M. Reddy, Nur Mohammad Ali Chisty, Anusha Bodepudi
Cloud computing uses few resources to give customers complete distant services via the internet. Data privacy, security, and reliability are major issues with cloud computing. Security is the biggest issue. This study discusses the biometrics framework and safe cloud computing identity management method. This paper discusses cloud computing security challenges and reviews cloud access framework approaches. It describes a novel fingerprint access-based authentication system to protect cloud services from DOS and DDoS attacks. This biometrics-based system can secure cloud services from illegal access. This study addresses cloud security and privacy via biometric face recognition. Cloud users' security and privacy are protected via biometrics recognition. This article discusses CPS and its applications, technologies, and standards. SIGNIFICANT DIFFICULTIES AND CHALLENGES ARE FOUND IN reviewing CPS security weaknesses, threats, and attacks. Presenting and analyzing current security measures and their key drawbacks. Finally, this extensive examination yields various recommendations.
{"title":"A Review of Cybersecurity and Biometric Authentication in Cloud Network","authors":"M. Reddy, Nur Mohammad Ali Chisty, Anusha Bodepudi","doi":"10.18034/ei.v10i1.652","DOIUrl":"https://doi.org/10.18034/ei.v10i1.652","url":null,"abstract":"Cloud computing uses few resources to give customers complete distant services via the internet. Data privacy, security, and reliability are major issues with cloud computing. Security is the biggest issue. This study discusses the biometrics framework and safe cloud computing identity management method. This paper discusses cloud computing security challenges and reviews cloud access framework approaches. It describes a novel fingerprint access-based authentication system to protect cloud services from DOS and DDoS attacks. This biometrics-based system can secure cloud services from illegal access. This study addresses cloud security and privacy via biometric face recognition. Cloud users' security and privacy are protected via biometrics recognition. This article discusses CPS and its applications, technologies, and standards. SIGNIFICANT DIFFICULTIES AND CHALLENGES ARE FOUND IN reviewing CPS security weaknesses, threats, and attacks. Presenting and analyzing current security measures and their key drawbacks. Finally, this extensive examination yields various recommendations.","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76127853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Network security is a framework that deals with issuing procedures and policies that will be used to establish and maintain security protocols in a particular organization. The functions related to the network security framework are oriented toward insulating the specific organization from external cyber threats and adversaries. On the other hand, a hybrid cloud is a type of cloud whose function is to allow the running and operating of different applications in various and different environments. The primary technique associated with developing hybrid clouds is the conjunctions between private and public clouds that will allow application portability and management for better and more efficient working of the clouds.
{"title":"Network Security Framework, Techniques, and Design for Hybrid Cloud","authors":"Sandesh Achar","doi":"10.18034/ei.v9i2.642","DOIUrl":"https://doi.org/10.18034/ei.v9i2.642","url":null,"abstract":"Network security is a framework that deals with issuing procedures and policies that will be used to establish and maintain security protocols in a particular organization. The functions related to the network security framework are oriented toward insulating the specific organization from external cyber threats and adversaries. On the other hand, a hybrid cloud is a type of cloud whose function is to allow the running and operating of different applications in various and different environments. The primary technique associated with developing hybrid clouds is the conjunctions between private and public clouds that will allow application portability and management for better and more efficient working of the clouds. \u0000 ","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80359389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nonlinear partial differential equations are mostly renowned for depicting the underlying behavior of nonlinear phenomena relating to the nature of the real world. In this paper, we discuss analytic solutions of fractional-order nonlinear Schrodinger types equations such as the space-time fractional nonlinear Schrodinger equation and the (2+1)-dimensional time-fractional Schrodinger equation. The considered equations are converted into ordinary differential equations with the help of wave variable transformation and then the recently established rational ( )-expansion method is employed to construct the exact solutions. The obtained solutions have appeared in the forms of a trigonometric function, hyperbolic function, and rational function which are compared with those of literature and claimed to be different. The graphical representations of the solutions are finally brought out for their physical appearances. The applied method is seemed to be efficient, concise, and productive which might be used for further research. Mathematics Subject Classifications: 35C08, 35R11
