Wahyu Andhyka Kusuma, A. Jantan, N. Admodisastro, N. Norowi
A deeper understanding and integration with system users' thoughts and emotional experiences are required for user-engaged development. User experience (UX) journey integrates user requirements and problem-solving approaches. The integration of data-driven techniques and user-centric approaches in software development is investigated in this study. It focuses on using the Markov chain model to predict developer productivity based on data gathered while creating personas across three projects. Organizations can gain valuable insights into user needs and requirements by conducting purposeful activities such as strength, weaknesses, opportunities, and threats (SWOT) analysis, competitor analysis, hypothesis formulation, identification of behavioral variables, mapping interviews, and defining characteristics and objectives. The model has predictive capabilities that allow for more informed decision-making, more efficient resource allocation, and better project planning. The goal of the activity and the model ensure the development of software products that effectively meet the needs of users, resulting in a higher success rate for software development initiatives. This study emphasizes the importance of integrating quantitative and qualitative analysis to drive successful software development projects and increase productivity while meeting user needs. According to the findings of the research conducted from the three projects completed, the proposed methods have similarities, and predictions using the Markov chain can determine the success of novice developers.
{"title":"Holistic personas to increase the novice developer productivity","authors":"Wahyu Andhyka Kusuma, A. Jantan, N. Admodisastro, N. Norowi","doi":"10.11591/eei.v13i3.6936","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6936","url":null,"abstract":"A deeper understanding and integration with system users' thoughts and emotional experiences are required for user-engaged development. User experience (UX) journey integrates user requirements and problem-solving approaches. The integration of data-driven techniques and user-centric approaches in software development is investigated in this study. It focuses on using the Markov chain model to predict developer productivity based on data gathered while creating personas across three projects. Organizations can gain valuable insights into user needs and requirements by conducting purposeful activities such as strength, weaknesses, opportunities, and threats (SWOT) analysis, competitor analysis, hypothesis formulation, identification of behavioral variables, mapping interviews, and defining characteristics and objectives. The model has predictive capabilities that allow for more informed decision-making, more efficient resource allocation, and better project planning. The goal of the activity and the model ensure the development of software products that effectively meet the needs of users, resulting in a higher success rate for software development initiatives. This study emphasizes the importance of integrating quantitative and qualitative analysis to drive successful software development projects and increase productivity while meeting user needs. According to the findings of the research conducted from the three projects completed, the proposed methods have similarities, and predictions using the Markov chain can determine the success of novice developers.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229324","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}
The paper presents a novel algorithm for the development of stability charts. The second-order differential homogeneous equation describing a double salient reluctance machine with a capacitance connected to its stator winding is transformed into hill’s equation. The circuit components are the stator coil time-varying inductance of a double salient reluctance machine, capacitance and resistance. All these are modeled by hill’s equation. The double salient reluctance machine acts as an energy conversion system. The maximum and minimum inductance of the energy conversion system is measured in laboratory by inductance, capacitance, and resistance (LCR) meter. These values help to determine the inductance modulation index. The inductance modulation indetx, the characteristic constant and the characteristic parameter obtained from modeling equations are used in the MATLAB/Simulink model. The MATLAB/Simulink simulations generate stable and unstable oscillations to form stability charts. The proposed stability charts are in good agreement with the Ince-Stritt stability chart, which is widely applied in physics, mechanics and in electrical engineering, especially where the state of stability of a system or an electric oscillatory circuit is to be determined.
{"title":"Development of stability charts for double salience reluctance machine modeled using hill’s equation","authors":"E. A. Yahaya, E. Ejiogu","doi":"10.11591/eei.v13i3.4113","DOIUrl":"https://doi.org/10.11591/eei.v13i3.4113","url":null,"abstract":"The paper presents a novel algorithm for the development of stability charts. The second-order differential homogeneous equation describing a double salient reluctance machine with a capacitance connected to its stator winding is transformed into hill’s equation. The circuit components are the stator coil time-varying inductance of a double salient reluctance machine, capacitance and resistance. All these are modeled by hill’s equation. The double salient reluctance machine acts as an energy conversion system. The maximum and minimum inductance of the energy conversion system is measured in laboratory by inductance, capacitance, and resistance (LCR) meter. These values help to determine the inductance modulation index. The inductance modulation indetx, the characteristic constant and the characteristic parameter obtained from modeling equations are used in the MATLAB/Simulink model. The MATLAB/Simulink simulations generate stable and unstable oscillations to form stability charts. The proposed stability charts are in good agreement with the Ince-Stritt stability chart, which is widely applied in physics, mechanics and in electrical engineering, especially where the state of stability of a system or an electric oscillatory circuit is to be determined.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"75 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231150","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 study evaluates the effectiveness of data augmentation on 1D convolutional neural network (CNN) and transformer models for speech emotion recognition (SER) on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset. The results show that data augmentation has a positive impact on improving emotion classification accuracy. Techniques such as noising, pitching, stretching, shifting, and speeding are applied to increase data variation and overcome class imbalance. The 1D CNN model with data augmentation achieved 94.5% accuracy, while the transformer model with data augmentation performed even better at 97.5%. This research is expected to contribute better insights for the development of accurate emotion recognition methods by using data augmentation with these models to improve classification accuracy on the RAVDESS dataset. Further research can explore larger and more diverse datasets and alternative model approaches.
