Pub Date : 2021-09-16DOI: 10.1109/scse53661.2021.9568330
Shakyani Jayasiriwardene, D. Meedeniya
At present, a significant demand has emerged for online educational tools that can be used as replacement for classroom education. Due to the ease of access, the preference of many users is focused on m-learning applications. This paper presents an architectural framework for an interactive mobile learning toolkit. This study explores different software design patterns and presents the implementation details of the prototype. As a case study, the application is applied for the primary education sector in Sri Lanka, as there is a lack of adaptive learning mobile toolkits that allow teachers and students to interact effectively. The study is concluded to be user-friendly, understandable, useful, and efficient through a System Usability Study.
{"title":"Architectural framework for an interactive learning toolkit","authors":"Shakyani Jayasiriwardene, D. Meedeniya","doi":"10.1109/scse53661.2021.9568330","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568330","url":null,"abstract":"At present, a significant demand has emerged for online educational tools that can be used as replacement for classroom education. Due to the ease of access, the preference of many users is focused on m-learning applications. This paper presents an architectural framework for an interactive mobile learning toolkit. This study explores different software design patterns and presents the implementation details of the prototype. As a case study, the application is applied for the primary education sector in Sri Lanka, as there is a lack of adaptive learning mobile toolkits that allow teachers and students to interact effectively. The study is concluded to be user-friendly, understandable, useful, and efficient through a System Usability Study.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"148 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":"122864515","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/scse53661.2021.9568362
H. D. W. Weerakkody, D. Niwunhella, A. Wijayanayake
Vehicle Routing Problem (VRP) is an extensively discussed area under supply chain literature, though it has variety of applications. Multi-product related VRP considers about optimizing the routes of vehicles distributing multiple commodities. Domestic distribution of goods of multiple clients from a third-party logistics distribution centre (DC) is one example of such an application. Compatibility of products is a major factor taken into consideration when consolidating and distributing multiple products in the same vehicle. From the literature, it was identified that, though compatibility is a major consideration, it has not been considered in the literature when developing vehicle routing models. Therefore, this study has been carried out with the objective of minimizing the cost of distribution in the multi-product VRP while considering the compatibility of the products distributed, using heterogeneous vehicle types. The extended mathematical model proposed has been validated using data obtained from a leading 3PL firm in Sri Lanka which has been simulated using the Supply Chain Guru software. The numerical results showcase that cost has been reduced when consolidating shipments in a 3PL DC. The study will contribute to literature with the finding that the compatibility factor of products can be considered when developing vehicle routing models for the multi-product related VRP.
{"title":"Solution approach to incompatibility of products in a multi-product and heterogeneous vehicle routing problem: An application in the 3PL industry","authors":"H. D. W. Weerakkody, D. Niwunhella, A. Wijayanayake","doi":"10.1109/scse53661.2021.9568362","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568362","url":null,"abstract":"Vehicle Routing Problem (VRP) is an extensively discussed area under supply chain literature, though it has variety of applications. Multi-product related VRP considers about optimizing the routes of vehicles distributing multiple commodities. Domestic distribution of goods of multiple clients from a third-party logistics distribution centre (DC) is one example of such an application. Compatibility of products is a major factor taken into consideration when consolidating and distributing multiple products in the same vehicle. From the literature, it was identified that, though compatibility is a major consideration, it has not been considered in the literature when developing vehicle routing models. Therefore, this study has been carried out with the objective of minimizing the cost of distribution in the multi-product VRP while considering the compatibility of the products distributed, using heterogeneous vehicle types. The extended mathematical model proposed has been validated using data obtained from a leading 3PL firm in Sri Lanka which has been simulated using the Supply Chain Guru software. The numerical results showcase that cost has been reduced when consolidating shipments in a 3PL DC. The study will contribute to literature with the finding that the compatibility factor of products can be considered when developing vehicle routing models for the multi-product related VRP.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"27 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":"122881177","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/scse53661.2021.9568352
M. Isaksson
{"title":"Keynote Speech: An Industry 4.0 Approach to Plastic Repair","authors":"M. Isaksson","doi":"10.1109/scse53661.2021.9568352","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568352","url":null,"abstract":"","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"154 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":"127306226","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/scse53661.2021.9568327
Prathap V. Jayasooriya, Geethal C. Siriwardana, T. R. Bandara
Vibration analysis is used to detect faults and anomalies in machinery and other mechanical systems that produce vibrations during operation. The study aimed to develop an algorithm that can detect and locate engine faults in automobiles by analyzing vibrational data produced during engine operation. Analysis was done on one type of engine fault - Spark Ignition Engine misfire. To detect anomalies in the vibrational pattern (waveform), analysis was carried out in both time and frequency domains. To obtain vibrational data an A VR - 32 (Arduino) based data acquisition device was built, and analysis was carried out in MA TLAB using scripts and functions. The developed algorithm isolates frequency components in the waveform that corresponds to engine faults and converts them into numerical quantities that are then compared with computed ranges. The algorithm was able to identify the presence of a misfire in the engine and could locate the cylinder in which the misfire occurs with significant accuracy.
