Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568278
Panchani Wickramarachchi, Lankeshwara Munasinghe
Social networks have shown an exponential growth in the recent past. It has estimated that nearly 4 billion people are currently using social networks. The growth of social networks can be explained using different models. Preferential Attachment (P A) is a widely used model, which is often used to link prediction in social networks. P A tells that the social network users prefer to get linked with popular users in the network. However, the popularity of a node depends not only on the node's degree but also on the node's activeness which is reflected by the amount of active links the node has at present. Activeness of a link can be quantified using the timestamp of the link. The present work introduces a novel method called Temporal Preferential Attachment (TPA) which is defined on the activeness and strength of a node. Strength of a node is the sum of weights of links attached to the node. Here, the weights of the links are assigned according to their activeness. Thus, TP A captures the temporal behaviors of nodes, which is a vital factor for new link formation. The novel method uses min - max scaling to scale the time differences between current time and the timestamps of the links. Here, the min value is the earliest timestamp of the links in the given network and max value is the latest timestamp of the links. The scaled time difference of a link is considered as the temporal weight of the link, which reflects its activeness. TP A was evaluated in terms of its link prediction performance using well-known social network data sets. The results show that TP A performs well in link prediction compared to P A, and show a significant improvement in prediction accuracy.
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Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568302
D. Jayasekara, A. Wijayanayake, A. Dissanayake
The principal agent problem revolves around the competing interest between shareholders and the employees. The organization focus is on maximizing shareholder wealth, while employees try to obtain the maximum benefits for themselves. As per the motivational theories, people have different types of needs. Therefore, management should focus on a wide range of factors to motivate the employees to work to their full potential in the interest of the organization. The study focuses on both employee and the management of a state bank. The organization is always eager to minimize the cost and maximize the profit. Game Theory was used to provide a mathematical framework for understanding the optimal outcome and what the tradeoffs are to achieve that outcome. The objective is to find the right balance between financial gains and employee satisfaction. To fulfill that objective, one needs to evaluate the benefits given to employees, the effectiveness of those benefits on employees and finally recommend an effective benefits allocation mix to the organization, which will address both employee and the top management of the bank.
{"title":"Application of Game Theory on financial benefits and employee satisfaction: Case study of a state bank of Sri Lanka","authors":"D. Jayasekara, A. Wijayanayake, A. Dissanayake","doi":"10.1109/scse53661.2021.9568302","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568302","url":null,"abstract":"The principal agent problem revolves around the competing interest between shareholders and the employees. The organization focus is on maximizing shareholder wealth, while employees try to obtain the maximum benefits for themselves. As per the motivational theories, people have different types of needs. Therefore, management should focus on a wide range of factors to motivate the employees to work to their full potential in the interest of the organization. The study focuses on both employee and the management of a state bank. The organization is always eager to minimize the cost and maximize the profit. Game Theory was used to provide a mathematical framework for understanding the optimal outcome and what the tradeoffs are to achieve that outcome. The objective is to find the right balance between financial gains and employee satisfaction. To fulfill that objective, one needs to evaluate the benefits given to employees, the effectiveness of those benefits on employees and finally recommend an effective benefits allocation mix to the organization, which will address both employee and the top management of the bank.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"64 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":"116455068","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/scse53661.2021.9568332
Full conference PDF.
完整的会议PDF。
{"title":"Full Proceedings Printed","authors":"","doi":"10.1109/scse53661.2021.9568332","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568332","url":null,"abstract":"Full conference PDF.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"1 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":"129356806","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/scse53661.2021.9568329
M. Amjath, S. Kuhanesan
This contribution focuses on developing an AI-driven warning device to ensure the safety of railway track workers. Recent studies clearly show that track workers safety has become a major challenge for the railway industry despite many precautionary measures that are implemented. In this regard, many technological solutions have been proposed and developed to warn track workers of the approaching trains. However, the cost and complexity are the drawbacks of these systems. Therefore, we introduce TrackWarn, a low-cost portable smart gadget that detects the sounds of the approaching trains and provides a warning signal to track workers via a phone call. TrackWarn uses a state-of-art Convolutional Neural Network (CNN) that utilizes environmental sounds and spectrograms to classify if the train is approaching or not. This model achieves an average classification accuracy of 92.46%. With the help of Arduino Nano 33 BLE Sense micro controller, the whole system becomes very handy and potable. This paper addresses the design of the TrackWarn and the results obtained with respect to the various test cases. Further, the performance and communication challenges are also described in detail.
