Pub Date : 2024-07-23DOI: 10.28991/esj-2024-sied1-010
H. Almarashdi
The COVID-19 pandemic has recently reshaped education and life around the world. Undoubtedly, the return to face-to-face learning has been affected after two years of distance learning. However, research that focuses on post-COVID-19 is still limited. Therefore, this study investigates how students perceive the experience of returning to face-to-face learning after distance learning within the context of the United Arab Emirates (UAE). It emphasizes the possibilities and challenges that could be faced in improving face-to-face mathematics education. This study applied an exploratory sequential mixed-method approach, which involved collecting qualitative data from 13 students through a focus group, and then quantitative data was collected from 243 Cycle 2 and 3 students. The qualitative data were coded and analyzed thematically, while descriptive analysis was used to analyze the quantitative data. The qualitative and quantitative results revealed consensus on the main challenges that students experience as they return to face-to-face learning. On top of these challenges are students' lack of study skills, excessive use of technology, and high levels of math test anxiety. Research findings showed students’ preference for face-to-face learning while adding some aspects of distance learning. The results of this study are also expected to be a reference in the development of a new sustainable paradigm of face-to-face learning and as study material for subsequent research related to rethinking math education after COVID-19. Doi: 10.28991/ESJ-2024-SIED1-010 Full Text: PDF
{"title":"Beyond COVID-19 Lockdowns: Rethinking Mathematics Education from a Student Perspective","authors":"H. Almarashdi","doi":"10.28991/esj-2024-sied1-010","DOIUrl":"https://doi.org/10.28991/esj-2024-sied1-010","url":null,"abstract":"The COVID-19 pandemic has recently reshaped education and life around the world. Undoubtedly, the return to face-to-face learning has been affected after two years of distance learning. However, research that focuses on post-COVID-19 is still limited. Therefore, this study investigates how students perceive the experience of returning to face-to-face learning after distance learning within the context of the United Arab Emirates (UAE). It emphasizes the possibilities and challenges that could be faced in improving face-to-face mathematics education. This study applied an exploratory sequential mixed-method approach, which involved collecting qualitative data from 13 students through a focus group, and then quantitative data was collected from 243 Cycle 2 and 3 students. The qualitative data were coded and analyzed thematically, while descriptive analysis was used to analyze the quantitative data. The qualitative and quantitative results revealed consensus on the main challenges that students experience as they return to face-to-face learning. On top of these challenges are students' lack of study skills, excessive use of technology, and high levels of math test anxiety. Research findings showed students’ preference for face-to-face learning while adding some aspects of distance learning. The results of this study are also expected to be a reference in the development of a new sustainable paradigm of face-to-face learning and as study material for subsequent research related to rethinking math education after COVID-19. Doi: 10.28991/ESJ-2024-SIED1-010 Full Text: PDF","PeriodicalId":11586,"journal":{"name":"Emerging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812347","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 agricultural sector's output holds paramount significance for the global population, serving as an indispensable resource for survival and consumption. Consequently, alterations in agricultural landscapes bear substantial implications for the world's food supply. The objectives of this research are to investigate the depletion of agricultural land, with a specific focus on Samut Songkhram Province—an agriculturally prominent region in Thailand renowned for supplying seafood and fruits to Bangkok. By employing advanced remote sensing and change detection methods and incorporating indices like NDVI, NDWI, and NDBI, the study meticulously analyzed land-use changes. The outcomes were rigorously scrutinized through supervised classification, validated by on-site inspections, and corroborated with data from pertinent agencies. Findings revealed that Samut Songkhram had sustained its prominence in agricultural land, constituting around 70% of the province's total area over the past two decades. However, this expanse has undergone persistent transformation during the last 20 years. Notably, the most substantial surge was observed in the conversion of agricultural land to urban and developed areas, particularly in the urban zones of Amphawa District, followed by Mueang Samut Songkhram and Bang Khonthi districts. This investigation illuminates a consistent downward trend in agricultural land, a vital source of sustenance for Thailand's population and the global community. Doi: 10.28991/ESJ-2024-08-02-020 Full Text: PDF
