Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9764991
Mirna Abou Mjahed, F. Ben Abdelaziz, H. Tarhini
By collaborating horizontally, firms are expected to achieve considerable improvement. Most optimization approaches in coalition-based supply chain collaboration are single objective targeting cost reduction or profit maximization. Real-world decision problems usually require the investigation of more than one criterion to achieve a sustainable progress. In this paper, we study the collaborative facility and fleet sharing among firms at the same horizontal layer of the supply networks and explore the benefits of forming coalitions. We consider the case of suppliers operating their own distribution centers. Such firms have incentives to minimize their operational costs by optimizing inventory levels at warehouses, replenishment process and distribution to customers. The aim is to look at the trade-off between cost of the logistic service (warehousing and transportation) and maintaining customer satisfaction and loyalty by keeping, when possible, delivery service internal to each firm. The problem is a coalition formation modeled as a cooperative multiobjective game.
{"title":"Coalition Formation for Horizontal Supply Chain Collaboration: A Multiobjective Approach","authors":"Mirna Abou Mjahed, F. Ben Abdelaziz, H. Tarhini","doi":"10.1109/DASA54658.2022.9764991","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9764991","url":null,"abstract":"By collaborating horizontally, firms are expected to achieve considerable improvement. Most optimization approaches in coalition-based supply chain collaboration are single objective targeting cost reduction or profit maximization. Real-world decision problems usually require the investigation of more than one criterion to achieve a sustainable progress. In this paper, we study the collaborative facility and fleet sharing among firms at the same horizontal layer of the supply networks and explore the benefits of forming coalitions. We consider the case of suppliers operating their own distribution centers. Such firms have incentives to minimize their operational costs by optimizing inventory levels at warehouses, replenishment process and distribution to customers. The aim is to look at the trade-off between cost of the logistic service (warehousing and transportation) and maintaining customer satisfaction and loyalty by keeping, when possible, delivery service internal to each firm. The problem is a coalition formation modeled as a cooperative multiobjective game.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115401142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9765002
Kim Carol Maligalig, Albertson D. Amante, Ryan R. Tejada, Roger S. Tamargo, Al Ferrer Santiago
Accidents can happen at any time and in any location, so emergency vehicles are essential in any emergency or life-threatening circumstance. However, due to lots of people owning cars, traffic jam is a severe problem in many cities. These traffic jams have an impact on emergency vehicles, particularly ambulances, as well as other vehicles such as fire trucks and police cars. The purpose of this research is to develop an emergency vehicle detection system that will assist law enforcement in mandating traffic when emergency vehicles are on the road. The researcher used deep learning, specifically the YOLov3 technique in developing the detection system wherein it will utilize CNN in implementation. The highest mAP value out of 25 models was obtained by the detection system is 98.78% by model 21.
{"title":"Machine Vision System of Emergency Vehicle Detection System Using Deep Transfer Learning","authors":"Kim Carol Maligalig, Albertson D. Amante, Ryan R. Tejada, Roger S. Tamargo, Al Ferrer Santiago","doi":"10.1109/DASA54658.2022.9765002","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765002","url":null,"abstract":"Accidents can happen at any time and in any location, so emergency vehicles are essential in any emergency or life-threatening circumstance. However, due to lots of people owning cars, traffic jam is a severe problem in many cities. These traffic jams have an impact on emergency vehicles, particularly ambulances, as well as other vehicles such as fire trucks and police cars. The purpose of this research is to develop an emergency vehicle detection system that will assist law enforcement in mandating traffic when emergency vehicles are on the road. The researcher used deep learning, specifically the YOLov3 technique in developing the detection system wherein it will utilize CNN in implementation. The highest mAP value out of 25 models was obtained by the detection system is 98.78% by model 21.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115632572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9765012
S. Kajanan, B. Kumara, Kuhaneswaran Banujan, S. Prasanth, K. Manitheepan
Analysis of Arterial Blood Gas (ABG) is an important investigation to measure oxygenation and blood acid levels. It is crucial in measuring the clinical status and contributes to an efficient and effective healthcare plan. Generally, ABG is applied in the emergency care units (ECU) and intensive care units (ICU). Most of the time, the doctors and nurses have difficulties identifying the type of respiratory failure with the help of ABG test results. So, during this research with the adaption of certain supervised machine learning approaches, namely Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Catboost, Random Forest, Naïve Bayes, Support Vector Machine (SVM), LightGBM, K-Nearest Neighbors (KNN), Neural Network (NN) and Decision Tree and have been incorporated with the intension of identifying the type of the respiratory failure with the highest accurate technique. To fulfil this purpose, 700 patient test results have been obtained from a public hospital in Sri Lanka. From the results discovered, XGBoost outperformed against all other techniques in identifying the type of respiratory failure with the highest accuracy of 98.65% and the lowest error rate of 1.35%. To ensure whether the XGBoost outperformed against the different percentages of training and testing data, K-fold cross-validation with five folds also has been performed with the dataset. The cross-validation produces results with an accuracy of 98.45% and the lowest error rate of 1.55%. In conclusion, XGBoost has been utilised in developing the prediction model. This would be a promising start for a future research scholar to adopt the hybrid techniques and the deep learning techniques to identify the causes of respiratory failure and the prediction of the type of respiratory failure.
