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Introduction to Data Science and Machine Learning最新文献

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Embedded Systems Based on Open Source Platforms 基于开源平台的嵌入式系统
Introduction to Data Science and Machine Learning Pub Date : 2019-12-13 DOI: 10.5772/intechopen.85806
Z. Bundalo, D. Bundalo
{"title":"Embedded Systems Based on Open Source Platforms","authors":"Z. Bundalo, D. Bundalo","doi":"10.5772/intechopen.85806","DOIUrl":"https://doi.org/10.5772/intechopen.85806","url":null,"abstract":"Ways and possibilities for design, implementation and application of microcom-puter-based embedded systems using open source hardware and software platforms are considered, proposed and described. It is proposed to use open source hardware and software microcomputer-based technologies for design and implementation of embedded systems in many practical needs and applications. Main advantages and possibilities of application and implementation of such embedded systems are considered and described. Two practically designed and implemented systems performing needed data acquisition and control are presented and described. Used technologies for realization of the systems and embedded applications of the solutions are described. Open source microcomputer boards, appropriate sensors, actuators and additional electronics are used for implementation of the systems hardware. Open source tools and programs and LINUX operating system are used for implementation of the systems software. Modular approach is applied in the systems design and realization. Easy system expandability, simplifying maintenance and adaptation of the system to user requirements and needs are enabled with such approach. Balance between functionality and cost of the systems was also achieved. Optimization according to user requirements and needs, low consumption of electrical energy and low cost of the system are main advantages of such systems compared with standard embedded systems. These systems are optimized and specialized systems for specific needs and requirements of users.","PeriodicalId":224487,"journal":{"name":"Introduction to Data Science and Machine Learning","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132569945","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: 3
Serialization in Object-Oriented Programming Languages 面向对象编程语言中的序列化
Introduction to Data Science and Machine Learning Pub Date : 2019-08-29 DOI: 10.5772/intechopen.86917
K. Grochowski, Michał Breiter, R. Nowak
{"title":"Serialization in Object-Oriented Programming Languages","authors":"K. Grochowski, Michał Breiter, R. Nowak","doi":"10.5772/intechopen.86917","DOIUrl":"https://doi.org/10.5772/intechopen.86917","url":null,"abstract":"This chapter depicts the process of converting object state into a format that can be transmitted or stored in currently used object-oriented programming languages. This process is called serialization (marshaling); the opposite is called deserialization (unmarshalling) processes. It is a low-level technique, and several technical issues should be considered like endianness, size of memory representation, representation of numbers, object references, recursive object connections and others. In this chapter we discuss these issues and give them solutions. We also include a short review of tools currently used, and we showed that meeting all requirements is not possible. Finally, we presented a new C++ library that supports forward compatibility.","PeriodicalId":224487,"journal":{"name":"Introduction to Data Science and Machine Learning","volume":"317 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132552720","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: 5
Best Practices in Accelerating the Data Science Process in Python 用Python加速数据科学过程的最佳实践
Introduction to Data Science and Machine Learning Pub Date : 2019-08-07 DOI: 10.5772/INTECHOPEN.84784
D. Larson
{"title":"Best Practices in Accelerating the Data Science Process in Python","authors":"D. Larson","doi":"10.5772/INTECHOPEN.84784","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.84784","url":null,"abstract":"The number of data science and big data projects is growing, and current software development approaches are challenged to support and contribute to the success and frequency of these projects. Much has been researched on how data science algorithm is used and the benefits of big data, but very little has been written about what best practices can be leveraged to accelerate and effectively deliver data science and big data projects. Big data characteristics such as volume, variety, velocity, and veracity complicate these projects. The proliferation of open-source technologies available to data scientists can also complicate the landscape. With the increase in data science and big data projects, organizations are struggling to deliver successfully. This paper addresses the data science and big data project process, the gaps in the process, best practices, and how these best practices are being applied in Python, one of the common data science open-source programming languages.","PeriodicalId":224487,"journal":{"name":"Introduction to Data Science and Machine Learning","volume":"18 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114128868","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
