International Journal of Computers and Applications最新文献

{"title":"Optimizing environmental monitoring in IoT: integrating DBSCAN with genetic algorithms for enhanced clustering","authors":"S. Regilan, L.K. Hema","doi":"10.1080/1206212x.2023.2277966","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2277966","url":null,"abstract":"AbstractIn our study, we introduce an advanced clustering method designed for IoT-based environmental monitoring. We’ve combined two powerful techniques, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithms (GA), to create a specialized approach called EC-GAD (Enhanced-Clustering using Genetic Algorithms and DBSCAN). This integrated system model relies on DBSCAN, a robust clustering algorithm capable of handling irregular shapes and varying data densities, to group sensor nodes based on their physical proximity. To improve clustering performance, we’ve harnessed Genetic Algorithms to optimize the parameters of DBSCAN. Through a repetitive process involving selection, crossover, and mutation, GA refines parameter settings based on the quality of environmental clustering as assessed by fitness metrics. Our approach is tailored specifically for IoT deployments in environmental monitoring, which involve collecting data from sensor nodes and integrating DBSCAN and GA. We’ve paid special attention to choosing an appropriate distance metric and fine-tuning DBSCAN parameters such as epsilon (ε) and minPts to match the unique needs of environmental monitoring applications. Furthermore, we’ve taken energy efficiency into account by implementing energy-aware node selection and optimizing cluster formation to minimize energy consumption.KEYWORDS: Environmental monitoringIoTclusteringDBSCANgenetic algorithms Disclosure statementNo potential conflict of interest was reported by the author(s).Ethical approvalThis article does not contain any studies with human participants performed by any of the authors.Data availability statementData sharing does not apply to this article as no new data has been created or analyzed in this study.Additional informationNotes on contributorsS. RegilanMr. S. Regilan working as a Research Scholar in the Department of Electronics and Communication Engineering. He has a track record of successful teaching, education reform and has been teaching Students for decades. He Completed his B.E in Electronics and Communication Engineering Department, in Bharath Niketan Engineering College, Anna University on 2011; M.E in Electronics and Communication Engineering Department, in Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation, Chennai on 2015. Pursuing Ph.D in Department of Electronics and Communication Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation, Chennai. He worked various recognized Institutions from 2011. He had 10+ years of academic experiences in the field of Electronics and Communication Engineering. He is member in various professional bodies like ISTE, IEEE societies. He participated and Presented many International & National Conferences/Workshop/Seminar/ Webinar in the field of Electronics and Communication Engineering. He published and indexed 5 papers in reputed journals under Scopus with good citations indexed. Ma","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"75 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135092732","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}

{"title":"Routing approaches in named data network: a survey and emerging research challenges","authors":"Sembati Yassine, Naja Najib, Jamali Abdellah","doi":"10.1080/1206212x.2023.2279811","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2279811","url":null,"abstract":"AbstractNamed Data Networking (NDN) has emerged as a promising information-centric networking paradigm that addresses the limitations of the traditional IP-based Internet architecture. The core principle of NDN relies on content naming instead of host addressing, to provide efficient, secure, and scalable content delivery. Routing is a critical component of NDN and is responsible for discovering and maintaining optimal paths to named content. This paper presents a comprehensive review of routing techniques in NDN, focusing on the design principles, algorithms, and performance metrics, especially in wired network architecture. We first summarize the NDN architecture and discuss its key components. We then delve into the fundamental routing concepts in NDN and categorize and examine various routing techniques, including link state, distance vector, and centralized approaches based on Software Defined Network. We also summarize the relevant research efforts proposed to address NDN routing challenges by focusing more on wired network architecture. Finally, we identify open research issues and future directions in NDN routing, emphasizing the need for scalable, efficient, and secure routing techniques that can fulfill the growing demands of the modern Internet. In conclusion, this review serves as a valuable reference for researchers and practitioners in NDN, offering a comprehensive understanding of the current state-of-the-art routing techniques, limitations, and potential future advancements.KEYWORDS: Software defined networkroutingnamed data networkscalabilittyoverheadwired network AcknowledgementsMy profound gratitude goes out to our mentors, Pr. Jamali Abdellah and Naja Najib, for their essential advice and assistance during the study process. I would especially like to thank my parents for their insightful advice. I appreciate the unwavering support and love of my family and friends. Finally, I would like to express my gratitude for the assistance and cooperation of the entire Department of Mathematics, Informatique, and Networks team.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"27 31","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135391922","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}

