发布求助
文献互助 智能选刊 最新文献

Handbook of Research on Emerging Trends and Applications of Machine Learning最新文献

筛选
英文 中文
Self-Driving Cars 自动驾驶汽车
Handbook of Research on Emerging Trends and Applications of Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-9643-1.ch023
P. Jha, K. S. Patnaik
{"title":"Self-Driving Cars","authors":"P. Jha, K. S. Patnaik","doi":"10.4018/978-1-5225-9643-1.ch023","DOIUrl":"https://doi.org/10.4018/978-1-5225-9643-1.ch023","url":null,"abstract":"Human errors are the main cause of vehicle crashes. Self-driving cars bear the promise to significantly reduce accidents by taking the human factor out of the equation, while in parallel monitor the surroundings, detect and react immediately to potentially dangerous situations and driving behaviors. Artificial intelligence tool trains the computers to do things like detect lane lines and identify cyclists by showing them millions of examples of the subject at hand. The chapter in this book discusses the technological advancement in transportation. It also covers the autonomy used according to The National Highway Traffic Safety Administration (NHTSA). The functional architecture of self-driving cars is further discussed. The chapter also talks about two algorithms for detection of lanes as well as detection of vehicles on the road for self-driving cars. Next, the ethical discussions surrounding the autonomous vehicle involving stakeholders, technologies, social environments, and costs vs. quality have been discussed.","PeriodicalId":321162,"journal":{"name":"Handbook of Research on Emerging Trends and Applications of Machine Learning","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116037804","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
Social Big Data Mining 社交大数据挖掘
Handbook of Research on Emerging Trends and Applications of Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-9643-1.ch025
Anisha P. Rodrigues, N. Chiplunkar, Roshan Fernandes
{"title":"Social Big Data Mining","authors":"Anisha P. Rodrigues, N. Chiplunkar, Roshan Fernandes","doi":"10.4018/978-1-5225-9643-1.ch025","DOIUrl":"https://doi.org/10.4018/978-1-5225-9643-1.ch025","url":null,"abstract":"Social media is used to share the data or information among the large group of people. Numerous forums, blogs, social networks, news reports, e-commerce websites, and many more online media play a role in sharing individual opinions. The data generated from these sources is huge and in unstructured format. Big data is a term used for data sets that are large or complex and that cannot be processed by traditional processing system. Sentimental analysis is one of the major data analytics applied on big data. It is a task of natural language processing to determine whether a text contains subjective information and what information it expresses. It helps in achieving various goals like the measurement of customer satisfaction, observing public mood on political movement, movie sales prediction, market intelligence, and many more. In this chapter, the authors present various techniques used for sentimental analysis and related work using these techniques. The chapter also presents open issues and challenges in sentimental analysis landscape.","PeriodicalId":321162,"journal":{"name":"Handbook of Research on Emerging Trends and Applications of Machine Learning","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117250431","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
Computer Vision-Based Assistive Technology for Helping Visually Impaired and Blind People Using Deep Learning Framework 使用深度学习框架帮助视障人士和盲人的基于计算机视觉的辅助技术
Handbook of Research on Emerging Trends and Applications of Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-9643-1.ch027
Mohamamd Farukh Hashmi, V. Gupta, Dheeravath Vijay, Vinaybhai Rathwa
{"title":"Computer Vision-Based Assistive Technology for Helping Visually Impaired and Blind People Using Deep Learning Framework","authors":"Mohamamd Farukh Hashmi, V. Gupta, Dheeravath Vijay, Vinaybhai Rathwa","doi":"10.4018/978-1-5225-9643-1.ch027","DOIUrl":"https://doi.org/10.4018/978-1-5225-9643-1.ch027","url":null,"abstract":"Millions of people in this world can't understand environment because they are blind or visually impaired. They also have navigation difficulties which leads to social awkwardness. They can use some other way to deal with their life and daily routines. It is very difficult for them to find something in unknown environment. Blind and visually impaired people face many difficulties in conversation because they can't decide whether the person is talking to them or someone else. Computer vision-based technologies have increased so much in this domain. Deep convolutional neural network has developed very fast in recent years. It is very helpful to use computer vision-based techniques to help the visually impaired. In this chapter, hearing is used to understand the world. Both sight sense and hearing have the same similarity: both visual object and audio can be localized. Many people don't realise that we are capable of identifying location of the source of sound by just hearing it.","PeriodicalId":321162,"journal":{"name":"Handbook of Research on Emerging Trends and Applications of Machine Learning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132697582","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
Tool Condition Monitoring Using Artificial Neural Network Models 基于人工神经网络模型的工具状态监测
Handbook of Research on Emerging Trends and Applications of Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-9643-1.ch026
Srinivasa P. Pai, Nagabhushana T. N.
{"title":"Tool Condition Monitoring Using Artificial Neural Network Models","authors":"Srinivasa P. Pai, Nagabhushana T. N.","doi":"10.4018/978-1-5225-9643-1.ch026","DOIUrl":"https://doi.org/10.4018/978-1-5225-9643-1.ch026","url":null,"abstract":"Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.","PeriodicalId":321162,"journal":{"name":"Handbook of Research on Emerging Trends and Applications of Machine Learning","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124833077","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
Sentiment Analysis on Social Media 社交媒体情感分析
Handbook of Research on Emerging Trends and Applications of Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-9643-1.ch024
R. Wadawadagi, V. Pagi
{"title":"Sentiment Analysis on Social Media","authors":"R. Wadawadagi, V. Pagi","doi":"10.4018/978-1-5225-9643-1.ch024","DOIUrl":"https://doi.org/10.4018/978-1-5225-9643-1.ch024","url":null,"abstract":"Due to the advent of Web 2.0, the size of social media content (SMC) is growing rapidly and likely to increase faster in the near future. Social media applications such as Instagram, Twitter, Facebook, etc. have become an integral part of our lives, as they prompt the people to give their opinions and share information around the world. Identifying emotions in SMC is important for many aspects of sentiment analysis (SA) and is a top-level agenda of many firms today. SA on social media (SASM) extends an organization's ability to capture and study public sentiments toward social events and activities in real time. This chapter studies recent advances in machine learning (ML) used for SMC analysis and its applications. The framework of SASM consists of several phases, such as data collection, pre-processing, feature representation, model building, and evaluation. This survey presents the basic elements of SASM and its utility. Furthermore, the study reports that ML has a significant contribution to SMC mining. Finally, the research highlights certain issues related to ML used for SMC.","PeriodicalId":321162,"journal":{"name":"Handbook of Research on Emerging Trends and Applications of Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131095849","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信