The Software to the Soft Target Assessment 软目标评估的软件
Introduction to Data Science and Machine Learning Pub Date : 2019-07-17 DOI: 10.5772/INTECHOPEN.87997
Lucia Ďuricová, M. Hromada, J. Mrázek
{"title":"The Software to the Soft Target Assessment","authors":"Lucia Ďuricová, M. Hromada, J. Mrázek","doi":"10.5772/INTECHOPEN.87997","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.87997","url":null,"abstract":"The soft targets are closely related to the risk of attack to the group of people (to the lives). This problem can cause fatal consequences for the population. The current situation on the world reflects the fear of the attack in the soft targets. We can see the fear to lose life at these public places and in all types of access to free buildings. Each of us spends time in the shopping centers or the park every day, and our children spend time in schools where they can be threatened. The characteris-tics between the soft targets belong to a considerable number of persons at the same time in the same area, and the current state of the security measures is not adequate to the threats yet. The main aim of the software to the assessment of the soft target is to protect the people in the soft targets, minimize the impact to the people (visitors), and help to solve the problem at the moment. The methodology is based on the assessment of the object according to the features (according to the criteria).","PeriodicalId":224487,"journal":{"name":"Introduction to Data Science and Machine Learning","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116521583","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
Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms 基于k -均值和模糊c -均值混合算法的图像分割
Introduction to Data Science and Machine Learning Pub Date : 2019-07-10 DOI: 10.5772/INTECHOPEN.86374
Raja Kishor Duggirala
{"title":"Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms","authors":"Raja Kishor Duggirala","doi":"10.5772/INTECHOPEN.86374","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.86374","url":null,"abstract":"Image segmentation is an essential technique of image processing for analyzing an image by partitioning it into non-overlapped regions each region referring to a set of pixels. Image segmentation approaches can be divided into four categories. They are thresholding, edge detection, region extraction and clustering. Clustering techniques can be used for partitioning datasets into groups according to the homogeneity of data points. The present research work proposes two algorithms involving hybridization of K-Means ( KM ) and Fuzzy C-Means ( FCM ) techniques as an attempt to achieve better clustering results. Along with the proposed hybrid algorithms, the present work also experiments with the standard K-Means and FCM algorithms. All the algorithms are experimented on four images. CPU Time, clustering fitness and sum of squared errors (SSE) are computed for measuring clustering performance of the algorithms. In all the experiments it is observed that the proposed hybrid algorithm KMandFCM is consistently producing better clustering results.","PeriodicalId":224487,"journal":{"name":"Introduction to Data Science and Machine Learning","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131324974","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: 4
The Methodological Standard to the Assessment of the Traffic Simulation in Real Time 交通仿真实时评价的方法标准
Introduction to Data Science and Machine Learning Pub Date : 2019-06-24 DOI: 10.5772/INTECHOPEN.86961
J. Mrázek, M. Hromada, Lucia Duricova Mrazkova
{"title":"The Methodological Standard to the Assessment of the Traffic Simulation in Real Time","authors":"J. Mrázek, M. Hromada, Lucia Duricova Mrazkova","doi":"10.5772/INTECHOPEN.86961","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.86961","url":null,"abstract":"The quantity of goods transported in the transport sector is increasing every year. As a result of the increase, the number of means of transport increases. The most popular sector is road transport, which is also referred to as the most dangerous in terms of safety. The assessment of the traffic situation on the planned route does not take place during its implementation. The consequences of long reaction times on emerging or already occurring incidents affect safety. This phenomenon can also trigger crisis situations in other critical infrastructure sectors. In more serious events, a cascading effect can occur between critical infrastructure elements that could lead to a domino effect. This phenomenon could be likened, for example, to blackout in power engineering. The conclusion of the chapter will include a case scenario as to how a methodological standard for traffic assessment should work on real-time crises.","PeriodicalId":224487,"journal":{"name":"Introduction to Data Science and Machine Learning","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131841610","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
