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检索条件"任意字段=2020 2nd International Conference on Applied Machine Learning and Data Science, ICAMLDS2020"

共 179 条记录，以下是81-90 订阅

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A Parallel Adaptive DBSCAN Algorithm Based on k-Dimensional Tree Partition 2

A Parallel Adaptive DBSCAN Algorithm Based on k-Dimensional ...

引用

2nd international conference on machine learning, Big data and Business Intelligence, MLBDBI 2020

作者： Lu, Xin Wang, Yu Yuan, Jiao Wang, Xun Fu, Kun Yang, Ke University of Electronic Science and Technology of China School of Software Chengdu China China Railway Eryuan Engineering Group Co. Ltd Chengdu China

ISBN: (纸本)9781728196381

The existing parallel DBSCAN (density based spatial clustering of applications with noise) algorithm needs to determine the parameter settings manually, and the datasets will be repeatedly accessed in the process of data partitioning and data merging, which reduces the efficiency of the algorithm excuting. Therefore, this paper proposes a parallel adaptive DBSCAN algorithm based on k-dimensional tree partition. It divides the dataset into several balanced data partitions by using k-dimensional tree, and carries out parallel computing in spark distributed computing framework, thus increasing the concurrent processing ability of the algorithm program and improving the I/O access speed. In addition, the improved adaptive DBSCAN parameter method is applied to each data partition for clustering analysis to obtain local clusters, which solves the random problem of manual setting parameters in the clustering process, and ensures the data quality of clustering mining. At the same time of creating local clusters, this algorithm also puts the mapping relationship between data points and adjacent points into the HashMap data structure of the master node, and uses it to merge local clusters into whole clusters, which can reduce the time cost of data merging. The experimental results show that the proposed algorithm can save about 18% running time compared with RDD-DBSCAN algorithm without reducing the clustering quality. With the increase of the number of cluster nodes, the running efficiency of the algorithm can be further improved, so it is suitable for processing massive data clustering analysis. © 2020 IEEE.

关键词： Clustering algorithms

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Understanding Online Public Sentiments: A machine learning-Based Analysis of English and Chinese Twitter Discourse during the 2019 Chinese National Day 2

Understanding Online Public Sentiments: A Machine Learning-B...

引用

2nd international Multidisciplinary Information Technology and Engineering conference, IMITEC 2020

作者： Xu, Yekai He, Qingqian Ni, Shiguang Big Data Lab Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen China Tsinghua International Relations Data Computing Lab School of Social Sciences Tsinghua University Beijing China School of Political Science and Public Administration China University of Political Science and Law Beijing China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9781728195209

As the Internet gradually penetrates people's daily lives, individual citizens are empowered to demonstrate and exchange opinions and sentiments at any time anywhere. Online communities are increasingly participating in the agenda-setting of public affairs and official policies. However, how to depict online public opinion and to what degree does it influence the real world are still unclear. This study addresses the above problems by analyzing Twitter discourse during the 2019 Chinese National Day with a machine learning-based approach. Over 300,000 English and Chinese tweets were collected between Sept 30 and Oct 3, and a hybrid method of support vector machine (SVM) and dictionary was applied to evaluate the sentiments of the collected tweets. This method avoids complex structures while yielding an average accuracy of over 96% in most classifiers used in the study. The results indicate alignment between the time of National Day celebration activities and the peak of sentiments revealed in both English and Chinese tweets, although the sentiments of the two languages tend to be in opposite directions. The sentiments of tweets also diverge from nation to nation, but are generally consistent with the country's official relations with China. The linguistic features of the tweets suggest different concerns for Twitter users who have different sentiments towards China. Future studies should prolong the collecting period and refine the algorithms used. Specific attention can also be paid to important countries pairs like China and the United States. © 2020 IEEE.

关键词： Sentiment analysis

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Comparative Analysis of Different machine learning Techniques 2nd

Comparative Analysis of Different Machine Learning Technique...