{"title":"Classify the Outcome of Arterial Blood Gas Test to Detect the Respiratory Failure Using Machine Learning","authors":"S. Kajanan, B. Kumara, Kuhaneswaran Banujan, S. Prasanth, K. Manitheepan","doi":"10.1109/DASA54658.2022.9765012","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765012","url":null,"abstract":"Analysis of Arterial Blood Gas (ABG) is an important investigation to measure oxygenation and blood acid levels. It is crucial in measuring the clinical status and contributes to an efficient and effective healthcare plan. Generally, ABG is applied in the emergency care units (ECU) and intensive care units (ICU). Most of the time, the doctors and nurses have difficulties identifying the type of respiratory failure with the help of ABG test results. So, during this research with the adaption of certain supervised machine learning approaches, namely Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Catboost, Random Forest, Naïve Bayes, Support Vector Machine (SVM), LightGBM, K-Nearest Neighbors (KNN), Neural Network (NN) and Decision Tree and have been incorporated with the intension of identifying the type of the respiratory failure with the highest accurate technique. To fulfil this purpose, 700 patient test results have been obtained from a public hospital in Sri Lanka. From the results discovered, XGBoost outperformed against all other techniques in identifying the type of respiratory failure with the highest accuracy of 98.65% and the lowest error rate of 1.35%. To ensure whether the XGBoost outperformed against the different percentages of training and testing data, K-fold cross-validation with five folds also has been performed with the dataset. The cross-validation produces results with an accuracy of 98.45% and the lowest error rate of 1.55%. In conclusion, XGBoost has been utilised in developing the prediction model. This would be a promising start for a future research scholar to adopt the hybrid techniques and the deep learning techniques to identify the causes of respiratory failure and the prediction of the type of respiratory failure.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125233460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9764970
Chanin Tungpantong, P. Nilsook, P. Wannapiroon
This research aims to apply confirmatory factor analysis to identify the enterprise architecture components for higher education institutions. The research sample comprised 300 personnel from agencies within higher education institutions, which are higher education institutions under the Ministry of Higher Education, Science, Research and Innovation that use the database system of educational quality assurance called Commission on Higher Education Quality Assessment online system (CHE QA Online). The selection resulted from multi-stage random sampling from 100 higher education instructions. The research tool was an online questionnaire form on factors influencing the enterprise architecture in the digital transformation for higher education institutions by 5-level rating scale based on the Likert's scale. The result revealed that the enterprise architecture factor is consistent with empirical data (p-value = 0.370), which comprise 5 components: 1) Business 2) Data/Information 3) Application 4) Infrastructure and 5) Security. The research findings help higher education institutions design their blueprint for the institutional transformation to a digital organization.
