A. Azar, Mohamed Tounsi, Suliman Mohamed Fati, Yasir Javed, S. Amin, Zafar Iqbal Khan, Shrooq A. Alsenan, Jothi Ganesan
Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.
{"title":"Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques","authors":"A. Azar, Mohamed Tounsi, Suliman Mohamed Fati, Yasir Javed, S. Amin, Zafar Iqbal Khan, Shrooq A. Alsenan, Jothi Ganesan","doi":"10.4018/ijskd.326629","DOIUrl":"https://doi.org/10.4018/ijskd.326629","url":null,"abstract":"Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.","PeriodicalId":53530,"journal":{"name":"International Journal of Sociotechnology and Knowledge Development","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84945255","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}
Sivagurunathan Shanmugam, Muthu Ganeshan V., Prathapchandran K., J. T.
Many application domains gain considerable advantages with the internet of things (IoT) network. It improves our lifestyle towards smartness in smart devices. IoT devices are mostly resource-constrained such as memory, battery, etc. So it is highly vulnerable to security attacks. Traditional security mechanisms can't be applied to these devices due to their restricted resources. A trust-based security mechanism plays an important role to ensure security in the IoT environment because it consumes only fewer resources. Thus, it is essential to evaluate the trustworthiness among IoT devices. The proposed model improves trusted routing in the IoT environment by detecting and isolating malicious nodes. This model uses reinforcement learning (RL) where the agent learns the behavior of the node and isolates the malicious nodes to improve the network performance. The model focuses on IoT with the routing protocol for low power and lossy network (RPL) and counters the blackhole attack.
{"title":"Mitigating Black Hole Attacks in Routing Protocols Using a Machine Learning-Based Trust Model","authors":"Sivagurunathan Shanmugam, Muthu Ganeshan V., Prathapchandran K., J. T.","doi":"10.4018/ijskd.310067","DOIUrl":"https://doi.org/10.4018/ijskd.310067","url":null,"abstract":"Many application domains gain considerable advantages with the internet of things (IoT) network. It improves our lifestyle towards smartness in smart devices. IoT devices are mostly resource-constrained such as memory, battery, etc. So it is highly vulnerable to security attacks. Traditional security mechanisms can't be applied to these devices due to their restricted resources. A trust-based security mechanism plays an important role to ensure security in the IoT environment because it consumes only fewer resources. Thus, it is essential to evaluate the trustworthiness among IoT devices. The proposed model improves trusted routing in the IoT environment by detecting and isolating malicious nodes. This model uses reinforcement learning (RL) where the agent learns the behavior of the node and isolates the malicious nodes to improve the network performance. The model focuses on IoT with the routing protocol for low power and lossy network (RPL) and counters the blackhole attack.","PeriodicalId":53530,"journal":{"name":"International Journal of Sociotechnology and Knowledge Development","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77981802","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 recently witnessed economic downturn prompted by the COVID-19 pandemic rekindled the interests of management professionals, policymakers, academicians, and researchers in corporate turnaround management. Hence, this study aims to ascertain the top management teams' characteristics for an effective turnaround in the manufacturing sector. Exploratory research design supported this study whereby 16 key informants were recruited for interviews. Adopting thematic analysis, the qualitative results showed the key five top management teams' characteristics for effective turnaround management, namely gender, personal features, educational qualifications, age, and experience. With these results, the study concludes that the heterogeneity of top management teams has a direct bearing on the successful turnaround attempts in the manufacturing sector during an economic crisis. As such, the study recommends the appointment of senior executives based on experience, age, gender, personal features, and educational qualifications.
