Pub Date : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029671
Smirthya Somaskantha Iyer, S. Kamalakkannan, A. Kulatunga
The quality of the student's learning process has always been a concern and the educational paradigm requires a change in the method of the teaching and learning process to improve the quality of the education system. As such, there are a variety of modern teaching and learning methods currently in use that include traditional classroom methods, online learning, learning management systems (LMS), and massive open online courses (MOOCs), and this paves the way for blended learning (BL). In that respect, BL is capable and can provide extensive knowledge and experience beyond current conventional BL practices. Accordingly, there are many advanced technologies and tools, especially Augmented Reality (AR) and Virtual Reality (VR) supporting the teaching and learning process, which can be integrated with BL practice. Hence, integrating AR technologies along with BL will be the advancement of current BL practice. Accordingly, this study aims to integrate AR with BL to educate Manufacturing and industrial engineering as a conceptual framework. The main focus of this study is to conceptualize a development of an AR application that can be used to achieve higher-order level manufacturing and industrial engineering knowledge both at home and in classroom environments. Consequently, the AR application will be incorporated through a QR code and LMS, enabling students to experience and understand the curriculum from anywhere on a digital platform, especially in home and classroom environments.
{"title":"Integration of Augmented Reality With Blended Learning to Educate Manufacturing and Industrial Engineering: A Conceptual Framework","authors":"Smirthya Somaskantha Iyer, S. Kamalakkannan, A. Kulatunga","doi":"10.1109/SKIMA57145.2022.10029671","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029671","url":null,"abstract":"The quality of the student's learning process has always been a concern and the educational paradigm requires a change in the method of the teaching and learning process to improve the quality of the education system. As such, there are a variety of modern teaching and learning methods currently in use that include traditional classroom methods, online learning, learning management systems (LMS), and massive open online courses (MOOCs), and this paves the way for blended learning (BL). In that respect, BL is capable and can provide extensive knowledge and experience beyond current conventional BL practices. Accordingly, there are many advanced technologies and tools, especially Augmented Reality (AR) and Virtual Reality (VR) supporting the teaching and learning process, which can be integrated with BL practice. Hence, integrating AR technologies along with BL will be the advancement of current BL practice. Accordingly, this study aims to integrate AR with BL to educate Manufacturing and industrial engineering as a conceptual framework. The main focus of this study is to conceptualize a development of an AR application that can be used to achieve higher-order level manufacturing and industrial engineering knowledge both at home and in classroom environments. Consequently, the AR application will be incorporated through a QR code and LMS, enabling students to experience and understand the curriculum from anywhere on a digital platform, especially in home and classroom environments.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129533449","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029583
Amit Kumar Bairwa, Sandeep Joshi
Mobile Ad-hoc Networks (MANETs) are a wireless network technology still in its early stages. Even when the network believes that all of its mobile nodes are trusted, it is difficult to detect the malicious nodes in the actual scenario. As a result, it is critical to propose a technique to ensure security by picking the best data forwarding path. Dingo Optimizer (DOX), a meta-heuristic model for secure routing, is presented in this paper. The multi-objective optimization model considers several Quality of Service (QoS) characteristics, including distance, energy latency, connection lifetime, and trust. A fitness function is proposed for the optimum route selection using the predicted multi-objective parameters. Three measures are used to assess the proposed algorithm's performance: Packet Delivery Ratio (PDR), End to End Delay (EED), and Routing Overhead (RO). Finally, it is compared with the MCFEER, PEER, ReTE-AODV, EESMR, and dynamic fuzzy clustering. The base protocol performance is always observed high, but if we compare the performance of the proposed protocol with the other developed protocols, then we can say that TRU-AODV has a high PDR. During the analysis of E2E, it's being observed that the proposed protocol has a minimum delay at every increasing node count.
