Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9765005
Rishabh Sharma, V. Kukreja
Lemon disease detection has been a hot topic of research for decades, thanks to the rising demand and supply for the commodity, which has increased the number of diseases found in the crop. Lemon citrus canker (LCC) is one of those diseases that has a draconian effect on lemon production, and to eliminate that factor, deep learning (DL) based convolutional long term network (CLTN) amalgamated model of convolutional neural networks (CNN) and long short term memory (LSTM) has been developed to build a system for detecting and classifying a 3000 image dataset of LCC disease based on four different disease levels. The implementation of the hybrid model resulted in a binary classification accuracy of 94.2%, while the best accuracy of 98.43% in the case of early level of LCC disease severity multi-classification. The proposed model is an effective model for image classification in terms of accuracy outcomes.
几十年来，柠檬病害检测一直是研究的热门话题，这要感谢对这种商品不断增长的需求和供应，这增加了作物中发现的病害数量。柠檬柑橘腐烂病(Lemon citrus canker, LCC)是严重影响柠檬生产的病害之一，为了消除这一影响因素，基于深度学习(DL)的卷积长期网络(convolutional long term network, CLTN)和长短期记忆(LSTM)的融合模型，建立了基于4个不同病害级别的3000张柑橘腐烂病图像数据集的检测和分类系统。混合模型的实现使二元分类准确率达到94.2%，而在早期LCC疾病严重程度多重分类的情况下，准确率最高为98.43%。从精度结果来看，该模型是一种有效的图像分类模型。
{"title":"Amalgamated convolutional long term network (CLTN) model for Lemon Citrus Canker Disease Multi-classification","authors":"Rishabh Sharma, V. Kukreja","doi":"10.1109/DASA54658.2022.9765005","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765005","url":null,"abstract":"Lemon disease detection has been a hot topic of research for decades, thanks to the rising demand and supply for the commodity, which has increased the number of diseases found in the crop. Lemon citrus canker (LCC) is one of those diseases that has a draconian effect on lemon production, and to eliminate that factor, deep learning (DL) based convolutional long term network (CLTN) amalgamated model of convolutional neural networks (CNN) and long short term memory (LSTM) has been developed to build a system for detecting and classifying a 3000 image dataset of LCC disease based on four different disease levels. The implementation of the hybrid model resulted in a binary classification accuracy of 94.2%, while the best accuracy of 98.43% in the case of early level of LCC disease severity multi-classification. The proposed model is an effective model for image classification in terms of accuracy outcomes.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128672323","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 : 2022-03-23DOI: 10.1109/DASA54658.2022.9765220
Chinmay Sumant, Vinayak Bhavsar, Binod Kumar Sinha, V. Bhatt
The present study is conducted on the ‘stock trading’ business and how consumers decide to buy and sell in the digital era. Currently, many apps are available on smartphones, such as ‘kite’ by Zerodha, Angel Broking Stock Trading App, ‘MO investor’ by Motilal Oswal, ‘IIFL Markets’ by IIFL securities, and many others which have more than ten lakhs of downloads on the Google Playstore. Lakhs of Indians, mostly millennials between the ages of 20 and 35, trade daily through these. Before the rise of these apps in the mid-2010s, people used to rely on their stockbrokers to place an order to sell or buy shares of a company. Information sources were just television and newspaper – and most people used to act on the advice of stockbrokers or their trusted acquaintances. Now, the scenario is completely different – stock trading individuals are continuously updated through their ‘Trading apps’ (such as Kite and others mentioned above) or are advised by gurus through social media apps like Instagram or LinkedIn or YouTube, etc. There are even Stocking Trading Advisory apps such as ‘Upstox’, ‘Smallcase’ etc., which advise consumers on purchase, sell, and hold decisions. This paper will focus on the impact of stock trading apps on the Indian Millennial (20 to 35 years) consumer behavior in the stock market.This study will also focus on identifying the ‘main parameters of value’ the customers consider when deciding to engage in online trading through stock trading apps. This study will further undertake a competitive analysis of the discovered ‘main parameters of value’ in the most used apps, limiting to Zerodha, Angel Broking, Motilal Oswal, and IIFL securities, etc., which have higher than ten lakhs downloads in google playstore. This study will conclude by identifying which App amongst these is most ahead in its journey to becoming an ideal product at this time.
