Pub Date : 2022-08-01DOI: 10.1109/acm57404.2022.00008
S. M. Islam
{"title":"Keynote Game Theory and Cybersecurity: Multiagent Security Issues, Mathematical Modelling and Computer Science Applications","authors":"S. M. Islam","doi":"10.1109/acm57404.2022.00008","DOIUrl":"https://doi.org/10.1109/acm57404.2022.00008","url":null,"abstract":"","PeriodicalId":322569,"journal":{"name":"2022 Algorithms, Computing and Mathematics Conference (ACM)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121048766","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 : 2022-08-01DOI: 10.1109/ACM57404.2022.00025
Geetha N k, Ragavi V
Graph Theory Matrix Approach (GTMA) is moving with great speed into the main stream of computer design, Information sciences, Information and Computer programming, Artificial Intelligence and design, Artificial Intelligent and various field of research. Application of GTMA is in diverse area such as Data structures, Communication networks and their security. A Graph-based approach centres on conserving the environment of security events by breaking down factors of observable data into a graph representation of all cyber vestiges, from all data aqueducts, counting for all once and present data. For secret communication, Ciphers can be converted into graphs. The Application of Graph Theory plays a vital role in various field of Engineering and Sciences. Especially Graph theory is commonly used as a tool of encryption. In this article some survey has been work done in the field of Cryptography and Network security is given.
{"title":"Graph Theory Matrix Approach in Cryptography and Network Security","authors":"Geetha N k, Ragavi V","doi":"10.1109/ACM57404.2022.00025","DOIUrl":"https://doi.org/10.1109/ACM57404.2022.00025","url":null,"abstract":"Graph Theory Matrix Approach (GTMA) is moving with great speed into the main stream of computer design, Information sciences, Information and Computer programming, Artificial Intelligence and design, Artificial Intelligent and various field of research. Application of GTMA is in diverse area such as Data structures, Communication networks and their security. A Graph-based approach centres on conserving the environment of security events by breaking down factors of observable data into a graph representation of all cyber vestiges, from all data aqueducts, counting for all once and present data. For secret communication, Ciphers can be converted into graphs. The Application of Graph Theory plays a vital role in various field of Engineering and Sciences. Especially Graph theory is commonly used as a tool of encryption. In this article some survey has been work done in the field of Cryptography and Network security is given.","PeriodicalId":322569,"journal":{"name":"2022 Algorithms, Computing and Mathematics Conference (ACM)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116328520","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 : 2022-08-01DOI: 10.1109/ACM57404.2022.00013
Varanasi L. V. S. K. B. Kasyap, Amrutha Macharla, Turlapati Kavya Sri, Devarasetty Syam Sai Akhil, S. Vinisha, Nimmagadda Vamsi Krishna
In the present day, Road boundary detection is one of the most focused problems as it is a causative for many road accidents. To ensure the passenger's safety an accurate model that can ensure road segmentation along with detection of the road boundary is inevitable. Road boundary detection in both structured and unstructured roads is a challenging task in machine vision and AI. Classic machine learning algorithms are proposed for this problem, however there exists many difficulties in deploying them in real time. This becomes laborious task which require huge computation in real time. This paper addresses a novel algorithm, RoadSDNet for road boundary detection and segmentation. This algorithm can be easily deployed in real time as it consumes very less computation time giving a significant accuracy compared with the other existing methods. This system can be implemented on AMD Ryzen 250 platform, allowing in easy installation over the vehicles. The hyperbola fitting techniques required for the interpolation of the disguised road is adopted from the Hough Transform and produced as the extended HT Network. This network ensures the smooth polynomial curve in accordance with the road track-line and tangent relationship. The proposed takes input only from the camera but not the other hardware components like LiDAR sensor, Proximity sensor. This can be considered as the novel contribution of the paper. The experiments performed on this model proves proposed method is robust and polent in the huge traffic also and works in the uncertain road conditions too giving noteworthy accuracy and precision.
