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Advances in Intelligent Systems and Computing 2021年 1174卷 v-vi页

作者： Sharma, Neha Balas, Valentina Emilia Chakrabarti, Amlan Martinovic, Jan Society for Data Science Pune Maharashtra India Department of Automatics and Applied Software Faculty of Engineering University of Arad Arad Romania A.K. Choudhury School of Information Technology West Bengal India Faculty of Engineering University of Calcutta Kolkata India IT4Innovations VSB-Technical University of Ostrava Ostrava Czech Republic

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Effects of Phytohormone Compatibility on Callus Growth of Rosa rugosa tunb

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IOP Conference Series: Earth and Environmental science 2020年第3期446卷

作者： Wenbo Chang Xue Wang Ying Lan Meini Wang Bin Wen Lan Li College of Science Jiamusi University Jiamusi City Heilongjiang Province 154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province 543000 China

To obtain superior quality callus and to regenerate root system of Rosa rugosa tunb and improve culture efficiency, this experiment used a multi-factor orthogonal test method to establish a sterile system with young leaves of R. rugosa tunb as experimental materialsand different concentrations of hormones in each stage. Explore the effects of plant callus growth. The results showed that the optimal disinfection method of explants was 70% alcohol 30s+0.1% HgCl2 8min. At this time, the browning rate was 46.70%, and the infection rate was 10.00%, which was 14.40% and 72.67% lower than that before disinfection. [Primary medium]: MS+1.5 mg/L 6-BA+3.0 mg/L NAA+3% sucrose+0.9% agar, the induction rate at this time is 75.00%, an increase of 62.50% compared with no added hormone. [Proliferation medium] MS+2.0mg/L 6-BA+0.2mg/L NAA+3% sucrose+0.9% agar, the callus diameter reached 2.49cm after proliferation, the growth was strong, and the callus was highly available. [Rooting medium] MS+0.4mg/L IBA+0.5g/L AC+3% sucrose+ 0.9% agar, the rooting rate is 75.00%, the root system is long and the growth is robust.

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Explore the designated frequency band (EMF) effect of radiation on biological characteristics of Ocimum basilicum

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IOP Conference Series: Earth and Environmental science 2020年第3期446卷

作者： Yaosheng Zhang Guoli Ren Wu Zhang Jiali Yang Xue Wang Yi Chu Lan Li College of Science Jiamusi University Jiamusi City Heilongjiang Province 154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province 543000 China

Explore and obtain the effects of irradiated Ocimum basilicum seeds on the biological mechanisms of germination and seedling growth in the specified frequency bands. In the experiment, two influencing factors of the specified frequency band and irradiation time were set, and different gradients of irradiated seeds were set. Non-magnetized treatment was used as a blank control group (CK) to determine the changes in the main chemical components in seeds and seedlings, and explore the radiation breeding biology mechanism. The results showed that 90 kHz-100 kHz had a significant inhibitory effect on seed germination, and the 50 kHz-70 kHz band had a significant effect on the germination of Ocimum basilicum, with 50 ± 2.0 kHz being the best. In the time gradient exploration, the main chemical content of seeds was the highest when irradiated for 20 min, and the growth effect of seedlings was the best. The specified frequency band (EMF) has the effect of promoting and inhibiting the germination and growth of basil. It stimulates the internal chemical composition to break the dormancy period of the seed, accelerates the anabolism, and increases the molecular assimilation of the building.

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Intervention of Nitrogen Evolution on Species Diversity of Broad-leaved Forest Plants in Xianweng Mountain

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IOP Conference Series: Earth and Environmental science 2020年第3期446卷

作者： Wenbo Chang Yanbin Sun Penglin Shen Qingsong Chen Tianlin Miao Lan Li College of Science Jiamusi University Jiamusi City Heilongjiang Province 154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province 543000 China

Nitrogen evolution is one of the important factors driving biodiversity change. It is generally believed that the species diversity structure will be changed, and under the conditions of exogenous nitrogen addition, grasses and deciduous shrubs have a competitive advantage over hybrid grasses and evergreen shrubs. For the competition between plants in the same life type, the interaction between different life-type plants is not involved. Therefore, from the changes in forest ecosystem structure and nitrogen elements, the response of different life-type plants to nitrogen evolution can be further explored. The lower plant species of the deciduous broad-leaved forest-Mongolian carp are studied, and different gradients of nitrogen (0, 40, 80, 120 kg Nha−1yr−1) are added to the four gradients of extraneous nitrogen effects of nitrogen evolution on forest biodiversity in the north temperate zone. The results of five-year continuous nitrogen addition experiments showed that nitrogen addition significantly reduced the species richness and diversity of understory plants, and changed the species composition of the community. Nitrogen addition increased the species richness of shrub plants and diversity, reduced the richness of herbaceous plants, and reduced the important value of grasses and increased the important value of hybrid grasses. The study showed that long-term nitrogen addition significantly changed the species composition of understory plants, and the response of different life-type plants to nitrogen addition was also different.