{"title":"Monitoring Agricultural Land Loss by Analyzing Changes in Land Use and Land Cover","authors":"Morakot Worachairungreung, Nayot Kulpanich, Kunyaphat Thanakunwutthirot, Phonpat Hemwan","doi":"10.28991/esj-2024-08-02-020","DOIUrl":"https://doi.org/10.28991/esj-2024-08-02-020","url":null,"abstract":"The agricultural sector's output holds paramount significance for the global population, serving as an indispensable resource for survival and consumption. Consequently, alterations in agricultural landscapes bear substantial implications for the world's food supply. The objectives of this research are to investigate the depletion of agricultural land, with a specific focus on Samut Songkhram Province—an agriculturally prominent region in Thailand renowned for supplying seafood and fruits to Bangkok. By employing advanced remote sensing and change detection methods and incorporating indices like NDVI, NDWI, and NDBI, the study meticulously analyzed land-use changes. The outcomes were rigorously scrutinized through supervised classification, validated by on-site inspections, and corroborated with data from pertinent agencies. Findings revealed that Samut Songkhram had sustained its prominence in agricultural land, constituting around 70% of the province's total area over the past two decades. However, this expanse has undergone persistent transformation during the last 20 years. Notably, the most substantial surge was observed in the conversion of agricultural land to urban and developed areas, particularly in the urban zones of Amphawa District, followed by Mueang Samut Songkhram and Bang Khonthi districts. This investigation illuminates a consistent downward trend in agricultural land, a vital source of sustenance for Thailand's population and the global community. Doi: 10.28991/ESJ-2024-08-02-020 Full Text: PDF","PeriodicalId":11586,"journal":{"name":"Emerging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140759631","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 : 2024-04-01DOI: 10.28991/esj-2024-08-02-01
José Cornejo, Jorge Cornejo, M. Vargas, M. Carvajal, Paul Perales, G. Rodríguez, C. Macias, S. Canizares, Paola Silva, Robert F. Cubas, M. C. Jimenez, Eddy P. Lincango, Luis Serrano, R. Palomares, S. Aspilcueta, Rocio Castillo-Larios, Lorna A Evans, J. A. De, La Cruz-Vargas, Marcelo Risk, Rafael J. Grossmann, Enrique F. Elli
Pub Date : 2024-04-01DOI: 10.28991/esj-2024-08-02-022
Siti Fatimah, S. Sarto, Moh. Fahrurrozi, Budhijanto Budhijanto
The synthesis of hybrid fiber based on bovine bone gelatin combined with polyvinyl alcohol-chitosan-oxidized sucrose (PVACOS) has been successfully carried out using the coaxial electrospinning technique. The presence of oxidized sucrose can improve the diameter and the tensile strength of hybrid fibers due to the formation of new covalent bonds. The combination of gelatin with PVACOS material aims to increase the strength of the hybrid fiber so that it has better tensile strength characteristics and improves the diameter of the resulting hybrid fiber. The characterization of the resulting material was tested using FTIR, SEM, EDX, XRD, and TGA. Based on FTIR analysis, there is an increase in absorption intensity in the 2900 cm-1 – 3000 cm-1 band, which indicates the occurrence of covalent bond interactions so that it can increase the bond strength between materials with the performance of crystalline materials. Apart from that, the morphological structure of the hybrid fibers was also investigated using scanning electron microscopy (SEM), and the resulting fiber diameter for Ge-Ch, Ge-Ch-PVA, Ge-PVACOS 3%, and Ge-PVACOS 5%, respectively, was 0.4049 µm. 0.3735 µm, 0.3388 µm, and 0.3206 µm. The tensile strengths of hybrid fiber for Ge-PVACOS 3% and Ge-PVACOS 5%, respectively, are 39.91935 N/m2 and 76.12507 N/m2. Statistical tests show that the concentration of oxidized sucrose has a significant influence on hybrid fiber performance. The significance values for diameter and tensile strength are 0.0486 and 0.0325, respectively. According to this performance, the Ge-PVACOS hybrid fiber is recommended as a material for advanced medical textiles. Doi: 10.28991/ESJ-2024-08-02-022 Full Text: PDF