{"title":"ETB-SRF: An Efficient Trust-Based Secure Routing framework in Mobile Ad-Hoc Networks","authors":"Amit Kumar Bairwa, Sandeep Joshi","doi":"10.1109/SKIMA57145.2022.10029583","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029583","url":null,"abstract":"Mobile Ad-hoc Networks (MANETs) are a wireless network technology still in its early stages. Even when the network believes that all of its mobile nodes are trusted, it is difficult to detect the malicious nodes in the actual scenario. As a result, it is critical to propose a technique to ensure security by picking the best data forwarding path. Dingo Optimizer (DOX), a meta-heuristic model for secure routing, is presented in this paper. The multi-objective optimization model considers several Quality of Service (QoS) characteristics, including distance, energy latency, connection lifetime, and trust. A fitness function is proposed for the optimum route selection using the predicted multi-objective parameters. Three measures are used to assess the proposed algorithm's performance: Packet Delivery Ratio (PDR), End to End Delay (EED), and Routing Overhead (RO). Finally, it is compared with the MCFEER, PEER, ReTE-AODV, EESMR, and dynamic fuzzy clustering. The base protocol performance is always observed high, but if we compare the performance of the proposed protocol with the other developed protocols, then we can say that TRU-AODV has a high PDR. During the analysis of E2E, it's being observed that the proposed protocol has a minimum delay at every increasing node count.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130921465","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029442
T. Siewierski, Adrian Malarek
The paper deals with economic optimization of the installed capacity of residential battery storage, as well as the day-ahead scheduling of energy storage to minimize the prosumer's price and volume risk at the balancing market in the case of small residential PV installations. A deterministic Linear Programming model has been used for battery dimensioning and a Stochastic Linear Programming model based on a day-ahead PV production forecast has been applied for the optimal use of available storage capacity. Both models have been tested using data from a small residential PV installation and household, prices from the Polish market. The paper focused on scenarios with a fully-fledged retail market, with balancing rules consistent with the wholesale segment. The results show that the current prices on the balancing market and high investment costs do not encourage the installation of energy storage, but the price trend of storage tank costs indicates that this situation may change soon. Stochastic linear programming turned out to be an adequate tool for planning the work of residential battery storage facilities, improving the economic effects of using battery storage with deterministic methods.
{"title":"Optimization of Battery Storage Capacity and Operation for Balancing Residential PV Installation","authors":"T. Siewierski, Adrian Malarek","doi":"10.1109/SKIMA57145.2022.10029442","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029442","url":null,"abstract":"The paper deals with economic optimization of the installed capacity of residential battery storage, as well as the day-ahead scheduling of energy storage to minimize the prosumer's price and volume risk at the balancing market in the case of small residential PV installations. A deterministic Linear Programming model has been used for battery dimensioning and a Stochastic Linear Programming model based on a day-ahead PV production forecast has been applied for the optimal use of available storage capacity. Both models have been tested using data from a small residential PV installation and household, prices from the Polish market. The paper focused on scenarios with a fully-fledged retail market, with balancing rules consistent with the wholesale segment. The results show that the current prices on the balancing market and high investment costs do not encourage the installation of energy storage, but the price trend of storage tank costs indicates that this situation may change soon. Stochastic linear programming turned out to be an adequate tool for planning the work of residential battery storage facilities, improving the economic effects of using battery storage with deterministic methods.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124489553","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}
This project studies the effects of charging electric vehicles on Phuentsholing's distribution network at various penetration levels. In the DIgSILENT power factory, an existing network was modeled and an analysis was performed for the aggregated domestic load with EV load based on two potential scenarios: low and high levels of uptake of EVs. The charging pattern of an electric vehicle may vary depending on the user's preferences. Two sub-EV charging scenarios (all EV charging at the same time and 50% of EV charging at the same time) were studied to analyze the impact. The scenarios are being formulated in consultation with the distribution engineers. Voltage stability and transformer loading are two significant areas that were assessed. The result indicated a technical violation in the year 2025 under high uptake when all EVs charged at the same time. Further, a worse case was observed in the year 2030, with high uptake, when all EVs charged at the same time. Low voltage issues and transformer overloading were the main causes of network instability. Random quick charger loads on distribution feeders contribute to load unbalance at the feeder level, whereas home chargers cause phase unbalance and voltage dips at the household level. These findings suggest that high EV penetration has a considerable impact on the optimal operation of a distribution system. A mitigation strategy for voltage correction and transformer loading has been presented based on the simulation results