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Graph-augmented convolutional networks on drug-drug interactions prediction

arXiv

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arXiv 2019年

作者： Zhong, Yi Chen, Xueyu Zhao, Yu Chen, Xiaoming Gao, Tingfang Weng, Zuquan College of Biological Science and Engineering Fuzhou University Fujian province China Centre for Big Data Research in Burns and Trauma College of Mathematics and Computer Science Fuzhou University Fujian province China School of Software Shandong University Shandong Province China

We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks. And this is implemented by combining graph convolutional neural network with an attentive pooling network to extract structural relations between drug pairs and make DDI predictions. The experiment results suggest a desirable performance achieving ROC at 0.988, F1-score at 0.956, and AUPR at 0.986. Besides, the model can tell how the two DDI drugs interact structurally by varying colored atoms. And this may be helpful for drug design during drug discovery. Copyright © 2019, The Authors. All rights reserved.

关键词： Drug interactions

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Towards a Personalized Item Recommendation Approach in Social Tagging Systems Using Intuitionistic Fuzzy DBSCAN 10

Towards a Personalized Item Recommendation Approach in Socia...

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10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018

作者： Guan, Chun Yuen, Kevin Kam Fung Yue, Yong Department of Computer Science University of Liverpool Liverpool United Kingdom Research Institute of Big Data Analytics Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China School of Business University of Social Sciences Singapore Singapore Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781538658369

In folksonomies, users annotate items with abundant personalized tags. The tags can be used in recommendation systems to produce meaningful information. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can be applied in recommendation systems. This paper proposes the Intuitionistic Fuzzy DBSCAN (IF-DBSCAN) for the personalized item recommendation in folksonomy. The IF-DBSCAN is used to cluster items with respect to the user-defined tags. The Intuitionistic Fuzzy Set (IFS) is used to represent tag values which are vague and uncertain. DBSCAN can cluster items with the tags represented by using IFSs into different groups. An example of movie recommendation is demonstrated for the applicability of the proposed method. © 2018 IEEE.

关键词： Recommender systems

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software Defect Distribution Prediction Model Based on NPE-SVM

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China Communications 2018年第5期15卷 173-182页

作者： Hua Wei Chun Shan Changzhen Hu Huizhong Sun Min Lei School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China China Information Technology Security Evaluation Center Beijing 100085China Beijing Key Laboratory of Software Security Engineering Technology School of SoftwareBeijing Institute of TechnologyBeijing 100081China Information Security Center Beijing University of Posts and TelecommunicationsBeijing 100876China Guizhou University Guizhou Provincial Key Laboratory of Public Big DataGuizhou Guiyang 550025China

During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper proposed a novel software defect prediction model based on neighborhood preserving embedded support vector machine(NPESVM) algorithm. The model uses SVM as the basic classifier of software defect distribution prediction model, and the NPE algorithm is combined to keep the local geometric structure of the data unchanged in the process of dimensionality reduction. The problem of precision reduction of SVM caused by data loss after attribute reduction is avoided. Compared with single SVM and LLE-SVM prediction algorithm, the prediction model in this paper improves the F-measure in aspect of software defect distribution prediction by 3%~4%.

关键词： data redundancy SVM NPE algorithm dimensionality reduction

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A Causal Roadmap for Generating High-Quality Real-World Evidence

