prediction in Human Complex Networks aims to predict the missing, deleted, or future link formations. These complex networks are represented graphically, consisting of nodes and links, also referred to as vertices and edges, respectively. We employ Link Prediction techniques on four different human-related networks to determine the most effective methods in the Human Complex domain. The techniques utilized are similarity-based, primarily focused on determining the similarity score of each network. We select four algorithms that demonstrated superior results in other complex networks and implement them on human-related networks. Our goal is to predict links that have been removed from the network in order to evaluate the prediction accuracy of the applied techniques. To accomplish this, we convert the datasets into adjacency matrices and divide them into training and probe sets. The training session is then conducted, followed by the testing of the data. The selected techniques are implemented to calculate the similarity score, and the accuracy is subsequently measured for each dataset. This approach facilitates a comprehensive comparative analysis of the various predicting techniques to determine the most effective one.","PeriodicalId":222021,"journal":{"name":"IETI Transactions on Data Analysis and Forecasting (iTDAF)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115730407","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

Survey of VR Products to Treat Social Phobia among College Students Based on Logistic Regression and K-Means Clustering Analysis 基于Logistic回归和k均值聚类分析的VR产品对大学生社交恐惧症治疗效果的调查

IETI Transactions on Data Analysis and Forecasting (iTDAF) Pub Date : 2023-07-06 DOI: 10.3991/itdaf.v1i2.34033

Quan Li, J. Qiu, Xiaoying Liu, Caimeng Huang, Ling Liang

{"title":"Survey of VR Products to Treat Social Phobia among College Students Based on Logistic Regression and K-Means Clustering Analysis","authors":"Quan Li, J. Qiu, Xiaoying Liu, Caimeng Huang, Ling Liang","doi":"10.3991/itdaf.v1i2.34033","DOIUrl":"https://doi.org/10.3991/itdaf.v1i2.34033","url":null,"abstract":"Social phobia or social anxiety disorder, is characterized by a fear of embarrassing situations in front of others, leading to long-term chronic anxiety disorders. The purpose of our study is to examine the market prospects of using virtual reality (VR) technology for the treatment of social phobia. Specifically, we aim to investigate the current prevalence of social phobia among college students in eight universities in Guilin and explore their willingness to adopt VR technology as a treatment option. To achieve this, we utilized various data collection methods, including questionnaire surveys, literature surveys, and field interviews. Through descriptive statistical analysis we gained insights into the respondents’ demographics and their perceptions of social phobia and its treatment. Subsequently, we constructed a binary logistic regression model to identify the influencing factors contributing to social phobia among college students. Additionally, we conducted factor analysis, which revealed that the aspects of service quality, safety, and environmental quality were or utmost concern. Finally, we employed K-Means cluster analysis to differentiate the distinctive characteristics of potential users and develop effective strategies for the advancement of VR technology in social phobia treatment.","PeriodicalId":222021,"journal":{"name":"IETI Transactions on Data Analysis and Forecasting (iTDAF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130327951","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

National Wealth 国家财富

IETI Transactions on Data Analysis and Forecasting (iTDAF) Pub Date : 2023-07-06 DOI: 10.3991/itdaf.v1i2.38231

Otilia Manta

{"title":"National Wealth","authors":"Otilia Manta","doi":"10.3991/itdaf.v1i2.38231","DOIUrl":"https://doi.org/10.3991/itdaf.v1i2.38231","url":null,"abstract":"One of the crucial factors that contribute to establishing well-being at the level of the world economies, as recognized by institutions such as the World Bank, is the management and distribution of the national wealth at both the national and global levels. Throughout history, each state has relied on the resources it possess and protects. In today’s world, it is more important than ever to have knowledge of and inventory these resources, not only to support one’s own economic sectors, but also to actively contribute to economies in need. The definition of national wealth is particularly significant in the context of ongoing discussions about limited resources at the global level. Moreover, it is crucial to highlight the defining elements of economic development at both the national and global levels. Such developments cannot be achieved without considering the unique characteristics of each state, including their strengths and weaknesses, particularly in terms of their resources. This paper aims to explore conceptual aspects and reference indicators for measuring national wealth. Doing so not only serves as an indicator of the sustainability of our economy but also provides a reference indicator for states worldwide.","PeriodicalId":222021,"journal":{"name":"IETI Transactions on Data Analysis and Forecasting (iTDAF)","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121494060","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

