Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215040
Hmkt Gunawardhana, B. Kumara, Kapila T. Rathnayake, P. Jayaweera
For e-commerce marketplaces, counterfeit goods are a major issue since they endanger public safety in addition to causing customer unhappiness and revenue loss. Traditional techniques to identify fake goods in online marketplaces take too long and have a narrow reach, hence they are ineffective. Machine learning algorithms have become a potential tool for swiftly and precisely identifying counterfeit goods in recent years. The usefulness of two machine learning algorithms in identifying fake goods in online marketplaces is examined in this research. The study assesses the performance using a sizable dataset of descriptions, title, prices, and seller names from many well-known e-commerce platforms. The study’s findings show that machine learning algorithms significantly affect the detection of fake goods in online marketplaces.
{"title":"Effectiveness of Machine Learning Algorithms on Battling Counterfeit Items in E-commerce Marketplaces","authors":"Hmkt Gunawardhana, B. Kumara, Kapila T. Rathnayake, P. Jayaweera","doi":"10.1109/SCSE59836.2023.10215040","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215040","url":null,"abstract":"For e-commerce marketplaces, counterfeit goods are a major issue since they endanger public safety in addition to causing customer unhappiness and revenue loss. Traditional techniques to identify fake goods in online marketplaces take too long and have a narrow reach, hence they are ineffective. Machine learning algorithms have become a potential tool for swiftly and precisely identifying counterfeit goods in recent years. The usefulness of two machine learning algorithms in identifying fake goods in online marketplaces is examined in this research. The study assesses the performance using a sizable dataset of descriptions, title, prices, and seller names from many well-known e-commerce platforms. The study’s findings show that machine learning algorithms significantly affect the detection of fake goods in online marketplaces.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117236417","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 : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215000
Archana Saini, Kalpna Guleria, Shagun Sharma
Early detection of eye diseases is crucial, particularly for individuals with a family history of eye diseases, people over 60 years of age, individuals with diabetes, and those who have a history of eye injuries or surgeries, as they are at a higher risk of developing eye diseases. Early detection and timely treatment are crucial in treating eye diseases and preventing permanent vision loss. Detecting eye diseases early on is crucial in preventing or slowing down the progression of vision loss and blindness. Unfortunately, many eye diseases, including diabetic retinopathy, glaucoma, and cataracts, do not have early warning signs or symptoms. Therefore, regular eye checkups and early detection of these diseases can be essential in preventing vision loss and improving the quality of life for those affected. Retinal fundus image screening is a commonly used technique for diagnosing eye disorders, but manual detection is time-consuming and labour-intensive. To address this issue, various researchers have turned to deep learning methods for the automated detection of retinal eye diseases. In this work, a convolutional neural network model has been developed for classifying eye diseases, demonstrating an impressive accuracy rate of 99.85%. This suggests that the model can correctly classify eye diseases in nearly 4 out of 5 cases. These findings have the potential to significantly improve the accuracy and efficiency of diagnosing eye diseases using retinal fundus images.
{"title":"An Efficient Deep Learning Model for Eye Disease Classification","authors":"Archana Saini, Kalpna Guleria, Shagun Sharma","doi":"10.1109/SCSE59836.2023.10215000","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215000","url":null,"abstract":"Early detection of eye diseases is crucial, particularly for individuals with a family history of eye diseases, people over 60 years of age, individuals with diabetes, and those who have a history of eye injuries or surgeries, as they are at a higher risk of developing eye diseases. Early detection and timely treatment are crucial in treating eye diseases and preventing permanent vision loss. Detecting eye diseases early on is crucial in preventing or slowing down the progression of vision loss and blindness. Unfortunately, many eye diseases, including diabetic retinopathy, glaucoma, and cataracts, do not have early warning signs or symptoms. Therefore, regular eye checkups and early detection of these diseases can be essential in preventing vision loss and improving the quality of life for those affected. Retinal fundus image screening is a commonly used technique for diagnosing eye disorders, but manual detection is time-consuming and labour-intensive. To address this issue, various researchers have turned to deep learning methods for the automated detection of retinal eye diseases. In this work, a convolutional neural network model has been developed for classifying eye diseases, demonstrating an impressive accuracy rate of 99.85%. This suggests that the model can correctly classify eye diseases in nearly 4 out of 5 cases. These findings have the potential to significantly improve the accuracy and efficiency of diagnosing eye diseases using retinal fundus images.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127846755","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 : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215013
Rahul Adihetti, S. Jayalal
The spread of fake news in the social media has grown significantly over the past few years. According to the New York Times, fake news is defined as “made-up articles meant to deceive.” Additionally, the way they are released is almost identical to that of conventional news organizations. The issue is that a significant number of news outlets outside the major and reliable ones are disseminating unreliable information. This problem is exacerbated by the ease with which anything can be published from anywhere on well-known social networking and social media platforms. People can use this to their advantage by disseminating any type of message on various social networking sites to accomplish their objectives. In the Sri Lankan context, content posted in Sinhala greatly impacts fake news in Sri Lanka. Because utilizing the Sinhala language to describe emotions and feelings makes it easier to connect with Sinhala-speaking people than using content that has been published in other languages, like English. The use of Sinhala on social media has grown over the past few years. Additionally, as the use of the Sinhala language expanded, so did the number of occurrences of fake news. Based on the literature, approaches to identifying fake news depend on the features of the news content. Therefore, this research proposed an autoencoder-based method for Sinhala fake news detection, which is an unsupervised method. The method uses Text, User, Propagation, and Image features from the news content. And also, this research found the best feature combination to detect Sinhala language fake news content, which is a combination of Text, User, and Image features. The method gained an accuracy of 98% and 88% in Precision, Recall, and F1 Score by outperforming other existing anomaly detection methods. The main stakeholder of this study was fact-checking organizations in Sri Lanka.
