Pub Date : 2023-07-06DOI: 10.3991/itdaf.v1i2.39987
E. Rusu, Otilia Manta
In the present research, the author presents the public procurement system as a significant lever for governments to accelerate the transition to more sustainable modes of consumption and production and, more generally, to contribute to the broader goal of sustainable development. Sustainable Public Procurement (SPP) is a process through which public entities procure goods, services, works, and utilities in optimal conditions, considering factors such as price and quality, in order to generate benefits entities while minimizing negative impacts on the environment. Consequently, this paper proposes some methodological issues that could contribute to the establishment of a SPP system, based on the model implemented and applied in EU countries. Additionally, this paper focuses on forecasting and presenting the current situations of public procurement as the basic element.
{"title":"Forecasting and Directions Regarding Sustainable Public Procurement","authors":"E. Rusu, Otilia Manta","doi":"10.3991/itdaf.v1i2.39987","DOIUrl":"https://doi.org/10.3991/itdaf.v1i2.39987","url":null,"abstract":"In the present research, the author presents the public procurement system as a significant lever for governments to accelerate the transition to more sustainable modes of consumption and production and, more generally, to contribute to the broader goal of sustainable development. Sustainable Public Procurement (SPP) is a process through which public entities procure goods, services, works, and utilities in optimal conditions, considering factors such as price and quality, in order to generate benefits entities while minimizing negative impacts on the environment. Consequently, this paper proposes some methodological issues that could contribute to the establishment of a SPP system, based on the model implemented and applied in EU countries. Additionally, this paper focuses on forecasting and presenting the current situations of public procurement as the basic element.","PeriodicalId":222021,"journal":{"name":"IETI Transactions on Data Analysis and Forecasting (iTDAF)","volume":"9 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":"133962620","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}
Pub Date : 2023-07-06DOI: 10.3991/itdaf.v1i2.41671
Hailing Ma
Achieving global carbon neutrality and reducing airborne pollution require innovative solutions. One potential solution is to replace fossil fuel-based energy with clean energy production and supply, necessitating the advancement of fuel cells, metal-air cells, supercapacitors, and water splitting. This paper analyzes data and predicts future trends for the quality of catalysts, carrier structure, construction characterization, environmental impact, and recycling preparation techniques for waste platinum catalysts. The findings presented in this study serve as inspiration for related research, aiding the successful promotion of clean energy technologies.
{"title":"The Applications of Platinum Catalysts in PEM Fuel Cells: Process and Data Analysis","authors":"Hailing Ma","doi":"10.3991/itdaf.v1i2.41671","DOIUrl":"https://doi.org/10.3991/itdaf.v1i2.41671","url":null,"abstract":"Achieving global carbon neutrality and reducing airborne pollution require innovative solutions. One potential solution is to replace fossil fuel-based energy with clean energy production and supply, necessitating the advancement of fuel cells, metal-air cells, supercapacitors, and water splitting. This paper analyzes data and predicts future trends for the quality of catalysts, carrier structure, construction characterization, environmental impact, and recycling preparation techniques for waste platinum catalysts. The findings presented in this study serve as inspiration for related research, aiding the successful promotion of clean energy technologies.","PeriodicalId":222021,"journal":{"name":"IETI Transactions on Data Analysis and Forecasting (iTDAF)","volume":"75 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":"127157431","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}
Pub Date : 2023-07-06DOI: 10.3991/itdaf.v1i2.40175
S. Akinola, Qing-Guo Wang, Peter O. Olukanmi, T. Marwala
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.
在诸如猴痘病毒(MPXV)等传染病发病时，监测数据对于跟踪疫情的进展至关重要。在多个欧洲国家记录病例后，MPXV的监测数据受到了相当大的关注。利用2022年5月9日至2022年8月10日期间获得的历史数据，对五个国家的MPXV累积病例轨迹进行了建模。我们的研究采用自回归综合移动平均线(ARIMA)、神经网络自回归(NNETAR)、指数平滑(ETS)和季节性naïve回归(SNAÏVE)进行训练和评估。本文的贡献如下:(1)利用Box-Cox变换作为预处理步骤增强了模型的稳定性;(2)对线性和非线性模型进行了实验;(3)对MPXV情况下脉冲上升期间前5个国家进行了模拟。使用三个指标评估结果:均方根误差(RMSE)、均方误差(MAE)和平均绝对百分比误差(MAPE)。ARIMA(0,1,3)(1,0,0)[7]模型在法国MAPE观测的保留集中产生了5.16的最低百分比误差。在ETS (A, A, A)模型中，德国MAE的最低百分比错误率为7.35。在NNETAR(1,1,2)[7]模型中，滞留观测中RMSE的最低百分比误差依次为:西班牙8.33，英国2.75，美国8.05。基于这些发现，我们可以得出结论，虽然转换证明对模型性能至关重要，但并非所有实验都需要转换，因为ARIMA在法国和ETS模型在德国仍然占主导地位。同时，NNETAR模型在西班牙、英国和美国的累积病例数方面表现优于其他国家。我们的实验允许早期识别，并有助于更好地理解使用线性和非线性模型组合预测MPXV病例。
{"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}
Pub Date : 2023-07-06DOI: 10.3991/itdaf.v1i2.39675
Syed Shah Hussain, Muhammad Arif, O. Inayat, Haji Gul
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.
{"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}
Pub Date : 2023-07-06DOI: 10.3991/itdaf.v1i2.34033
Quan Li, J. Qiu, Xiaoying Liu, Caimeng Huang, Ling Liang
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.
{"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}
Pub Date : 2023-07-06DOI: 10.3991/itdaf.v1i2.38231
Otilia Manta
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.