引用

2nd international conference on Futuristic Trends in Networks and Computing Technologiess, FTNCT 2019

作者： Srivastava, Nidhi Lamba, Tripti Agarwal, Manisha Banasthali Vidyapith Vanasthali India IITM GGSIPU New Delhi India

ISBN: (纸本)9789811544507

Artificial learning (AI) is one of the areas of computer science which develops the system in a way that our system starts learning and gives the reaction as a human brain does. This paper discussed one of the branches of AI i.e. machine learning as the name suggests it focuses on the development of the computers especially their programs that access the data that can be used for self-development. The main objective of the paper is to compare the best machine learning model by using the performance parameter R squared and Mean square Error (MSE). The data set was taken from the promise repository for the analysis. Feature selection technique Boruta was applied to find the important/confirmed variables from the dataset which are having information regarding the JAVA projects. This paper finds out that among different algorithms which one outperforms when the comparative analysis was done to find the best model. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Mean square error

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Centroid Sort: A Clustering-based Technique for Accelerating Sorting Algorithms 2

Centroid Sort: A Clustering-based Technique for Accelerating...

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2nd international Multidisciplinary Information Technology and Engineering conference, IMITEC 2020

作者： Olukanmi, Peter Popoola, Peter Olusanya, Michael Kimberley South Africa School of Mathematics Computer Science and Statistics University of KwaZulu-Natal Department of Computer Science South Africa

ISBN: (纸本)9781728195209

Sorting does not only occupy a central place in computer science;it also constitutes a building block in machine learning algorithms. This paper draws a useful connection between sorting and clustering, leading to a general approach for accelerating sorting algorithms. Basically, clustering is presented as an effective way to implement the well-known divide-and-conquer strategy. The key idea is that unless an algorithm takes (sub-)linear time, which is an impossible average complexity for sorting, if a k-means-type clustering is applied to the input data, the sum of the individual sorting times will be less than the time for the original input, and sorted versions of the partitions only need be concatenated. Quadratic time, which is common among well-known algorithms, can be accelerated up to k times. Although any clustering algorithm can be used, to minimize overhead, we propose a simplified one-pass k-means-type algorithm comprising a single iteration of Lloyds standard k-means algorithm. We sample k items as partition prototypes;then each item in the data is grouped with its nearest prototype. As proof of concept, we present experiments involving 3 well-known quadratic-time algorithms, namely Insertion Sort, Bubble Sort and Selection Sort. © 2020 IEEE.

关键词： Sorting

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User Evaluation Sentiment Analysis Model Based on machine learning 2

User Evaluation Sentiment Analysis Model Based on Machine Le...

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2nd international conference on Consumer Electronics and Computer Engineering, ICCECE 2022

作者： Lu, Zhixiang Chen, Yanhong School of Mathematics and Science Leshan Normal University Department of Applied Statistics Leshan China School of Educational Science Leshan Normal University Department of Psychology Leshan China

ISBN: (纸本)9781665408868

In order to obtain a correlation between the online evaluation of three online sales products provided by Amazon and their corresponding star rating, we use the tf-idf algorithm for text mining on three online product evaluations, extract each product based on the AFINN sentiment dictionary of sentiment words with tf-idf values included in consumer reviews, and add their intersections into a thesaurus. We also extract the brand information implied in the information and observe a certain difference in the consumer evaluation of each brand. Based on our established thesaurus, we conduct a sentiment analysis of consumer text reviews, assign corresponding sentiment scores, and obtain the corresponding review score variables. Then, we find a significant correlation between the consumer sentiment score and its corresponding star rating. Based on the star rating, verified purchase, and helpful votes variables in the data, we use machine learning models to learn the sample characteristics of each star rating. The final three-product decision tree model has an accuracy of 78.33%. The accuracy rate of the 5-star evaluation is 99.41%, which can better distinguish the extreme evaluations of consumers to a certain extent. An evaluation model based on product brand, consumer star rating, and market share of product brand is established to predict the potential success or failure of the product to a certain extent. © 2022 IEEE.