{"title":"DFMCloudsim: an extension of cloudsim for modeling and simulation of data fragments migration over distributed data centers","authors":"Laila Bouhouch, Mostapha Zbakh, Claude Tadonki","doi":"10.1080/1206212x.2023.2277554","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2277554","url":null,"abstract":"AbstractDue to the increasing volume of data for applications running on geographically distributed Cloud systems, the need for efficient data management has emerged as a crucial performance factor. Alongside basic task scheduling, the management of input data on distributed Cloud systems has become a genuine challenge, particularly with data-intensive applications. Ideally, each dataset should be stored in the same data center as its consumer tasks so as to lead to local data accesses only. However, when a given task does not need all items within one of its input datasets, sending that dataset entirely might lead to a severe time overhead. To address this concern, a data fragmentation strategy can be considered in order to partition the datasets and process them in that form. Such a strategy should be flexible enough to support any user-defined partitioning, and suitable enough to minimize the overhead of transferring the data in their fragmented form. To simulate and estimate the basic statistics of both fragmentation and migration mechanisms prior to an implementation in a real Cloud, we chose Cloudsim, with the goal of enhancing it with the corresponding extensions. Cloudsim is a popular simulator for Cloud Computing investigations. Our proposed extension is named DFMCloudsim, its goal is to provide an efficient module for implementing fragmentation and data migration strategies. We validate our extension using various simulated scenarios. The results indicate that our extension effectively achieves its main objectives and can reduce data transfer overhead by 74.75% compared to our previous work.Keywords: Cloud computingbig datacloudsimdata fragmentationdata migration AcknowledgmentsL. B.: prepared the manuscript, and performed analysis and experiments. M. Z., C. T.: helped in the initial solution design. All authors reviewed the paper and approved the final version of the manuscript.Availability of data and materialsAll of the material is owned by the authors and can be accessed by email request.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsLaila BouhouchLaila Bouhouch received her engineer degree in Computer Science at ENSA (National School of Applied Sciences) at Ibn Zohr University, Agadir, Morocco, in 2017. She is currently a Ph.D. student in the Department of Computer Science, Laboratory CEDOC ST2I, ENSIAS, Rabat, Morocco. Her research interests include big data management in workflow systems, cloud computing and distributed systems.Mostapha ZbakhMostapha Zbakh received his Ph.D. in computer sciences from Polytechnic Faculty of Mons, Belgium, in 2001. He is currently a Professor at ENSIAS (National School of Computer Science and System Analysis) at Mohammed V University, Rabat, Morocco, since 2002. His research interests include load balancing, parallel and distributed systems, HPC, Big data and Cloud computing.Claude TadonkiClaude Tadonki currently holds ","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"13 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818487","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}

{"title":"Malware image classification: comparative analysis of a fine-tuned CNN and pre-trained models","authors":"Santosh Kumar Majhi, Abhipsa Panda, Suresh Kumar Srichandan, Usha Desai, Biswaranjan Acharya","doi":"10.1080/1206212x.2023.2270804","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2270804","url":null,"abstract":"AbstractA crucial part is played by malware detection and classification in ensuring the safety and security of computer systems. In this work, a comprehensive study has been presented for the classification of harmful or malware images that uses a Convolutional Neural Network (CNN) which has been finely tuned and its performance has been compared with five pre-trained models: ResNet50, InceptionResNetV2, VGG16, Xception and InceptionV3. The suggested CNN framework has been trained using the dataset MalImg_9010, consisting of 9,376 grayscale images resized to 128 × 128 pixels. The models have been evaluated based on their F1 score, recall, precision, and accuracy. The experiments that were conducted demonstrate that the fine-tuned CNN model achieves an impressive 0.965 as the F1 score and a 95.57% accuracy. Furthermore, the comparison with pre-trained models reveals the dominance of the presented framework concerning the F1 score and accuracy. The output of the conducted simulation suggests that the fine-tuned CNN approach shows promise for accurate malware image classification. Additionally, the paper discusses potential improvements, such as increasing the number of training epochs and incorporating larger and more diverse malware datasets, including RGB images and a broader range of malware families. The current research article gives valuable observations on various models’ effectiveness for classifying malware images and highlights the future scopes for research incorporating this domain.KEYWORDS: Malware image classificationdata privacydata protectionartificial intelligencedeep learning Disclosure statementThe authors declare that they have no known competing financial or personal relationships that could be viewed as influencing the work reported in this paper. On behalf of all authors, the corresponding author states that there is no conflict of interest.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"78 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976060","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}