{"title":"Impact of Stock Trading Apps on Indian Millennial Consumer Behavior in the Stock Market","authors":"Chinmay Sumant, Vinayak Bhavsar, Binod Kumar Sinha, V. Bhatt","doi":"10.1109/DASA54658.2022.9765220","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765220","url":null,"abstract":"The present study is conducted on the ‘stock trading’ business and how consumers decide to buy and sell in the digital era. Currently, many apps are available on smartphones, such as ‘kite’ by Zerodha, Angel Broking Stock Trading App, ‘MO investor’ by Motilal Oswal, ‘IIFL Markets’ by IIFL securities, and many others which have more than ten lakhs of downloads on the Google Playstore. Lakhs of Indians, mostly millennials between the ages of 20 and 35, trade daily through these. Before the rise of these apps in the mid-2010s, people used to rely on their stockbrokers to place an order to sell or buy shares of a company. Information sources were just television and newspaper – and most people used to act on the advice of stockbrokers or their trusted acquaintances. Now, the scenario is completely different – stock trading individuals are continuously updated through their ‘Trading apps’ (such as Kite and others mentioned above) or are advised by gurus through social media apps like Instagram or LinkedIn or YouTube, etc. There are even Stocking Trading Advisory apps such as ‘Upstox’, ‘Smallcase’ etc., which advise consumers on purchase, sell, and hold decisions. This paper will focus on the impact of stock trading apps on the Indian Millennial (20 to 35 years) consumer behavior in the stock market.This study will also focus on identifying the ‘main parameters of value’ the customers consider when deciding to engage in online trading through stock trading apps. This study will further undertake a competitive analysis of the discovered ‘main parameters of value’ in the most used apps, limiting to Zerodha, Angel Broking, Motilal Oswal, and IIFL securities, etc., which have higher than ten lakhs downloads in google playstore. This study will conclude by identifying which App amongst these is most ahead in its journey to becoming an ideal product at this time.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124772798","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 : 2022-03-23DOI: 10.1109/DASA54658.2022.9765123
Fatma Kutlu Gündoǧdu, Esra Ilbahar, A. Karaşan, I. Kaya, B. Özkaya
Day by day, with the increment in the world’s temperature, the ways of reducing greenhouse gas emissions (GHGE) have been started to be investigated more to slow down this process. To create a sustainable action plan and a road map, the governments and the international agencies have been started to take steps. Based on this aim, United Nations (UN) determined the most effective factors on GHGE with respect to their possible reduction amounts to take an action. On the other hand, the world bank identified related indicators of GHGE for the governments to create their individual agendas to work for a sustainable and affordable environment and city plans. This work proposes a methodology consisting of spherical fuzzy TOPSIS (SF-TOPSIS) and fuzzy inference system (FIS) for prioritizing the pre-determined sectors with respect to CO2 emission reduction based on the climate change indicators. The SF-TOPSIS technique is used to obtain input data of the FIS by considering the distance to ideal solutions of the evaluated sectors for Turkey. Through the application, it is obtained that Transport, Energy, and Industry sectors are determined as the most effective against the CO2 reduction based on the current ecosystem of Turkey. Since Turkey is a developing country and one of the G20 countries, its current focus areas are mainly increasing productivity considering high-level technologies, the supplement of inadequate and sufficient energy for both the industry and the householders, and investments in infrastructure for a better and faster transformation. Considering these aspects, the country’s primary investments areas are on industry, energy, and transportation to reach a better place considering the annual gross domestic product. Therefore, the obtained results are quite applicable and meaningful, and this study can be a good starting point for further actions.