{"title":"Electric Vehicle Impact Assessment on Distribution Network of Phuentsholing","authors":"Manoj Sharma, Tempa Sangay, Kuenzang Rangrik, Ngawang Dorji, Sangay Chophel","doi":"10.1109/SKIMA57145.2022.10029476","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029476","url":null,"abstract":"This project studies the effects of charging electric vehicles on Phuentsholing's distribution network at various penetration levels. In the DIgSILENT power factory, an existing network was modeled and an analysis was performed for the aggregated domestic load with EV load based on two potential scenarios: low and high levels of uptake of EVs. The charging pattern of an electric vehicle may vary depending on the user's preferences. Two sub-EV charging scenarios (all EV charging at the same time and 50% of EV charging at the same time) were studied to analyze the impact. The scenarios are being formulated in consultation with the distribution engineers. Voltage stability and transformer loading are two significant areas that were assessed. The result indicated a technical violation in the year 2025 under high uptake when all EVs charged at the same time. Further, a worse case was observed in the year 2030, with high uptake, when all EVs charged at the same time. Low voltage issues and transformer overloading were the main causes of network instability. Random quick charger loads on distribution feeders contribute to load unbalance at the feeder level, whereas home chargers cause phase unbalance and voltage dips at the household level. These findings suggest that high EV penetration has a considerable impact on the optimal operation of a distribution system. A mitigation strategy for voltage correction and transformer loading has been presented based on the simulation results","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122679499","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029572
Lijuan Ren, A. Seklouli, Tao Wang, Haiqing Zhang, A. Bouras
Long-lasting and difficult-to-treat, hypertension frequently leads to serious and life-threatening diseases. As a result, early risk assessment and prevention of hypertension are crucial. The majority of research currently available ignore the preprocessing analysis of real medical data, particularly the analysis of missing values, in favor of using clean data to increase the performance of hypertension prediction. Thus, in this study, real but incomplete data were subjected to preprocessing analysis including missing value analysis and feature divergence analysis, and then a Bayesian optimization technique was employed to find the optimal random forest model. Experimental results showed that proper missing value strategy (i.e., MissForest) can slightly enhance the data quality and produce slightly better predictive performance (from 0.001% to 0.069%) even the missing rate is less than 1%. Additionally, compared to using the original features, removing some features with little divergence can lower the dimensionality and even marginally enhance performance by 0.161% in terms of median AUC across 50 runs. Furthermore, the optimal random forest can demonstrate better hypertension discrimination in real medical data. In our case, the optimal random forest can improve the performance of the non-optimized forest by up to 3.51%.
{"title":"Hypertension Prediction Using Optimal Random Forest and Real Medical Data","authors":"Lijuan Ren, A. Seklouli, Tao Wang, Haiqing Zhang, A. Bouras","doi":"10.1109/SKIMA57145.2022.10029572","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029572","url":null,"abstract":"Long-lasting and difficult-to-treat, hypertension frequently leads to serious and life-threatening diseases. As a result, early risk assessment and prevention of hypertension are crucial. The majority of research currently available ignore the preprocessing analysis of real medical data, particularly the analysis of missing values, in favor of using clean data to increase the performance of hypertension prediction. Thus, in this study, real but incomplete data were subjected to preprocessing analysis including missing value analysis and feature divergence analysis, and then a Bayesian optimization technique was employed to find the optimal random forest model. Experimental results showed that proper missing value strategy (i.e., MissForest) can slightly enhance the data quality and produce slightly better predictive performance (from 0.001% to 0.069%) even the missing rate is less than 1%. Additionally, compared to using the original features, removing some features with little divergence can lower the dimensionality and even marginally enhance performance by 0.161% in terms of median AUC across 50 runs. Furthermore, the optimal random forest can demonstrate better hypertension discrimination in real medical data. In our case, the optimal random forest can improve the performance of the non-optimized forest by up to 3.51%.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123087634","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029495
Ahmed H. Okilly, Jonghyuk Lee, Namhun Kim, S. Kwak, J. Baek
power quality is an essential consideration in design of the power supply in high power industrial applications. Thus, this paper covers the design of the different magnetic components, switching element selection considerations, and the voltage current controllers optimal design on the basis of small-signal stability modeling and an adequate stability criterion to enhance the high-power factor (PF), high efficiency and lower current distortions. The proposed converter contains two stages, the power factor correction (PFC) stage and the isolated phase-shift PWM zero-voltage switching (ZVS) DC-DC converter stage. The proposed two-stage converter's total harmonic distortions (THDs), voltage and current ripples, conversion efficiency, and PF performance are investigated using PSIM simulations under various operating conditions. This study designs an industrial 2000 W, 54 V telecom AC-DC supply with a PF of more than 99%, a THD of about 5 %, and conversion efficiency of around 93% at full load.