{"title":"Confirmatory Factor Analysis of Enterprise Architecture for Higher Education Institutions","authors":"Chanin Tungpantong, P. Nilsook, P. Wannapiroon","doi":"10.1109/DASA54658.2022.9764970","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9764970","url":null,"abstract":"This research aims to apply confirmatory factor analysis to identify the enterprise architecture components for higher education institutions. The research sample comprised 300 personnel from agencies within higher education institutions, which are higher education institutions under the Ministry of Higher Education, Science, Research and Innovation that use the database system of educational quality assurance called Commission on Higher Education Quality Assessment online system (CHE QA Online). The selection resulted from multi-stage random sampling from 100 higher education instructions. The research tool was an online questionnaire form on factors influencing the enterprise architecture in the digital transformation for higher education institutions by 5-level rating scale based on the Likert's scale. The result revealed that the enterprise architecture factor is consistent with empirical data (p-value = 0.370), which comprise 5 components: 1) Business 2) Data/Information 3) Application 4) Infrastructure and 5) Security. The research findings help higher education institutions design their blueprint for the institutional transformation to a digital organization.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116712588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9765232
Aakash Gupta, Nataraj Das
Following the pandemic, customers, preference for using e-commerce has accelerated. Since much information is available in multiple reviews (sometimes running in thousands) for a single product, it can create decision paralysis for the buyer. This scenario disempowers the consumer, who cannot be expected to go over so many reviews since its time consuming and can confuse them. Various commercial tools are available, that use a scoring mechanism to arrive at an adjusted score. It can alert the user to potential review manipulations. This paper proposes a framework that fine-tunes a generative pre-trained transformer to understand these reviews better. Furthermore, using "common-sense" to make better decisions. These models have more than 13 billion parameters. To fine-tune the model for our requirement, we use the curie engine from generative pre-trained transformer (GPT3). By using generative models, we are introducing abstractive summarization. Instead of using a simple extractive method of summarizing the reviews. This brings out the true relationship between the reviews and not simply copy-paste. This introduces an element of "common sense" for the user and helps them to quickly make the right decisions. The user is provided the pros and cons of the processed reviews. Thus the user/customer can take their own decisions.
{"title":"ProdRev: A DNN framework for empowering customers using generative pre-trained transformers","authors":"Aakash Gupta, Nataraj Das","doi":"10.1109/DASA54658.2022.9765232","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765232","url":null,"abstract":"Following the pandemic, customers, preference for using e-commerce has accelerated. Since much information is available in multiple reviews (sometimes running in thousands) for a single product, it can create decision paralysis for the buyer. This scenario disempowers the consumer, who cannot be expected to go over so many reviews since its time consuming and can confuse them. Various commercial tools are available, that use a scoring mechanism to arrive at an adjusted score. It can alert the user to potential review manipulations. This paper proposes a framework that fine-tunes a generative pre-trained transformer to understand these reviews better. Furthermore, using \"common-sense\" to make better decisions. These models have more than 13 billion parameters. To fine-tune the model for our requirement, we use the curie engine from generative pre-trained transformer (GPT3). By using generative models, we are introducing abstractive summarization. Instead of using a simple extractive method of summarizing the reviews. This brings out the true relationship between the reviews and not simply copy-paste. This introduces an element of \"common sense\" for the user and helps them to quickly make the right decisions. The user is provided the pros and cons of the processed reviews. Thus the user/customer can take their own decisions.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116922730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9765102
U. Maiwada, Kamaluddeen Usman Danyaro, A. Sarlan
The usage of femtocells, or micro macrocell base stations, increases energy efficiency which enhance quality of service for both indoor and outdoor customers. Femtocells were used in 4GP’s (Fourth Generation Project) LTE (Long Term Evolution) and advanced LTE like the 5G/6G networks to improve indoor coverage and capacity of the network. However, the random deployment of femtocells, as well as the large number and size variables, make controlling mobility even more difficult because mobile users increase day by day. This research investigates energy efficiency for femtocell mobility state detection algorithms to increase the QoS in LTE and advanced LTE networks (5G/6G networks). Several systems for detecting movement are currently in place. However, when it comes to cell type information and parameter scaling difficulties, they are found wanting as they help to improve the QoS. Overall handover performance suffers because of this gap that present techniques fail to address. As a remedy, this study presents an Improved Mobility State Detection Mechanism (IMSDM). As a result of the findings, IMSDM appears to be a viable way to improve energy efficiency for handover performance deterioration to increase the QoS and information about the cell type problems. It did not minimize the probability on Radio Link Failure (RLF), but it did give a decent trade-off among RLF likelihood since it is reduced and the quantity of Ping-Pong handovers.