这项贡献的重点是开发一种人工智能驱动的警报装置，以确保铁路轨道工人的安全。最近的研究清楚地表明，尽管实施了许多预防措施，但轨道工人的安全已成为铁路行业面临的主要挑战。在这方面，已经提出并开发了许多技术解决方案，以警告轨道工人接近的火车。然而，成本和复杂性是这些系统的缺点。因此，我们推出了TrackWarn，这是一种低成本的便携式智能设备，可以探测到驶近的火车的声音，并通过电话向跟踪工人提供警告信号。TrackWarn使用最先进的卷积神经网络(CNN)，利用环境声音和频谱图来分类火车是否正在接近。该模型的平均分类准确率为92.46%。在Arduino Nano 33 BLE Sense微控制器的帮助下，整个系统变得非常方便便携。本文讨论了TrackWarn的设计和各种测试用例的结果。此外，还详细描述了性能和通信方面的挑战。
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Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568308
Siventhirarajah Sangeevan
The study proposes a deep learning-based pesticides prescription system for leaf diseases of home garden crops in Sri Lanka. It is an intelligent system to get suitable pesticides prescriptions for plant leaf diseases. Home gardening has become popular and is rapid because of the current pandemic situation. However, plant diseases are a major problem in gardening activities, even in a home garden or in a commercial garden. Identifying and finding a solution for the plant disease is a big challenge for home gardeners rather than commercial farmers. The proposed system of deep learning-based pesticides prescription system for leaf diseases of home garden crops in Sri Lanka will be the best solution for identifying and finding a solution to the plant diseases. The system is using a trained model for prescribing pesticides. The model was built using the deep learning method and trained in the supervised learning process. The convolutional neural network algorithm was used in the model. Transfer learning with AlexN et pre-trained model was used to increase the performance in the proposed solution and the best accuracy of 88.64 % was achieved in the experiments.
{"title":"Deep learning-based pesticides prescription system for leaf diseases of home garden crops in Sri Lanka","authors":"Siventhirarajah Sangeevan","doi":"10.1109/scse53661.2021.9568308","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568308","url":null,"abstract":"The study proposes a deep learning-based pesticides prescription system for leaf diseases of home garden crops in Sri Lanka. It is an intelligent system to get suitable pesticides prescriptions for plant leaf diseases. Home gardening has become popular and is rapid because of the current pandemic situation. However, plant diseases are a major problem in gardening activities, even in a home garden or in a commercial garden. Identifying and finding a solution for the plant disease is a big challenge for home gardeners rather than commercial farmers. The proposed system of deep learning-based pesticides prescription system for leaf diseases of home garden crops in Sri Lanka will be the best solution for identifying and finding a solution to the plant diseases. The system is using a trained model for prescribing pesticides. The model was built using the deep learning method and trained in the supervised learning process. The convolutional neural network algorithm was used in the model. Transfer learning with AlexN et pre-trained model was used to increase the performance in the proposed solution and the best accuracy of 88.64 % was achieved in the experiments.","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":"129182922","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/scse53661.2021.9568331
Nimasha Arambepola, Lankeshwara Munasinghe
Having a rich human resource is critical for an organization to move towards success. Especially, for business organizations such as technology companies, the human resource is the driving factor of the company's growth which depends on employees' motivation, skills and quality of work. Employees often change their jobs when they are not satisfied with it. Different factors may cause a change in the level of job satisfaction of an employee. For example, the dynamic nature of the Information Technology (IT) industry is an impactful factor that determines the job satisfaction of IT professionals. Foreseeing the employees' job satisfaction makes it easy for a company to take swift actions to improve the job satisfaction of its employees. In this research, we analyzed the effectiveness of machine learning (ML) methods for predicting job satisfaction using employee job profiles. There are job-specific factors in each job domain, and those factors may influence job satisfaction levels. Therefore, this research focused on the following fundamental questions: 1) How do existing ML models perform when predicting job satisfaction of software developers? 2) Can the job satisfaction prediction models be generalized to the other job roles in the IT industry? This study compared the performance of classification models: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Neural Network (NN) in predicting the level of job satisfaction. Our experiments used two benchmark datasets: Stack Overflow developer survey and IBM HR analytics dataset. The experimental analysis shows that both employee-related factors and company-related factors contribute similarly to predicting job satisfaction. On average, the above ML models predict the job satisfaction of software developers with an accuracy of around 79%.
{"title":"What makes job satisfaction in the information technology industry?","authors":"Nimasha Arambepola, Lankeshwara Munasinghe","doi":"10.1109/scse53661.2021.9568331","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568331","url":null,"abstract":"Having a rich human resource is critical for an organization to move towards success. Especially, for business organizations such as technology companies, the human resource is the driving factor of the company's growth which depends on employees' motivation, skills and quality of work. Employees often change their jobs when they are not satisfied with it. Different factors may cause a change in the level of job satisfaction of an employee. For example, the dynamic nature of the Information Technology (IT) industry is an impactful factor that determines the job satisfaction of IT professionals. Foreseeing the employees' job satisfaction makes it easy for a company to take swift actions to improve the job satisfaction of its employees. In this research, we analyzed the effectiveness of machine learning (ML) methods for predicting job satisfaction using employee job profiles. There are job-specific factors in each job domain, and those factors may influence job satisfaction levels. Therefore, this research focused on the following fundamental questions: 1) How do existing ML models perform when predicting job satisfaction of software developers? 2) Can the job satisfaction prediction models be generalized to the other job roles in the IT industry? This study compared the performance of classification models: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Neural Network (NN) in predicting the level of job satisfaction. Our experiments used two benchmark datasets: Stack Overflow developer survey and IBM HR analytics dataset. The experimental analysis shows that both employee-related factors and company-related factors contribute similarly to predicting job satisfaction. On average, the above ML models predict the job satisfaction of software developers with an accuracy of around 79%.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"8 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":"126953354","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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