{"title":"Vibration analysis to detect and locate engine misfires","authors":"Prathap V. Jayasooriya, Geethal C. Siriwardana, T. R. Bandara","doi":"10.1109/scse53661.2021.9568327","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568327","url":null,"abstract":"Vibration analysis is used to detect faults and anomalies in machinery and other mechanical systems that produce vibrations during operation. The study aimed to develop an algorithm that can detect and locate engine faults in automobiles by analyzing vibrational data produced during engine operation. Analysis was done on one type of engine fault - Spark Ignition Engine misfire. To detect anomalies in the vibrational pattern (waveform), analysis was carried out in both time and frequency domains. To obtain vibrational data an A VR - 32 (Arduino) based data acquisition device was built, and analysis was carried out in MA TLAB using scripts and functions. The developed algorithm isolates frequency components in the waveform that corresponds to engine faults and converts them into numerical quantities that are then compared with computed ranges. The algorithm was able to identify the presence of a misfire in the engine and could locate the cylinder in which the misfire occurs with significant accuracy.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"9 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":"125385255","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/scse53661.2021.9568349
M. Ranathunga, A. Wijayanayake, D. Niwunhella
Logistics is one of the primary areas of operation within cutting-edge supply chain operations. In the e-commerce supply chain also logistics operations play a vital part. The logistics operations must be controlled effectively and efficiently since they deal with the high-cost besides environmental impacts. In e-commerce logistics operations, first-mile and last-mile delivery operations are considered as the operations with the highest costs incurred. So, e-commerce service providers are interested in optimizing their first-mile and last-mile delivery operations. Though it is known that the integration of first-mile pickup and last-mile deliveries will minimize the cost of transportation, there are more practical concerns to be taken into account when combining the first-mile pickup and last-mile delivery operations. Capacitated Vehicle Routing Problem (CVRP) is discussed in the literature as a solution approach for this kind of problems. The objective of this study is to provide a comprehensive overview of the current CVRP related literature, including models, algorithmic solution approaches, objectives, and industrial applications, with a focus of identifying interesting study paths for the future to improve distribution in e-commerce logistics networks by combining first-mile pickup and last-mile delivery operations. The findings of the study have demonstrated that constraints and features of Vehicle Routing Problem with Backhauls are very attractive with today's e-commerce operations, and the majority of the cited publications employed approximation methods rather than precise algorithms to solve these types of models.
{"title":"Solution approaches for combining first-mile pickup and last-mile delivery in an e-commerce logistic network: A systematic literature review","authors":"M. Ranathunga, A. Wijayanayake, D. Niwunhella","doi":"10.1109/scse53661.2021.9568349","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568349","url":null,"abstract":"Logistics is one of the primary areas of operation within cutting-edge supply chain operations. In the e-commerce supply chain also logistics operations play a vital part. The logistics operations must be controlled effectively and efficiently since they deal with the high-cost besides environmental impacts. In e-commerce logistics operations, first-mile and last-mile delivery operations are considered as the operations with the highest costs incurred. So, e-commerce service providers are interested in optimizing their first-mile and last-mile delivery operations. Though it is known that the integration of first-mile pickup and last-mile deliveries will minimize the cost of transportation, there are more practical concerns to be taken into account when combining the first-mile pickup and last-mile delivery operations. Capacitated Vehicle Routing Problem (CVRP) is discussed in the literature as a solution approach for this kind of problems. The objective of this study is to provide a comprehensive overview of the current CVRP related literature, including models, algorithmic solution approaches, objectives, and industrial applications, with a focus of identifying interesting study paths for the future to improve distribution in e-commerce logistics networks by combining first-mile pickup and last-mile delivery operations. The findings of the study have demonstrated that constraints and features of Vehicle Routing Problem with Backhauls are very attractive with today's e-commerce operations, and the majority of the cited publications employed approximation methods rather than precise algorithms to solve these types of models.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"46 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":"125759666","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/scse53661.2021.9568320
M. P. A. V. Gunawardhana, C. Jayatissa, J. A. Seneviratne
Thought identification has been the ultimate goal of brain-computer interface systems. However, due to the complex nature of brain signals, classification is difficult. But recent developments in deep learning have made the classification of multivariate time series data relatively easy. Studies have been carried out in the recent past to classify thoughts based on signals from medical-grade EEG devices. This study explores the possibility of thought identification using a commercially available EEG device using deep learning techniques. The crucial part of any EEG experiment is contamination-free data collection. Keeping the subject's mind concentrated only in the decided state is important, yet challenging. To address this issue, we have developed a graphical user interface (GUI) based program that allows stimulus controlling and data recording. With the use of the low-cost commercially available EEG device, accuracies up to 89% were achieved for the classification of high contrast signals. However, tests on complex thought identification did not produce statistically significant results over the chance accuracy.