{"title":"Wave Structures for Nonlinear Schrodinger Types Fractional Partial Differential Equations Arise in Physical Sciences","authors":"Mst. Nasrin Nahar, M. Islam, Diganta Broto Kar","doi":"10.18034/EI.V9I2.560","DOIUrl":"https://doi.org/10.18034/EI.V9I2.560","url":null,"abstract":"Nonlinear partial differential equations are mostly renowned for depicting the underlying behavior of nonlinear phenomena relating to the nature of the real world. In this paper, we discuss analytic solutions of fractional-order nonlinear Schrodinger types equations such as the space-time fractional nonlinear Schrodinger equation and the (2+1)-dimensional time-fractional Schrodinger equation. The considered equations are converted into ordinary differential equations with the help of wave variable transformation and then the recently established rational ( )-expansion method is employed to construct the exact solutions. The obtained solutions have appeared in the forms of a trigonometric function, hyperbolic function, and rational function which are compared with those of literature and claimed to be different. The graphical representations of the solutions are finally brought out for their physical appearances. The applied method is seemed to be efficient, concise, and productive which might be used for further research. \u0000Mathematics Subject Classifications: 35C08, 35R11","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73266130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gradient boosting machines, the learning process successively fits fresh prototypes to offer a more precise approximation of the response parameter. The principle notion associated with this algorithm is that a fresh base-learner construct to be extremely correlated with the “negative gradient of the loss function” related to the entire ensemble. The loss function's usefulness can be random, nonetheless, for a clearer understanding of this subject, if the “error function is the model squared-error loss”, then the learning process would end up in sequential error-fitting. This study is aimed at delineating the significance of the gradient boosting algorithm in data management systems. The article will dwell much the significance of gradient boosting algorithm in text classification as well as the limitations of this model. The basic methodology as well as the basic-learning algorithm of the gradient boosting algorithms originally formulated by Friedman, is presented in this study. This may serve as an introduction to gradient boosting algorithms. This article has displayed the approach of gradient boosting algorithms. Both the hypothetical system and the plan choices were depicted and outlined. We have examined all the basic stages of planning a specific demonstration for one’s experimental needs. Elucidation issues have been tended to and displayed as a basic portion of the investigation. The capabilities of the gradient boosting algorithms were examined on a set of real-world down-to-earth applications such as text classification.
{"title":"Significant of Gradient Boosting Algorithm in Data Management System","authors":"S. Hosen, Ruhul Amin","doi":"10.18034/EI.V9I2.559","DOIUrl":"https://doi.org/10.18034/EI.V9I2.559","url":null,"abstract":"Gradient boosting machines, the learning process successively fits fresh prototypes to offer a more precise approximation of the response parameter. The principle notion associated with this algorithm is that a fresh base-learner construct to be extremely correlated with the “negative gradient of the loss function” related to the entire ensemble. The loss function's usefulness can be random, nonetheless, for a clearer understanding of this subject, if the “error function is the model squared-error loss”, then the learning process would end up in sequential error-fitting. This study is aimed at delineating the significance of the gradient boosting algorithm in data management systems. The article will dwell much the significance of gradient boosting algorithm in text classification as well as the limitations of this model. The basic methodology as well as the basic-learning algorithm of the gradient boosting algorithms originally formulated by Friedman, is presented in this study. This may serve as an introduction to gradient boosting algorithms. This article has displayed the approach of gradient boosting algorithms. Both the hypothetical system and the plan choices were depicted and outlined. We have examined all the basic stages of planning a specific demonstration for one’s experimental needs. Elucidation issues have been tended to and displayed as a basic portion of the investigation. The capabilities of the gradient boosting algorithms were examined on a set of real-world down-to-earth applications such as text classification.","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87742899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md. Shahadat Hossain, Md Anwar Hossain, A. Abadin, M. Ahmed
The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.
{"title":"Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network","authors":"Md. Shahadat Hossain, Md Anwar Hossain, A. Abadin, M. Ahmed","doi":"10.18034/EI.V9I2.551","DOIUrl":"https://doi.org/10.18034/EI.V9I2.551","url":null,"abstract":"The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80876543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantum Computing in high-frequency trading and fraud detection is an analysis of quantum computing and how it can be used by the different industries especially finance. It is an evolution of computing from the traditional computing method. Quantum computing is a process that is concentrated on creating systems and technology based on quantum theory rules. Quantum theory describes the energy on atomic and subatomic levels. Quantum computing uses quantum bits (qubits) which are more advanced than the traditional bits used by traditional computers. This article focuses on deploying quantum computers in solving problems that cannot be efficiently solved using traditional computers. In the finance sector, such as banking, insurance, and high-frequency trading, quantum computers can help optimize service by providing targeting and predictive analytics to reduce risk, provide personalized customer service, and provide the needed security framework against fraud.