{"title":"Sinhala Language Fake News Detection In Social Media Using Autoencoder-Based Method","authors":"Rahul Adihetti, S. Jayalal","doi":"10.1109/SCSE59836.2023.10215013","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215013","url":null,"abstract":"The spread of fake news in the social media has grown significantly over the past few years. According to the New York Times, fake news is defined as “made-up articles meant to deceive.” Additionally, the way they are released is almost identical to that of conventional news organizations. The issue is that a significant number of news outlets outside the major and reliable ones are disseminating unreliable information. This problem is exacerbated by the ease with which anything can be published from anywhere on well-known social networking and social media platforms. People can use this to their advantage by disseminating any type of message on various social networking sites to accomplish their objectives. In the Sri Lankan context, content posted in Sinhala greatly impacts fake news in Sri Lanka. Because utilizing the Sinhala language to describe emotions and feelings makes it easier to connect with Sinhala-speaking people than using content that has been published in other languages, like English. The use of Sinhala on social media has grown over the past few years. Additionally, as the use of the Sinhala language expanded, so did the number of occurrences of fake news. Based on the literature, approaches to identifying fake news depend on the features of the news content. Therefore, this research proposed an autoencoder-based method for Sinhala fake news detection, which is an unsupervised method. The method uses Text, User, Propagation, and Image features from the news content. And also, this research found the best feature combination to detect Sinhala language fake news content, which is a combination of Text, User, and Image features. The method gained an accuracy of 98% and 88% in Precision, Recall, and F1 Score by outperforming other existing anomaly detection methods. The main stakeholder of this study was fact-checking organizations in Sri Lanka.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124389666","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 : 2023-06-29DOI: 10.1109/scse59836.2023.10215033
{"title":"Keynote 1: Innovation in the Age of AI Unpacking 2023’s AI Innovations and Their Sweeping Global Implications","authors":"","doi":"10.1109/scse59836.2023.10215033","DOIUrl":"https://doi.org/10.1109/scse59836.2023.10215033","url":null,"abstract":"","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134353198","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 : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215005
Shalitha Alahakon, Tharindu Siriwardana, Deshan Udupihilla, T. Wickramasinghe, S. Rajapaksha
Retailers are crucial in supply chains, acting as the bridge between consumers and resources. However, there is limited analytic-based literature on block design in grocery stores. This paper employs an algorithmic approach with optimization techniques to efficiently design the interior space of a provided supermarket. The objective is to create an analytical method for handling design issues without relying on human-centered approaches. Using data from supermarket store arrangements, the paper showcases efficient space utilization by aligning item measurements with customer needs. Decision variables offer decision makers a precise collection of non-dominated designs. Previous studies demonstrate the effectiveness of this approach in analytically designing a data-driven structure for supermarket block layouts. The model identifies layouts that maximize space utilization while meeting industry standards. Although primarily focused on Asian retailers, the approach is generally applicable due to the similarity of grocery store layouts worldwide. The method and results are easily translatable for other retailers.