{"title":"Synthesis and Characterization of Hybridfiber from Gelatin Modified by PVACOS Using Coaxial Electrospinning Techniques as an Advanced Medical Textile Material","authors":"Siti Fatimah, S. Sarto, Moh. Fahrurrozi, Budhijanto Budhijanto","doi":"10.28991/esj-2024-08-02-022","DOIUrl":"https://doi.org/10.28991/esj-2024-08-02-022","url":null,"abstract":"The synthesis of hybrid fiber based on bovine bone gelatin combined with polyvinyl alcohol-chitosan-oxidized sucrose (PVACOS) has been successfully carried out using the coaxial electrospinning technique. The presence of oxidized sucrose can improve the diameter and the tensile strength of hybrid fibers due to the formation of new covalent bonds. The combination of gelatin with PVACOS material aims to increase the strength of the hybrid fiber so that it has better tensile strength characteristics and improves the diameter of the resulting hybrid fiber. The characterization of the resulting material was tested using FTIR, SEM, EDX, XRD, and TGA. Based on FTIR analysis, there is an increase in absorption intensity in the 2900 cm-1 – 3000 cm-1 band, which indicates the occurrence of covalent bond interactions so that it can increase the bond strength between materials with the performance of crystalline materials. Apart from that, the morphological structure of the hybrid fibers was also investigated using scanning electron microscopy (SEM), and the resulting fiber diameter for Ge-Ch, Ge-Ch-PVA, Ge-PVACOS 3%, and Ge-PVACOS 5%, respectively, was 0.4049 µm. 0.3735 µm, 0.3388 µm, and 0.3206 µm. The tensile strengths of hybrid fiber for Ge-PVACOS 3% and Ge-PVACOS 5%, respectively, are 39.91935 N/m2 and 76.12507 N/m2. Statistical tests show that the concentration of oxidized sucrose has a significant influence on hybrid fiber performance. The significance values for diameter and tensile strength are 0.0486 and 0.0325, respectively. According to this performance, the Ge-PVACOS hybrid fiber is recommended as a material for advanced medical textiles. Doi: 10.28991/ESJ-2024-08-02-022 Full Text: PDF","PeriodicalId":11586,"journal":{"name":"Emerging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140779396","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 : 2024-04-01DOI: 10.28991/esj-2024-08-02-05
Hung Van Tran
This study fills the gap in the literature by applying novel quantile regression and spectral Granger causality frameworks to evaluate the asymmetric effect of GDP, globalization, green growth, and renewable energy consumption on CO2 emissions in India. The results suggest that in all quantiles, green growth, globalization, and renewable energy consumption impact environmental quality negatively, and the effect of economic growth on CO2 emissions is positive in most of the quantiles. In addition, the nexus between the regressors and CO2 emissions is significant across different time horizons. More specifically, the results from the spectral Granger causality test unveil that all the indicators would predict CO2emissions across various time scales. Several policy implications have been proposed based on the research’s findings so that India might move toward achieving sustainable development. Doi: 10.28991/ESJ-2024-08-02-05 Full Text: PDF
{"title":"Asymmetric Role of Economic Growth, Globalization, Green Growth, and Renewable Energy in Achieving Environmental Sustainability","authors":"Hung Van Tran","doi":"10.28991/esj-2024-08-02-05","DOIUrl":"https://doi.org/10.28991/esj-2024-08-02-05","url":null,"abstract":"This study fills the gap in the literature by applying novel quantile regression and spectral Granger causality frameworks to evaluate the asymmetric effect of GDP, globalization, green growth, and renewable energy consumption on CO2 emissions in India. The results suggest that in all quantiles, green growth, globalization, and renewable energy consumption impact environmental quality negatively, and the effect of economic growth on CO2 emissions is positive in most of the quantiles. In addition, the nexus between the regressors and CO2 emissions is significant across different time horizons. More specifically, the results from the spectral Granger causality test unveil that all the indicators would predict CO2emissions across various time scales. Several policy implications have been proposed based on the research’s findings so that India might move toward achieving sustainable development. Doi: 10.28991/ESJ-2024-08-02-05 Full Text: PDF","PeriodicalId":11586,"journal":{"name":"Emerging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140786046","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 : 2024-04-01DOI: 10.28991/esj-2024-08-02-08
