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2019 international conference on applied machine learning and data science, icamlds 2019

作者： Jiang, Xiulian Ju, Peng Song, Yining Mao, Lingcheng Xie, Guifang Xuzhou University of Technology Xuzhou Jiangsu221008 China University of Chinese Academy of Sciences Beijing100000 China Xinjiang Astronomical Observatory CAS Urumqi Xinjiang830011 China

The diversified development of network applications and the informatization of social life are triggering explosive growth of data. The core journal articles based on big data research in the 2011-2018 database, which are included in the CNKI database, are used as research objects, and CiteSpace, Netdraw, and Ucinet are used as tools for visualizing knowledge maps. Visualize the growth trend of big data literature, authors and institutional cooperation networks;use co-occurrence analysis and cluster analysis to mine research hotspots of big data;use mutation word detection and time zone view to study evolution path of big data development. The research hotspots of big data are shown in three directions of big data technology, application field and challenges brought by big data. The evolution path of the field of big data shows that in early stage, technology research related to big data was mainly conducted, and later, it developed into the research trend of technology and application. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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Study on the surface ceramization of aluminum alloy

Study on the surface ceramization of aluminum alloy

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2019 international conference on applied machine learning and data science, icamlds 2019

作者： Wu, Zhifu Hu, Liyun School of Materials Science and Engineering Baise University 533000 China Library of Baise University 533000 China

Aluminum alloy has been widely used as a metal material. However, it is usually vulnerable to corrosion, so the protection of aluminum alloy surface is particularly important. At present, the commonly used methods of surface protection of aluminium alloys are anodic oxidation, plasma micro-arc oxidation, chemical oxidation and so on. In this experiment, chemical protective coatings were prepared by chemical oxidation of Aluminium 1100 and 6063 treated with H2O2 or KMnO4 solution. The results showed that the oxidation capacity of H2O2 solution from 3% concentration to 15% concentration to Al1100 was successively enhanced. Moreover, the ceramic coating produced when the concentration of H2O2 is 12%-15% has a better effect, and the structures are cauliflower, concave and convex, and hydrophobic. It is expected that the surface has lotus leaf effect. © Published under licence by IOP Publishing Ltd.

关键词： Aluminum alloys

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Automated Genre Classification of Books Using machine learning and Natural Language Processing 9

Automated Genre Classification of Books Using Machine Learni...

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9th international conference on Cloud Computing, data science and Engineering (CONFLUENCE)

作者： Gupta, Shikha Agarwal, Mohit Jain, Satbir Delhi Univ Dept Comp Engn NSIT Delhi India KIET Dept Informat Technol Ghaziabad India

ISBN: (纸本)9781538659335

In today's world due to ever increasing demand to make computers perform tasks of humans, machine learning is used. It is a tedious task to manually read the entire book and classify it based on its genre. Novice writers find it troublesome to figure out the genre of their book, which can affect its reach to the right audience. The proposed method gains knowledge from a large number of words from the books and transforms them into a feature matrix. During transformation, the size of the initial matrix is reduced using Wordnet and Principle Component Analysis. Then, AdaBoost classifier is applied to predict the genres of the books.

关键词： Natural Language Processing machine learning Genre Classification WordNet TF-IDF Decision Tree AdaBoost Principle Component Analysis (PCA)

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Matrix factorization recommendation algorithms based on knowledge map representation learning1

Matrix factorization recommendation algorithms based on know...

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2019 international conference on applied machine learning and data science, icamlds 2019

作者： Huang, Xuejian Min, Lu Wang, Gensheng Gan, Tian Li, Zhipeng Computer Practice Teaching Center Jiangxi University of Finance and Economics Nanchang330013 China School of Humanities Jiangxi University of Finance and Economics Nanchang330013 China

The matrix factorization recommendation algorithm does not consider characteristics of the recommendation object itself, resulting in poor recommendation results. Therefore, a matrix factorization recommendation algorithm based on knowledge map representation learning is proposed. Firstly, the recommendation object is represented as a low dimensional semantic vector by using the knowledge map distributed representation learning algorithm. Then the semantic similarity between objects is calculated, and the semantic similarity is incorporated into the objective optimization function of matrix factorization, so that the feature vectors obtained by matrix factorization can also contain semantic knowledge, which makes up for the shortcoming of matrix factorization recommendation algorithm that does not consider characteristics of the recommendation object itself from the semantic perspective. The experimental results show that the improved algorithm has higher accuracy, recall and coverage than the traditional matrix factorization recommendation algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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Pan-cancer clinical and molecular landscape of MTAP deletion in nationwide and international comprehensive genomic data

