Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568284
Fanoon Raheem, Nihla Iqbal
Foreign exchange rate prediction can be considered crucial in today's world. The exchange rate of a country plays a vital role in its economic growth. The Central Bank of a country holds the authority in managing the exchange rate and its policies. The study predicts the foreign exchange rate of American Dollar to Sri Lankan Rupee using FbProphet model; a time-series forecasting model developed and introduced by Facebook. The daily exchange rate values for USD/LKR were obtained and the values are predicted for another twenty-four months starting from November 2020. R Squared value is calculated to verify the fitting of the model and the value is 0.98, which indicates that the model for prediction very well fits for the data set used. And further, Mean Squared Error and Mean Absolute Error are calculated to measure the performance of the model. These metric measurements show that the model is appropriate for the data set which has been selected for the research study.
{"title":"Forecasting foreign exchange rate: Use of FbProphet","authors":"Fanoon Raheem, Nihla Iqbal","doi":"10.1109/scse53661.2021.9568284","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568284","url":null,"abstract":"Foreign exchange rate prediction can be considered crucial in today's world. The exchange rate of a country plays a vital role in its economic growth. The Central Bank of a country holds the authority in managing the exchange rate and its policies. The study predicts the foreign exchange rate of American Dollar to Sri Lankan Rupee using FbProphet model; a time-series forecasting model developed and introduced by Facebook. The daily exchange rate values for USD/LKR were obtained and the values are predicted for another twenty-four months starting from November 2020. R Squared value is calculated to verify the fitting of the model and the value is 0.98, which indicates that the model for prediction very well fits for the data set used. And further, Mean Squared Error and Mean Absolute Error are calculated to measure the performance of the model. These metric measurements show that the model is appropriate for the data set which has been selected for the research study.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123246808","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 : 2021-09-16DOI: 10.1109/scse53661.2021.9568286
M. Perera, A. Wijayanayake, S. Peter
Disruptions to a company supply chain, has serious implications, and if not addressed lead to even business closure. The article explores the supply chain risks faced by the apparel industry during an epidemic outbreak and the strategies that could be taken to mitigate them. A systematic review of the literature was initially conducted to identify the supply chain risks and mitigation strategies, and expert interviews were then used to reinforce the findings and then identify the focus areas. Supply chain risks were mapped in a vulnerability matrix with risk association, using a diagrammatic format, and a framework was developed using the supply chain risks and strategies. The developed framework shows that most of the risks can be mitigated by local sourcing and giving incentives to customers. A generalized model was developed based on cost and time considerations but using the same process it can be customized using different factors and risks depending on the experience and needs of the company.
{"title":"Framework to mitigate supply chain disruptions in the apparel industry during an epidemic outbreak","authors":"M. Perera, A. Wijayanayake, S. Peter","doi":"10.1109/scse53661.2021.9568286","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568286","url":null,"abstract":"Disruptions to a company supply chain, has serious implications, and if not addressed lead to even business closure. The article explores the supply chain risks faced by the apparel industry during an epidemic outbreak and the strategies that could be taken to mitigate them. A systematic review of the literature was initially conducted to identify the supply chain risks and mitigation strategies, and expert interviews were then used to reinforce the findings and then identify the focus areas. Supply chain risks were mapped in a vulnerability matrix with risk association, using a diagrammatic format, and a framework was developed using the supply chain risks and strategies. The developed framework shows that most of the risks can be mitigated by local sourcing and giving incentives to customers. A generalized model was developed based on cost and time considerations but using the same process it can be customized using different factors and risks depending on the experience and needs of the company.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126536126","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 : 2021-09-16DOI: 10.1109/scse53661.2021.9568314
Lakmini Herath, D. Meedeniya, M. A. J. C. Marasingha, V. Weerasinghe
Autism spectrum disorder (ASD) is one of the most common neurodevelopment disorders that severely affect patients in performing their day-to-day activities and social interactions. Early and accurate diagnosis can help decide the correct therapeutic adaptations for the patients to lead an almost normal life. The present practices of diagnosis of ASD are highly subjective and time-consuming. Today, as a popular solution, understanding abnormalities in brain functions using brain imagery such as functional magnetic resonance imaging (fMRI), is being performed using machine learning. This study presents a transfer learning-based approach using Inception v3 for ASD classification with fMRI data. The approach transforms the raw 4D fMRI dataset to 2D epi, stat map, and glass brain images. The classification results show higher accuracy values with pre-trained weights. Thus, the pre-trained ImageNet models with transfer learning provides a viable solution for diagnosing ASD from fMRI images.