{"title":"RoadSDNet: A Robust Algorithm for Road Boundary Detection and Segmentation using Mixed Networks and Hough Transform","authors":"Varanasi L. V. S. K. B. Kasyap, Amrutha Macharla, Turlapati Kavya Sri, Devarasetty Syam Sai Akhil, S. Vinisha, Nimmagadda Vamsi Krishna","doi":"10.1109/ACM57404.2022.00013","DOIUrl":"https://doi.org/10.1109/ACM57404.2022.00013","url":null,"abstract":"In the present day, Road boundary detection is one of the most focused problems as it is a causative for many road accidents. To ensure the passenger's safety an accurate model that can ensure road segmentation along with detection of the road boundary is inevitable. Road boundary detection in both structured and unstructured roads is a challenging task in machine vision and AI. Classic machine learning algorithms are proposed for this problem, however there exists many difficulties in deploying them in real time. This becomes laborious task which require huge computation in real time. This paper addresses a novel algorithm, RoadSDNet for road boundary detection and segmentation. This algorithm can be easily deployed in real time as it consumes very less computation time giving a significant accuracy compared with the other existing methods. This system can be implemented on AMD Ryzen 250 platform, allowing in easy installation over the vehicles. The hyperbola fitting techniques required for the interpolation of the disguised road is adopted from the Hough Transform and produced as the extended HT Network. This network ensures the smooth polynomial curve in accordance with the road track-line and tangent relationship. The proposed takes input only from the camera but not the other hardware components like LiDAR sensor, Proximity sensor. This can be considered as the novel contribution of the paper. The experiments performed on this model proves proposed method is robust and polent in the huge traffic also and works in the uncertain road conditions too giving noteworthy accuracy and precision.","PeriodicalId":322569,"journal":{"name":"2022 Algorithms, Computing and Mathematics Conference (ACM)","volume":"50 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131946349","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 : 2022-08-01DOI: 10.1109/ACM57404.2022.00011
W. Auccahuasi, Sandra Meza, Kety Sifuentes, Daysi Mancco, Lucas Herrera, C. Ovalle, Hernando Martín Campos Martinez, K. Rojas, Miryam Inciso-Rojas, Aly Auccahuasi
With the development of information and communication technologies, it has been possible to integrate advanced hardware solutions, presenting embedded systems, capable of presenting solutions in various areas, one of them is related to the presentation of low cost multispectral cameras, which have integrated several working bands, these can be placed in drones, which allows to capture images in several bands. In this work we performed an algorithm to analyze an image captured with a 6-band camera, which performs an analysis to determine the number of bands, the separation into individual bands and the operation of band algebra, the results show that you can analyze and process multispectral images, making various operations, depending on the use, the algorithm presented can be used with images that have different numbers of bands as well as different resolutions. The algorithm was implemented using the MATLAB tool, in the realization of all the processes.
{"title":"Algorithm for Processing and Visualizing Multispectral Images Captured by Drones","authors":"W. Auccahuasi, Sandra Meza, Kety Sifuentes, Daysi Mancco, Lucas Herrera, C. Ovalle, Hernando Martín Campos Martinez, K. Rojas, Miryam Inciso-Rojas, Aly Auccahuasi","doi":"10.1109/ACM57404.2022.00011","DOIUrl":"https://doi.org/10.1109/ACM57404.2022.00011","url":null,"abstract":"With the development of information and communication technologies, it has been possible to integrate advanced hardware solutions, presenting embedded systems, capable of presenting solutions in various areas, one of them is related to the presentation of low cost multispectral cameras, which have integrated several working bands, these can be placed in drones, which allows to capture images in several bands. In this work we performed an algorithm to analyze an image captured with a 6-band camera, which performs an analysis to determine the number of bands, the separation into individual bands and the operation of band algebra, the results show that you can analyze and process multispectral images, making various operations, depending on the use, the algorithm presented can be used with images that have different numbers of bands as well as different resolutions. The algorithm was implemented using the MATLAB tool, in the realization of all the processes.","PeriodicalId":322569,"journal":{"name":"2022 Algorithms, Computing and Mathematics Conference (ACM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116128787","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 : 2022-08-01DOI: 10.1109/ACM57404.2022.00018
W. Auccahuasi, Sandra Meza, Emelyn Porras, Milagros Reyes, Lucas Herrera, C. Ovalle, Hernando Martín Campos Martinez, K. Rojas, Miryam Inciso-Rojas, Aly Auccahuasi
In these times we are living, which has been affected mainly by the pandemic of Covid-19, in this sense many national and private institutions, from different business areas, are starting business models related to the manufacture of medical equipment, in this sense, in this paper, we indicate a method to develop medical equipment, under the SCRUM methodology, to leverage resources and improve project management, the methodology develops six major groups of activities known as Sprint, the first related to the analysis of requirements, the second with the analysis of the regulations to be met, the third related to the design of the prototype consisting of hardware and software components, the fourth related to quality testing by measuring the patterns, the fifth related to testing on patients and the sixth with the evaluation with an entity that qualifies and issues the final authorization of use, we explain the methodology in general so that it can be applied and scaled to different types of equipment.