关键词： machine learning

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Smart Scheduling for Flexible and Hybrid Production with Multi-Agent Deep Reinforcement learning 2

Smart Scheduling for Flexible and Hybrid Production with Mul...

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2nd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2021

作者： Wang, Shiyong Li, Jiaxian Luo, Yongchao School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China School of Electrical Engineering Guangzhou City University of Technology Guangzhou China

ISBN: (纸本)9781665428767

As a consequence of growing personalized consumption, the demand for customized production processes is steadily increasing. Therefore, production systems should have a flexible structure, redundant resources, and dynamic scheduling mechanism to incorporate hybrid production of small-lot or even one-item orders. A greater flexibility will result in a higher uncertainty of production performance which can be mitigated by the introduction of smart scheduling mechanisms. In this paper, the scheduling problem of flexible and hybrid production is modelled as a workpiece-machine and workpiece-workpiece interaction problem. As an approach to problem solving, the Multi-Agent Deep Reinforcement learning algorithm is applied. Simulation results confirm that the scheduling algorithm converges in the training process resulting in a clear performance improvement with respect to processing time. © 2021 IEEE.

关键词： Reinforcement learning

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ICDSMLA 2020 1

引用

丛书名： Lecture Notes in Electrical Engineering

1000年

作者： Amit Kumar Sabrina Senatore Vinit Kumar Gunjan

ISBN: (数字)9789811636905

ISBN: (纸本)9789811636899;9789811636929

This book gathers selected high-impact articles from the 2nd international conference on data science, machine learning & Applications 2020. It highlights the latest developments in the areas of artificial intelligence, machine learning, soft computing, human–computer interaction and various data science and machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise.

关键词： Computational Intelligence IT in Business Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks Cyber-physical systems, IoT Professional Computing

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Predicting career decisions among graduates of tafseer and hadith 2

Predicting career decisions among graduates of tafseer and h...

引用

2nd international conference on Computer and Information sciences, ICCIS 2020

作者： Jamal, Khairunnas Kurniawan, Rahmad Husti, Ilyas Zailani Nazri, Mohd Zakree Ahmad Arifin, Johar Faculty of Usuluddin Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Tafseer and Hadits Pekanbaru Indonesia Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Informatics Engineering Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor43600 Malaysia

ISBN: (纸本)9781728154671

The overall aim of the research was to identify influential factors that best predict career decisions or job choice among graduates of the Department of Tafseer and Hadith at the Universitas Islam Negeri Sultan Syarif Kasim Riau. Instead of using a longitudinal, cohort study using statistical analysis, machine learning techniques such as Decision Tree and Naïve Bayes was applied to search for unknown patterns or rules. This study compares the performance of the machine learning methods in discovering hidden patterns or factors that influenced the alumni career decisions. One of the primary concern of the university is whether their career choice after graduation are relevant or match to their field of studies. Our studies show that CGPA, cohort, additional expertise, and gender are the main factors that influenced alumni career success. We found that a cohort of graduate students was unable to find relevant professions in their field of studies. The experimental result shows that Naïve Bayes outperforms Decision Tree with the best accuracy score of 97.1 % and 92.6% subsequently. Thus, it can be concluded that the prediction model and analysis using Naïve Bayes have the potential to be used effectively. Despite its low performance, Decision Tree able to extract the main factors that influenced an alumnus career efficiently. These findings are valuable and useful both for the institution to better understand and improve the quality of its program and graduates, and also the community of machine learning in understanding the techniques behaviors with small datasets. © 2020 IEEE.