{"title":"MuDeLA: multi-level deep learning approach for intrusion detection systems","authors":"Wathiq Laftah Al-Yaseen, Ali Kadhum Idrees","doi":"10.1080/1206212x.2023.2275084","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2275084","url":null,"abstract":"AbstractIn recent years, deep learning techniques have achieved significant results in several fields, like computer vision, speech recognition, bioinformatics, medical image analysis, and natural language processing. On the other hand, deep learning for intrusion detection has been widely used, particularly the implementation of convolutional neural networks (CNN), multilayer perceptron (MLP), and autoencoders (AE) to classify normal and abnormal. In this article, we propose a multi-level deep learning approach (MuDeLA) for intrusion detection systems (IDS). The MuDeLA is based on CNN and MLP to enhance the performance of detecting attacks in the IDS. The MuDeLA is evaluated by using various well-known benchmark datasets like KDDCup'99, NSL-KDD, and UNSW-NB15 in order to expand the comparison with different related work results. The outcomes show that the proposed MuDeLA achieves high efficiency for multiclass classification compared with the other methods, where the accuracy reaches 95.55% for KDDCup'99, 88.12% for NSL-KDD, and 90.52% for UNSW-NB15.Keywords: Intrusion detection systemmultilevel learning modeldeep learningconvolution neural networkmultilayer perceptron Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsWathiq Laftah Al-YaseenWathiq Laftah Al-Yaseen is currently a Lecturer in the Department of Computer Systems Techniques at Kerbala Technical Institute in Al-Furat Al-Awsat Technical University, Kerbala, Iraq. He received his Master of Computer Science from the University of Babylon, Iraq. He received his PhD of Computer Science from FTSM/UKM, Malaysia. His research interests include artificial intelligence, network security, machine learning, data mining and bioinformatics.Ali Kadhum IdreesAli Kadhum Idrees received his BSc and MSc in Computer Science from the University of Babylon, Iraq in 2000 and 2003 respectively. He received his PhD in Computer Science (wireless networks) in 2015 from the University of Franche-Comte (UFC), France. He is currently an Assistant Professor in Computer Science at the University of Babylon, Iraq. He has several research papers in wireless sensor networks (WSNs) and computer networks. His research interests include wireless networks, WSNs, SDN, IoT, distributed computing, data mining and optimisation in communication networks.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"116 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135325829","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}

{"title":"Feature selection in P2P lending based on hybrid genetic algorithm with machine learning","authors":"Muhammad Sam'an, Muhammad Munsarif, None Safuan, Yahya Nur Ifriza","doi":"10.1080/1206212x.2023.2276553","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2276553","url":null,"abstract":"","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"14 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135871582","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}

{"title":"Prioritizing software regression testing using reinforcement learning and hidden Markov model","authors":"Neelam Rawat, Vikas Somani, Arun Kr. Tripathi","doi":"10.1080/1206212x.2023.2273585","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2273585","url":null,"abstract":"AbstractSoftware regression testing is an essential testing practice that ensures that changes made to the source code of an application do not affect its functionality and quality. Within this research, we introduce a novel method for prioritizing software test cases using a fusion of reinforcement learning and hidden Markov model to enhance the efficiency of the testing process. The primary objective of this research paper is to maximize the likelihood of selecting test cases that have the highest priority of uncovering defects in new code changes introduced into the codebase. To assess the efficacy of our suggested methodology, we experimented on the test cases of five web applications. Our results demonstrate that our proposed approach can accurately identify critical test cases while minimizing false positives, as evidenced by an F1 score of 0.849. This outcome can help prioritize testing efforts, saving time, and resources while improving the overall efficiency of the testing process.Keywords: Regression testingtest case prioritization (TCP)hidden Markov model (HMM)reinforcement learning (RL) Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsNeelam RawatMs. Neelam Rawat is a dedicated research scholar in the field of Computer Science & Engineering at Sangam University. With an extensive portfolio that includes over 15 publications, 3 patents, and 2 authored books, she is actively engaged in pioneering research. Her primary areas of expertise lie in the domains of machine learning, deep learning, software testing, software engineering, quality assurance, and management.Vikas SomaniDr. Vikas Somani (PhD, M.Tech, MCA,BCA) has more than 16 years of Teaching and Industrial Experience. Currently he is Associate Professor and Assistant Dean, School of Engineering and Technology at the Sangam University, Bhilwara. He has diversified research interests in the areas of Cloud Computing, Artificial Intelligence, Machine Learning, Block chain and Internet of Things (IoT). He is a Member of IEEE, CSI, IAENG, ACM, IRED. He has published over 35 Research Paper in International, National Journal and Conferences and attended around 50 Workshops and STP. He has also Supervised/Guided more than 20 Research Work. Currently, under his 6 research scholars are working. He has Three Patent awarded and granted/design one from Government of India Patent Office and another from Germany Patent Office. He has also published Five Patents.Arun Kr. TripathiDr. Arun Kr. Tripathi has more than 21 years of Teaching experience and completed Ph.D. in Computer Applications with specialization in Wireless Networks. Presently he is appointed as Head of Computer Applications with and an additional responsibility of Head Cyber Security and Forensic Science Division. His major research interests are Computer Network, Network Security, IoT, Machine Learning etc. with over 70 published works in reputed Journal","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"296 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067877","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}