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ESMO Open 2025年第4期10卷 104535-104535页

作者： Ikushima, H. Watanabe, K. Shinozaki-Ushiku, A. Oda, K. Kage, H. Department of Respiratory Medicine of Medicine The University of Tokyo Tokyo Japan Next-Generation Precision Medicine Development Laboratory of Medicine The University of Tokyo Tokyo Japan Division of Integrative Genomics of Medicine The University of Tokyo Tokyo Japan Department of Pathology of Medicine The University of Tokyo Tokyo Japan

Background: Early-phase clinical trials of protein arginine methyltransferase 5 (PRMT5) inhibitors as synthetic lethal strategies have shown promising efficacy in methylthioadenosine phosphorylase (MTAP)-deleted tumors. To refine and expand this promising therapeutic approach within the framework of precision oncology, it is critical to comprehensively characterize the clinical and molecular profiles of MTAP-deleted tumors. Materials and methods: This pan-cancer retrospective cohort study analyzed clinico-genomic data from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database, which includes 99.7% of patients who underwent comprehensive genomic profiling (CGP) in Japan between June 2019 and November 2023. machine learning and explainable artificial intelligence methods were applied to identify clinical predictors of MTAP deficiency. Findings were validated and compared using The Cancer Genome Atlas (TCGA) and American Association for Cancer Research (AACR) Genomics Evidence Neoplasia Information Exchange (GENIE) datasets. Results: Among 51 828 pan-cancer patients in the C-CAT cohort, MTAP deletion was observed in 4964 cases (9.6%), with a high prevalence in pancreatic (18.4%), biliary tract (15.6%), and lung (14.3%) cancers. MTAP deletion was associated with distinct clinical features, including male sex (56.0% versus 47.8%), older age (mean 62.4 versus 59.8 years), and shorter interval from diagnosis to CGP (median 380.0 versus 567.0 days). In pancreatic cancer, MTAP deletion was more common in KRAS-mutant tumors (19.8%) compared with KRAS wild-type tumors (8.9%). Across cancer types, MTAP deletion was less frequent in RB1-mutant tumors (pan-cancer: 3.2%, pancreatic: 7.6%, lung: 2.5%, biliary tract: 5.4%) than in RB1 wild-type tumors (9.9%, 18.7%, 16.1%, 16.0%). These findings were validated using the TCGA (n = 9896) and GENIE (n = 178 034) datasets. In lung adenocarcinoma, MTAP deletion was found in 22.8% of EGFR-mutated tumors, 25.0% of ALK-tr

关键词： comprehensive genomic profiling explainable artificial intelligence MTAP PRMT5 synthetic lethality

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Deterministic Dual-stack Query Tree RFID tag anti-collision algorithm

Deterministic Dual-stack Query Tree RFID tag anti-collision ...

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2019 international conference on applied machine learning and data science, icamlds 2019

作者： Zhou, Yanling Clemen, Thomas Artificial Intelligence and Big Data College Hefei University Hefei230601 China Department of Computer Science Hamburg University of Applied Sciences Berliner Tor 7 Hamburg20099 Germany

Tag collision problems are a major issue affecting the performance of RFID systems. The probabilistic tag anti-collision algorithm has tag starvation and cannot identify some tags. This paper proposes a deterministic dual-stack query tree algorithm (DDQT). It successfully implements the anti-collision algorithm of tags by generating new query codes and tag identification functions. At the same time, the DDQT algorithm greatly saves memory space by adding a prefix code stack and a suffix code stack. The DDQT algorithm is simple, and there is no limit on the length of the electronic tag to be queried. Compared with the previous query-tree algorithms, the DDQT algorithm has obvious improvements in query comparison times, empty query times, and occupied memory space. It can effectively remove idle nodes, shorten recognition time and save memory resources, thus improving the speed and accuracy of label recognition to some extent. © Published under licence by IOP Publishing Ltd.

关键词： Trees (mathematics)

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Job Recommendation System based on machine learning and data Mining Techniques using RESTful API and Android IDE 9

Job Recommendation System based on Machine Learning and Data...