{"title":"Application of the scrum methodology in the design of medical equipment prototypes","authors":"W. Auccahuasi, Sandra Meza, Emelyn Porras, Milagros Reyes, Lucas Herrera, C. Ovalle, Hernando Martín Campos Martinez, K. Rojas, Miryam Inciso-Rojas, Aly Auccahuasi","doi":"10.1109/ACM57404.2022.00018","DOIUrl":"https://doi.org/10.1109/ACM57404.2022.00018","url":null,"abstract":"In these times we are living, which has been affected mainly by the pandemic of Covid-19, in this sense many national and private institutions, from different business areas, are starting business models related to the manufacture of medical equipment, in this sense, in this paper, we indicate a method to develop medical equipment, under the SCRUM methodology, to leverage resources and improve project management, the methodology develops six major groups of activities known as Sprint, the first related to the analysis of requirements, the second with the analysis of the regulations to be met, the third related to the design of the prototype consisting of hardware and software components, the fourth related to quality testing by measuring the patterns, the fifth related to testing on patients and the sixth with the evaluation with an entity that qualifies and issues the final authorization of use, we explain the methodology in general so that it can be applied and scaled to different types of equipment.","PeriodicalId":322569,"journal":{"name":"2022 Algorithms, Computing and Mathematics Conference (ACM)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131849838","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 : 2022-08-01DOI: 10.1109/ACM57404.2022.00027
Rakhi J. Bharadwaj, Sandeep Shinde, Sakshi Oswal
The current bus transportation system relies on experience-based manual decisions for route planning and timings which may result in longer ride times and total distance travelled as well as increasing cost and carbon emissions along with usage of resources more than required. On the other hand, timetables are often outdated and created based on static information resulting in suboptimal results and an increase in waiting time of passengers due to unreliable scheduling of buses. We propose a three-fold solution to the current system by Route Optimization which provides the most effective route connections concerning traffic and population using a genetic algorithm, Dynamic Timetable Generation considering peak hour traffic and seasonal patterns, and Application which provides real-time information and recommendation about buses, automatic personalized notifications about new stops and timings on modification of routes/timetables.