arXiv

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arXiv 2023年

作者： Dang, Lauren E. Gruber, Susan Lee, Hana Dahabreh, Issa Stuart, Elizabeth A. Williamson, Brian D. Wyss, Richard Díaz, Iván Ghosh, Debashis Kıcıman, Emre Alemayehu, Demissie Hoffman, Katherine L. Vossen, Carla Y. Huml, Raymond A. Ravn, Henrik Kvist, Kajsa Pratley, Richard Shih, Mei-Chiung Pennello, Gene Martin, David Waddy, Salina P. Barr, Charles E. Akacha, Mouna Buse, John B. van der Laan, Mark Petersen, Maya Department of Biostatistics University of California BerkeleyCA United States TL Revolution CambridgeMA United States Office of Biostatistics Office of Translational Sciences Center for Drug Evaluation and Research U.S. Food and Drug Administration Silver SpringMD United States Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Department of Mental Health Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Biostatistics Division Kaiser Permanente Washington Health Research Institute SeattleWA United States Division of Pharmacoepidemiology and Pharmacoeconomics Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Biostatistics Department of Population Health New York University Grossman School of Medicine New YorkNY United States Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Microsoft Research RedmondWA United States Global Biometrics and Data Management Pfizer Inc. New YorkNY United States Department of Epidemiology Mailman School of Public Health Columbia University New YorkNY United States Syneos Health Clinical Solutions Amsterdam Netherlands Syneos Health Clinical Solutions MorrisvilleNC United States Novo Nordisk Søborg Denmark AdventHealth Translational Research Institute OrlandoFL United States Cooperative Studies Program Coordinating Center VA Palo Alto Health Care System Palo AltoCA United States Department of Biomedical Data Science Stanford University StanfordCA United States Division of Imaging Diagnostics and Software Reliability Office of Science and Engineering Laboratories Center for Devices and Radiological Health U.S. Food and Drug Administration Silver SpringMD United States Global Real World Evidence Group Moderna CambridgeMA United States National Center for Advancing Translational Sciences BethesdaMD United States G

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized controlled trials with outcomes assessed using RWD to fully observational studies. Yet many RWE study proposals lack sufficient detail to evaluate adequacy, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to pre-specify analytic study designs;it addresses a wide range of guidance within a single framework. By requiring transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on pre-specified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers, with companion papers demonstrating application of the Causal Roadmap for specific use cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Convergence of edge computing and deep learning: A comprehensive survey

arXiv

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arXiv 2019年

作者： Han, Yiwen Wang, Xiaofei Leung, Victor C.M. Niyato, Dusit Yan, Xueqiang Chen, Xu College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada School of Computer Science and Engineering Nanyang Technological University Singapore 2012 Lab of Huawei Technologies Shenzhen China School of Data and Computer Science Sun Yat-sen University Guangzhou China

Ubiquitous sensors and smart devices from factories and communities guarantee massive amounts of data and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, artificial intelligence (especially deep learning) applications and services have experienced a thriving development process. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of "providing artificial intelligence for every person and every organization at everywhere". Thus, recently, a better solution is unleashing deep learning services from the cloud to the edge near to data sources. Therefore, edge intelligence, aiming to facilitate the deployment of deep learning services by edge computing, has received great attention. In addition, deep learning, as the main representative of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually benefited edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both;2) the practical implementation methods and enabling technologies, namely deep learning training and inference in the customized edge computing framework;3) existing challenges and future trends of more pervasive and fine-grained intelligence. We believe that this survey can help readers to garner information scattered across the communication, networking, and deep learning, understand the connections between enabling technologies, and promotes further discussions on the fusion of edge intelligence and intelligent edge. Copyright © 2019, The Authors. All rights reserved.

关键词： Edge computing

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Feature-Selected and -Preserved Sampling for High-Dimensional Stream data Summary

Feature-Selected and -Preserved Sampling for High-Dimensiona...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： ling Lin Qian Yu Wen Ji Yang Gao State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Electronics and Information Engineering College Yili Normal University Yining Xinjiang China School of Data and Computer Science Shandong Womens University China

Along with the prosperity of the Mobile Internet, a large amount of stream data has emerged. Stream data cannot be completely stored in memory because of its massive volume and continuous arrival. Moreover, it should be accessed only once and handled in time due to the high cost of multiple accesses. Therefore, the intrinsic nature of stream data calls facilitates the development of a summary in the main memory to enable fast incremental learning and to allow working in limited time and memory. Sampling techniques are one of the commonly used methods for constructing data stream summaries. Given that the traditional random sampling algorithm deviates from the real data distribution and does not consider the true distribution of the stream data attributes, we propose a novel sampling algorithm based on feature-selected and -preserved algorithm. We first use matrix approximation to select important features in stream data. Then, the feature-preserved sampling algorithm is used to generate high-quality representative samples over a sliding window. The sampling quality of our algorithm could guarantee a high degree of consistency between the distribution of attribute values in the population (the entire data) and that in the sample. Experiments on real datasets show that the proposed algorithm can select a representative sample with high efficiency.

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