{"title":"Beyond cocaine and heroin use: a stacking ensemble-based framework for predicting the likelihood of subsequent substance use disorder using demographics and personality traits","authors":"Amina Bouhadja, Abdelkrim Bouramoul","doi":"10.1080/1206212x.2023.2273011","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2273011","url":null,"abstract":"","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907507","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}

{"title":"AT-densenet with salp swarm optimization for outlier prediction","authors":"Chigurupati Ravi Swaroop, K. Raja","doi":"10.1080/1206212x.2023.2273015","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2273015","url":null,"abstract":"","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907681","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}

{"title":"Security enhancement in a cloud environment using a hybrid chaotic algorithm with multifactor verification for user authentication","authors":"Megha Gupta, Laxmi Ahuja, Ashish Seth","doi":"10.1080/1206212x.2023.2267839","DOIUrl":"https://doi.org/10.1080/1206212x.2023.2267839","url":null,"abstract":"AbstractA hybrid chaotic-based DNA and multifactor authentication strategy are created to improve the protection of the cloud environment. Initially, multimodal data are collected from the data owner then the information is compressed by utilizing the deflate compression approach. The data is then encrypted using hybrid chaotic-based DNA cryptography to increase the security of data. In this hybrid algorithm DNA is used for the key generation and chaotic algorithm is utilized for the encryption process. On the other hand, a multifactor authentication method is created to access data from the cloud to block access by unauthorized users. In that technique, users are requested to enter the registered Password with the generated OTP from the mobile number. Then, the device serial number is another factor to verify the accessing device. Likewise, the user's fingerprint and iris recognition are also validated for accessing the data. The cloud-based data can be accessible following users' successful authentication. The simulation analysis shows that the encryption and decryption time reached for image, string and integer data is 24, 0.065, 37, 0.14, 28 and 0.14 s, respectively, for the cloud security algorithm. The proposed algorithm effectively mitigates space consumption and provides improved data security in a cloud environment.KEYWORDS: Cloud environmentsecuritycompressiondeflatehybrid chaotic-DNAmultifactor authentication AcknowledgementsAuthor express their deep sense of gratitude to the Founder President of Amity University, Dr. Ashok K. Chauhan for his keen interest in promoting research in the Amity University and have always been an inspiration for achieving great heights.Disclosure statementNo potential conflict of interest was reported by the author(s).Compliance with ethical standardsThis article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the journal’s editorial board decides not to accept it for publication.Additional informationFundingThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.Notes on contributorsMegha GuptaMegha Gupta is research scholar pursuing Ph.D from AIIT, Amity University, and Noida under guidance of Prof. (Dr.) Laxmi Ahuja & Co-Guide Prof. (Dr.) Ashish Seth. She is Gold Medalist in M.tech from Jamia Hamdard, New Delhi. She received her B.TECH degree with HONOURS from B.M.I.E.T Sonipat. Her research areas include Cloud Computing, Security, Software Engineering, Software Testing, Computer Networks, Database Management systems and Big Data. She has published several research papers in reputed international and national journals and also participated and presented research papers in various international, national conferences.Laxmi AhujaProf. (Dr.) Laxmi Ahuja Ph.D(CSE) working as Professor in Amity Institute of Information Technology with the role ranging from Lecturer to Prof","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135882961","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}