{"title":"Dynamic Timetable and Route Optimized Public Transport System","authors":"Rakhi J. Bharadwaj, Sandeep Shinde, Sakshi Oswal","doi":"10.1109/ACM57404.2022.00027","DOIUrl":"https://doi.org/10.1109/ACM57404.2022.00027","url":null,"abstract":"The current bus transportation system relies on experience-based manual decisions for route planning and timings which may result in longer ride times and total distance travelled as well as increasing cost and carbon emissions along with usage of resources more than required. On the other hand, timetables are often outdated and created based on static information resulting in suboptimal results and an increase in waiting time of passengers due to unreliable scheduling of buses. We propose a three-fold solution to the current system by Route Optimization which provides the most effective route connections concerning traffic and population using a genetic algorithm, Dynamic Timetable Generation considering peak hour traffic and seasonal patterns, and Application which provides real-time information and recommendation about buses, automatic personalized notifications about new stops and timings on modification of routes/timetables.","PeriodicalId":322569,"journal":{"name":"2022 Algorithms, Computing and Mathematics Conference (ACM)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116645880","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 : 2022-08-01DOI: 10.1109/ACM57404.2022.00012
Ameya Parkar, Rajni Bhalla
Sentiment Analysis is gathering a lot of attention nowadays as a lot of online data is gathered through blogs, ecommerce websites, product reviews, etc. which people are expressing online. This data is extracted by companies to judge if their products are having a positive outlook or a negative outlook. However, when people express their opinions, they mention not only about the entity but also about the aspects of the entity. A lot of research has gone ahead on gathering opinions on aspects, especially explicit aspects. But little work is done on gathering implicit aspects. This paper provides a survey on different techniques used by researchers to gather implicit aspects. At the end, we propose a methodology to extract implicit aspects from reviews. We propose co-occurrence matrix for all opinions and aspects followed by clustering technique to gather all aspects which are similar in one cluster followed by classification using machine learning techniques. The proposed framework will give suggestions to different researchers in the domain on extracting implicit aspects.
{"title":"A survey paper on the latest techniques for implicit feature extraction using CCC method","authors":"Ameya Parkar, Rajni Bhalla","doi":"10.1109/ACM57404.2022.00012","DOIUrl":"https://doi.org/10.1109/ACM57404.2022.00012","url":null,"abstract":"Sentiment Analysis is gathering a lot of attention nowadays as a lot of online data is gathered through blogs, ecommerce websites, product reviews, etc. which people are expressing online. This data is extracted by companies to judge if their products are having a positive outlook or a negative outlook. However, when people express their opinions, they mention not only about the entity but also about the aspects of the entity. A lot of research has gone ahead on gathering opinions on aspects, especially explicit aspects. But little work is done on gathering implicit aspects. This paper provides a survey on different techniques used by researchers to gather implicit aspects. At the end, we propose a methodology to extract implicit aspects from reviews. We propose co-occurrence matrix for all opinions and aspects followed by clustering technique to gather all aspects which are similar in one cluster followed by classification using machine learning techniques. The proposed framework will give suggestions to different researchers in the domain on extracting implicit aspects.","PeriodicalId":322569,"journal":{"name":"2022 Algorithms, Computing and Mathematics Conference (ACM)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125718707","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 : 2022-08-01DOI: 10.1109/ACM57404.2022.00009
A. Ramachandran, Swetha Ashok, Remya Nair T
Social media is one of the most significant parts of our daily life. Our social media profiles are a reflection of our emotions. Instagram is the world's most popular photo-based social networking platform, with a reasonably high number of users ranging from regular people to artists, public figures, and top authorities. Users on Instagram may add captions to their images to make them more interesting. In this study, we are focusing on conducting sentiment analysis on Instagram captions by applying three different algorithms. We are concluding that the Logistic Regression algorithm is outperforming along with SMOTE and VADER compared to XG Boost and Random Forest algorithms. We started by acquiring data and dividing it down into little tokens, then we remove connection words and give clean data via the stop word removal mechanism. The cleaned data is then passed via the NLTK (Natural Language Toolkit) passer, which uses the VADER sentiment unit to produce sentiment based on the data. Then applying different algorithms XGBoost, Logistic Regression, and Random Forest on the produced sentiment. The accuracy of algorithms such as XGBoost, Logistic Regression, and Random Forest on sentiment data was also analyzed and tested and can be concluded that Logistic Regression performed well on these kinds of data with more accuracy. Through this work, the accuracy is lifted to a better level and thereby getting a truthful idea of the Instagram captions.