{"title":"Thought identification through visual stimuli presentation from a commercially available EEG device","authors":"M. P. A. V. Gunawardhana, C. Jayatissa, J. A. Seneviratne","doi":"10.1109/scse53661.2021.9568320","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568320","url":null,"abstract":"Thought identification has been the ultimate goal of brain-computer interface systems. However, due to the complex nature of brain signals, classification is difficult. But recent developments in deep learning have made the classification of multivariate time series data relatively easy. Studies have been carried out in the recent past to classify thoughts based on signals from medical-grade EEG devices. This study explores the possibility of thought identification using a commercially available EEG device using deep learning techniques. The crucial part of any EEG experiment is contamination-free data collection. Keeping the subject's mind concentrated only in the decided state is important, yet challenging. To address this issue, we have developed a graphical user interface (GUI) based program that allows stimulus controlling and data recording. With the use of the low-cost commercially available EEG device, accuracies up to 89% were achieved for the classification of high contrast signals. However, tests on complex thought identification did not produce statistically significant results over the chance accuracy.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"47 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131623349","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/scse53661.2021.9568319
J. Premachandra, Ppnv Kumara
Climate variability in recent years has critically affected the usual aspects of human lives, where the agriculture sector can be considered as one of the most vulnerable. Sri Lanka is also facing these climate changes over the past few decades. It has resulted in rainfall pattern changes where the expected rain may not occur during the expected time and amount. The mismatch between the rainfall pattern and traditional seasonal cultivation schedule has critically affected the agricultural sustainability. Even with the current technological advancements, weather prediction is one of the most technically and scientifically challenging tasks. This paper presents a novel machine learning-based approach for predicting rainfall for precision agriculture in Sri Lanka and it can be recognized as the first attempt to validate machine learning models to predict the weather in Sri Lankan context for precision agriculture. By analyzing the nature of the weather in Sri Lanka, the relationship of weather attributes with agriculture, availability, and accessibility, seven attributes are selected including rain gauge, relative humidity, average temperature, wind speed, wind direction where solar radiation and ozone concentration are uniquely selected for Sri Lankan context. For the prediction model, cross-validated data are trained and tested with four machine learning algorithms: Multiple Linear Regression, K-Nearest Neighbors, Support Vector Machine, and Random Forest. Currently, Support Vector Machine, K-Nearest Neighbors models have achieved accuracies of 88.57%, 88.66%. Random Forest has been recognized as the best-fitted model with 89.16% accuracy. The results depict a significant accuracy in this novel approach for Sri Lankan weather prediction.