{"title":"Autism spectrum disorder diagnosis support model using Inception V3","authors":"Lakmini Herath, D. Meedeniya, M. A. J. C. Marasingha, V. Weerasinghe","doi":"10.1109/scse53661.2021.9568314","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568314","url":null,"abstract":"Autism spectrum disorder (ASD) is one of the most common neurodevelopment disorders that severely affect patients in performing their day-to-day activities and social interactions. Early and accurate diagnosis can help decide the correct therapeutic adaptations for the patients to lead an almost normal life. The present practices of diagnosis of ASD are highly subjective and time-consuming. Today, as a popular solution, understanding abnormalities in brain functions using brain imagery such as functional magnetic resonance imaging (fMRI), is being performed using machine learning. This study presents a transfer learning-based approach using Inception v3 for ASD classification with fMRI data. The approach transforms the raw 4D fMRI dataset to 2D epi, stat map, and glass brain images. The classification results show higher accuracy values with pre-trained weights. Thus, the pre-trained ImageNet models with transfer learning provides a viable solution for diagnosing ASD from fMRI images.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129347323","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 : 2021-09-16DOI: 10.1109/scse53661.2021.9568360
A. P. K. J. Prabodhika, D. Niwunhella, A. Wijayanayake
Many manufacturers and retailers often outsource their logistics functions to Logistics Service Providers (LSPs) to focus more on their core business process. Due to the competitiveness and the popularity of the sustainability concept, those organizations evaluate their prospective LSPs not only based on economic aspects like cost, service quality but also on social and environmental aspects as well when selecting LSPs. This paper proposes a methodology that can be used by organizations when evaluating and selecting LSPs based on their sustainability performance. Analytic Network Process (ANP) is used in evaluating the LSPs' sustainable performance since multiple dimensions and indicators need to be incorporated when measuring the sustainability performance. A Linear Programming Problem (LPP) model was proposed which allows the organizations to decide both desired number of LSPs and the volume to be allocated for those selected LSPs. The proposed methodology is flexible as it depends on the sustain ability requirements of the organization when selecting LSPs. Both the indicators and their relative importance are up to the organization to decide.
{"title":"Model to optimize the quantities of delivery products prioritizing the sustainability performance","authors":"A. P. K. J. Prabodhika, D. Niwunhella, A. Wijayanayake","doi":"10.1109/scse53661.2021.9568360","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568360","url":null,"abstract":"Many manufacturers and retailers often outsource their logistics functions to Logistics Service Providers (LSPs) to focus more on their core business process. Due to the competitiveness and the popularity of the sustainability concept, those organizations evaluate their prospective LSPs not only based on economic aspects like cost, service quality but also on social and environmental aspects as well when selecting LSPs. This paper proposes a methodology that can be used by organizations when evaluating and selecting LSPs based on their sustainability performance. Analytic Network Process (ANP) is used in evaluating the LSPs' sustainable performance since multiple dimensions and indicators need to be incorporated when measuring the sustainability performance. A Linear Programming Problem (LPP) model was proposed which allows the organizations to decide both desired number of LSPs and the volume to be allocated for those selected LSPs. The proposed methodology is flexible as it depends on the sustain ability requirements of the organization when selecting LSPs. Both the indicators and their relative importance are up to the organization to decide.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130767920","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 : 2021-09-16DOI: 10.1109/scse53661.2021.9568303
B. Madhavi, Ruwan Wickramarachchi
Decision-making during a crisis impacts the performance of an entire organization. Due to the COVID-19 pandemic, many organizations had undergone supply chain disruptions due to the forward and backward propagation of disruptions in the global supply chain networks, implying the importance of building up resilience in the supply chain networks. This study intends to systematically review the existing literature to determine the impact of optimal decision-making during crises to build up supply chain resilience. The paper has focused on the need for evaluating the impact of the COVID-19 pandemic on the FMCG industry and how supply chain resilience would improve in performance during such crises. The study also assessed the existing decision support systems for resilience in a supply chain network and their applicability during a crisis. Some of these models could be used to facilitate decision-making during an epidemic as well. Precisely determining resilience factors affected during an unexpected circumstance would enhance the value of the decision support system in use. Furthermore, it was concluded that the use of quantitative models should be further investigated, as most published work focuses on the conceptualization of a restricted number of resilience factors instead of the development of integrated, comprehensive approaches.