Augmented Post Systems: Syntax, Semantics, and Applications 扩充邮政系统:语法、语义和应用
Introduction to Data Science and Machine Learning Pub Date : 2019-05-06 DOI: 10.5772/INTECHOPEN.86207
I. Sheremet
{"title":"Augmented Post Systems: Syntax, Semantics, and Applications","authors":"I. Sheremet","doi":"10.5772/INTECHOPEN.86207","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.86207","url":null,"abstract":"Augmented Post systems (APS) are string-operating Prolog-like knowledge representation, affiliated with the “ Set of Strings ” Framework (SSF). APS descriptive and logical inference capabilities provide natural integration of Big Data with online analytic processing. This chapter is dedicated to strict formal definition of APS syntax, mathematical and operational semantics, and to its most valuable implementational issues, as well as to APS application to Big Data, Internet of Things, cyberphysical industry, and cybersecurity areas. every activated F-production is interpreted as residual body of S-production (lines 7 – 9). Every variable declaration δ 0 , obtained as a result of this interpretation, is used for the creation of new message by substitution of δ 0 to actor s . This message is used as input value for function F recursive application. So the wave of messages, trig-gered by initial message, is generated, modeling well-known blackboard architecture . This wave propagation of any programs (including DBMS, software/hardware drivers, providing activation and operation of various devices, as well as network-ing middleware) may be applied. Operator terminate (line 11) stops F execution, so no return to the parent call is performed.","PeriodicalId":224487,"journal":{"name":"Introduction to Data Science and Machine Learning","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133830292","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
“Set of Strings” Framework for Big Data Modeling “Set of Strings”大数据建模框架
Introduction to Data Science and Machine Learning Pub Date : 2019-04-27 DOI: 10.5772/INTECHOPEN.85602
I. Sheremet
{"title":"“Set of Strings” Framework for Big Data Modeling","authors":"I. Sheremet","doi":"10.5772/INTECHOPEN.85602","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.85602","url":null,"abstract":"The most complicated task for big data modeling in comparison with relational approach is its variety, being a consequence of heterogeneity of sources of data, accumulated in the integrated storage space. “Set of Strings” Framework (SSF) provides unified solution of this task by representation of database as updated finite set of facts, being strings, in which structure is defined by current metadatabase, which is also an updated set of the context-free generating rules. This chapter is dedicated to SSF formal and substantial description.","PeriodicalId":224487,"journal":{"name":"Introduction to Data Science and Machine Learning","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125014361","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
The K-Means Algorithm Evolution k -均值算法进化
Introduction to Data Science and Machine Learning Pub Date : 2019-04-03 DOI: 10.5772/INTECHOPEN.85447
J. Pérez-Ortega, N. N. Almanza-Ortega, Andrea Vega-Villalobos, Rodolfo A. Pazos-Rangel, Crispín Zavala-Díaz, A. Martínez-Rebollar
{"title":"The K-Means Algorithm Evolution","authors":"J. Pérez-Ortega, N. N. Almanza-Ortega, Andrea Vega-Villalobos, Rodolfo A. Pazos-Rangel, Crispín Zavala-Díaz, A. Martínez-Rebollar","doi":"10.5772/INTECHOPEN.85447","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.85447","url":null,"abstract":"Clustering is one of the main methods for getting insight on the underlying nature and structure of data. The purpose of clustering is organizing a set of data into clusters, such that the elements in each cluster are similar and different from those in other clusters. One of the most used clustering algorithms presently is K -means, because of its easiness for interpreting its results and implementation. The solution to the K -means clustering problem is NP-hard, which justifies the use of heuristic methods for its solution. To date, a large number of improvements to the algorithm have been proposed, of which the most relevant were selected using systematic review methodology. As a result, 1125 documents on improvements were retrieved, and 79 were left after applying inclusion and exclusion criteria. The improvements selected were classified and summarized according to the algorithm steps: initialization, classification, centroid calculation, and convergence. It is remarkable that some of the most successful algorithm variants were found. Some articles on trends in recent years were included, concerning K -means improvements and its use in other areas. Finally, it is considered that the main improvements may inspire the development of new heuristics for K -means or other clustering algorithms.","PeriodicalId":224487,"journal":{"name":"Introduction to Data Science and Machine Learning","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127134510","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: 23
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