{"title":"Enhancing speech emotion recognition with deep learning using multi-feature stacking and data augmentation","authors":"Khasyi Al Mukarram, M. A. Mukhlas, Amalia Zahra","doi":"10.11591/eei.v13i3.6049","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6049","url":null,"abstract":"This study evaluates the effectiveness of data augmentation on 1D convolutional neural network (CNN) and transformer models for speech emotion recognition (SER) on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset. The results show that data augmentation has a positive impact on improving emotion classification accuracy. Techniques such as noising, pitching, stretching, shifting, and speeding are applied to increase data variation and overcome class imbalance. The 1D CNN model with data augmentation achieved 94.5% accuracy, while the transformer model with data augmentation performed even better at 97.5%. This research is expected to contribute better insights for the development of accurate emotion recognition methods by using data augmentation with these models to improve classification accuracy on the RAVDESS dataset. Further research can explore larger and more diverse datasets and alternative model approaches.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"41 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231559","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}
Mounir Grari, Mimoun Yandouzi, Berrahal Mohammed, Mohammed Boukabous, Idriss Idrissi
Forests play a vital role in maintaining ecological equilibrium and serving as vital habitats for wildlife. They regulate global climate, safeguard soil and water resources, and provide crucial ecosystem services such as air and water purification, essential for human well-being and sustainable development. Forest fires wreak havoc on ecosystems and wildlife, emitting harmful pollutants, disrupting communities, and increasing the risk of erosion and landslides. Detecting forest fires through satellite imaging, aerial reconnaissance, and ground-based sensors is pivotal for early detection and containment, safeguarding human lives, wildlife, and preserving natural resources for future generations. Utilizing drones and deep learning (DL) algorithms can significantly enhance early fire detection and minimize their devastating impact. In this paper, we examine teachable machine, a Google tool for creating DL models. We compare the top model generated by teachable machine for fire and smoke detection to models obtained through transfer learning from established DL models in image recognition and computer vision (CV), such as VGG16, VGG19, MobileNet, MobileNetv2, and MobileNetv3. The results underscore the significance of employing the teachable machine model in specific fire and smoke detection scenarios.
{"title":"Comparative study of teachable machine for forest fire and smoke detection by drone","authors":"Mounir Grari, Mimoun Yandouzi, Berrahal Mohammed, Mohammed Boukabous, Idriss Idrissi","doi":"10.11591/eei.v13i3.6578","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6578","url":null,"abstract":"Forests play a vital role in maintaining ecological equilibrium and serving as vital habitats for wildlife. They regulate global climate, safeguard soil and water resources, and provide crucial ecosystem services such as air and water purification, essential for human well-being and sustainable development. Forest fires wreak havoc on ecosystems and wildlife, emitting harmful pollutants, disrupting communities, and increasing the risk of erosion and landslides. Detecting forest fires through satellite imaging, aerial reconnaissance, and ground-based sensors is pivotal for early detection and containment, safeguarding human lives, wildlife, and preserving natural resources for future generations. Utilizing drones and deep learning (DL) algorithms can significantly enhance early fire detection and minimize their devastating impact. In this paper, we examine teachable machine, a Google tool for creating DL models. We compare the top model generated by teachable machine for fire and smoke detection to models obtained through transfer learning from established DL models in image recognition and computer vision (CV), such as VGG16, VGG19, MobileNet, MobileNetv2, and MobileNetv3. The results underscore the significance of employing the teachable machine model in specific fire and smoke detection scenarios.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"57 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232259","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}
Nowadays the good or bad study program can be seen from the accreditation rank that it obtains from the institution of college accreditation. However, it is frequently found at college that there are some study programs that have the same accreditation. This encourages the college to do another approach which can do this study program ranking from a different point of view. This research developed a model of decision support system to do ranking towards 25 study programs existed in the environment of Sriwijaya State Polytechnic. Hybrid method employed the combination of analytical hierarchy process (AHP) and simple additive weighting (SAW) to do the ranking. Actual weighting model was used in the calculation based on the fact obtained in each study program, and in line with the criteria which had been determined. As many as 7 relevant criteria and 21 sub criteria were used in this model. The results of this research showed that the model which had been developed can give recommendation in the form of study program ranking with actual condition based on the data attached to each study program.