{"title":"Prioritization of the Potential Sectors for CO2 Emission Reduction based on International Policies: A Case of Turkey","authors":"Fatma Kutlu Gündoǧdu, Esra Ilbahar, A. Karaşan, I. Kaya, B. Özkaya","doi":"10.1109/DASA54658.2022.9765123","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765123","url":null,"abstract":"Day by day, with the increment in the world’s temperature, the ways of reducing greenhouse gas emissions (GHGE) have been started to be investigated more to slow down this process. To create a sustainable action plan and a road map, the governments and the international agencies have been started to take steps. Based on this aim, United Nations (UN) determined the most effective factors on GHGE with respect to their possible reduction amounts to take an action. On the other hand, the world bank identified related indicators of GHGE for the governments to create their individual agendas to work for a sustainable and affordable environment and city plans. This work proposes a methodology consisting of spherical fuzzy TOPSIS (SF-TOPSIS) and fuzzy inference system (FIS) for prioritizing the pre-determined sectors with respect to CO2 emission reduction based on the climate change indicators. The SF-TOPSIS technique is used to obtain input data of the FIS by considering the distance to ideal solutions of the evaluated sectors for Turkey. Through the application, it is obtained that Transport, Energy, and Industry sectors are determined as the most effective against the CO2 reduction based on the current ecosystem of Turkey. Since Turkey is a developing country and one of the G20 countries, its current focus areas are mainly increasing productivity considering high-level technologies, the supplement of inadequate and sufficient energy for both the industry and the householders, and investments in infrastructure for a better and faster transformation. Considering these aspects, the country’s primary investments areas are on industry, energy, and transportation to reach a better place considering the annual gross domestic product. Therefore, the obtained results are quite applicable and meaningful, and this study can be a good starting point for further actions.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129435432","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 : 2022-03-23DOI: 10.1109/DASA54658.2022.9765179
N. Indumathi, R. Ramalakshmi, Ayodeji Olalekan Salau, Tayo Uthman Badrudeen, Chukwunonso Anthony Mmonyi
The town of Sivakasi in Tamil Nadu's Virudhunagar district in India produces majority of the country's consumption of firework items. Handling numerous chemicals is a necessary part of the firework industry's manufacturing process. As a result, the firework industry is commonly reported to be highly dangerous because of the hazardous nature of the chemicals used to create the sparkling effects during the ignition of firework crackers. Previous research have focused on harmful behaviors and hazardous conditions, pointing to human error as the primary cause of many accidents. According to the findings of this study, the majority of explosions were caused by the improper handling of hazardous chemicals and carelessness when making fireworks. Therefore, a method was presented in this paper which aims to examine the likelihood of human error in the fireworks industry. The proposed method uses task analysis and prediction of human error to shape the performance elements. The presented model can also be used to examine potential accident scenarios. The results show that the presented greedy-based process compared with the rule mining-based approach gives better accuracy and outcomes for the prediction of human error possibilities in the fireworks industry.
