Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121809
Tiffany Zhan
Deep learning has been increasingly used in various applications such as image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. In deep learning, a convolutional neural network (CNN) is regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The full connectivity of these networks makes them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include penalizing parameters during training or trimming connectivity. CNNs use relatively little pre-processing compared to other image classification algorithms. Given the rise in popularity and use of deep neural network learning, the problem of tuning hyperparameters is increasingly prominent tasks in constructing efficient deep neural networks. In this paper, the tuning of deep neural network learning (DNN) hyper-parameters is explored using an evolutionary based approach popularized for use in estimating solutions to problems where the problem space is too large to get an exact solution.
{"title":"Hyper-Parameter Tuning in Deep Neural Network Learning","authors":"Tiffany Zhan","doi":"10.5121/csit.2022.121809","DOIUrl":"https://doi.org/10.5121/csit.2022.121809","url":null,"abstract":"Deep learning has been increasingly used in various applications such as image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. In deep learning, a convolutional neural network (CNN) is regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The full connectivity of these networks makes them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include penalizing parameters during training or trimming connectivity. CNNs use relatively little pre-processing compared to other image classification algorithms. Given the rise in popularity and use of deep neural network learning, the problem of tuning hyperparameters is increasingly prominent tasks in constructing efficient deep neural networks. In this paper, the tuning of deep neural network learning (DNN) hyper-parameters is explored using an evolutionary based approach popularized for use in estimating solutions to problems where the problem space is too large to get an exact solution.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77009137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121803
Joshua Tian, Y. Sun
In recent years, we have seen a huge increase in air conditioning usage [4]. However, much of this energy put into air conditioning is being wasted, which contributes to a far less environmentally friendly world and is inconvenient for many [5][6]. This paper develops a smart vent and a mobile app to regulate temperatures in different rooms of a home to create an efficient solution to save energy. This conservation of energy allows both the environment to be preserved as well as the financial burden of families in need to be alleviated. Controlled studies of the system provide evidence of the system's automated ability to be energy efficient.
{"title":"A Context-Aware and Adaptive System to Automate the Control of the AC Windshield using AI and Internet of Things","authors":"Joshua Tian, Y. Sun","doi":"10.5121/csit.2022.121803","DOIUrl":"https://doi.org/10.5121/csit.2022.121803","url":null,"abstract":"In recent years, we have seen a huge increase in air conditioning usage [4]. However, much of this energy put into air conditioning is being wasted, which contributes to a far less environmentally friendly world and is inconvenient for many [5][6]. This paper develops a smart vent and a mobile app to regulate temperatures in different rooms of a home to create an efficient solution to save energy. This conservation of energy allows both the environment to be preserved as well as the financial burden of families in need to be alleviated. Controlled studies of the system provide evidence of the system's automated ability to be energy efficient.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72936648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121814
Yu-Zi Ji, Mingze Gao, Yu Sun
The concepts of physics play an important role in many fields of people’s lives, and physics learning is abstract, challenging, and sometimes intimidating [1]. However, how to motivate students to learn physics in a fun way becomes a question. This paper develops a gamic, interactive, educational application to allow students to learn abstract physics in an illustrative way. We have implemented a visual physics lab by using a 3D game engine supporting the immersive environment of visualization and providing a playful learning tool for physics experiments at the same time [2].
{"title":"LabBuddy: A Game-based Interactive and Immersive Educational Platform for Physics Lab Learning using Artificial Intelligence and 3D Game Engine","authors":"Yu-Zi Ji, Mingze Gao, Yu Sun","doi":"10.5121/csit.2022.121814","DOIUrl":"https://doi.org/10.5121/csit.2022.121814","url":null,"abstract":"The concepts of physics play an important role in many fields of people’s lives, and physics learning is abstract, challenging, and sometimes intimidating [1]. However, how to motivate students to learn physics in a fun way becomes a question. This paper develops a gamic, interactive, educational application to allow students to learn abstract physics in an illustrative way. We have implemented a visual physics lab by using a 3D game engine supporting the immersive environment of visualization and providing a playful learning tool for physics experiments at the same time [2].","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87059106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121810
Michel Kamel, Anis Hoayek, M. Batton-Hubert
Alarms data is a very important source of information for network operation center (NOC) teams to aggregate and display alarming events occurring within a network element. However, on a large network, a long list of alarms is generated almost continuously. Intelligent analytical reporting of these alarms is needed to help the NOC team to eliminate noise and focus on primary events. Hence, there is a need for an anomaly detection model to learn from and use historical alarms data to achieve this. It is also important to indicate the root cause of anomalies so that immediate corrective action can be taken. In this paper, we aim to design an anomaly detection model in the context of alarms data (categorical data) in the field of telecommunication and that can be used as a first step for further root cause analysis. To do this, we introduce a new algorithm to derive four features based on historical data and aggregate them to generate a final score that is optimized through supervised labels for greater accuracy. These four features reflect the likelihood of occurrence of events, the sequence of events and the importance of relatively new events not seen in the historical data. Certain assumptions are tested on the data using the relevant statistical tests. After validating these assumptions, we measure the accuracy on labelled data, revealing that the proposed algorithm performs with a high anomaly detection accuracy.