{"title":"Decision-making models for a resilient supply chain in FMCG companies during a pandemic: A systematic literature review","authors":"B. Madhavi, Ruwan Wickramarachchi","doi":"10.1109/scse53661.2021.9568303","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568303","url":null,"abstract":"Decision-making during a crisis impacts the performance of an entire organization. Due to the COVID-19 pandemic, many organizations had undergone supply chain disruptions due to the forward and backward propagation of disruptions in the global supply chain networks, implying the importance of building up resilience in the supply chain networks. This study intends to systematically review the existing literature to determine the impact of optimal decision-making during crises to build up supply chain resilience. The paper has focused on the need for evaluating the impact of the COVID-19 pandemic on the FMCG industry and how supply chain resilience would improve in performance during such crises. The study also assessed the existing decision support systems for resilience in a supply chain network and their applicability during a crisis. Some of these models could be used to facilitate decision-making during an epidemic as well. Precisely determining resilience factors affected during an unexpected circumstance would enhance the value of the decision support system in use. Furthermore, it was concluded that the use of quantitative models should be further investigated, as most published work focuses on the conceptualization of a restricted number of resilience factors instead of the development of integrated, comprehensive approaches.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124331799","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 : 2021-09-16DOI: 10.1109/scse53661.2021.9568292
Malni Kumarathunga, R. Calheiros, A. Ginige
Using scenario transformation methodology, we identified four scenarios that indicated a lack of trusted parties to sell harvest has forced smallholder farmers to sell the harvest to brokers who often collect the harvest at the farm gate at the lowest possible prices and sell in the market for large profits. As blockchain smart contracts provide a mechanism to reduce risk and establish trust between unknown trading partners, we transformed these into a scenario that establishes trust between farmer and unknown broker using smart contracts, generating a trust-enabled market. This scenario enables farmers to search for the optimum farm-gate price without relying on known brokers. The scenario is further enhanced to enable a Many-one-Many market linkage, facilitating automatic aggregated marketing. The paper presents the functional prototype of the scenario, explaining the functionality of the transformed system.
{"title":"Technology-enabled online aggregated market for smallholder farmers to obtain enhanced farm-gate prices","authors":"Malni Kumarathunga, R. Calheiros, A. Ginige","doi":"10.1109/scse53661.2021.9568292","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568292","url":null,"abstract":"Using scenario transformation methodology, we identified four scenarios that indicated a lack of trusted parties to sell harvest has forced smallholder farmers to sell the harvest to brokers who often collect the harvest at the farm gate at the lowest possible prices and sell in the market for large profits. As blockchain smart contracts provide a mechanism to reduce risk and establish trust between unknown trading partners, we transformed these into a scenario that establishes trust between farmer and unknown broker using smart contracts, generating a trust-enabled market. This scenario enables farmers to search for the optimum farm-gate price without relying on known brokers. The scenario is further enhanced to enable a Many-one-Many market linkage, facilitating automatic aggregated marketing. The paper presents the functional prototype of the scenario, explaining the functionality of the transformed system.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127760643","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 : 2021-09-16DOI: 10.1109/scse53661.2021.9568285
W. H. D. Fernando, S. Sotheeswaran
This paper presents an approach to detect traffic signs using You Only Look Once version 4 (YOLOv4) model. The traffic sign detection and recognition system (TSDR) play an essential role in the intelligent transportation system (ITS). TSDR can be utilized for driver assistance and, eventually, driverless cars to reduce accidents. When driving an automobile, the driver's attention is usually drawn to the road. On the other hand, most traffic signs are situated on the side of the road, which may have contributed to the collision. TSDR allows drivers to view traffic sign information without having to divert their attention. Due to the existence of a large background, clutter, fluctuating degrees of illumination, varying sizes of traffic signs, and changing weather conditions, TSDR is an important but difficult process in intelligent transport systems. Many efforts have been made to find answers to the major issues that they face. The objective of this study addresses road traffic sign detection and recognition using a technique that initially detects the bounding box of a traffic sign. Then the detected traffic sign will be recognized for usage in a speeded-up process. Since safe driving necessitates real-time traffic sign detection, the YOLOv4 network was employed in this research. YOLOv4 was evaluated on our dataset, which consisted of manual annotations to identify 43 distinctive traffic signs classes. It was able to achieve an average recognition accuracy of 84.7%. Overall, the work adds by presenting a basic yet effective model for real-time detection and recognition of traffic signs.