{"title":"Understanding Top Management Teams' Characteristics for Effective Turnaround Management","authors":"Mufaro Dzingirai, N. Baporikar","doi":"10.4018/ijskd.312572","DOIUrl":"https://doi.org/10.4018/ijskd.312572","url":null,"abstract":"The recently witnessed economic downturn prompted by the COVID-19 pandemic rekindled the interests of management professionals, policymakers, academicians, and researchers in corporate turnaround management. Hence, this study aims to ascertain the top management teams' characteristics for an effective turnaround in the manufacturing sector. Exploratory research design supported this study whereby 16 key informants were recruited for interviews. Adopting thematic analysis, the qualitative results showed the key five top management teams' characteristics for effective turnaround management, namely gender, personal features, educational qualifications, age, and experience. With these results, the study concludes that the heterogeneity of top management teams has a direct bearing on the successful turnaround attempts in the manufacturing sector during an economic crisis. As such, the study recommends the appointment of senior executives based on experience, age, gender, personal features, and educational qualifications.","PeriodicalId":53530,"journal":{"name":"International Journal of Sociotechnology and Knowledge Development","volume":"88 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75709082","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}
Myocardial infarction is the most hazardous cardiovascular disease for humans; generally, it is acknowledged as a heart attack, which may result in death. Thus, rapid and precise detection of myocardial infarction is essential to reduce the mortality rate. This paper proposes the Taylor-enhanced invasive weed sine cosine optimization algorithm-based deep convolutional neural network (Taylor IIWSCOA-enabled DCNN) model to classify myocardial infarction. Here, the DCNN classifier is used to predict and categorize myocardial infarction, and the classifier is tuned by the Taylor IIWSCOA to attain superior efficiency. The Taylor IIWSCOA is designed by integrating SCA, IIWO approach, and the Taylor series. The proposed Taylor IIWSCOA-based DCNN approach outperforms other conventional approaches with an accuracy of 0.9412, sensitivity of 0.9535, and specificity of 0.9485.
{"title":"Design and Development of Hybrid Optimization-Enabled Deep Learning Model for Myocardial Infarction","authors":"Shamal S. Bulbule, Shridevi Soma","doi":"10.4018/ijskd.313589","DOIUrl":"https://doi.org/10.4018/ijskd.313589","url":null,"abstract":"Myocardial infarction is the most hazardous cardiovascular disease for humans; generally, it is acknowledged as a heart attack, which may result in death. Thus, rapid and precise detection of myocardial infarction is essential to reduce the mortality rate. This paper proposes the Taylor-enhanced invasive weed sine cosine optimization algorithm-based deep convolutional neural network (Taylor IIWSCOA-enabled DCNN) model to classify myocardial infarction. Here, the DCNN classifier is used to predict and categorize myocardial infarction, and the classifier is tuned by the Taylor IIWSCOA to attain superior efficiency. The Taylor IIWSCOA is designed by integrating SCA, IIWO approach, and the Taylor series. The proposed Taylor IIWSCOA-based DCNN approach outperforms other conventional approaches with an accuracy of 0.9412, sensitivity of 0.9535, and specificity of 0.9485.","PeriodicalId":53530,"journal":{"name":"International Journal of Sociotechnology and Knowledge Development","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88470269","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}
A. Redjati, Amira Boulmaiz, M. Boughazi, Karima Boukari, Billel Meghni
Given its location on the migration route of the Western Palearctic, the complex of wetlands of El-Kala (North-East Algeria) forms the most important and diverse area of the Mediterranean for migratory birds in the Maghreb. The knowledge of these birds allows one to acquire crucial information on the state of health of considered environments as well as annual statistics of this population. Some of which are threatened with extinction. Because of the dense vegetation, the main feature characterizing the birds' habitat, the identification of bird species from their images is made a complicated task. In addition, there is a high degree of similarity between classes and features. In this paper and in order to solve these problems, a new method named DarkBirdNet based on deep learning has been developed. This method is derived from the predefined DarkNet53 model and aims at detecting and classifying bird species in Algeria.
{"title":"A Novel Deep Learning Model for Recognition of Endangered Water-Bird Species","authors":"A. Redjati, Amira Boulmaiz, M. Boughazi, Karima Boukari, Billel Meghni","doi":"10.4018/ijskd.315750","DOIUrl":"https://doi.org/10.4018/ijskd.315750","url":null,"abstract":"Given its location on the migration route of the Western Palearctic, the complex of wetlands of El-Kala (North-East Algeria) forms the most important and diverse area of the Mediterranean for migratory birds in the Maghreb. The knowledge of these birds allows one to acquire crucial information on the state of health of considered environments as well as annual statistics of this population. Some of which are threatened with extinction. Because of the dense vegetation, the main feature characterizing the birds' habitat, the identification of bird species from their images is made a complicated task. In addition, there is a high degree of similarity between classes and features. In this paper and in order to solve these problems, a new method named DarkBirdNet based on deep learning has been developed. This method is derived from the predefined DarkNet53 model and aims at detecting and classifying bird species in Algeria.","PeriodicalId":53530,"journal":{"name":"International Journal of Sociotechnology and Knowledge Development","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85875352","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}
Rose A. Arceňo, Jason G. Tuang-tuang, Romil Asoque, Rey Cesar Olorvida, N. Egloso, Edwin Ramones, Rey Ann Bande, Ruby Mary Encenzo, Janeth Aclao, Romel Mejos, Jessa Turalba, Ronald Lacaba, Fatima Maturan, Samantha Shane Evangelista, Joerabell Lourdes Aro, L. Ocampo
This work employs the decision-making trial and evaluation laboratory (DEMATEL) analysis in elucidating the holistic relationships among the factors that affect the intention of consumers to purchase products via e-commerce. In demonstrating the DEMATEL, a case study evaluating 13 factors of consumer purchase intention of food products via Facebook e-commerce platforms, derived from a focus group discussion, was carried out in this work. The context of the analysis is positioned during the early phase of the COVID-19 pandemic, where strict physical distancing measures were implemented. The findings of this work suggest that reliability, food product quality and safety, and convenience are the key factors influencing consumer purchase intention, with reliability as the most prominent. These results offer practical insights that would aid decision-makers in food enterprises in allocating resources and designing initiatives to attract consumers to purchase food products through e-commerce platforms. Some managerial takeaways are outlined in this work.
