Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning最新文献_第2页

Ontology Learning using Hybrid Machine Learning Algorithms for Disaster Risk Management 基于混合机器学习算法的灾害风险管理本体学习

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432306

Jennifer O. Contreras, Melvin A. Ballera, E. Festijo

{"title":"Ontology Learning using Hybrid Machine Learning Algorithms for Disaster Risk Management","authors":"Jennifer O. Contreras, Melvin A. Ballera, E. Festijo","doi":"10.1145/3432291.3432306","DOIUrl":"https://doi.org/10.1145/3432291.3432306","url":null,"abstract":"Disaster is inevitable but manageable thru careful planning, preparation and immediate response strategies. During typhoons, earthquakes and other calamities, agreement about language is vital to understand each other well to avoid high number of deaths, delay in access to basic needs and slow response time. However, some of the people involved in this domain find it hard to coordinate and respond to different emergency situations due to lack of familiarization and knowledge about the different terms or concepts. In disaster risk management, the consistency and reusability of the sharing of information is important to avoid possible risks. Due to this reason, an ontology is incorporated to aid in the disaster management process. The use of ontology enables quick retrieving and incorporating \"consistent data\" and information related to disaster management which plays an important for making decisions efficiently. This paper aims to implement and evaluate the accuracy of Support Vector Machine (SVM) and Neural Network (NN) learning-based ontology for disaster risk management to enhance the classification of concepts (keywords) generated for the domain ontology. The experiment shows that the hybrid SVM and NN machine learning algorithm outperformed the accuracy of SVM and NN based on the precision, recall and F-Measure criterion.","PeriodicalId":126684,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126097798","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}

引用次数: 0

Clinical Decision Support System Based on KNN/Ontology Extraction Method 基于KNN/本体提取方法的临床决策支持系统

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432305

Suqun Cao, Lingao Wang, Rendong Ji, Chao Wang, L. Yao, Lin Kai, A. Abdalla, S. k.

{"title":"Clinical Decision Support System Based on KNN/Ontology Extraction Method","authors":"Suqun Cao, Lingao Wang, Rendong Ji, Chao Wang, L. Yao, Lin Kai, A. Abdalla, S. k.","doi":"10.1145/3432291.3432305","DOIUrl":"https://doi.org/10.1145/3432291.3432305","url":null,"abstract":"The complexity of the knowledge structure in the clinical cases, involving a wide range of attributes, results in making its case similarity calculation more complex. The existing medical ontologies, due to different expressions of the same concepts in computer information retrieval, causes difficulties in terms of sharing useful information in different database systems. This paper constructs a new decision support system based on KNN/ontology method was proposed. The detail of the methods and processes of common clinical case knowledge acquisition in combination with the method of obtaining structured information has been presented. The clinical case data similarity calculation method based on various types such as symptom information, medical history information, complications, surgical information, diagnostic results and other information, for record of a clinical diagnosis and treatment process. The validity of the similarity calculation method and the weight calculation method is verified by the clinical case data. The proposed methods can be effective for improving the quality and level of clinical services for medical service organizations.","PeriodicalId":126684,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121649170","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}

引用次数: 0

Feature Selection and Classification Methods for Predicting Search Engine Ranking 预测搜索引擎排名的特征选择和分类方法

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432309

Willy K. Portier, Yujian Li, B. A. Kouassi

{"title":"Feature Selection and Classification Methods for Predicting Search Engine Ranking","authors":"Willy K. Portier, Yujian Li, B. A. Kouassi","doi":"10.1145/3432291.3432309","DOIUrl":"https://doi.org/10.1145/3432291.3432309","url":null,"abstract":"In the two-past decade, by using the methods of machine learning, the accuracy of performing computer-aided tasks successfully improved. Search engines (Google, Baidu, Bing...) use classification methods to rank the billion pages available on the world wide web. Rankings are made according to the algorithms with various features, which classify each page for a search engine request. The purpose of this paper is to analyze the performance of various machine learning models applied on features selected through different techniques. A dataset, composed of 31 features with 28,000 observations, has been evaluated considering only the characteristics with the highest correlation. To achieve that goal three filter methods were used (Chi-square, Gini index and Fisher) and three wrapper methods (Forward Selection, Backward Elimination and Bidirectional Elimination). To continue the research various classification algorithms were tested to create combination models with previous filtered and wrapper methods. Then, a comparison was done to determine the optimal features' combinations, to improve the correct prediction for an URL to be on Google Top10 SERP. From the research, it can be concluded that for this dataset, the Random Forest model combined with the Fisher filter method or Backward Elimination wrapper method could produce the best results among others.","PeriodicalId":126684,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121595889","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}