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9th international conference on Cloud Computing, data science and Engineering (CONFLUENCE)

作者： Jain, Harsh Kakkar, Misha App St Software Pvt Ltd Gurgaon Haryana India Amity Univ Dept Comp Sci Noida Uttar Pradesh India

ISBN: (纸本)9781538659335

In the current Capitalist world with an abundance of different state-of-the-art industries and fields cropping up, ushering in an influx of jobs for motivated and talented professionals, it is not difficult to identify your field and to persevere to get a job in the respective field but lack of information and awareness render the task difficult. This problem is being tackled by Job Recommendation systems. But not every aspect from the wide spectrum of factors is incorporated in the existing systems. For the "Job Recommendation System - Vitae" machine learning and data mining techniques were applied to a RESTful Web Server application that bridges the gap between the Frontend (Android Application) and the Backend (MongoDB instance) using APIs. The data communicated through APIs is fed into the database and the Recommendation System uses that data to synthesize the results. To make the existing systems even more reliable, here efforts have been done to come up with the idea of a system that uses a wide variety of factors and is not only a one-way recommendation system.

关键词： data mining machine learning Recommendation Systems Content Based filtering RESTful API Android Application Job Recommendations

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Research on the guidance learning and monitoring system of autonomous learning for College English

Research on the guidance learning and monitoring system of a...

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2019 international conference on applied machine learning and data science, icamlds 2019

作者： Wen, Lu College of Foreign Studies Guilin University of Electronic Technology Guilin China

This paper aims to find out the influence of guidance learning and monitoring on autonomous learning for College English. By applying the qualitative and quantitative research approach, we tested the efficiency of the guidance learning and monitoring system of autonomous learning by a semester-long teaching practice. We compared the final score with that of the previous semester. After analyzing the data, we concluded that compared with the previous semester, the grades of performance scores of the experimental semester were significantly improved as well as the final scores. This shows that the effect of implementation of guidance learning and monitoring system to autonomous learning in College English is obvious. Finally, this paper provides some strategies to improve the effect of autonomous learning, and points out that there is a harmony relationship between autonomy and monitoring. © Published under licence by IOP Publishing Ltd.

关键词： Monitoring

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data comparison of the effects of feeding ginkgo leaf fermentation and Chinese herbal medicine on meat ducks

Data comparison of the effects of feeding ginkgo leaf fermen...

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2019 international conference on applied machine learning and data science, icamlds 2019

作者： Wang, Jie Ren, Shanmao Fang, Xixiu Jiangsu Agri-animal Husbandry Vocational College Jiangsu China

In order to study the effect of feeding Ginkgo leaf fermentation and Chinese herbal medicine on meat duck, we added Ginkgo leaf fermentation and Chinese herbal medicine additive to the daily feed of meat ducks, and set up a control group (basic feed), a western medicine group (basic feed+150mg/kg aureomycin), an experimental group 1 (basic feed + Chinese herbal medicine 0.1%) and an experimental group 2 (basic feed + 0.1% Chinese herbal medicine + 2.5% Ginkgo leaf ferment). We fed the ducks for 45 days, and then completed the measurement of production performance and blood index. Based on the measurement method of related data, we used Excel and spss25 software for data statistics and analysis. The results showed that the diet supplemented with 0.1% Chinese herbal medicine and 2.5% Ginkgo leaf fermentation could significantly increase daily growth weight of meat ducks during the whole growth period (P © Published under licence by IOP Publishing Ltd.

关键词： Fermentation

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Classification of Mutual Fund Investment Types with Advanced machine learning Models 4

Classification of Mutual Fund Investment Types with Advanced...

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4th IEEE/ACIS international conference on Big data, Cloud Computing, and data science, BCD 2019

作者： Tao, Tao Yan, Kexin Yang, Shuyan Department of Computer and Mathematical Sciences University of Toronto Scarborough Toronto Canada Department of Statistical and Actuarial Sciences Western University London Canada Department of Mathematics University of Waterloo Waterloo Canada

ISBN: (纸本)9781728108865

In the past, studies tended to classify mutual funds according to arbitrary definitions. In this paper, we applied machine learning techniques to assist the process. Compared to artificial selection, our classification method offers high accuracy. Hence, machine learning techniques can provide predictive insights and tremendous power in mutual fund investment decision and risk management. © 2019 IEEE.

关键词： Risk management

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