本文提出了一种使用You Only Look Once version 4 (YOLOv4)模型检测交通标志的方法。交通标志检测与识别系统(TSDR)在智能交通系统(ITS)中起着至关重要的作用。TSDR可以用于驾驶员辅助，并最终用于无人驾驶汽车，以减少事故。驾驶汽车时，司机的注意力通常被吸引到道路上。另一方面，大多数交通标志都位于道路的一侧，这可能是导致碰撞的原因。TSDR允许司机在不转移注意力的情况下查看交通标志信息。由于存在大背景、杂波、光照程度波动、交通标志大小变化以及天气条件的变化，TSDR是智能交通系统中一个重要但困难的过程。为解决他们所面临的重大问题作出了许多努力。本研究的目的是利用一种最初检测交通标志边界框的技术来解决道路交通标志的检测和识别问题。然后，检测到的交通标志将被识别并加速使用。由于安全驾驶需要实时检测交通标志，因此本研究采用了YOLOv4网络。YOLOv4在我们的数据集上进行了评估，该数据集由手动注释组成，以识别43种不同的交通标志类别。平均识别准确率达到84.7%。总的来说，该工作通过提出一个基本而有效的模型来实时检测和识别交通标志。
{"title":"Automatic road traffic signs detection and recognition using ‘You Only Look Once’ version 4 (YOLOv4)","authors":"W. H. D. Fernando, S. Sotheeswaran","doi":"10.1109/scse53661.2021.9568285","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568285","url":null,"abstract":"This paper presents an approach to detect traffic signs using You Only Look Once version 4 (YOLOv4) model. The traffic sign detection and recognition system (TSDR) play an essential role in the intelligent transportation system (ITS). TSDR can be utilized for driver assistance and, eventually, driverless cars to reduce accidents. When driving an automobile, the driver's attention is usually drawn to the road. On the other hand, most traffic signs are situated on the side of the road, which may have contributed to the collision. TSDR allows drivers to view traffic sign information without having to divert their attention. Due to the existence of a large background, clutter, fluctuating degrees of illumination, varying sizes of traffic signs, and changing weather conditions, TSDR is an important but difficult process in intelligent transport systems. Many efforts have been made to find answers to the major issues that they face. The objective of this study addresses road traffic sign detection and recognition using a technique that initially detects the bounding box of a traffic sign. Then the detected traffic sign will be recognized for usage in a speeded-up process. Since safe driving necessitates real-time traffic sign detection, the YOLOv4 network was employed in this research. YOLOv4 was evaluated on our dataset, which consisted of manual annotations to identify 43 distinctive traffic signs classes. It was able to achieve an average recognition accuracy of 84.7%. Overall, the work adds by presenting a basic yet effective model for real-time detection and recognition of traffic signs.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129203953","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 : 2021-09-16DOI: 10.1109/scse53661.2021.9568348
J. Jayakody, W. Wijayanayake
An Information Technology (IT) project deals with IT infrastructure, information systems, or computers for delivering an IT product within a temporary period. Proper application of software development methodologies assists software designers to run IT projects to the success of achieving the satisfaction of project stakeholders. Because of the issues raised by traditional software development methodologies such as the Waterfall model, the IT industry began to employ Agile methodology for IT project management. However, due to the separation of software development and operation teams, Agile methodology also caused problems. DevOps is a new approach adapted to the Agile methodology that collaborates the software development and operation teams in order to provide continuous development of high-quality software in a short period of time. However, there are practical issues reported since DevOps approach is still in its infancy in the IT industry. The purpose of this research is to analyze the use of the DevOps concept in IT Projects by evaluating the challenges and mitigating strategies practiced by software development firms in order to ensure the success of IT projects. This purpose was achieved by performing a literature study and soliciting recommendations from industry professionals using a questionnaire survey. The findings reveal the critical challenges and prioritization of challenges experienced by software firms while adopting DevOps, as well as their practices for overcoming those challenges. The research findings will help IT project development teams and future researchers to develop strategies for making the success of DevOps adoption with Agile methodology in the IT industry.