引用次数: 1

Instruction Level Disassembly through Electromagnetic Side-Chanel: Machine Learning Classification Approach with Reduced Combinatorial Complexity 通过电磁侧通道的指令级拆卸:降低组合复杂度的机器学习分类方法

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432300

V. M. Vaidyan, A. Tyagi

引用次数: 6

Review on Machine learning and its application in Atmospheric science and Human Behavior Recognition 机器学习及其在大气科学和人类行为识别中的应用综述

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432311

Hongyu Chen, Meiqiu Jiang, Yixin Liu, Junlin He, Haoru Li

引用次数: 1

Camera Re-localization by Training Multi-dataset Simultaneously via Convolutional Neural Network 基于卷积神经网络同时训练多数据集的摄像机再定位

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432296

Yixin Wang, Erwu Liu, Rui Wang

引用次数: 0

The Impact of Cloud Computing Infrastructure Capability on Enterprise Agility: Based on the Perspective of IT Business Alignment 云计算基础设施能力对企业敏捷性的影响:基于IT业务一致性的视角

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3433642

Ge Zhang, Lin Fu, Yikai Liang

{"title":"The Impact of Cloud Computing Infrastructure Capability on Enterprise Agility: Based on the Perspective of IT Business Alignment","authors":"Ge Zhang, Lin Fu, Yikai Liang","doi":"10.1145/3432291.3433642","DOIUrl":"https://doi.org/10.1145/3432291.3433642","url":null,"abstract":"Based on the IT business alignment perspective, this paper explores the influence mechanism of cloud computing technology on enterprise agility. Based on questionnaire data from 204 cloud-based enterprises, this paper uses structural equation model to explore the relationship among cloud computing infrastructure capability, IT business alignment and enterprise agility. The research results show that: (1) Two dimensions of cloud computing infrastructure capability (namely, the flexibility and integration capability of cloud computing) have significant positive effects on the two dimensions of IT business alignment (namely, intellectual alignment and social alignment); (2) Social alignment in IT business alignment has a significant positive impact on enterprise agility; (3) Intellectual alignment in IT business alignment plays a partial intermediary role between cloud computing infrastructure capability and enterprise agility, while social alignment in IT business alignment plays a full intermediary role between agility capability and enterprise agility, and plays a partial intermediary role between integration capability and enterprise agility.","PeriodicalId":126684,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning","volume":"343 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114732778","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}

引用次数: 0

Regeneration of Gamma Oscillations in Large-scale Neural Network with Complicated Structure Based on CUDA 基于CUDA的复杂结构大型神经网络γ振荡再生

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432304

Xiaochun Gu, Xia Peng, Fang Han, Zhijie Wang

引用次数: 0

Feature Selection using Machine Learning Techniques Based on Search Engine Parameters 基于搜索引擎参数的机器学习特征选择

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432308

Willy K. Portier, Yujian Li, B. A. Kouassi

{"title":"Feature Selection using Machine Learning Techniques Based on Search Engine Parameters","authors":"Willy K. Portier, Yujian Li, B. A. Kouassi","doi":"10.1145/3432291.3432308","DOIUrl":"https://doi.org/10.1145/3432291.3432308","url":null,"abstract":"In the last two decades, Internet visibility became mandatory for any companies wishing to get exposure and get revenues. Among many ways to be visible on the Internet, one of the most important is to be on top of search engines' results for keywords relative to companies' business. It is the art of Search Engine Optimization (SEO), which is a collection of techniques to get more traffic from a search engine. More a website is SEO optimized, thus more search engines give it a high ranking on results' pages for a maximal exposure. So, Google, with 90% market share worldwide, is the main search engine outside of China (Baidu) and Russia (Yandex), and its algorithm is like a black box all marketers want to discover. Google claims to have more than 200 features in his algorithm made to rank results for queries among billions of pages. This article tries different machine learning methods to determine the most important parameters using a selection of 30 features in a dataset made with around 28,000 observations. A binary classification approach was done to detect if a keyword can be found or not in Top10 search engine result. During the simulation, the importance of features was determined to find the most important parameters used for building related search results. According to the research result, it leads that there are three kinds of parameters which influence the process of ranking the results on search engine Google for web pages: editorial features, notoriety features and technical features. Moreover, few features with minimum importance were found, for example, the low importance of using \"https\" protocol in a web resource.","PeriodicalId":126684,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115702462","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}

引用次数: 2

Irregular Meshes for Color Management 不规则网格颜色管理

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432298

S. Vishnyakov, A. Kononov, Y. Vishnyakova

引用次数: 0