{"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}
Pub Date : 2023-07-06DOI: 10.3991/itdaf.v1i2.41347
Saif Ur Rehman, XiaoYong Zhou, Guanhua Zhao, A. Arif, Iqra Naeem
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.
{"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}
Pub Date : 2023-04-03DOI: 10.3991/itdaf.v1i1.37009
Shanshan Yang, J. Qiu, Xinze Wang
In order to thoroughly investigate the development status of Gongcheng Camellia oleifera, study the development path, this paper uses SPSS software, KMO test and Bartlett spherical test were carried out on the data, and it was found that there was correlation between the variables, and factor analysis was effective. Then, Amos software is used to model it, and the structural equation model diagram is obtained; Realize the visualization of survey data with the help of software fine Bi; Finally, deeply excavate and use the data to explore the factors that affect the marketing and promotion of Gongcheng Camellia oleifera, and put forward feasible solutions to improve the sales volume of Gongcheng Camellia oleifera, promote industrial poverty alleviation, and help rural revitalization.
{"title":"The Development Path of Guilin Gongcheng Camellia oleifera Based on SEM Data Processing and Analysis","authors":"Shanshan Yang, J. Qiu, Xinze Wang","doi":"10.3991/itdaf.v1i1.37009","DOIUrl":"https://doi.org/10.3991/itdaf.v1i1.37009","url":null,"abstract":"In order to thoroughly investigate the development status of Gongcheng Camellia oleifera, study the development path, this paper uses SPSS software, KMO test and Bartlett spherical test were carried out on the data, and it was found that there was correlation between the variables, and factor analysis was effective. Then, Amos software is used to model it, and the structural equation model diagram is obtained; Realize the visualization of survey data with the help of software fine Bi; Finally, deeply excavate and use the data to explore the factors that affect the marketing and promotion of Gongcheng Camellia oleifera, and put forward feasible solutions to improve the sales volume of Gongcheng Camellia oleifera, promote industrial poverty alleviation, and help rural revitalization.","PeriodicalId":222021,"journal":{"name":"IETI Transactions on Data Analysis and Forecasting (iTDAF)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114824884","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}
Pub Date : 2023-04-03DOI: 10.3991/itdaf.v1i1.35723
Mustapha Adamu Mohammed, Obeng Bismark, S. Alornyo, M. Asante, Bernard Obo Essah
Melanoma, a high-level variant of skin cancer is very difficult to distinguish from other skin cancer types in patients. The presence of large variety of sizes of lesions, fuzzy boundaries and irregular shaped nature, with low contrast between skin lesions and surrounding fresh areas makes it clinically difficult to detect and treat melanoma. In this paper, we propose Residual Full Convolutional Network (ResFCNET) skin lesion recognition model that combines residual learning and full convolutional network to perform semantic segmentation of skin lesion. Based on secondary feature extraction and classification, experiment was done to verify the effectiveness of our model using ISBI 2016 and ISBI 2017 dataset. Results showed that residual convolution neural network obtain high precision classification. This technique is novel and provides a compelling insight for medical image segmentation.
{"title":"ResFCNET: A Skin Lesion Segmentation Method Based on a Deep Residual Fully Convolutional Neural Network","authors":"Mustapha Adamu Mohammed, Obeng Bismark, S. Alornyo, M. Asante, Bernard Obo Essah","doi":"10.3991/itdaf.v1i1.35723","DOIUrl":"https://doi.org/10.3991/itdaf.v1i1.35723","url":null,"abstract":"Melanoma, a high-level variant of skin cancer is very difficult to distinguish from other skin cancer types in patients. The presence of large variety of sizes of lesions, fuzzy boundaries and irregular shaped nature, with low contrast between skin lesions and surrounding fresh areas makes it clinically difficult to detect and treat melanoma. In this paper, we propose Residual Full Convolutional Network (ResFCNET) skin lesion recognition model that combines residual learning and full convolutional network to perform semantic segmentation of skin lesion. Based on secondary feature extraction and classification, experiment was done to verify the effectiveness of our model using ISBI 2016 and ISBI 2017 dataset. Results showed that residual convolution neural network obtain high precision classification. This technique is novel and provides a compelling insight for medical image segmentation.","PeriodicalId":222021,"journal":{"name":"IETI Transactions on Data Analysis and Forecasting (iTDAF)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125792765","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}
Pub Date : 2023-04-03DOI: 10.3991/itdaf.v1i1.34341
Li Fei-Fei, J. Qiu
China has a high degree of population aging, pension has become a hot topic. Based on this, this study focuses on the private doctor service assisted pension market, and investigates doctors, young people and the elderly respectively. The correlation analysis was used to obtain the relationship between respondents' willingness to accept private doctors and various factors. Further with the help of Logistic regression model, it is concluded that doctors in private hospitals, young and middle-aged elderly men and elderly people living alone and with high cost of living are more willing to accept the service of private doctors.
{"title":"Investigation and Analysis of Private Doctors Helping the Elderly Care Market","authors":"Li Fei-Fei, J. Qiu","doi":"10.3991/itdaf.v1i1.34341","DOIUrl":"https://doi.org/10.3991/itdaf.v1i1.34341","url":null,"abstract":"China has a high degree of population aging, pension has become a hot topic. Based on this, this study focuses on the private doctor service assisted pension market, and investigates doctors, young people and the elderly respectively. The correlation analysis was used to obtain the relationship between respondents' willingness to accept private doctors and various factors. Further with the help of Logistic regression model, it is concluded that doctors in private hospitals, young and middle-aged elderly men and elderly people living alone and with high cost of living are more willing to accept the service of private doctors.","PeriodicalId":222021,"journal":{"name":"IETI Transactions on Data Analysis and Forecasting (iTDAF)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115844681","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}
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