{"title":"Consumer Purchase Intention for Food Products in Facebook E-Commerce Platforms During COVID-19 Lockdowns","authors":"Rose A. Arceňo, Jason G. Tuang-tuang, Romil Asoque, Rey Cesar Olorvida, N. Egloso, Edwin Ramones, Rey Ann Bande, Ruby Mary Encenzo, Janeth Aclao, Romel Mejos, Jessa Turalba, Ronald Lacaba, Fatima Maturan, Samantha Shane Evangelista, Joerabell Lourdes Aro, L. Ocampo","doi":"10.4018/ijskd.313929","DOIUrl":"https://doi.org/10.4018/ijskd.313929","url":null,"abstract":"This work employs the decision-making trial and evaluation laboratory (DEMATEL) analysis in elucidating the holistic relationships among the factors that affect the intention of consumers to purchase products via e-commerce. In demonstrating the DEMATEL, a case study evaluating 13 factors of consumer purchase intention of food products via Facebook e-commerce platforms, derived from a focus group discussion, was carried out in this work. The context of the analysis is positioned during the early phase of the COVID-19 pandemic, where strict physical distancing measures were implemented. The findings of this work suggest that reliability, food product quality and safety, and convenience are the key factors influencing consumer purchase intention, with reliability as the most prominent. These results offer practical insights that would aid decision-makers in food enterprises in allocating resources and designing initiatives to attract consumers to purchase food products through e-commerce platforms. Some managerial takeaways are outlined in this work.","PeriodicalId":53530,"journal":{"name":"International Journal of Sociotechnology and Knowledge Development","volume":"354 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76477234","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}
Corona virus has affected the world education system and the distance education system. The medium of instruction changed from traditional to technological in this pandemic. This study has been done in Saudi Arabia to measure the benefits and barriers of distance education during the corona virus pandemic. A total of 1500 questioners were distributed and collected through random sampling to measure the response of respondents in schools. To analyze the data, t-test and ANOVA techniques were used. From the study it has been found that due to technology, teachers have benefited in achieving their goals in delivering the classes and results were achieved as desired.
{"title":"Corona Virus Pandemic Situation and Its Effect on Distance Education in Saudi Arabia","authors":"K. Alotaibi","doi":"10.4018/ijskd.313928","DOIUrl":"https://doi.org/10.4018/ijskd.313928","url":null,"abstract":"Corona virus has affected the world education system and the distance education system. The medium of instruction changed from traditional to technological in this pandemic. This study has been done in Saudi Arabia to measure the benefits and barriers of distance education during the corona virus pandemic. A total of 1500 questioners were distributed and collected through random sampling to measure the response of respondents in schools. To analyze the data, t-test and ANOVA techniques were used. From the study it has been found that due to technology, teachers have benefited in achieving their goals in delivering the classes and results were achieved as desired.","PeriodicalId":53530,"journal":{"name":"International Journal of Sociotechnology and Knowledge Development","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74770841","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 current study was conducted on 391 young Egyptian respondents from different regions in Egypt. This study investigates the level of Egyptian youths’ dependence on social media to obtain information about the 2020 US presidential elections and the effects of social media use applying the Uses and Dependency Model developed by Rubin & Windahl 1986. The results indicated that the dependency on social media, specifically Facebook, had clear cognitive, emotional and behavioral effects toward the 2020 US presidential election. Gender, type of education, nature of the respondent's work, place of residence, and relatives residing in America, had a high degree of control over the occurrence of these effects.