{"title":"Quantum Computing in High Frequency Trading and Fraud Detection","authors":"Apoorva Ganapathy","doi":"10.18034/EI.V9I2.549","DOIUrl":"https://doi.org/10.18034/EI.V9I2.549","url":null,"abstract":"Quantum Computing in high-frequency trading and fraud detection is an analysis of quantum computing and how it can be used by the different industries especially finance. It is an evolution of computing from the traditional computing method. Quantum computing is a process that is concentrated on creating systems and technology based on quantum theory rules. Quantum theory describes the energy on atomic and subatomic levels. Quantum computing uses quantum bits (qubits) which are more advanced than the traditional bits used by traditional computers. This article focuses on deploying quantum computers in solving problems that cannot be efficiently solved using traditional computers. In the finance sector, such as banking, insurance, and high-frequency trading, quantum computers can help optimize service by providing targeting and predictive analytics to reduce risk, provide personalized customer service, and provide the needed security framework against fraud.","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85261353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Friction stir welding is a modern innovation in the welding processes technology, there are several ways in which this technology has to be investigated in order to refine and make it economically responsible. Aluminum alloys have strong mechanical properties when they are welded by using the Friction Stir welding. Therefore, certain parameters of the welding process need to be examined to achieve the required mechanical properties. In this project, a literature survey has been performed about the friction stir welding process and its parameters for 6xxx series aluminum alloys.
{"title":"A Survey of the Parameters of the Friction Stir Welding Process of Aluminum Alloys 6xxx Series","authors":"M. Essa, Fahad Salem Alhajri","doi":"10.18034/EI.V9I1.548","DOIUrl":"https://doi.org/10.18034/EI.V9I1.548","url":null,"abstract":"Friction stir welding is a modern innovation in the welding processes technology, there are several ways in which this technology has to be investigated in order to refine and make it economically responsible. Aluminum alloys have strong mechanical properties when they are welded by using the Friction Stir welding. Therefore, certain parameters of the welding process need to be examined to achieve the required mechanical properties. In this project, a literature survey has been performed about the friction stir welding process and its parameters for 6xxx series aluminum alloys. \u0000 ","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74296104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The most appropriate method to uncover patterns from clinical records for each patient record is to create a bag with a variety of examples in the form of symptoms. The goal of medical diagnosis is to find useful ones first and then map them to one or more diseases. Patients are often represented as vectors in some aspect. Pathologists and dermatopathologists diagnose basal cell carcinomas (BCC), one of the most frequent cutaneous cancers in humans, on a regular basis. Improving histological diagnosis by producing diagnosis ideas, i.e. computer-assisted diagnoses, is a hotly debated research topic aimed at improving safety, quality, and efficiency. Due to their improved performance, machine learning approaches are rapidly being used. Typical images obtained by scanning histological sections, on the other hand, frequently have a resolution insufficient for today's state-of-the-art neural networks. Furthermore, weak labels hamper network training because just a small portion of the image signals the disease class, while the majority of the image is strikingly comparable to the non-disease class. The goal of this work is to see if attention-based deep learning models can detect basal cell carcinomas in histological sections and overcome the ultra-high resolution and poor labeling of full slide images. With an AUC of 0.99, we show that attention-based models can achieve nearly flawless classification performance.
{"title":"Diagnosing Epidermal basal Squamous Cell Carcinoma in High-resolution, and Poorly Labeled Histopathological Imaging","authors":"Mani Manavalan","doi":"10.18034/ei.v8i2.574","DOIUrl":"https://doi.org/10.18034/ei.v8i2.574","url":null,"abstract":"The most appropriate method to uncover patterns from clinical records for each patient record is to create a bag with a variety of examples in the form of symptoms. The goal of medical diagnosis is to find useful ones first and then map them to one or more diseases. Patients are often represented as vectors in some aspect. Pathologists and dermatopathologists diagnose basal cell carcinomas (BCC), one of the most frequent cutaneous cancers in humans, on a regular basis. Improving histological diagnosis by producing diagnosis ideas, i.e. computer-assisted diagnoses, is a hotly debated research topic aimed at improving safety, quality, and efficiency. Due to their improved performance, machine learning approaches are rapidly being used. Typical images obtained by scanning histological sections, on the other hand, frequently have a resolution insufficient for today's state-of-the-art neural networks. Furthermore, weak labels hamper network training because just a small portion of the image signals the disease class, while the majority of the image is strikingly comparable to the non-disease class. The goal of this work is to see if attention-based deep learning models can detect basal cell carcinomas in histological sections and overcome the ultra-high resolution and poor labeling of full slide images. With an AUC of 0.99, we show that attention-based models can achieve nearly flawless classification performance.","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75529868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to train neural network algorithms for multiple machine learning tasks, like the division of distinct categories of objects, various deep learning approaches employ data. Convolutional neural networks deep learning algorithms are quite strong when it comes to image processing. With the recent development of multi-layer convolutional neural networks for high-level tasks like object recognition, object acquisition, and recent semantic classification, the field has seen great success in this approach. The two-phase approach is frequently employed in semantic segregation. In the second step of becoming a standard global graphical model, communication networks are educated to deliver good local intelligence with a pixel. Convolutional Neural Networks (CNN or ConvNet) are complicated neural server networks in the field of artificial intelligence. Because of their great accuracy, convolutional neural networks (CNNs) are frequently utilized in picture categorization and recognition. In the late 1990s, Yann LeCun, a computer scientist, was based on the human notion of cognition and came up with the idea. When constructing a network, CNN uses a hierarchical model that eventually results in a convolution layer in which all neurons are linked and output is processed. Using an example of an image processing application, this article demonstrates how the CNN architecture is implemented in its entirety. You can utilize this to better comprehend the advantages of this current photography website.