{"title":"Predictive Analytics of Human Errors in the Fireworks Industry","authors":"N. Indumathi, R. Ramalakshmi, Ayodeji Olalekan Salau, Tayo Uthman Badrudeen, Chukwunonso Anthony Mmonyi","doi":"10.1109/DASA54658.2022.9765179","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765179","url":null,"abstract":"The town of Sivakasi in Tamil Nadu's Virudhunagar district in India produces majority of the country's consumption of firework items. Handling numerous chemicals is a necessary part of the firework industry's manufacturing process. As a result, the firework industry is commonly reported to be highly dangerous because of the hazardous nature of the chemicals used to create the sparkling effects during the ignition of firework crackers. Previous research have focused on harmful behaviors and hazardous conditions, pointing to human error as the primary cause of many accidents. According to the findings of this study, the majority of explosions were caused by the improper handling of hazardous chemicals and carelessness when making fireworks. Therefore, a method was presented in this paper which aims to examine the likelihood of human error in the fireworks industry. The proposed method uses task analysis and prediction of human error to shape the performance elements. The presented model can also be used to examine potential accident scenarios. The results show that the presented greedy-based process compared with the rule mining-based approach gives better accuracy and outcomes for the prediction of human error possibilities in the fireworks industry.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126672642","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}
This research examines the state revenue from international tourism on the rising usage of digital payment in Thailand from 2011 to 2020. The data are analyzed through multiple linear regression using the annual data of the World Bank and Thailand government. Surprisingly, the international tourism variable can explain 91 percent of the variation in digital payment usage in Thailand with the model is proven to be fit. Jarque Bera test shows the residuals are normal. Breusch-Godfrey Serial Correlation Lagrange Multiplier test proves that the residuals are free from serial correlation. Breusch-Pagan Godfrey depicts that the residuals are free from heteroskedasticity. In conclusion, there is a significant influence of the international tourism receipts on accelerated usage of digital payment in Thailand.
{"title":"Acceleration Of International Tourism Improves Digital Payments Usage: The Case Of Thailand","authors":"Dzakiyy Hadiyan Achyar, Zata Hasyyati, Hazhiyah Yumni, Fathir Wafda","doi":"10.1109/DASA54658.2022.9765033","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765033","url":null,"abstract":"This research examines the state revenue from international tourism on the rising usage of digital payment in Thailand from 2011 to 2020. The data are analyzed through multiple linear regression using the annual data of the World Bank and Thailand government. Surprisingly, the international tourism variable can explain 91 percent of the variation in digital payment usage in Thailand with the model is proven to be fit. Jarque Bera test shows the residuals are normal. Breusch-Godfrey Serial Correlation Lagrange Multiplier test proves that the residuals are free from serial correlation. Breusch-Pagan Godfrey depicts that the residuals are free from heteroskedasticity. In conclusion, there is a significant influence of the international tourism receipts on accelerated usage of digital payment in Thailand.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129076522","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 : 2022-03-23DOI: 10.1109/DASA54658.2022.9765242
Shweta Pande, A. Patil, S. Petkar
Nowadays with technological advancements, ma-chine learning is widely used in healthcare sector to help patients and doctors. Machine learning offers various tools for healthcare to diagnose various diseases in effective manner. In clinical diagnosis machine learning is used to analyse audio recording of coughs in order to detect respiratory illness. To clear lung and throat from any foreign substance, human body’s inundate mechanism create a substance called Cough. Audio recordings of coughs consists of patterns and depending on the pattern, cough can be classified as wet cough and dry cough. The COUGHVID dataset consists of more than 20,000 audio recordings of cough which includes wide range of subject such as gender, ages, geographic locations, from which more than 2000 recording are labelled by medical experts to diagnose abnormalities present in cough. In this paper, fusion of different cepstral based statistical features and classification using machine learning algorithm is presented. After analysis, it is observed that through ADASYN oversampling highest accuracy of 85.84%, f1 score of 86.80% and the area under the curve as 0.857 is achieved for MLP model.