{"title":"US presidential elections on social media","authors":"","doi":"10.4018/ijskd.297977","DOIUrl":"https://doi.org/10.4018/ijskd.297977","url":null,"abstract":"The current study was conducted on 391 young Egyptian respondents from different regions in Egypt. This study investigates the level of Egyptian youths’ dependence on social media to obtain information about the 2020 US presidential elections and the effects of social media use applying the Uses and Dependency Model developed by Rubin & Windahl 1986. The results indicated that the dependency on social media, specifically Facebook, had clear cognitive, emotional and behavioral effects toward the 2020 US presidential election. Gender, type of education, nature of the respondent's work, place of residence, and relatives residing in America, had a high degree of control over the occurrence of these effects.","PeriodicalId":53530,"journal":{"name":"International Journal of Sociotechnology and Knowledge Development","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74918993","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 digital payment system has many advantages over cash transactions. In India, the adoption of digital payment has increased during the COVID-19 pandemic, but still, the usage of cash is extremely high. This study attempts to determine the factors influencing the adoption of digital payment and the barriers to the adoption during COVID-19. Exploratory factor analysis has been carried out on primary data collected from 409 respondents using a closed-ended questionnaire. The study reveals efficiency parameters, perceived utility, social influence, and facilitating conditions as significant influencing factors. The barriers identified are technological barrier, value barrier, risk barrier, usage and image barriers. The digital payment industry may use the findings of this study to enhance the influencing factors and remove the barriers such as improving the performance and reducing the efforts of payment applications and providing better technology and increasing awareness about digital fraud.
{"title":"Factors Influencing Adoption of Digital Payment Systems During COVID-19","authors":"Sweta Lakhaiyar, Mukta Mani","doi":"10.4018/ijskd.315292","DOIUrl":"https://doi.org/10.4018/ijskd.315292","url":null,"abstract":"The digital payment system has many advantages over cash transactions. In India, the adoption of digital payment has increased during the COVID-19 pandemic, but still, the usage of cash is extremely high. This study attempts to determine the factors influencing the adoption of digital payment and the barriers to the adoption during COVID-19. Exploratory factor analysis has been carried out on primary data collected from 409 respondents using a closed-ended questionnaire. The study reveals efficiency parameters, perceived utility, social influence, and facilitating conditions as significant influencing factors. The barriers identified are technological barrier, value barrier, risk barrier, usage and image barriers. The digital payment industry may use the findings of this study to enhance the influencing factors and remove the barriers such as improving the performance and reducing the efforts of payment applications and providing better technology and increasing awareness about digital fraud.","PeriodicalId":53530,"journal":{"name":"International Journal of Sociotechnology and Knowledge Development","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88051048","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}
A. R. D. Kelin, B. Nagarajan, Sasikumar Rajendran, Muthumari S.
Consistently, many bugs are raised, which are not completely settled, and countless designers are utilizing open sources or outsider assets, which prompts security issues. Bug-triage is the impending mechanized bug report framework to appoint individual security teams for a more than adequate pace of bug reports submitted from various IDEs inside the association (on-premises). We can lessen the time and cost of bug following and allocate it to the fitting group by foreseeing which division it has a place in within an association. In this paper, the authors are executing an automatic bug tracking system (ABTS) to allocate the group for the revealed bug involving the text examination for bug naming and characterization AI calculation for anticipating designer. Hybrid natural language processing and machine learning techniques are used for automatic bug identification to improve the performance of software organization products.
{"title":"Automatic Bug Classification System to Improve the Software Organization Product Performance","authors":"A. R. D. Kelin, B. Nagarajan, Sasikumar Rajendran, Muthumari S.","doi":"10.4018/ijskd.310066","DOIUrl":"https://doi.org/10.4018/ijskd.310066","url":null,"abstract":"Consistently, many bugs are raised, which are not completely settled, and countless designers are utilizing open sources or outsider assets, which prompts security issues. Bug-triage is the impending mechanized bug report framework to appoint individual security teams for a more than adequate pace of bug reports submitted from various IDEs inside the association (on-premises). We can lessen the time and cost of bug following and allocate it to the fitting group by foreseeing which division it has a place in within an association. In this paper, the authors are executing an automatic bug tracking system (ABTS) to allocate the group for the revealed bug involving the text examination for bug naming and characterization AI calculation for anticipating designer. Hybrid natural language processing and machine learning techniques are used for automatic bug identification to improve the performance of software organization products.","PeriodicalId":53530,"journal":{"name":"International Journal of Sociotechnology and Knowledge Development","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85289903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：