Viviana Moya, Angélica Quito, Andrea Pilco, Juan P. Vásconez, Christian Vargas
In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDF
{"title":"Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis","authors":"Viviana Moya, Angélica Quito, Andrea Pilco, Juan P. Vásconez, Christian Vargas","doi":"10.28991/esj-2024-08-02-08","DOIUrl":"https://doi.org/10.28991/esj-2024-08-02-08","url":null,"abstract":"In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDF","PeriodicalId":11586,"journal":{"name":"Emerging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140779130","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 : 2024-04-01DOI: 10.28991/esj-2024-08-02-07
A. Nekrasov, A. Khachaturian, Colin J. Fidge
The rapid development of aircraft and unmanned aerial vehicles (UAV) increases their use, including in polar areas, which are characterized by their remoteness and rather harsh conditions. The dominant trends in airborne radar development are expanding their functionality and increasing the altitude of their applicability. Our study focuses on the functionality enhancement of airborne high-altitude conical scanning radars currently used for circular clouds and precipitation observations as well as for sea wind measurements. Recently, we showed how a semicircular observation scheme, instead of a circular one, can double the maximum applicable altitude of sea wind measurements made with such radars. Here we apply this approach to show how an airborne high-altitude conical scanning radar’s functionality can also be expanded for sea water/ice discrimination within a semicircular observation scheme, again doubling the maximum discrimination altitude compared to circular observations. The discrimination is performed in scatterometer mode using the minimum statistical distance of the measured normalized radar cross sections (NRCSs) to the geophysical model functions (GMFs) of the sea water and ice underlying surfaces. However, as no sea ice GMF is available for the considered horizontal transmit and receive polarization at the Ku band, we instead used a substitute sea ice GMF having the same azimuth isotropic property setting for its NRCSs as the averaged value of the measured azimuth NRCSs within the semicircular observations scheme. Our analysis found that incidence angles of 30°, 45°, and 60° are well suited to our sea water/ice discrimination method, and that incidence angles higher than 30° are preferable as they provide a higher difference in the statistical distance of the measured NRCSs to the sea ice and water GMFs, whereas an incidence angle of 30° provides the highest applicable altitude for sea water/ice discrimination and wind retrieval. We also demonstrated the ability of the sea water/ice discrimination procedure’s implementation for any airborne wind scatterometer or multimode radar operated in scatterometer mode over freezing seas to avoid entirely erroneous sea wind measurement results when a sea ice surface is observed. The obtained results can also be used for enhancing aircraft and UAV radars and for developing new remote sensing systems. Doi: 10.28991/ESJ-2024-08-02-07 Full Text: PDF
{"title":"Using Semicircular Sampling to Increase Sea Water/Ice Discrimination Altitude","authors":"A. Nekrasov, A. Khachaturian, Colin J. Fidge","doi":"10.28991/esj-2024-08-02-07","DOIUrl":"https://doi.org/10.28991/esj-2024-08-02-07","url":null,"abstract":"The rapid development of aircraft and unmanned aerial vehicles (UAV) increases their use, including in polar areas, which are characterized by their remoteness and rather harsh conditions. The dominant trends in airborne radar development are expanding their functionality and increasing the altitude of their applicability. Our study focuses on the functionality enhancement of airborne high-altitude conical scanning radars currently used for circular clouds and precipitation observations as well as for sea wind measurements. Recently, we showed how a semicircular observation scheme, instead of a circular one, can double the maximum applicable altitude of sea wind measurements made with such radars. Here we apply this approach to show how an airborne high-altitude conical scanning radar’s functionality can also be expanded for sea water/ice discrimination within a semicircular observation scheme, again doubling the maximum discrimination altitude compared to circular observations. The discrimination is performed in scatterometer mode using the minimum statistical distance of