{"title":"A novel approach for weather prediction for agriculture in Sri Lanka using Machine Learning techniques","authors":"J. Premachandra, Ppnv Kumara","doi":"10.1109/scse53661.2021.9568319","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568319","url":null,"abstract":"Climate variability in recent years has critically affected the usual aspects of human lives, where the agriculture sector can be considered as one of the most vulnerable. Sri Lanka is also facing these climate changes over the past few decades. It has resulted in rainfall pattern changes where the expected rain may not occur during the expected time and amount. The mismatch between the rainfall pattern and traditional seasonal cultivation schedule has critically affected the agricultural sustainability. Even with the current technological advancements, weather prediction is one of the most technically and scientifically challenging tasks. This paper presents a novel machine learning-based approach for predicting rainfall for precision agriculture in Sri Lanka and it can be recognized as the first attempt to validate machine learning models to predict the weather in Sri Lankan context for precision agriculture. By analyzing the nature of the weather in Sri Lanka, the relationship of weather attributes with agriculture, availability, and accessibility, seven attributes are selected including rain gauge, relative humidity, average temperature, wind speed, wind direction where solar radiation and ozone concentration are uniquely selected for Sri Lankan context. For the prediction model, cross-validated data are trained and tested with four machine learning algorithms: Multiple Linear Regression, K-Nearest Neighbors, Support Vector Machine, and Random Forest. Currently, Support Vector Machine, K-Nearest Neighbors models have achieved accuracies of 88.57%, 88.66%. Random Forest has been recognized as the best-fitted model with 89.16% accuracy. The results depict a significant accuracy in this novel approach for Sri Lankan weather prediction.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"65 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":"116713585","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/scse53661.2021.9568293
Shehara Grabau, I. Hewapathirana
Since the early civilizations, transportation has played a significant role, from fulfilling basic human needs to contributing towards major economic growths all over the world. With the advancement in technology, the demand for smooth and hassle-free transportation increased and it is particularly true for road transportation in Sri Lanka as well. As a result, the expressway road network was introduced to Sri Lanka in 2011. Although a toll is payable for the use of expressways, many vehicle users prefer to utilize the expressway due to the extensive amount of time saved. Time is of utmost importance for expressway users. Hence, long queues and waiting time at toll plazas where the toll payment is made should be minimized. This study is aimed at analyzing the performance at the Peliyagoda toll plaza of the Colombo-Katunayake expressway where the formation of long queues and long waiting time in queues can be observed during peak hours. Due to the high complexity of using the analytical approach in obtaining the performance measures, a simulation approach was used with Arena Simulation Software. Few setup improvements were identified, and each of the setups were simulated to obtain the performance measures. Based on the comparison of the results, recommendations and suggestions to improve the efficiency of the operations at the Peliyagoda toll plaza have been outlined.
{"title":"Simulation analysis of an expressway toll plaza","authors":"Shehara Grabau, I. Hewapathirana","doi":"10.1109/scse53661.2021.9568293","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568293","url":null,"abstract":"Since the early civilizations, transportation has played a significant role, from fulfilling basic human needs to contributing towards major economic growths all over the world. With the advancement in technology, the demand for smooth and hassle-free transportation increased and it is particularly true for road transportation in Sri Lanka as well. As a result, the expressway road network was introduced to Sri Lanka in 2011. Although a toll is payable for the use of expressways, many vehicle users prefer to utilize the expressway due to the extensive amount of time saved. Time is of utmost importance for expressway users. Hence, long queues and waiting time at toll plazas where the toll payment is made should be minimized. This study is aimed at analyzing the performance at the Peliyagoda toll plaza of the Colombo-Katunayake expressway where the formation of long queues and long waiting time in queues can be observed during peak hours. Due to the high complexity of using the analytical approach in obtaining the performance measures, a simulation approach was used with Arena Simulation Software. Few setup improvements were identified, and each of the setups were simulated to obtain the performance measures. Based on the comparison of the results, recommendations and suggestions to improve the efficiency of the operations at the Peliyagoda toll plaza have been outlined.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"24 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":"131649446","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/scse53661.2021.9568288
Sobana Selvaratnam, B. Yogarajah, T. Jeyamugan, N. Ratnarajah
Machine learning models for predictions enable researchers to make effective decisions based on historical data. Automobile price prediction studies have been a most interesting research area in machine learning nowadays. The independent variables to model the price and the price predictions are equally important for automobile consumers and manufacturers. Automobile consulting companies determine how prices vary in relation to the independent variables and they can then adjust the automobile's design, commercial strategy, and other factors to fulfill specified price targets. Furthermore, the model will assist management in comprehending a company's pricing patterns. The ability of machine learning systems to predict outcomes is entirely dependent on the effective selection of features. In this paper, we determine the influencing features on automobile price using an integrated approach of LASSO and stepwise selection regression algorithms. We use multiple linear regression to build the model using the selected features. From the experimental results using the automobile dataset from the UCI machine learning repository, the influencing features on automobile price are width, engine size, city mpg, stroke, make, aspiration, number of doors, body style, and drive wheels. Training data accuracy for predicting price was found to be 92 %, and testing data accuracy was found to be 87%. The proposed approach supports selecting the most important characteristics of predicting the price of automobiles efficiently and effectively. This research will aid in the development of a model that uses the selected attributes to predict the price of automobiles using machine learning technologies.