关键词： Decision trees

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2nd ACNS Workshop on Application Intelligence and Blockchain Security, AIBlock 2020, 1st ACNS Workshop on Artificial Intelligence in Hardware Security, AIHWS 2020, 2nd ACNS Workshop on Artificial Intelligence and Industrial IoT Security, AIoTS 2020, 2nd ACNS Workshop on Cloud Security and Privacy, Cloud S and P 2020, 1st ACNS Workshop on Secure Cryptographic Implementation, SCI 2020, 1st ACNS Workshop on Security in Mobile Technologies, SecMT 2020, and 2nd ACNS Workshop on Security in machine learning and its Applications, SiMLA 2020, held in parallel with the 18th international conference on applied Cryptography and Network Security, ACNS 2020

2nd ACNS Workshop on Application Intelligence and Blockchain...

引用

2nd ACNS Workshop on Application Intelligence and Blockchain Security, AIBlock 2020, 1st ACNS Workshop on Artificial Intelligence in Hardware Security, AIHWS 2020, 2nd ACNS Workshop on Artificial Intelligence and Industrial IoT Security, AIoTS 2020, 2nd ACNS Workshop on Cloud Security and Privacy, Cloud S and P 2020, 1st ACNS Workshop on Secure Cryptographic Implementation, SCI 2020, 1st ACNS Workshop on Security in Mobile Technologies, SecMT 2020, and 2nd ACNS Workshop on Security in machine learning and its Applications, SiMLA 2020, held in parallel with the 18th international conference on applied Cryptography and Network Security, ACNS 2020

ISBN: (纸本)9783030616373

The proceedings contain 31 papers. The special focus in this conference is on applied Cryptography and Network Security. The topics include: Evolvable hardware architectures on FPGA for side-channel security;simple electromagnetic analysis against activation functions of deep neural networks;performance analysis of multilayer perceptron in profiling side-channel analysis;The forgotten hyperparameter: Introducing dilated convolution for boosting CNN-based side-channel attacks;ARM-AFL: Coverage-guided fuzzing framework for ARM-based IoT devices;post-exploitation and persistence techniques against programmable logic controller;investigation of cyber attacks on a water distribution system;computing neural networks with homomorphic encryption and verifiable computing;attribute-based symmetric searchable encryption;towards inclusive privacy protections in the cloud;secure management of IoT devices based on blockchain non-fungible tokens and physical unclonable functions;a study on microarchitectural covert channel vulnerabilities in infrastructure-as-a-service;on new zero-knowledge proofs for fully anonymous lattice-based group signature scheme with verifier-local revocation;Proofs of ownership on encrypted cloud data via Intel SGX;on the verification of signed messages;applications and developments of the lattice attack in side channel attacks;exploring the security of certificate transparency in the wild;daVinci: Android app analysis beyond frida via dynamic system call instrumentation;mobHide: App-level runtime data anonymization on mobile;evaluation of the adoption and privacy risks of google prompts;on the evolution of security issues in android app versions;bitcoin blockchain steganographic analysis;unsupervised labelling of stolen handwritten digit embeddings with density matching;minority reports defense: Defending against adversarial patches;dynamic group key agreement for resource-constrained devices using blockchains;tokenization of real estate using blockchain

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Construction of Identity Based Signcryption Using learning with Rounding 2nd

Construction of Identity Based Signcryption Using Learning w...

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2nd international conference on machine learning, Image Processing, Network Security and data sciences, MInd 2020

作者： Dharminder, Dharminder Mishra, Dheerendra Department of Mathematics The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9789811563171

Current technology, quantum computing is a big threat to the security based on essentially number-theoretic cryptographic constructions. Shor’s algorithm forces one to understanding and working on quantum resistant problems. Therefore, we need a reliable communication especially which can provide both confidentiality and authenticity in a single step. This paper presents an identity based signcryption using learning with rounding (IBSCLR) scheme in a random lattice. The security is based on the worst-case hardness of learning with rounding (LWR) problem. This scheme uses short signature and ensures security in widely acceptable standard model. Furthermore, the scheme illustrates security in the quantum era and can be applied to practical application vehicular, crowdsourcing, internet of things based structure in the modern computation world. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Authentication

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