{"title":"Design and Optimal Control of a Two-Stage Efficient and High PF AC-DC Converter for High-Power Density Industrial Applications","authors":"Ahmed H. Okilly, Jonghyuk Lee, Namhun Kim, S. Kwak, J. Baek","doi":"10.1109/SKIMA57145.2022.10029495","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029495","url":null,"abstract":"power quality is an essential consideration in design of the power supply in high power industrial applications. Thus, this paper covers the design of the different magnetic components, switching element selection considerations, and the voltage current controllers optimal design on the basis of small-signal stability modeling and an adequate stability criterion to enhance the high-power factor (PF), high efficiency and lower current distortions. The proposed converter contains two stages, the power factor correction (PFC) stage and the isolated phase-shift PWM zero-voltage switching (ZVS) DC-DC converter stage. The proposed two-stage converter's total harmonic distortions (THDs), voltage and current ripples, conversion efficiency, and PF performance are investigated using PSIM simulations under various operating conditions. This study designs an industrial 2000 W, 54 V telecom AC-DC supply with a PF of more than 99%, a THD of about 5 %, and conversion efficiency of around 93% at full load.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128952188","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029571
M. Promentilla, Fidero Kuok
As Information and Communication Technologies (ICT) advances, blockchain can be an enabling technology with potential applications in smart cities to improve their service in waste management and resource efficiency, among others. However, it is imperative to identify the potential barriers to blockchain adoption in smart cities and how they are interrelated to each other. This paper thus proposes a novel T-spherical Neutrosophic Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL)-Analytic Network Process (ANP) to analyze the interdependencies and rank of these barriers while capturing the ambiguity and impreciseness of human judgment. An illustrative case study is presented to explore the barriers to blockchain technology adoption to waste management in future smart cities of Cambodia. Findings from such a problematique approach could provide insights for policymakers and accelerate the blockchain implementation in convergence to the creation of smart cities.
{"title":"AT-Spherical Neutrosophic Fuzzy DEMATEL-ANP Approach to Problematiques of Blockchain Technology in Smart Cities","authors":"M. Promentilla, Fidero Kuok","doi":"10.1109/SKIMA57145.2022.10029571","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029571","url":null,"abstract":"As Information and Communication Technologies (ICT) advances, blockchain can be an enabling technology with potential applications in smart cities to improve their service in waste management and resource efficiency, among others. However, it is imperative to identify the potential barriers to blockchain adoption in smart cities and how they are interrelated to each other. This paper thus proposes a novel T-spherical Neutrosophic Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL)-Analytic Network Process (ANP) to analyze the interdependencies and rank of these barriers while capturing the ambiguity and impreciseness of human judgment. An illustrative case study is presented to explore the barriers to blockchain technology adoption to waste management in future smart cities of Cambodia. Findings from such a problematique approach could provide insights for policymakers and accelerate the blockchain implementation in convergence to the creation of smart cities.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121408740","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029488
Salah Godhbani, S. Elkosantini, W. Suh, S. Lee
Recently, intelligent transportation system (ITS) is considered as one of the most important issues in smart city applications. Its supports urban and regional development and promotes economic growth, social development, and enhances human well-being. ITS integrates new technologies of information and communication including sensors, social media IoT devices which can generate a massive amount of heterogeneous and multimodal data known as big data term. In this context, Data Fusion techniques (DF) seem promising and have emerged from transportation applications and hold a promising opportunity to deal with imperfect raw data for capturing reliable, valuable and accurate information. In literature many DF techniques based on machine learning remarkably renovates fusion techniques by offering the strong ability of computing and predicting. In this paper, we propose new Hybrid method based on Deep Learning combine two independent model such as CNN, LSTM models to fuse multimodal and spatial temporal data. The proposed model uses Extended Kalman Filter (EKF) to combine result of the proposed DL classifiers. In the other side, the proposed approach uses CBOA algorithm for feature selection in order to provide effective exploration of significant features with faster convergence