{"title":"A Factual Sentiment Analysis on Instagram Data – A Comparative Study Using Machine Learning Algorithms","authors":"A. Ramachandran, Swetha Ashok, Remya Nair T","doi":"10.1109/ACM57404.2022.00009","DOIUrl":"https://doi.org/10.1109/ACM57404.2022.00009","url":null,"abstract":"Social media is one of the most significant parts of our daily life. Our social media profiles are a reflection of our emotions. Instagram is the world's most popular photo-based social networking platform, with a reasonably high number of users ranging from regular people to artists, public figures, and top authorities. Users on Instagram may add captions to their images to make them more interesting. In this study, we are focusing on conducting sentiment analysis on Instagram captions by applying three different algorithms. We are concluding that the Logistic Regression algorithm is outperforming along with SMOTE and VADER compared to XG Boost and Random Forest algorithms. We started by acquiring data and dividing it down into little tokens, then we remove connection words and give clean data via the stop word removal mechanism. The cleaned data is then passed via the NLTK (Natural Language Toolkit) passer, which uses the VADER sentiment unit to produce sentiment based on the data. Then applying different algorithms XGBoost, Logistic Regression, and Random Forest on the produced sentiment. The accuracy of algorithms such as XGBoost, Logistic Regression, and Random Forest on sentiment data was also analyzed and tested and can be concluded that Logistic Regression performed well on these kinds of data with more accuracy. Through this work, the accuracy is lifted to a better level and thereby getting a truthful idea of the Instagram captions.","PeriodicalId":322569,"journal":{"name":"2022 Algorithms, Computing and Mathematics Conference (ACM)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122463914","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 : 2022-08-01DOI: 10.1109/ACM57404.2022.00020
Anitha Rani Palakayala, Kuppusamy P
Parkinson's Disease (PD) is an acute ailment, that occurs as a result of the loss of cells in the substantia nigra of the brain that makes dopamine. It has a huge negative impact on a human's quality of life. People affected with PD have trouble in speaking, writing, and walking. Brain is the main part that will be affected first, in persons with PD. It can be diagnosed with several motor symptoms like tremor, rigidity, slow movement and postural instability. Studies revealed that 90% of people with PD have issues with their speaking. As the disease impact grows, the patient's tone becomes highly distorted. Speech analysis has been used drastically, in order to construct the telemonitoring and tele diagnosing models for prediction. The most important goal of this research is to look at the survey work done considering different symptoms, to diagnose PD. Many machine learning and deep learning algorithms are being employed till date and as a result, Deep learning algorithms resulted with the best accuracy of 99.34% and Machine learning algorithms resulted with an accuracy of 97.1%, when scanned brain images are considered for analysis, to classify PD. Developing a better detection system to identify PD at the early stages, is highly demanding. Artificial intelligence is serving as a great learning tool that is adding value to problem-solving situations, particularly in the field of medical diagnosis.
{"title":"Survey of Parkinson's Disease Detection using Different Symptoms","authors":"Anitha Rani Palakayala, Kuppusamy P","doi":"10.1109/ACM57404.2022.00020","DOIUrl":"https://doi.org/10.1109/ACM57404.2022.00020","url":null,"abstract":"Parkinson's Disease (PD) is an acute ailment, that occurs as a result of the loss of cells in the substantia nigra of the brain that makes dopamine. It has a huge negative impact on a human's quality of life. People affected with PD have trouble in speaking, writing, and walking. Brain is the main part that will be affected first, in persons with PD. It can be diagnosed with several motor symptoms like tremor, rigidity, slow movement and postural instability. Studies revealed that 90% of people with PD have issues with their speaking. As the disease impact grows, the patient's tone becomes highly distorted. Speech analysis has been used drastically, in order to construct the telemonitoring and tele diagnosing models for prediction. The most important goal of this research is to look at the survey work done considering different symptoms, to diagnose PD. Many machine learning and deep learning algorithms are being employed till date and as a result, Deep learning algorithms resulted with the best accuracy of 99.34% and Machine learning algorithms resulted with an accuracy of 97.1%, when scanned brain images are considered for analysis, to classify PD. Developing a better detection system to identify PD at the early stages, is highly demanding. Artificial intelligence is serving as a great learning tool that is adding value to problem-solving situations, particularly in the field of medical diagnosis.","PeriodicalId":322569,"journal":{"name":"2022 Algorithms, Computing and Mathematics Conference (ACM)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527707","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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