{"title":"Feature selection in automobile price prediction: An integrated approach","authors":"Sobana Selvaratnam, B. Yogarajah, T. Jeyamugan, N. Ratnarajah","doi":"10.1109/scse53661.2021.9568288","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568288","url":null,"abstract":"Machine learning models for predictions enable researchers to make effective decisions based on historical data. Automobile price prediction studies have been a most interesting research area in machine learning nowadays. The independent variables to model the price and the price predictions are equally important for automobile consumers and manufacturers. Automobile consulting companies determine how prices vary in relation to the independent variables and they can then adjust the automobile's design, commercial strategy, and other factors to fulfill specified price targets. Furthermore, the model will assist management in comprehending a company's pricing patterns. The ability of machine learning systems to predict outcomes is entirely dependent on the effective selection of features. In this paper, we determine the influencing features on automobile price using an integrated approach of LASSO and stepwise selection regression algorithms. We use multiple linear regression to build the model using the selected features. From the experimental results using the automobile dataset from the UCI machine learning repository, the influencing features on automobile price are width, engine size, city mpg, stroke, make, aspiration, number of doors, body style, and drive wheels. Training data accuracy for predicting price was found to be 92 %, and testing data accuracy was found to be 87%. The proposed approach supports selecting the most important characteristics of predicting the price of automobiles efficiently and effectively. This research will aid in the development of a model that uses the selected attributes to predict the price of automobiles using machine learning technologies.","PeriodicalId":319650,"journal":{"name":"2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"55 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":"125713003","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/scse53661.2021.9568338
Abdul Cader Mohamed Nafrees, F. Shibly
Tourism that uses smart technology and practices to boost resource management and sustainability while growing their businesses' overall competitiveness is known as smart tourism. Information and communication technologies (ICTs) have had a profound impact on the tourism industry, and they continue to be the key drivers of tourism innovation. ICTs have fundamentally changed the way tourism products are developed, presented, and offered, according to the literature. Any empirical studies or experiments must be focused on accepted or formed hypotheses. In this regard, grounded theory measures were used for interpretation, while a systematic review was performed to assess the research scope from current studies and works. The main goal of the study is to investigate and propose long-lasting and stable smart technologies for implementing smart tourism. Grounded theory is a concept that uses methodical rules to gather and dissect data in order to construct an unbiased theory. Fewer studies on smart technology in tourism have been conducted, with a majority of them concentrating on IoT, virtual and augmented reality, big data, cloud computing, and mobile applications. In either case, there is space for further investigation into this important field of study. As a result, this paper is a vital first step toward a clearer understanding of how smart technology can be applied to the tourism industry. The number of available research work on smart technologies in tourism were fewer from the selected journals and conference proceedings, which led to the accessibility of lesser data for analysis.
{"title":"Smart technologies in tourism: a study using systematic review and grounded theory","authors":"Abdul Cader Mohamed Nafrees, F. Shibly","doi":"10.1109/scse53661.2021.9568338","DOIUrl":"https://doi.org/10.1109/scse53661.2021.9568338","url":null,"abstract":"Tourism that uses smart technology and practices to boost resource management and sustainability while growing their businesses' overall competitiveness is known as smart tourism. Information and communication technologies (ICTs) have had a profound impact on the tourism industry, and they continue to be the key drivers of tourism innovation. ICTs have fundamentally changed the way tourism products are developed, presented, and offered, according to the literature. Any empirical studies or experiments must be focused on accepted or formed hypotheses. In this regard, grounded theory measures were used for interpretation, while a systematic review was performed to assess the research scope from current studies and works. The main goal of the study is to investigate and propose long-lasting and stable smart technologies for implementing smart tourism. Grounded theory is a concept that uses methodical rules to gather and dissect data in order to construct an unbiased theory. Fewer studies on smart technology in tourism have been conducted, with a majority of them concentrating on IoT, virtual and augmented reality, big data, cloud computing, and mobile applications. In either case, there is space for further investigation into this important field of study. As a result, this paper is a vital first step toward a clearer understanding of how smart technology can be applied to the tourism industry. The number of available research work on smart technologies in tourism were fewer from the selected journals and conference proceedings, which led to the accessibility of lesser data for analysis.","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":"132671284","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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