{"title":"Dry and Wet Cough Detection using Fusion of Cepstral base Statistical Features","authors":"Shweta Pande, A. Patil, S. Petkar","doi":"10.1109/DASA54658.2022.9765242","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765242","url":null,"abstract":"Nowadays with technological advancements, ma-chine learning is widely used in healthcare sector to help patients and doctors. Machine learning offers various tools for healthcare to diagnose various diseases in effective manner. In clinical diagnosis machine learning is used to analyse audio recording of coughs in order to detect respiratory illness. To clear lung and throat from any foreign substance, human body’s inundate mechanism create a substance called Cough. Audio recordings of coughs consists of patterns and depending on the pattern, cough can be classified as wet cough and dry cough. The COUGHVID dataset consists of more than 20,000 audio recordings of cough which includes wide range of subject such as gender, ages, geographic locations, from which more than 2000 recording are labelled by medical experts to diagnose abnormalities present in cough. In this paper, fusion of different cepstral based statistical features and classification using machine learning algorithm is presented. After analysis, it is observed that through ADASYN oversampling highest accuracy of 85.84%, f1 score of 86.80% and the area under the curve as 0.857 is achieved for MLP model.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129091978","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 : 2022-03-23DOI: 10.1109/DASA54658.2022.9765157
A. A. N. Perwira Redi, Gerlyn Calica Altes, Justine Kyle Coronel Chan, Arni C. Acla, Parida Jewpanya, A. A. N. Agung Redioka, Yogi Tri Prasetyo, M. N. Young
Coronavirus 2019, popularly known as COVID-19 and declared a pandemic by the World Health Organization (WHO) in 2020, has affected billions of people and claimed millions of lives. Leaders and corporations worldwide have worked feverishly to develop a vaccine to combat the virus. After numerous tests and trials, COVID-19 vaccines were developed. Given the magnitude of the need for vaccination, these vaccines should not go to waste due to expiration from slow-paced rollouts or oversupply. This study aims to maximize near-expired COVID-19 vaccines in cases of oversupply by distributing them in neighbouring facilities at a low delivery cost and by utilizing P-median modelling. All gathered data were loaded into and run through the AMPL simulation model, with varying P-values or the number of facilities to be located to act as suppliers to the remaining demand nodes. Following the model simulation, it was observed that the P-value is inversely proportional to the cost; therefore, the cost of delivering near-expired COVID-19 vaccines to the demand clusters decreases as the P-value increases. Through the simulation model, the researchers determined which node facilities, if opened, would incur the lowest delivery cost.
{"title":"Facility Location Problem to Identify The Optimal Allocation of Near-Expired COVID-19 Vaccines","authors":"A. A. N. Perwira Redi, Gerlyn Calica Altes, Justine Kyle Coronel Chan, Arni C. Acla, Parida Jewpanya, A. A. N. Agung Redioka, Yogi Tri Prasetyo, M. N. Young","doi":"10.1109/DASA54658.2022.9765157","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765157","url":null,"abstract":"Coronavirus 2019, popularly known as COVID-19 and declared a pandemic by the World Health Organization (WHO) in 2020, has affected billions of people and claimed millions of lives. Leaders and corporations worldwide have worked feverishly to develop a vaccine to combat the virus. After numerous tests and trials, COVID-19 vaccines were developed. Given the magnitude of the need for vaccination, these vaccines should not go to waste due to expiration from slow-paced rollouts or oversupply. This study aims to maximize near-expired COVID-19 vaccines in cases of oversupply by distributing them in neighbouring facilities at a low delivery cost and by utilizing P-median modelling. All gathered data were loaded into and run through the AMPL simulation model, with varying P-values or the number of facilities to be located to act as suppliers to the remaining demand nodes. Following the model simulation, it was observed that the P-value is inversely proportional to the cost; therefore, the cost of delivering near-expired COVID-19 vaccines to the demand clusters decreases as the P-value increases. Through the simulation model, the researchers determined which node facilities, if opened, would incur the lowest delivery cost.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124112459","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 : 2022-03-23DOI: 10.1109/DASA54658.2022.9765260
Mourya Pathapati, Saikat Gochhait
The advancements in digitization are transforming the healthcare industry, one of the prominent industries producing critical data through patient care. The management of structured and processed data is becoming a challenge. Collecting, storing, and analyzing the data by efficiently reducing the complexity of data management makes the healthcare industry one of the most valuable industries. Creating meaningful and accurate disease predictions is critical in the healthcare sector. A study was conducted using VOSviewer software, which led to four clusters of keywords from different domains based on occurrences and relevance taken from 1500 documents from 1995 to 2021 from Web of Science. These keywords were mapped to the fields impacting the data management in Healthcare to explore the potential problems based on several types of research to establish a framework with an exploratory analysis. The methodology applied in this analysis describes the progress in data management in Healthcare and can let researchers, scholars, and healthcare professionals gain insights for facilitating the healthcare decision-makers.