Enhancing Construction Site Safety in Pakistan: A Proposed Health and Safety Framework Based on the Analytical Hierarchy Process 加强巴基斯坦建筑工地安全:基于层次分析法的健康与安全框架建议

IETI Transactions on Data Analysis and Forecasting (iTDAF) Pub Date : 2023-07-06 DOI: 10.3991/itdaf.v1i2.41347

Saif Ur Rehman, XiaoYong Zhou, Guanhua Zhao, A. Arif, Iqra Naeem

{"title":"Enhancing Construction Site Safety in Pakistan: A Proposed Health and Safety Framework Based on the Analytical Hierarchy Process","authors":"Saif Ur Rehman, XiaoYong Zhou, Guanhua Zhao, A. Arif, Iqra Naeem","doi":"10.3991/itdaf.v1i2.41347","DOIUrl":"https://doi.org/10.3991/itdaf.v1i2.41347","url":null,"abstract":"Developing infrastructure is crucial for the economic growth of countries like Pakistan, which are facing financial challenges. However, the construction industry is complex and uncertain, with various associated risks. The purpose of this study is to develop a proactive health and safety strategy by identifying the risk factors that pose a threat to the safety of construction workers in Pakistan. Pairwise comparison matrices were constructed using the Analytical Hierarchy Process (AHP) approach for individual groups and the total sample. This process generated weights, consistency indices, and consistency ratios to validate the data. The fuzzy comprehensive evaluation method (FCEM) was used to evaluate the identified threats. Based on the identified health and safety risk factors, a general matrix, as well as first and second- level ambiguous relations were created. Additionally, a fuzzy comprehensive evaluation matrix was developed. The centesimal values of the goal layer were found to be higher (3.72) than the values of the factors, including unsafe acts (13.08), accidents and hazards (25.14), policies and management (12.15), managing workers at the worksite (6.12), and management of worksite (5.07). The results indicated that all these factors significantly affect health and security in construction projects. Based on these findings, corrective measures could be implemented at the strategic and planning levels to strengthen and regulate these barriers.","PeriodicalId":222021,"journal":{"name":"IETI Transactions on Data Analysis and Forecasting (iTDAF)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123371044","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

The Development Path of Guilin Gongcheng Camellia oleifera Based on SEM Data Processing and Analysis 基于SEM数据处理与分析的桂林恭城油茶发展路径

IETI Transactions on Data Analysis and Forecasting (iTDAF) Pub Date : 2023-04-03 DOI: 10.3991/itdaf.v1i1.37009

Shanshan Yang, J. Qiu, Xinze Wang

引用次数: 0

ResFCNET: A Skin Lesion Segmentation Method Based on a Deep Residual Fully Convolutional Neural Network 基于深度残差全卷积神经网络的皮肤病灶分割方法

IETI Transactions on Data Analysis and Forecasting (iTDAF) Pub Date : 2023-04-03 DOI: 10.3991/itdaf.v1i1.35723

Mustapha Adamu Mohammed, Obeng Bismark, S. Alornyo, M. Asante, Bernard Obo Essah

引用次数: 0

Investigation and Analysis of Private Doctors Helping the Elderly Care Market 私人医生助老市场调查与分析

IETI Transactions on Data Analysis and Forecasting (iTDAF) Pub Date : 2023-04-03 DOI: 10.3991/itdaf.v1i1.34341

Li Fei-Fei, J. Qiu

引用次数: 0