{"title":"The model of decision support system using hybrid method and actual weighting for the study program ranking","authors":"M. Amin, Yevi Dwitayanti","doi":"10.11591/eei.v13i3.7038","DOIUrl":"https://doi.org/10.11591/eei.v13i3.7038","url":null,"abstract":"Nowadays the good or bad study program can be seen from the accreditation rank that it obtains from the institution of college accreditation. However, it is frequently found at college that there are some study programs that have the same accreditation. This encourages the college to do another approach which can do this study program ranking from a different point of view. This research developed a model of decision support system to do ranking towards 25 study programs existed in the environment of Sriwijaya State Polytechnic. Hybrid method employed the combination of analytical hierarchy process (AHP) and simple additive weighting (SAW) to do the ranking. Actual weighting model was used in the calculation based on the fact obtained in each study program, and in line with the criteria which had been determined. As many as 7 relevant criteria and 21 sub criteria were used in this model. The results of this research showed that the model which had been developed can give recommendation in the form of study program ranking with actual condition based on the data attached to each study program.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"5 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141233654","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}
Ensemble learning, which involves combining the opinions of multiple experts to arrive at a better result, has been used for centuries. In this work, a review of the major voting methods in ensemble learning is explored. This work will focus on a new method for combining the results of individual learners. The method depends on the relative accuracy and diversity of teams. Instead of trying to assign weight to each different trainer, the concept of diversity teams is presented. Each team will vote as one player; however, the individual accuracies of each learner still be implemented. The concept of relaxing parameters that deal with each team as one player is presented. Our experiments demonstrate that traditional ensemble voting methods outperform individual learners. There is a limit to the superiority of the ensemble learner that any ensemble learner cannot go beyond. The proposed voting method gives the same results as the traditional ensemble voting methods, however, a different diversity of the proposed method from the traditional voting method or for different values of the relaxing parameter can be achieved.
{"title":"Ensemble learning based on relative accuracy approach and diversity teams","authors":"Mahmoud B. Rokaya, Kholod D. Alsufiani","doi":"10.11591/eei.v13i3.6003","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6003","url":null,"abstract":"Ensemble learning, which involves combining the opinions of multiple experts to arrive at a better result, has been used for centuries. In this work, a review of the major voting methods in ensemble learning is explored. This work will focus on a new method for combining the results of individual learners. The method depends on the relative accuracy and diversity of teams. Instead of trying to assign weight to each different trainer, the concept of diversity teams is presented. Each team will vote as one player; however, the individual accuracies of each learner still be implemented. The concept of relaxing parameters that deal with each team as one player is presented. Our experiments demonstrate that traditional ensemble voting methods outperform individual learners. There is a limit to the superiority of the ensemble learner that any ensemble learner cannot go beyond. The proposed voting method gives the same results as the traditional ensemble voting methods, however, a different diversity of the proposed method from the traditional voting method or for different values of the relaxing parameter can be achieved.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"112 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141234497","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}
Lightweight communication protocols for wireless sensor networks (WSNs) are unfolding for machine to machine (M2M) communications and thus there is always going to be a possible conflict of interest on which protocol is best suited for any particular application. The two protocols of interest in this study are the message queue telemetry transport protocol for sensor network (MQTT-SN), a variant of message queue telemetry transport (MQTT) protocol and the constrained application protocol (CoAP). There have been studies that reveal that these protocols perform differently based on the underlying network conditions. CoAP experience lower delays than MQTT for higher packet loss and higher delays for lower packet loss. MQTT default communication via a broker is easier to scale compared to CoAP direct request-response paradigm. Although this is a huge advantage over CoAP, it presents the single point-of-failure problem. In this paper we propose an integration of MQTT-CoAP protocol using an abstraction layer that enables both MQTT-SN and CoAP protocol to be used in the same sensor node. Resources are managed by directly modifying sensor node configuration using CoAP protocol. Performance evaluation of these protocols under the integrated scenario shows acceptable levels of latency and energy consumption for internet of thing (IoT) operations.