{"title":"Application of Convolution Neural Network for Digital Image Processing","authors":"Venkata Naga Satya Surendra Chimakurthi","doi":"10.18034/ei.v8i2.592","DOIUrl":"https://doi.org/10.18034/ei.v8i2.592","url":null,"abstract":"In order to train neural network algorithms for multiple machine learning tasks, like the division of distinct categories of objects, various deep learning approaches employ data. Convolutional neural networks deep learning algorithms are quite strong when it comes to image processing. With the recent development of multi-layer convolutional neural networks for high-level tasks like object recognition, object acquisition, and recent semantic classification, the field has seen great success in this approach. The two-phase approach is frequently employed in semantic segregation. In the second step of becoming a standard global graphical model, communication networks are educated to deliver good local intelligence with a pixel. Convolutional Neural Networks (CNN or ConvNet) are complicated neural server networks in the field of artificial intelligence. Because of their great accuracy, convolutional neural networks (CNNs) are frequently utilized in picture categorization and recognition. In the late 1990s, Yann LeCun, a computer scientist, was based on the human notion of cognition and came up with the idea. When constructing a network, CNN uses a hierarchical model that eventually results in a convolution layer in which all neurons are linked and output is processed. Using an example of an image processing application, this article demonstrates how the CNN architecture is implemented in its entirety. You can utilize this to better comprehend the advantages of this current photography website. \u0000 ","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83396546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Long short-term memory networks (LSTM) offer precision in the prediction that has never been seen before in ungauged basins. Using k-fold validation, we trained and evaluated several LSTMs in this study on 531 basins from the CAMELS data set. This allowed us to make predictions in basins for which we did not have any training data. The implication is that there is usually sufficient information in available catchment attributes data about similarities and differences between catchment-level rainfall-runoff behaviors to generate out-of-sample simulations that are generally more accurate than current models when operating under ideal (i.e., calibrated) conditions, i.e., when using under idealized conditions. In other words, existing models are generally less accurate when working under idealized conditions than out-of-sample simulations. We found evidence that including physical limits in LSTM models improves simulations, which we believe should be the primary focus of future research on physics-guided artificial intelligence. Putting in place additional physical constraints on the LSTM models.
{"title":"Maximizing the Potential of Artificial Intelligence to Perform Evaluations in Ungauged Washbowls","authors":"Sandesh Achar","doi":"10.18034/ei.v8i2.636","DOIUrl":"https://doi.org/10.18034/ei.v8i2.636","url":null,"abstract":"Long short-term memory networks (LSTM) offer precision in the prediction that has never been seen before in ungauged basins. Using k-fold validation, we trained and evaluated several LSTMs in this study on 531 basins from the CAMELS data set. This allowed us to make predictions in basins for which we did not have any training data. The implication is that there is usually sufficient information in available catchment attributes data about similarities and differences between catchment-level rainfall-runoff behaviors to generate out-of-sample simulations that are generally more accurate than current models when operating under ideal (i.e., calibrated) conditions, i.e., when using under idealized conditions. In other words, existing models are generally less accurate when working under idealized conditions than out-of-sample simulations. We found evidence that including physical limits in LSTM models improves simulations, which we believe should be the primary focus of future research on physics-guided artificial intelligence. Putting in place additional physical constraints on the LSTM models.","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73690302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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