{"title":"An improved mobility state detection mechanism for femtocells in LTE networks","authors":"U. Maiwada, Kamaluddeen Usman Danyaro, A. Sarlan","doi":"10.1109/DASA54658.2022.9765102","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765102","url":null,"abstract":"The usage of femtocells, or micro macrocell base stations, increases energy efficiency which enhance quality of service for both indoor and outdoor customers. Femtocells were used in 4GP’s (Fourth Generation Project) LTE (Long Term Evolution) and advanced LTE like the 5G/6G networks to improve indoor coverage and capacity of the network. However, the random deployment of femtocells, as well as the large number and size variables, make controlling mobility even more difficult because mobile users increase day by day. This research investigates energy efficiency for femtocell mobility state detection algorithms to increase the QoS in LTE and advanced LTE networks (5G/6G networks). Several systems for detecting movement are currently in place. However, when it comes to cell type information and parameter scaling difficulties, they are found wanting as they help to improve the QoS. Overall handover performance suffers because of this gap that present techniques fail to address. As a remedy, this study presents an Improved Mobility State Detection Mechanism (IMSDM). As a result of the findings, IMSDM appears to be a viable way to improve energy efficiency for handover performance deterioration to increase the QoS and information about the cell type problems. It did not minimize the probability on Radio Link Failure (RLF), but it did give a decent trade-off among RLF likelihood since it is reduced and the quantity of Ping-Pong handovers.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121245472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9765071
Halbast Rashid Ismael, S. Ameen
The unprecedented COVID-19 incident created many challenges for higher education institutions. This case brought online examinations and E-learning to the spotlight after many universities refuged to e-assessment and online teaching. However, one of the main obstacles with online teaching and learning is the e-assessments transparency especially in Iraq universities and Kurdistan Region universities. Thus, the aim of the paper is to understand the experiences of students and lecturers in Duhok Polytechnic University (DPU) situated at KRG Iraq with online assessment. The paper investigates via questionnaire designed for this purpose the DPU participants with online assessments to show how are they are familiar with online exams, determine the most important problems that appeared during online examinations. The results from the questionnaire are analyzed and assessed determine factors affecting the quality of online learning and e-assessment transparency. Finally, solutions suggestions with best measures to assure the transparency and quality of online examination are recommended.
{"title":"Investigation and Development of Transparent Online Assessment: A Case Study at DPU","authors":"Halbast Rashid Ismael, S. Ameen","doi":"10.1109/DASA54658.2022.9765071","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765071","url":null,"abstract":"The unprecedented COVID-19 incident created many challenges for higher education institutions. This case brought online examinations and E-learning to the spotlight after many universities refuged to e-assessment and online teaching. However, one of the main obstacles with online teaching and learning is the e-assessments transparency especially in Iraq universities and Kurdistan Region universities. Thus, the aim of the paper is to understand the experiences of students and lecturers in Duhok Polytechnic University (DPU) situated at KRG Iraq with online assessment. The paper investigates via questionnaire designed for this purpose the DPU participants with online assessments to show how are they are familiar with online exams, determine the most important problems that appeared during online examinations. The results from the questionnaire are analyzed and assessed determine factors affecting the quality of online learning and e-assessment transparency. Finally, solutions suggestions with best measures to assure the transparency and quality of online examination are recommended.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124902517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9765095
Nattaphon Rangsaritvorakarn, Suthep Nimsai, Korawit Fakkhong, C. Jongsureyapart
This study aimed to explore and compare machine learning performance to predict the customer purchasing decision within premium beef shops. The sampling locations were Thailand. The population used in the study consisted of 436 valid responses from 5 premium beef shops. The data was obtained by using questionnaires consisting of gender, age, three questions of product, one question for the price, one question for the place, and three questions for appearances. The study was used four classifier’s algorithms: k- nearest neighbors, decision tree, random forest, and xgboost model. The models were compared to find the highest accuracy for premium beef customer behavior data set. Random forest algorithms were evaluated to have the best performance in predicting premium beef purchasing decisions in Thailand. The model has an accuracy of 88.62 percent, precision of 88.46 percent, recall of 85.19 percent, f1 of 86.79 percent, and AUC of 95 percent. The two important elements that influence purchasing decisions are price and product age. The most accurate algorithms can be used to forecast consumer product purchases and comprehend the principles of elements that influence buying decisions.