{"title":"Integration of MQTT-SN and CoAP protocol for enhanced data communications and resource management in WSNs","authors":"Emmanuel Nwankwo, Michael David, E. Onwuka","doi":"10.11591/eei.v13i3.5158","DOIUrl":"https://doi.org/10.11591/eei.v13i3.5158","url":null,"abstract":"Lightweight communication protocols for wireless sensor networks (WSNs) are unfolding for machine to machine (M2M) communications and thus there is always going to be a possible conflict of interest on which protocol is best suited for any particular application. The two protocols of interest in this study are the message queue telemetry transport protocol for sensor network (MQTT-SN), a variant of message queue telemetry transport (MQTT) protocol and the constrained application protocol (CoAP). There have been studies that reveal that these protocols perform differently based on the underlying network conditions. CoAP experience lower delays than MQTT for higher packet loss and higher delays for lower packet loss. MQTT default communication via a broker is easier to scale compared to CoAP direct request-response paradigm. Although this is a huge advantage over CoAP, it presents the single point-of-failure problem. In this paper we propose an integration of MQTT-CoAP protocol using an abstraction layer that enables both MQTT-SN and CoAP protocol to be used in the same sensor node. Resources are managed by directly modifying sensor node configuration using CoAP protocol. Performance evaluation of these protocols under the integrated scenario shows acceptable levels of latency and energy consumption for internet of thing (IoT) operations.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"114 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141234575","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}
Muhammad Nadiy Zaiaami, N. E. Abd Rashid, Nor Najwa Ismail, I. P. Ibrahim, S. A. Enche Ab Rahim, Nor Ayu Zalina Zakaria
A traditional maritime radar system is utilized for ship detection and tracking through onshore transmitters and receivers. However, it faces challenges when it comes to detecting small boats. In contrast, unmanned surface vehicles (USVs) have been designed to monitor maritime traffic. They excel in detecting vessels of various sizes and enhance the capabilities and resolution of maritime radar systems. Nevertheless, just like conventional radar systems, USVs encounter difficulties due to environmental interference and clutter, affecting the accuracy of target signal detection. This research proposes a comprehensive numerical assessment to tackle the clutter issue associated with USVs. This involves gathering clutter signal data, performing numerical analysis, and employing distribution fitting techniques that leverage mathematical distributions to unravel data complexity. The root mean square error (RMSE) is applied in this analysis to validate the efficacy of the distribution model. The results of this study aim to formulate a clutter model that can enhance radar performance in detecting small vessels within cluttered environments.
{"title":"Clutter evalution of unmanned surface vehicles for maritime traffic monitoring","authors":"Muhammad Nadiy Zaiaami, N. E. Abd Rashid, Nor Najwa Ismail, I. P. Ibrahim, S. A. Enche Ab Rahim, Nor Ayu Zalina Zakaria","doi":"10.11591/eei.v13i3.6836","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6836","url":null,"abstract":"A traditional maritime radar system is utilized for ship detection and tracking through onshore transmitters and receivers. However, it faces challenges when it comes to detecting small boats. In contrast, unmanned surface vehicles (USVs) have been designed to monitor maritime traffic. They excel in detecting vessels of various sizes and enhance the capabilities and resolution of maritime radar systems. Nevertheless, just like conventional radar systems, USVs encounter difficulties due to environmental interference and clutter, affecting the accuracy of target signal detection. This research proposes a comprehensive numerical assessment to tackle the clutter issue associated with USVs. This involves gathering clutter signal data, performing numerical analysis, and employing distribution fitting techniques that leverage mathematical distributions to unravel data complexity. The root mean square error (RMSE) is applied in this analysis to validate the efficacy of the distribution model. The results of this study aim to formulate a clutter model that can enhance radar performance in detecting small vessels within cluttered environments.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235236","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}
Smriti Mishra, Ranjan Kumar, S. K. Tiwari, Priya Ranjan
The COVID-19 pandemic has highlighted the importance of accurately predicting disease severity to ensure timely intervention and effective allocation of healthcare resources, which can ultimately improve patient outcomes. This study aims to develop an efficient machine learning (ML) model based on patient demographic and clinical data. It utilizes advanced feature engineering techniques to reduce the dimensionality of dataset and address the issue of highly imbalanced data using synthetic minority oversampling technique (SMOTE). The study employs several ensemble learning models, including XGBoost, Random Forest, AdaBoost, voting ensemble, enhanced-weighted voting ensemble, and stack-based ensembles with support vector machine (SVM) and Gaussian Naïve Bayes as meta-learners, to develop the proposed model. The results indicate that the proposed model outperformed the top-performing models reported in previous studies. It achieved an accuracy of 0.978, sensitivity of 1.0, precision of 0.875, F1-score of 0.934, and receiver operating characteristic area under the curve (ROC-AUC) of 0.965. The study identified several features that significantly correlated with COVID-19 severity, which included respiratory rate (breaths per minute), c-reactive proteins, age, and total leukocyte count (TLC) count. The proposed approach presents a promising method for accurate COVID-19 severity prediction, which may prove valuable in assisting healthcare providers in making informed decisions about patient care.