{"title":"Anomaly Detection based on Alarms Data","authors":"Michel Kamel, Anis Hoayek, M. Batton-Hubert","doi":"10.5121/csit.2022.121810","DOIUrl":"https://doi.org/10.5121/csit.2022.121810","url":null,"abstract":"Alarms data is a very important source of information for network operation center (NOC) teams to aggregate and display alarming events occurring within a network element. However, on a large network, a long list of alarms is generated almost continuously. Intelligent analytical reporting of these alarms is needed to help the NOC team to eliminate noise and focus on primary events. Hence, there is a need for an anomaly detection model to learn from and use historical alarms data to achieve this. It is also important to indicate the root cause of anomalies so that immediate corrective action can be taken. In this paper, we aim to design an anomaly detection model in the context of alarms data (categorical data) in the field of telecommunication and that can be used as a first step for further root cause analysis. To do this, we introduce a new algorithm to derive four features based on historical data and aggregate them to generate a final score that is optimized through supervised labels for greater accuracy. These four features reflect the likelihood of occurrence of events, the sequence of events and the importance of relatively new events not seen in the historical data. Certain assumptions are tested on the data using the relevant statistical tests. After validating these assumptions, we measure the accuracy on labelled data, revealing that the proposed algorithm performs with a high anomaly detection accuracy.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81290622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121815
Fatema Nafa, Enoc Gonzalez, Gurpreet Kaur
Breast cancer is one of the most common diseases that causes the death of several women around the world. So, early detection is required to help decrease breast cancer mortality rates and save the lives of cancer patients. Hence early detection is a significant process to have a healthy lifestyle. Machine learning provides the greatest support to detect breast cancer in the early stage, since it cannot be cured and brings great complications to our health system. In this paper, novel models are generated for prediction of breast cancer using Gaussian Naive Bayes (GNB), Neighbour’s Classifier, Support Vector Classifier (SVC) and Decision Tree Classifier (CART). This paper presents a comparative machine learning study based to detect breast cancer by employing four different Machine Learning models. In this paper, experiment analysis carried out on a Wisconsin Breast Cancer dataset to evaluate the performance for the models. The computation of the model is simple; hence enabling an efficient process for prediction. The best overall accuracy for breast cancer detection is achieved equal to 94%. using Gaussian Naive Bayes.
{"title":"An Approach using Machine Learning Model for Breast Cancer Prediction","authors":"Fatema Nafa, Enoc Gonzalez, Gurpreet Kaur","doi":"10.5121/csit.2022.121815","DOIUrl":"https://doi.org/10.5121/csit.2022.121815","url":null,"abstract":"Breast cancer is one of the most common diseases that causes the death of several women around the world. So, early detection is required to help decrease breast cancer mortality rates and save the lives of cancer patients. Hence early detection is a significant process to have a healthy lifestyle. Machine learning provides the greatest support to detect breast cancer in the early stage, since it cannot be cured and brings great complications to our health system. In this paper, novel models are generated for prediction of breast cancer using Gaussian Naive Bayes (GNB), Neighbour’s Classifier, Support Vector Classifier (SVC) and Decision Tree Classifier (CART). This paper presents a comparative machine learning study based to detect breast cancer by employing four different Machine Learning models. In this paper, experiment analysis carried out on a Wisconsin Breast Cancer dataset to evaluate the performance for the models. The computation of the model is simple; hence enabling an efficient process for prediction. The best overall accuracy for breast cancer detection is achieved equal to 94%. using Gaussian Naive Bayes.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78897437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121823
Xu Wang, Longzhen Zhou, Zusheng Zhang, Yingbo Wu, Longxi Li
In the context of low-carbon innovation, reasonable subsidy, innovation, and pricing strategies are important to achieve resource decarbonization and supply-demand matching, while the quality differentiation of resources has a significant impact on the strategy formulation. In this paper, we study low-carbon innovation and government subsidy in different innovation scenarios with two providers offering differentiated manufacturing resources on a resource trading platform, integrating two variables of resource quality difference and demand-side lowcarbon preference. Using utility theory and the Stackelberg game, a decision model of low carbon innovation and government subsidy is constructed, and the equilibrium solution is obtained with inverse induction. Then, the low-carbon innovation and subsidy strategies under different innovation scenarios are compared and the effects of relative coefficients of quality and innovation cost coefficients on the strategies are analyzed. The findings show that when the difference in resource quality is small, the level of green innovation is higher in the low carbon innovation scenario with high-quality resources compared to the low carbon innovation scenario with low-quality resources, and the rate of government subsidy for innovation investment is also higher. In case of the large difference in resource quality, the relative magnitudes of green innovation level and government subsidy rate for innovation inputs in different scenarios are related to innovation cost coefficients.