the measured normalized radar cross sections (NRCSs) to the geophysical model functions (GMFs) of the sea water and ice underlying surfaces. However, as no sea ice GMF is available for the considered horizontal transmit and receive polarization at the Ku band, we instead used a substitute sea ice GMF having the same azimuth isotropic property setting for its NRCSs as the averaged value of the measured azimuth NRCSs within the semicircular observations scheme. Our analysis found that incidence angles of 30°, 45°, and 60° are well suited to our sea water/ice discrimination method, and that incidence angles higher than 30° are preferable as they provide a higher difference in the statistical distance of the measured NRCSs to the sea ice and water GMFs, whereas an incidence angle of 30° provides the highest applicable altitude for sea water/ice discrimination and wind retrieval. We also demonstrated the ability of the sea water/ice discrimination procedure’s implementation for any airborne wind scatterometer or multimode radar operated in scatterometer mode over freezing seas to avoid entirely erroneous sea wind measurement results when a sea ice surface is observed. The obtained results can also be used for enhancing aircraft and UAV radars and for developing new remote sensing systems. Doi: 10.28991/ESJ-2024-08-02-07 Full Text: PDF","PeriodicalId":11586,"journal":{"name":"Emerging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140760097","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 : 2024-04-01DOI: 10.28991/esj-2024-08-02-024
Siti Fatimah Abdul Razak, S. Yogarayan, Md. Shohel Sayeed, Muhammad Izzat Faiz Mohd Derafi
The visionary paradigm of Agriculture 5.0 integrates Industry 4.0 principles into agricultural practices. Our scoping review explores the landscape of Agriculture 5.0, emphasizing the pivotal role of Explainable AI (XAI) in shaping this domain. Guided by the Preferred Reporting Items for Systematic Review and Meta-Analysis Scoping Review, we rigorously analyzed 84 articles published from 2018 to September 2023. Our findings highlight XAI’s potential within Agriculture 5.0, recognizing its influence on intelligent farming. We propose a conceptual framework for integrating XAI, emphasizing its impact on model transparency and user trust. Despite transformative applications, existing literature often lacks XAI discussions. Our objective is to bridge this gap and provide a reference for academics, practitioners, policymakers, and educators in the field of smart agriculture that is both environmentally friendly and technologically advanced. Doi: 10.28991/ESJ-2024-08-02-024 Full Text: PDF
{"title":"Agriculture 5.0 and Explainable AI for Smart Agriculture: A Scoping Review","authors":"Siti Fatimah Abdul Razak, S. Yogarayan, Md. Shohel Sayeed, Muhammad Izzat Faiz Mohd Derafi","doi":"10.28991/esj-2024-08-02-024","DOIUrl":"https://doi.org/10.28991/esj-2024-08-02-024","url":null,"abstract":"The visionary paradigm of Agriculture 5.0 integrates Industry 4.0 principles into agricultural practices. Our scoping review explores the landscape of Agriculture 5.0, emphasizing the pivotal role of Explainable AI (XAI) in shaping this domain. Guided by the Preferred Reporting Items for Systematic Review and Meta-Analysis Scoping Review, we rigorously analyzed 84 articles published from 2018 to September 2023. Our findings highlight XAI’s potential within Agriculture 5.0, recognizing its influence on intelligent farming. We propose a conceptual framework for integrating XAI, emphasizing its impact on model transparency and user trust. Despite transformative applications, existing literature often lacks XAI discussions. Our objective is to bridge this gap and provide a reference for academics, practitioners, policymakers, and educators in the field of smart agriculture that is both environmentally friendly and technologically advanced. Doi: 10.28991/ESJ-2024-08-02-024 Full Text: PDF","PeriodicalId":11586,"journal":{"name":"Emerging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140782680","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 : 2024-04-01DOI: 10.28991/esj-2024-08-02-014
K. Goh, Sugiyarto Surono, M. Y. F. Afiatin, K. R. Mahmudah, N. Irsalinda, Mesith Chaimanee, Choo Wou Onn