数字化的进步正在改变医疗保健行业，这是通过患者护理产生关键数据的重要行业之一。结构化和已处理数据的管理正在成为一个挑战。通过有效降低数据管理的复杂性来收集、存储和分析数据，使医疗保健行业成为最有价值的行业之一。创建有意义和准确的疾病预测在医疗保健部门至关重要。使用VOSviewer软件进行了一项研究，该研究基于1995年至2021年来自Web of Science的1500份文档的出现次数和相关性，得出了来自不同领域的四组关键词。将这些关键字映射到影响医疗保健数据管理的领域，以探索基于几种类型研究的潜在问题，并通过探索性分析建立框架。本分析中应用的方法描述了医疗保健领域数据管理的进展，可以让研究人员、学者和医疗保健专业人员获得促进医疗保健决策者的见解。
{"title":"Intelligent Data Management to Facilitate Decision-Making in Healthcare","authors":"Mourya Pathapati, Saikat Gochhait","doi":"10.1109/DASA54658.2022.9765260","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765260","url":null,"abstract":"The advancements in digitization are transforming the healthcare industry, one of the prominent industries producing critical data through patient care. The management of structured and processed data is becoming a challenge. Collecting, storing, and analyzing the data by efficiently reducing the complexity of data management makes the healthcare industry one of the most valuable industries. Creating meaningful and accurate disease predictions is critical in the healthcare sector. A study was conducted using VOSviewer software, which led to four clusters of keywords from different domains based on occurrences and relevance taken from 1500 documents from 1995 to 2021 from Web of Science. These keywords were mapped to the fields impacting the data management in Healthcare to explore the potential problems based on several types of research to establish a framework with an exploratory analysis. The methodology applied in this analysis describes the progress in data management in Healthcare and can let researchers, scholars, and healthcare professionals gain insights for facilitating the healthcare decision-makers.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116515931","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}
The application of e-commerce platforms for retailing agricultural products has been increasingly adopted for several benefits namely market expansion and connection, brand establishment, price improvement as well as the motivation for farmers to actively ameliorate their farming practices, product quality and package. However, this retail method is still lagging far behind in Vietnam - despite the need for digitalization to solve persistent problems, namely the imbalanced supply, demand and accompanied price loss in the traditional distribution channel. Thus, this research aims to investigate the factors that impact the Vietnamese farmers’ intention to adopt e-commerce platforms for fresh produce retail. The paper applies the integrated Technology Acceptance Model and Technology-Organization-Environment framework. Through an online survey, a sample of 344 farmers who produced fruits and vegetables across Vietnam was drawn to confirm the hypotheses of this study. The results showed that three factors, namely "Perceived usefulness" (PU), "Perceived Ease of use" (PEOU) and the environmental context, directly and positively affect "Intention to Adopt" (INT). Besides, both the technological context and the organizational context is positively associated with PU and PEOU. Findings are valuable to the development in e-commerce platforms and policies to promote Vietnamese farmers’ intention of using e-commerce platforms to retail agricultural products.