{"title":"Developing and Training a Mathematical Model for Optimizing a Given Interior Space of a Supermarket","authors":"Shalitha Alahakon, Tharindu Siriwardana, Deshan Udupihilla, T. Wickramasinghe, S. Rajapaksha","doi":"10.1109/SCSE59836.2023.10215005","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215005","url":null,"abstract":"Retailers are crucial in supply chains, acting as the bridge between consumers and resources. However, there is limited analytic-based literature on block design in grocery stores. This paper employs an algorithmic approach with optimization techniques to efficiently design the interior space of a provided supermarket. The objective is to create an analytical method for handling design issues without relying on human-centered approaches. Using data from supermarket store arrangements, the paper showcases efficient space utilization by aligning item measurements with customer needs. Decision variables offer decision makers a precise collection of non-dominated designs. Previous studies demonstrate the effectiveness of this approach in analytically designing a data-driven structure for supermarket block layouts. The model identifies layouts that maximize space utilization while meeting industry standards. Although primarily focused on Asian retailers, the approach is generally applicable due to the similarity of grocery store layouts worldwide. The method and results are easily translatable for other retailers.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130049703","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 : 2023-06-29DOI: 10.1109/SCSE59836.2023.10214987
A. Withanaarachchi, A.M. Himashi Silva
The Sri Lankan apparel manufacturing business, a major contributor to the country’s export revenue, has been attempting to adopt industry 4.0. Only a few developing nations have been able to capture the maximum benefits of the fourth industrial revolution. The purpose of this study is to identify the critical factors that must be considered for the successful implementation of industry 4.0 in the Sri Lankan apparel manufacturing sector. Throughout the research, a quantitative approach was used. Initially, the six most significant critical factors and two moderating variables were determined by a review of prior research and the opinions of industry professionals. Partial Least Square – Structural Equation Modelling (PLS-SEM) was used to analyze the relationship between the factors. Greater financial investments, organizational strategy, workforce, a dynamic organizational culture, the involvement of top management, and the availability of IT infrastructure have a significant positive impact on the successful implementation of industry 4.0 in the Sri Lankan apparel manufacturing sector, as determined by the final findings of the data analysis. In addition, the availability and accessibility of support services have a significant positive moderating effect on financial investments, when successfully implementing industry 4.0 in the Sri Lankan apparel industry. In addition, the advancement of digital technologies has a significant positive moderating effect on financial investments and, a significant negative effect on organizational strategy and the involvement of top management when successfully implementing industry 4.0 in the Sri Lankan apparel industry. The outcomes of this study assist the managers of the Sri Lankan clothing manufacturing sector in comprehending the critical factors that must be considered when successfully implementing industry 4.0 technologies.
{"title":"Critical Success Factors Affecting the Successful Implementation of Industry 4.0 in The Sri Lankan Apparel Manufacturing Industry","authors":"A. Withanaarachchi, A.M. Himashi Silva","doi":"10.1109/SCSE59836.2023.10214987","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10214987","url":null,"abstract":"The Sri Lankan apparel manufacturing business, a major contributor to the country’s export revenue, has been attempting to adopt industry 4.0. Only a few developing nations have been able to capture the maximum benefits of the fourth industrial revolution. The purpose of this study is to identify the critical factors that must be considered for the successful implementation of industry 4.0 in the Sri Lankan apparel manufacturing sector. Throughout the research, a quantitative approach was used. Initially, the six most significant critical factors and two moderating variables were determined by a review of prior research and the opinions of industry professionals. Partial Least Square – Structural Equation Modelling (PLS-SEM) was used to analyze the relationship between the factors. Greater financial investments, organizational strategy, workforce, a dynamic organizational culture, the involvement of top management, and the availability of IT infrastructure have a significant positive impact on the successful implementation of industry 4.0 in the Sri Lankan apparel manufacturing sector, as determined by the final findings of the data analysis. In addition, the availability and accessibility of support services have a significant positive moderating effect on financial investments, when successfully implementing industry 4.0 in the Sri Lankan apparel industry. In addition, the advancement of digital technologies has a significant positive moderating effect on financial investments and, a significant negative effect on organizational strategy and the involvement of top management when successfully implementing industry 4.0 in the Sri Lankan apparel industry. The outcomes of this study assist the managers of the Sri Lankan clothing manufacturing sector in comprehending the critical factors that must be considered when successfully implementing industry 4.0 technologies.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121442093","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 : 2023-06-29DOI: 10.1109/scse59836.2023.10215002
{"title":"SCSE 2023 Cover Page","authors":"","doi":"10.1109/scse59836.2023.10215002","DOIUrl":"https://doi.org/10.1109/scse59836.2023.10215002","url":null,"abstract":"","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124523905","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 : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215028
T. A. Gamage, E. Sandamali, Pradeep Kalansooriya
Electroencephalogram (EEG) based emotion recognition approaches have proven to be successful with the latest technologies, and therefore, driver emotion recognition is also being widely discussed for enhancing road safety. This paper reveals a unique approach to driver emotion recognition for the calm, fear, sad, and anger emotional states where calm is the desired state of mind while driving. Emotiv EPOC X 14 channel EEG headset is utilised for the EEG collection, and ten subjects are involved in the experiment. EEG preprocessing of the collected EEG data is done using the EEGLAB toolbox in Matlab. EEG feature extraction is performed using Matlab, and feature selection and classification model training is done using the Classification Learner app in Matlab. ANOVA and ReliefF are employed as the feature selection algorithms, and Support Vector Machine (SVM) and Naïve Bayes classifiers are utilised for the emotion classification. The outcomes reveal that the highest mean accuracy of 95% is achieved from the Coarse Gaussian SVM classifier, while the lowest mean accuracy of 85% is obtained from the Fine Gaussian SVM classifier detecting the calm, fear, sad, and anger emotional states. In addition, all the other trained classifier models have an accuracy between 85% and 95%. Therefore, the findings suggest that the proposed EEG-based implementation approach of an emotion classification model for drivers is highly successful and can be employed in future research in the paradigm of driver emotion recognition as well. Besides, this research presents a critical literature review concerning critical aspects of EEG-based emotion recognition research.