Deep learning, specifically the Convolutional Neural Network (CNN), has been a significant technology tool for image processing and human health. CNNs, which mimic the working principles of the human brain, can learn robust representations of images. However, CNNs are susceptible to noise interference, which can impact classification performance. Choosing the right activation function can improve CNNs performance and accuracy. This research aims to test the accuracy of CNN with ResNet50, VGG16, and GoogleNet architectures combined with several activation functions such as ReLU, Leaky ReLU, Sigmoid, and Tanh in the classification of images that experience Poisson noise. Poisson noise is applied to each test data to evaluate CNN accuracy. The data used in this study consists of three scenarios of different numbers of classes, namely 3 classes, 5 classes, and 10 classes. The results showed that combining ResNet50 with the ReLU activation function produced the best performance in class recognition in each scenario of the number of classes experiencing Poisson noise interference. The model achieved 97% accuracy for 3-class data, 95% for 5-class data, and 90% for 10-class data. These results show that using ResNet50 with the ReLU activation function can provide excellent resistance to Poisson noise in image processing. It was found that as the number of classes increases, the accuracy of image recognition tends to decrease. This shows that the more complex the image classification task is with a larger number of classes, the more difficult it is for CNNs to distinguish between different classes. Doi: 10.28991/ESJ-2024-08-02-014 Full Text: PDF
{"title":"Comparison of Activation Functions in Convolutional Neural Network for Poisson Noisy Image Classification","authors":"K. Goh, Sugiyarto Surono, M. Y. F. Afiatin, K. R. Mahmudah, N. Irsalinda, Mesith Chaimanee, Choo Wou Onn","doi":"10.28991/esj-2024-08-02-014","DOIUrl":"https://doi.org/10.28991/esj-2024-08-02-014","url":null,"abstract":"Deep learning, specifically the Convolutional Neural Network (CNN), has been a significant technology tool for image processing and human health. CNNs, which mimic the working principles of the human brain, can learn robust representations of images. However, CNNs are susceptible to noise interference, which can impact classification performance. Choosing the right activation function can improve CNNs performance and accuracy. This research aims to test the accuracy of CNN with ResNet50, VGG16, and GoogleNet architectures combined with several activation functions such as ReLU, Leaky ReLU, Sigmoid, and Tanh in the classification of images that experience Poisson noise. Poisson noise is applied to each test data to evaluate CNN accuracy. The data used in this study consists of three scenarios of different numbers of classes, namely 3 classes, 5 classes, and 10 classes. The results showed that combining ResNet50 with the ReLU activation function produced the best performance in class recognition in each scenario of the number of classes experiencing Poisson noise interference. The model achieved 97% accuracy for 3-class data, 95% for 5-class data, and 90% for 10-class data. These results show that using ResNet50 with the ReLU activation function can provide excellent resistance to Poisson noise in image processing. It was found that as the number of classes increases, the accuracy of image recognition tends to decrease. This shows that the more complex the image classification task is with a larger number of classes, the more difficult it is for CNNs to distinguish between different classes. Doi: 10.28991/ESJ-2024-08-02-014 Full Text: PDF","PeriodicalId":11586,"journal":{"name":"Emerging Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140792684","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 : 2024-04-01DOI: 10.28991/esj-2024-08-02-010
Tu Ngoc Tran
This study estimates the effect of macroeconomic and control factors on the capital adequacy ratio of commercial banks in Vietnam. Using the feasible generalized least squares method (FGLS), the following factors are statistically significant in affecting the capital adequacy ratio: national governance quality, economic growth, COVID-19, bank size, liquidity, and leverage. This study also highlights the role of compliance in maintaining capital adequacy during a global crisis, such as the COVID-19 outbreak, because commercial banks need more capital to absorb shocks in the financial instability period caused by the pandemic. Besides, the author emphasizes that in developing countries, especially Vietnam, the government needs to ensure national governance quality, such as political stability and regulatory quality, to increase additional capital buffers to protect them from losses or bankruptcies. Furthermore, the author conducts robustness tests to enhance the reliability and impartiality of the research findings. Doi: 10.28991/ESJ-2024-08-02-010 Full Text: PDF
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