{"title":"An efficient synthetic minority oversampling technique-based ensemble learning model to detect COVID-19 severity","authors":"Smriti Mishra, Ranjan Kumar, S. K. Tiwari, Priya Ranjan","doi":"10.11591/eei.v13i3.6774","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6774","url":null,"abstract":"The COVID-19 pandemic has highlighted the importance of accurately predicting disease severity to ensure timely intervention and effective allocation of healthcare resources, which can ultimately improve patient outcomes. This study aims to develop an efficient machine learning (ML) model based on patient demographic and clinical data. It utilizes advanced feature engineering techniques to reduce the dimensionality of dataset and address the issue of highly imbalanced data using synthetic minority oversampling technique (SMOTE). The study employs several ensemble learning models, including XGBoost, Random Forest, AdaBoost, voting ensemble, enhanced-weighted voting ensemble, and stack-based ensembles with support vector machine (SVM) and Gaussian Naïve Bayes as meta-learners, to develop the proposed model. The results indicate that the proposed model outperformed the top-performing models reported in previous studies. It achieved an accuracy of 0.978, sensitivity of 1.0, precision of 0.875, F1-score of 0.934, and receiver operating characteristic area under the curve (ROC-AUC) of 0.965. The study identified several features that significantly correlated with COVID-19 severity, which included respiratory rate (breaths per minute), c-reactive proteins, age, and total leukocyte count (TLC) count. The proposed approach presents a promising method for accurate COVID-19 severity prediction, which may prove valuable in assisting healthcare providers in making informed decisions about patient care.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"10 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230529","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}
Soil moisture (SM) is a crucial criterion for agronomics and the management of water resources, particularly in areas where the socio-economic status and significant source of income depend upon agriculture and related sectors. This paper intends to estimate SM over the vegetative area using a generalized regression neural network (GRNN) and ground scatterometer and compare the results with SM retrieved using Sentinel-1 data. At the same time, random forest regression (RFR) and support vector regression (SVR) models are used for SM estimation. Correlation analysis results concluded that L-band HV-polarization at 300 incidence angle showed the highest correlation with the measured field parameters. This study investigated backscattering coefficients, VV/VH polarization ratio and polarization phase difference over wheat’s entire growth phase to estimate SM. The results indicate that the GRNN with backscattering coefficients and polarization ratio provided the highest accuracy compared to the random forest (RF) and SVR with the root mean square error of 0.093 over the Yavatmal District, Maharashtra, India.
{"title":"Soil moisture estimation using ground scatterometer and Sentinel-1 data","authors":"Geeta T. Desai, Abhay N. Gaikwad","doi":"10.11591/eei.v13i3.6433","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6433","url":null,"abstract":"Soil moisture (SM) is a crucial criterion for agronomics and the management of water resources, particularly in areas where the socio-economic status and significant source of income depend upon agriculture and related sectors. This paper intends to estimate SM over the vegetative area using a generalized regression neural network (GRNN) and ground scatterometer and compare the results with SM retrieved using Sentinel-1 data. At the same time, random forest regression (RFR) and support vector regression (SVR) models are used for SM estimation. Correlation analysis results concluded that L-band HV-polarization at 300 incidence angle showed the highest correlation with the measured field parameters. This study investigated backscattering coefficients, VV/VH polarization ratio and polarization phase difference over wheat’s entire growth phase to estimate SM. The results indicate that the GRNN with backscattering coefficients and polarization ratio provided the highest accuracy compared to the random forest (RF) and SVR with the root mean square error of 0.093 over the Yavatmal District, Maharashtra, India.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"16 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141234172","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：