{"title":"Challenges for adopting DevOps in information technology projects","authors":"J. Jayakody, W. Wijayanayake","doi":"10.1109/scse53661.2021.9568348","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568348","url":null,"abstract":"An Information Technology (IT) project deals with IT infrastructure, information systems, or computers for delivering an IT product within a temporary period. Proper application of software development methodologies assists software designers to run IT projects to the success of achieving the satisfaction of project stakeholders. Because of the issues raised by traditional software development methodologies such as the Waterfall model, the IT industry began to employ Agile methodology for IT project management. However, due to the separation of software development and operation teams, Agile methodology also caused problems. DevOps is a new approach adapted to the Agile methodology that collaborates the software development and operation teams in order to provide continuous development of high-quality software in a short period of time. However, there are practical issues reported since DevOps approach is still in its infancy in the IT industry. The purpose of this research is to analyze the use of the DevOps concept in IT Projects by evaluating the challenges and mitigating strategies practiced by software development firms in order to ensure the success of IT projects. This purpose was achieved by performing a literature study and soliciting recommendations from industry professionals using a questionnaire survey. The findings reveal the critical challenges and prioritization of challenges experienced by software firms while adopting DevOps, as well as their practices for overcoming those challenges. The research findings will help IT project development teams and future researchers to develop strategies for making the success of DevOps adoption with Agile methodology in the IT industry.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129179356","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 : 2021-09-16DOI: 10.1109/scse53661.2021.9568333
J. M. D. Senanayake, Nadeeka Pathirana
Identifying an appropriate target audience is essential to market a product or a service. A proper mechanism should be followed to generate these potential leads and target audiences. The majority of people who were born between 1981 and 2012 hold top positions in companies. These people are regular social media and website users, since they represent generations Y and Z. They usually keep digital footprints. Therefore, if an accurate method is followed, it is possible to identify potential contact points by analysing publicly available data. In this research, a novel lead generation mechanism based on analysing social media and web data has been proposed and named L YZGen (Leads of $Y$ and $Z$ Generations). The input to the L YZGen model was an optimised search query based on the user requirement. The model used web crawling, named entity recognition (NER), and pattern identification. The model found and analysed freely available data from social media and other websites. Initially, person name identification was performed. An extensive search was carried out to retrieve peoples' contact points such as email addresses, contact numbers, designations, based on the identified names. Cross verification of the analysed details was conducted as the next step. The results generator provided the final output, which contained the leads and details. Generated details were verified with responses captured via a survey and identified that the model could detect lead details with 87.3 % average accuracy. The model used only the open data posted on the internet by the people. Therefore, it did not violate extensive privacy or security concerns. The generated results can be used, in several ways, including communicating promotional details to the potential target audience.
{"title":"LYZGen: A mechanism to generate leads from Generation Y and Z by analysing web and social media data","authors":"J. M. D. Senanayake, Nadeeka Pathirana","doi":"10.1109/scse53661.2021.9568333","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568333","url":null,"abstract":"Identifying an appropriate target audience is essential to market a product or a service. A proper mechanism should be followed to generate these potential leads and target audiences. The majority of people who were born between 1981 and 2012 hold top positions in companies. These people are regular social media and website users, since they represent generations Y and Z. They usually keep digital footprints. Therefore, if an accurate method is followed, it is possible to identify potential contact points by analysing publicly available data. In this research, a novel lead generation mechanism based on analysing social media and web data has been proposed and named L YZGen (Leads of $Y$ and $Z$ Generations). The input to the L YZGen model was an optimised search query based on the user requirement. The model used web crawling, named entity recognition (NER), and pattern identification. The model found and analysed freely available data from social media and other websites. Initially, person name identification was performed. An extensive search was carried out to retrieve peoples' contact points such as email addresses, contact numbers, designations, based on the identified names. Cross verification of the analysed details was conducted as the next step. The results generator provided the final output, which contained the leads and details. Generated details were verified with responses captured via a survey and identified that the model could detect lead details with 87.3 % average accuracy. The model used only the open data posted on the internet by the people. Therefore, it did not violate extensive privacy or security concerns. The generated results can be used, in several ways, including communicating promotional details to the potential target audience.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116890170","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 : 2021-09-16DOI: 10.1109/scse53661.2021.9568365
N. Wiratunga
{"title":"Keynote Speech: Learning to Personalise Human Activity Recognition","authors":"N. Wiratunga","doi":"10.1109/scse53661.2021.9568365","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568365","url":null,"abstract":"","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131865060","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：