{"title":"ADL based Framework For Multimodal Data Fusion in Traffic Jam prediction","authors":"Salah Godhbani, S. Elkosantini, W. Suh, S. Lee","doi":"10.1109/SKIMA57145.2022.10029488","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029488","url":null,"abstract":"Recently, intelligent transportation system (ITS) is considered as one of the most important issues in smart city applications. Its supports urban and regional development and promotes economic growth, social development, and enhances human well-being. ITS integrates new technologies of information and communication including sensors, social media IoT devices which can generate a massive amount of heterogeneous and multimodal data known as big data term. In this context, Data Fusion techniques (DF) seem promising and have emerged from transportation applications and hold a promising opportunity to deal with imperfect raw data for capturing reliable, valuable and accurate information. In literature many DF techniques based on machine learning remarkably renovates fusion techniques by offering the strong ability of computing and predicting. In this paper, we propose new Hybrid method based on Deep Learning combine two independent model such as CNN, LSTM models to fuse multimodal and spatial temporal data. The proposed model uses Extended Kalman Filter (EKF) to combine result of the proposed DL classifiers. In the other side, the proposed approach uses CBOA algorithm for feature selection in order to provide effective exploration of significant features with faster convergence","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125263008","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029576
Sharif Ahmed, M. A. Hossain, Zia Ush-Shamszaman
The National Health Service (NHS) has 1.3 million staff who care for the people of England with skill, compassion, and dedication. Between 1 January 2020 - 31 March 2020, there were 84,393 advertised vacancy full-time equivalents in NHS England. Among them, around 40% is Nursing and Midwifery Registered Staff Group. To tackle the staff shortage, NHS needs to spend about £480m per year on temporary staff. This paper presents an investigation of the three complementary methods to analyse retention data: (i) one that explores comparisons for engagement and retention within a year, (ii) differences across organisations in any observable or unobservable predictors and finally, (iii) the focuses on changes within NHS trusts across several years. Generally, a higher level of staff engagement indicates a better retention rate; but the findings of this investigation demonstrate a reverse result. Highly likely unmeasured external factors may influence the outputs, and possible factors may be organisation restructure, local labour market variations and more subtle between trust types. The latent Growth Curve model statistical technique is used to estimate growth trajectories. Using this technique, we estimated the change over time in staff retention. It shows a clear positive link between changes in engagement and changes in retention. Results show a 1% increase in the initial year of involvement (2015 involvement) is associated with a rise of 3.0 per cent in retention rate each year in the following periods.
{"title":"A Statistical Analysis of the Staff Data to Evaluate the Influence of the Retention Factors in the NHS England","authors":"Sharif Ahmed, M. A. Hossain, Zia Ush-Shamszaman","doi":"10.1109/SKIMA57145.2022.10029576","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029576","url":null,"abstract":"The National Health Service (NHS) has 1.3 million staff who care for the people of England with skill, compassion, and dedication. Between 1 January 2020 - 31 March 2020, there were 84,393 advertised vacancy full-time equivalents in NHS England. Among them, around 40% is Nursing and Midwifery Registered Staff Group. To tackle the staff shortage, NHS needs to spend about £480m per year on temporary staff. This paper presents an investigation of the three complementary methods to analyse retention data: (i) one that explores comparisons for engagement and retention within a year, (ii) differences across organisations in any observable or unobservable predictors and finally, (iii) the focuses on changes within NHS trusts across several years. Generally, a higher level of staff engagement indicates a better retention rate; but the findings of this investigation demonstrate a reverse result. Highly likely unmeasured external factors may influence the outputs, and possible factors may be organisation restructure, local labour market variations and more subtle between trust types. The latent Growth Curve model statistical technique is used to estimate growth trajectories. Using this technique, we estimated the change over time in staff retention. It shows a clear positive link between changes in engagement and changes in retention. Results show a 1% increase in the initial year of involvement (2015 involvement) is associated with a rise of 3.0 per cent in retention rate each year in the following periods.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"38 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125737648","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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