{"title":"Low-Carbon Innovation Decision Considering Quality Differences and Government Subsidies under the Three-Party Trading Platform","authors":"Xu Wang, Longzhen Zhou, Zusheng Zhang, Yingbo Wu, Longxi Li","doi":"10.5121/csit.2022.121823","DOIUrl":"https://doi.org/10.5121/csit.2022.121823","url":null,"abstract":"In the context of low-carbon innovation, reasonable subsidy, innovation, and pricing strategies are important to achieve resource decarbonization and supply-demand matching, while the quality differentiation of resources has a significant impact on the strategy formulation. In this paper, we study low-carbon innovation and government subsidy in different innovation scenarios with two providers offering differentiated manufacturing resources on a resource trading platform, integrating two variables of resource quality difference and demand-side lowcarbon preference. Using utility theory and the Stackelberg game, a decision model of low carbon innovation and government subsidy is constructed, and the equilibrium solution is obtained with inverse induction. Then, the low-carbon innovation and subsidy strategies under different innovation scenarios are compared and the effects of relative coefficients of quality and innovation cost coefficients on the strategies are analyzed. The findings show that when the difference in resource quality is small, the level of green innovation is higher in the low carbon innovation scenario with high-quality resources compared to the low carbon innovation scenario with low-quality resources, and the rate of government subsidy for innovation investment is also higher. In case of the large difference in resource quality, the relative magnitudes of green innovation level and government subsidy rate for innovation inputs in different scenarios are related to innovation cost coefficients.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75821319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121802
A. Skaf, Samir Dawaliby, Arezki Aberkane
This paper discusses the computational complexity of the quay crane scheduling problem (QCSP) in a maritime port. To prove that a problem is NP-complete, there should be no polynomial time algorithm for the exact solution, and only heuristic approaches are used to obtain near-optimal solutions but in reasonable time complexity. To address this, first we formulate the QCSP as a mixed integer linear programming to solve it to optimal, and next we theoretically prove that the examined problem is NP-complete.
{"title":"The NP-Completeness of Quay Crane Scheduling Problem","authors":"A. Skaf, Samir Dawaliby, Arezki Aberkane","doi":"10.5121/csit.2022.121802","DOIUrl":"https://doi.org/10.5121/csit.2022.121802","url":null,"abstract":"This paper discusses the computational complexity of the quay crane scheduling problem (QCSP) in a maritime port. To prove that a problem is NP-complete, there should be no polynomial time algorithm for the exact solution, and only heuristic approaches are used to obtain near-optimal solutions but in reasonable time complexity. To address this, first we formulate the QCSP as a mixed integer linear programming to solve it to optimal, and next we theoretically prove that the examined problem is NP-complete.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90923836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121819
Muqing Bai, Yu Sun
In an era of machine learning, many fields outside of computer science have implemented machine learning as a tool [5]. In the financial world, a variety of machine learning models are used to predict the future prices of a stock in order to optimize profit. This paper preposes a stock prediction algorithm that focuses on the correlation between the price of a stock and its public sentiments shown on social media [6].We trained different machine learning algorithms to find the best model at predicting stock prices given its sentiment. And for the public to access this model, a web-based server and a mobile application is created. We used Thunkable, a powerful no code platform, to produce our mobile application [7]. It allows anyone to check the predictions of stocks, helping people with their investment decisions.