{"title":"Determinants of Vietnamese Farmers’ Intention to Adopt Ecommerce Platforms for Fresh Produce Retail: An Integrated TOE-TAM Framework","authors":"Chau Minh Ngoc Nguyen, Linh Hoang Vu, Hieu Duc Phan, Thang Duc Nguyen, Anh Quynh Trinh","doi":"10.1109/DASA54658.2022.9765134","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765134","url":null,"abstract":"The application of e-commerce platforms for retailing agricultural products has been increasingly adopted for several benefits namely market expansion and connection, brand establishment, price improvement as well as the motivation for farmers to actively ameliorate their farming practices, product quality and package. However, this retail method is still lagging far behind in Vietnam - despite the need for digitalization to solve persistent problems, namely the imbalanced supply, demand and accompanied price loss in the traditional distribution channel. Thus, this research aims to investigate the factors that impact the Vietnamese farmers’ intention to adopt e-commerce platforms for fresh produce retail. The paper applies the integrated Technology Acceptance Model and Technology-Organization-Environment framework. Through an online survey, a sample of 344 farmers who produced fruits and vegetables across Vietnam was drawn to confirm the hypotheses of this study. The results showed that three factors, namely \"Perceived usefulness\" (PU), \"Perceived Ease of use\" (PEOU) and the environmental context, directly and positively affect \"Intention to Adopt\" (INT). Besides, both the technological context and the organizational context is positively associated with PU and PEOU. Findings are valuable to the development in e-commerce platforms and policies to promote Vietnamese farmers’ intention of using e-commerce platforms to retail agricultural products.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":" 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113947176","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 : 2022-03-23DOI: 10.1109/DASA54658.2022.9765182
Deena AlHudaib, Minwir M. Al-Shammari
E-commerce plays an important and prominent role in the modern era, especially with the continued emergence of new technologies, which opened new horizons for entrepreneurs and business owners of small and medium enterprises (SMEs) to pursue business growth. Currently, SMEs are no longer limited to practicing their business activities locally but internationally. Digitalization has a vital role in elevating the state of competitiveness between firms, which prompts many SMEs to acquire technologies that facilitate the business transition to e-commerce considering gaining a competitive advantage over their rivals and maintaining relevance in their field. This research will further explore the different challenges SMEs faced in Bahrain during the Covid-19 period and analyze the various obstacles faced during e-commerce adaptation. The analysis considers three main categories: organizational Readiness, environmental Readiness, and technological Readiness. This study aims to demonstrate SMEs' willingness to transition their business activities to e-commerce after the devastating repercussions of the Covid-19 pandemic. A questionnaire was designed and shared with 110 employees working at SMEs, and 100 responses were received and selected to be the research sample size. The research revealed that SMEs in Bahrain faced many obstacles to transform into e-commerce businesses during the pandemic and among the challenges were the financial cost of such transformation. The study provided further recommendations for future studies.
{"title":"The Adoption of E-Commerce by Businesses in Bahrain During Covid-19","authors":"Deena AlHudaib, Minwir M. Al-Shammari","doi":"10.1109/DASA54658.2022.9765182","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765182","url":null,"abstract":"E-commerce plays an important and prominent role in the modern era, especially with the continued emergence of new technologies, which opened new horizons for entrepreneurs and business owners of small and medium enterprises (SMEs) to pursue business growth. Currently, SMEs are no longer limited to practicing their business activities locally but internationally. Digitalization has a vital role in elevating the state of competitiveness between firms, which prompts many SMEs to acquire technologies that facilitate the business transition to e-commerce considering gaining a competitive advantage over their rivals and maintaining relevance in their field. This research will further explore the different challenges SMEs faced in Bahrain during the Covid-19 period and analyze the various obstacles faced during e-commerce adaptation. The analysis considers three main categories: organizational Readiness, environmental Readiness, and technological Readiness. This study aims to demonstrate SMEs' willingness to transition their business activities to e-commerce after the devastating repercussions of the Covid-19 pandemic. A questionnaire was designed and shared with 110 employees working at SMEs, and 100 responses were received and selected to be the research sample size. The research revealed that SMEs in Bahrain faced many obstacles to transform into e-commerce businesses during the pandemic and among the challenges were the financial cost of such transformation. The study provided further recommendations for future studies.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124054898","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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