{"title":"An Experimental Comparison of Classification Algorithms for Premium Beef Customer Buying Intention","authors":"Nattaphon Rangsaritvorakarn, Suthep Nimsai, Korawit Fakkhong, C. Jongsureyapart","doi":"10.1109/DASA54658.2022.9765095","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765095","url":null,"abstract":"This study aimed to explore and compare machine learning performance to predict the customer purchasing decision within premium beef shops. The sampling locations were Thailand. The population used in the study consisted of 436 valid responses from 5 premium beef shops. The data was obtained by using questionnaires consisting of gender, age, three questions of product, one question for the price, one question for the place, and three questions for appearances. The study was used four classifier’s algorithms: k- nearest neighbors, decision tree, random forest, and xgboost model. The models were compared to find the highest accuracy for premium beef customer behavior data set. Random forest algorithms were evaluated to have the best performance in predicting premium beef purchasing decisions in Thailand. The model has an accuracy of 88.62 percent, precision of 88.46 percent, recall of 85.19 percent, f1 of 86.79 percent, and AUC of 95 percent. The two important elements that influence purchasing decisions are price and product age. The most accurate algorithms can be used to forecast consumer product purchases and comprehend the principles of elements that influence buying decisions.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125145688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9765275
M. Masuduzzaman, R. Nugraha, S. Shin
Monitoring the CO2 gas level in a smart factory is essential as the high levels of CO2 gas negatively affect the human body, causing various physical problems. This paper presents an Internet of Things (IoT) based CO2 gas level monitoring and automated decision-making system inside a smart factory using the unmanned aerial vehicle (UAV) and multi-access edge computing (MEC) technique. Firstly, different IoT device is used to continuously monitor and detect the CO2 gas level data using gas sensors. Due to the drawback of sink node failure and the centralized data collection technique of wireless sensor networks, a UAV-based continuous CO2 gas level monitoring approach has been introduced in this study. Moreover, the MEC-enabled data processing technique is utilized by offloading the sensor data from the UAV considering its limited battery capacity and low processing power. Finally, a blockchain-based secure decision-making system is designed to evacuate the smart factory premises by alerting all employees in an emergency case of an excessive level of CO2 gas existence. Result analysis shows that the IoT devices can successfully monitor and detect the CO2 gas level in the smart factory using the UAV. Furthermore, the UAV can securely offload sensor data to the MEC server to analyze and make an automated decision to alert all employees in a smart factory to evacuate if CO2 levels are too high.
{"title":"IoT-based CO2 Gas-level Monitoring and Automated Decision-making System in Smart Factory using UAV-assisted MEC","authors":"M. Masuduzzaman, R. Nugraha, S. Shin","doi":"10.1109/DASA54658.2022.9765275","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765275","url":null,"abstract":"Monitoring the CO2 gas level in a smart factory is essential as the high levels of CO2 gas negatively affect the human body, causing various physical problems. This paper presents an Internet of Things (IoT) based CO2 gas level monitoring and automated decision-making system inside a smart factory using the unmanned aerial vehicle (UAV) and multi-access edge computing (MEC) technique. Firstly, different IoT device is used to continuously monitor and detect the CO2 gas level data using gas sensors. Due to the drawback of sink node failure and the centralized data collection technique of wireless sensor networks, a UAV-based continuous CO2 gas level monitoring approach has been introduced in this study. Moreover, the MEC-enabled data processing technique is utilized by offloading the sensor data from the UAV considering its limited battery capacity and low processing power. Finally, a blockchain-based secure decision-making system is designed to evacuate the smart factory premises by alerting all employees in an emergency case of an excessive level of CO2 gas existence. Result analysis shows that the IoT devices can successfully monitor and detect the CO2 gas level in the smart factory using the UAV. Furthermore, the UAV can securely offload sensor data to the MEC server to analyze and make an automated decision to alert all employees in a smart factory to evacuate if CO2 levels are too high.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126852067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1109/DASA54658.2022.9765098
Nadia Shahrin Chandni, M. Ismail, A. M. Muzahidul Islam
Air pollution is invariably responsible for our health deterioration in many ways. In most cases, this health deterioration may cause severe illness to death. It is possible to reduce the effect of air pollution only if we get the real-time solution. As we spend most of our time in the building or in an indoor space it would be wise to monitor the surrounding air and get a notification through message or alert using IoT-based devices while there will be the presence of air pollutants. This paper is based on the review of different journal and survey papers. It reviews the different systems which can provide an IoT solution for Indoor Air Quality Monitoring to develop a smart health care environment.
{"title":"IoT Driven Solution for Indoor Air Quality Monitoring System to Develop a Smart Healthcare Environment: A Review Based Study","authors":"Nadia Shahrin Chandni, M. Ismail, A. M. Muzahidul Islam","doi":"10.1109/DASA54658.2022.9765098","DOIUrl":"https://doi.org/10.1109/DASA54658.2022.9765098","url":null,"abstract":"Air pollution is invariably responsible for our health deterioration in many ways. In most cases, this health deterioration may cause severe illness to death. It is possible to reduce the effect of air pollution only if we get the real-time solution. As we spend most of our time in the building or in an indoor space it would be wise to monitor the surrounding air and get a notification through message or alert using IoT-based devices while there will be the presence of air pollutants. This paper is based on the review of different journal and survey papers. It reviews the different systems which can provide an IoT solution for Indoor Air Quality Monitoring to develop a smart health care environment.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128719327","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：