{"title":"An Intelligent and Social-Oriented Sentiment Analytical Model for Stock Market Prediction using Machine Learning and Big Data Analysis","authors":"Muqing Bai, Yu Sun","doi":"10.5121/csit.2022.121819","DOIUrl":"https://doi.org/10.5121/csit.2022.121819","url":null,"abstract":"In an era of machine learning, many fields outside of computer science have implemented machine learning as a tool [5]. In the financial world, a variety of machine learning models are used to predict the future prices of a stock in order to optimize profit. This paper preposes a stock prediction algorithm that focuses on the correlation between the price of a stock and its public sentiments shown on social media [6].We trained different machine learning algorithms to find the best model at predicting stock prices given its sentiment. And for the public to access this model, a web-based server and a mobile application is created. We used Thunkable, a powerful no code platform, to produce our mobile application [7]. It allows anyone to check the predictions of stocks, helping people with their investment decisions.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91181575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121821
Fei Wang, Zhenxi Li, Xiaofeng Wang
Distributed Denial of Service (DDoS) is Achilles' heel of cloud security. This paper thus focuses on detection of such attack, and more importantly, victim identification to promote attack reaction. We present a collaborative system, called F-LOW. Profiting from bitwise-based hash function, split sketch, and lightweight IP reconstruction, F-LOW can defeat shortcomings of principle component analysis (PCA) and regular sketch. Outperforming previous work, our system fits all Four-LOW properties, low profile, low dimensional, low overhead and low transmission, of a promising DDoS countermeasure. Through simulation and theoretical analysis, we demonstrate such properties and remarkable efficacy of our approach in DDoS mitigation.
{"title":"F-low: A Promising Countermeasure Against DDoS Attacks based on Split Sketch and PCA","authors":"Fei Wang, Zhenxi Li, Xiaofeng Wang","doi":"10.5121/csit.2022.121821","DOIUrl":"https://doi.org/10.5121/csit.2022.121821","url":null,"abstract":"Distributed Denial of Service (DDoS) is Achilles' heel of cloud security. This paper thus focuses on detection of such attack, and more importantly, victim identification to promote attack reaction. We present a collaborative system, called F-LOW. Profiting from bitwise-based hash function, split sketch, and lightweight IP reconstruction, F-LOW can defeat shortcomings of principle component analysis (PCA) and regular sketch. Outperforming previous work, our system fits all Four-LOW properties, low profile, low dimensional, low overhead and low transmission, of a promising DDoS countermeasure. Through simulation and theoretical analysis, we demonstrate such properties and remarkable efficacy of our approach in DDoS mitigation.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86036267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121824
M. Ćurković, A. Curkovic, D. Vucina, D. Samardžić
Image segmentation and segmentation of geometry are one of the basic requirements for reverse engineering, shape synthesis, and shape optimization. In terms of shape optimization and shape synthesis where the original geometry should be faithfully replaced with some mathematical parametric model (NURBS, hierarchical NURBS, T-Spline, …) segmentation of geometry may be done directly on 3D geometry and its corresponding parametric values in the 2D parametric domain. In our approach, we are focused on segmentation of 2D parametric domain as an image instead of 3D geometry. The reason for this lies in our dynamic hierarchical parametric model, which controls the results of various operators from image processing applied to the parametric domain.
{"title":"Image Segmentation in Shape Synthesis, Shape Optimization, And Reverse Engineering","authors":"M. Ćurković, A. Curkovic, D. Vucina, D. Samardžić","doi":"10.5121/csit.2022.121824","DOIUrl":"https://doi.org/10.5121/csit.2022.121824","url":null,"abstract":"Image segmentation and segmentation of geometry are one of the basic requirements for reverse engineering, shape synthesis, and shape optimization. In terms of shape optimization and shape synthesis where the original geometry should be faithfully replaced with some mathematical parametric model (NURBS, hierarchical NURBS, T-Spline, …) segmentation of geometry may be done directly on 3D geometry and its corresponding parametric values in the 2D parametric domain. In our approach, we are focused on segmentation of 2D parametric domain as an image instead of 3D geometry. The reason for this lies in our dynamic hierarchical parametric model, which controls the results of various operators from image processing applied to the parametric domain.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89233367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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