基于脑电图(EEG)的情绪识别方法已被最新技术证明是成功的，因此驾驶员情绪识别也被广泛讨论以提高道路安全。本文揭示了一种独特的方法来识别司机的情绪平静，恐惧，悲伤和愤怒的情绪状态，而冷静是驾驶时的理想心态。EEG采集采用Emotiv EPOC X 14通道脑电耳机，实验共涉及10名受试者。利用Matlab中的EEGLAB工具箱对采集到的脑电信号进行预处理。利用Matlab进行脑电特征提取，利用Matlab中的classification Learner app进行特征选择和分类模型训练。使用ANOVA和ReliefF作为特征选择算法，使用支持向量机(SVM)和Naïve贝叶斯分类器进行情感分类。结果表明，粗高斯SVM分类器检测平静、恐惧、悲伤和愤怒情绪状态的平均准确率最高，达到95%，而细高斯SVM分类器检测平静、恐惧、悲伤和愤怒情绪状态的平均准确率最低，为85%。此外，所有其他训练的分类器模型的准确率在85%到95%之间。因此，研究结果表明，基于脑电图的驾驶员情绪分类模型的实现方法是非常成功的，也可以用于未来驾驶员情绪识别范式的研究。此外，本研究对基于脑电图的情绪识别研究的关键方面进行了批判性的文献综述。
{"title":"DrivEmo: A Novel Approach for EEG-Based Emotion Classification for Drivers","authors":"T. A. Gamage, E. Sandamali, Pradeep Kalansooriya","doi":"10.1109/SCSE59836.2023.10215028","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215028","url":null,"abstract":"Electroencephalogram (EEG) based emotion recognition approaches have proven to be successful with the latest technologies, and therefore, driver emotion recognition is also being widely discussed for enhancing road safety. This paper reveals a unique approach to driver emotion recognition for the calm, fear, sad, and anger emotional states where calm is the desired state of mind while driving. Emotiv EPOC X 14 channel EEG headset is utilised for the EEG collection, and ten subjects are involved in the experiment. EEG preprocessing of the collected EEG data is done using the EEGLAB toolbox in Matlab. EEG feature extraction is performed using Matlab, and feature selection and classification model training is done using the Classification Learner app in Matlab. ANOVA and ReliefF are employed as the feature selection algorithms, and Support Vector Machine (SVM) and Naïve Bayes classifiers are utilised for the emotion classification. The outcomes reveal that the highest mean accuracy of 95% is achieved from the Coarse Gaussian SVM classifier, while the lowest mean accuracy of 85% is obtained from the Fine Gaussian SVM classifier detecting the calm, fear, sad, and anger emotional states. In addition, all the other trained classifier models have an accuracy between 85% and 95%. Therefore, the findings suggest that the proposed EEG-based implementation approach of an emotion classification model for drivers is highly successful and can be employed in future research in the paradigm of driver emotion recognition as well. Besides, this research presents a critical literature review concerning critical aspects of EEG-based emotion recognition research.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122730296","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 : 2023-06-29DOI: 10.1109/SCSE59836.2023.10214988
K. Nawurunnage, A. Prasadika, A. Wijayanayake
The growing need to address the threat of global warming and greenhouse gas emissions has placed immense pressure on logistics companies to adopt sustainable practices. With logistics operations being a significant source of greenhouse gas emissions, incorporating green supply chain management practices (GSCM) has become crucial to achieving environmental sustainability within the third-party logistics (3PL) industry. Exploring the existing literature under the concepts of Total Quality Management and Green Supply Chain Management reveals the need for future investigations into how those practices might potentially improve the logistics firm’s performance to achieve sustainability. Therefore, the main objective of this study is to identify the interrelationships of TQM practices and supply chain performance third-party logistics industry in terms of overall performance and identify the suitable TQM practices that can be applied to enhance the overall performance of Sri Lankan 3PLs and assess moderating effect of GSCM practices on that TQM-performance relationships. An online survey instrument was used to collect the data from executives, senior executives, and managers of 3PL firms in Sri Lanka. The statistical data analysis was done using PLS-SEM. The results found that top management support, customer focus, statistical process control, and continuous improvements are the significant total quality management practice for overall performance in the Sri Lankan 3PL industry. The study’s findings are useful for the top management of 3PLs, policymakers, and academia to identify the level of GSCM implementation within the industry, and results provide insights into further considerations regarding the implementation of GSCM practices and TQM practices to achieve the supply chain performance of the 3PLs while achieving sustainability.
{"title":"TQM Practices on Supply Chain Performance of Third-Party Logistics Services in Sri Lanka: The Moderating Role of Green Supply Chain Practices","authors":"K. Nawurunnage, A. Prasadika, A. Wijayanayake","doi":"10.1109/SCSE59836.2023.10214988","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10214988","url":null,"abstract":"The growing need to address the threat of global warming and greenhouse gas emissions has placed immense pressure on logistics companies to adopt sustainable practices. With logistics operations being a significant source of greenhouse gas emissions, incorporating green supply chain management practices (GSCM) has become crucial to achieving environmental sustainability within the third-party logistics (3PL) industry. Exploring the existing literature under the concepts of Total Quality Management and Green Supply Chain Management reveals the need for future investigations into how those practices might potentially improve the logistics firm’s performance to achieve sustainability. Therefore, the main objective of this study is to identify the interrelationships of TQM practices and supply chain performance third-party logistics industry in terms of overall performance and identify the suitable TQM practices that can be applied to enhance the overall performance of Sri Lankan 3PLs and assess moderating effect of GSCM practices on that TQM-performance relationships. An online survey instrument was used to collect the data from executives, senior executives, and managers of 3PL firms in Sri Lanka. The statistical data analysis was done using PLS-SEM. The results found that top management support, customer focus, statistical process control, and continuous improvements are the significant total quality management practice for overall performance in the Sri Lankan 3PL industry. The study’s findings are useful for the top management of 3PLs, policymakers, and academia to identify the level of GSCM implementation within the industry, and results provide insights into further considerations regarding the implementation of GSCM practices and TQM practices to achieve the supply chain performance of the 3PLs while achieving sustainability.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116662574","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 : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215030
Achini Nisansala, Rukshani Puvnendran
The ability to recognize different postures of any living creature is a prerequisite for getting an accurate idea about their mental and physical well-being. Dogs are the most friendly and social canine breeds that provide love and security for human companions being their best friend at all times. The present study aimed at paying the initiatives at exploring important information about the wellbeing of the dogs with their sleeping postures. The paper studies and compared the classification performance of three deep transfer learning algorithms: VGG16, Xception, and ResNet50, and Convolutional Neural Network on a manually collected and augmented dataset of nearly 4000 images consisting of four different sleeping postures of dogs. Our model reveals that ResNet50 outperforms all other algorithms and achieved the highest accuracy of S7.35%. Overall, our finding would help disabled and special requirement dogs and their owners to identify canine’s health conditions and requirements using the sleeping postures and provide a more comfortable and better life for them.
{"title":"Canine Sleeping Posture Identification using Transfer Learning","authors":"Achini Nisansala, Rukshani Puvnendran","doi":"10.1109/SCSE59836.2023.10215030","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215030","url":null,"abstract":"The ability to recognize different postures of any living creature is a prerequisite for getting an accurate idea about their mental and physical well-being. Dogs are the most friendly and social canine breeds that provide love and security for human companions being their best friend at all times. The present study aimed at paying the initiatives at exploring important information about the wellbeing of the dogs with their sleeping postures. The paper studies and compared the classification performance of three deep transfer learning algorithms: VGG16, Xception, and ResNet50, and Convolutional Neural Network on a manually collected and augmented dataset of nearly 4000 images consisting of four different sleeping postures of dogs. Our model reveals that ResNet50 outperforms all other algorithms and achieved the highest accuracy of S7.35%. Overall, our finding would help disabled and special requirement dogs and their owners to identify canine’s health conditions and requirements using the sleeping postures and provide a more comfortable and better life for them.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129004842","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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