AI for Emerging Verticals: Human-robot computing, sensing and networking最新文献

Predictive mobility management in cellular networks 蜂窝网络中的预测性移动性管理

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/pbpc034e_ch6

引用次数: 0

Visual object tracking by quadrotor AR.Drone using artificial neural networks and fuzzy logic controller 基于人工神经网络和模糊控制器的四旋翼ar无人机视觉目标跟踪

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/pbpc034e_ch5

引用次数: 0

Artificial intelligence and data analytics in 5G and beyond-5G wireless networks 5G及超5G无线网络中的人工智能和数据分析

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/pbpc034e_ch7

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Robot intelligence for real-world applications 现实世界应用的机器人智能

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/pbpc034e_ch4

引用次数: 0

Artificial intelligence for localization of ultrawide bandwidth (UWB) sensor nodes 超宽带传感器节点定位的人工智能

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/PBPC034E_CH9

Fuhu Che, Abbas Ahmed, Q. Ahmed, M. Z. Shakir

引用次数: 6

Surface water pollution monitoring using the Internet of Things (IoT) and machine learning 利用物联网(IoT)和机器学习监测地表水污染

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/pbpc034e_ch17

Hamza Khurshid, R. Mumtaz, Noor Alvi, F. Shafait, Sheraz Ahmed, M. I. Malik, A. Dengel, Quanita Kiran

{"title":"Surface water pollution monitoring using the Internet of Things (IoT) and machine learning","authors":"Hamza Khurshid, R. Mumtaz, Noor Alvi, F. Shafait, Sheraz Ahmed, M. I. Malik, A. Dengel, Quanita Kiran","doi":"10.1049/pbpc034e_ch17","DOIUrl":"https://doi.org/10.1049/pbpc034e_ch17","url":null,"abstract":"Water is one of the basic resources required for human survival. However, pollution of water has become a global problem. 2.4 billion people worldwide live without any form of water sanitation. This work focuses on case study of water pollution in Pakistan where only 20% of the population has an access to good-quality water. Drinking bad-quality water causes diseases such as hepatitis, diarrhea and typhoid. Moreover, people living close to the industrial areas are more prone to drinking polluted water and catching diseases as a result. Yet, there is no system that can monitor the quality of water or help in disease prevention. In this work, an Internet of Things (IoT)-enabled water quality monitoring system is developed that works as a stand-alone portable solution for monitoring water quality accurately and in real time. The real-time results are stored in a cloud database. The public web portal shows these results in the form of data sheets, maps and charts for analyzing data. Further, this data along with the collected data of past water quality is used to generate machine learning (ML) models for prediction of water quality. As a consequence, a model for prediction of water quality is trained and tested on a test set. The predictions on the test set resulted in a mean squared error (MSE) of 0.264.","PeriodicalId":187411,"journal":{"name":"AI for Emerging Verticals: Human-robot computing, sensing and networking","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132311915","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

Back Matter 回到问题

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/pbpc034e_bm

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A Cascaded Machine LearningApproach for indoor classification and localization using adaptive feature selection 基于自适应特征选择的室内分类和定位级联机器学习方法

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/pbpc034e_ch10

引用次数: 3

Machine learning-based affect detection within the context of human–horse interaction 基于机器学习的人马互动情境下的情感检测

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/PBPC034E_CH3

Turke Althobaiti, Stamos Katsigiannis, D. West, H. Rabah, N. Ramzan

引用次数: 0

Artificial intelligence for affective computing: an emotion recognition case study 情感计算的人工智能:情感识别案例研究

AI for Emerging Verticals: Human-robot computing, sensing and networking Pub Date : 2020-11-24 DOI: 10.1049/PBPC034E_CH2

Pablo Arnau-González, Stamos Katsigiannis, M. Arevalillo-Herráez, N. Ramzan

{"title":"Artificial intelligence for affective computing: an emotion recognition case study","authors":"Pablo Arnau-González, Stamos Katsigiannis, M. Arevalillo-Herráez, N. Ramzan","doi":"10.1049/PBPC034E_CH2","DOIUrl":"https://doi.org/10.1049/PBPC034E_CH2","url":null,"abstract":"This chapter provides an introduction on the beneﬁts of artiﬁcial intelligence techniques for the ﬁeld of affective computing, through a case study about emotion recognition via brain (electroencephalography - EEG) signals. Readers are ﬁrst provided with a general description of the ﬁeld, followed by the main models of human affect, with special emphasis to Russell’s Circumplex model and the Pleasure-Arousal-Dominance (PAD) model. Finally, an AI-based method for the detection of affect elicited via multimedia stimuli is presented. The method combines both connectivity-based and channel-based EEG features with a selection method that considerably reduces the dimensionality of the data and allows for efﬁcient classiﬁcation. In particular, the Relative Energy (RE) and its logarithm in the spatial domain, as well as the spectral power (SP) in the frequency domain are computed for the four typically used EEG frequency bands ( α , β , γ and θ ), and complemented with the mutual information measured over all EEG channel pairs. The resulting features are then reduced by using a hybrid method that combines supervised and unsupervised feature selection. Detection results are compared to state-of-the-art methods on the DEAP benchmarking dataset for emotion analysis, which is composed of labelled EEG recordings from 32 individuals, acquired while watching 40 music videos. The acquired results demonstrate the potential of AI-based methods for emotion recognition, an application that can signiﬁcantly beneﬁt the ﬁelds of human-computer interaction (HCI) and of quality-of-experience (QoE).","PeriodicalId":187411,"journal":{"name":"AI for Emerging Verticals: Human-robot computing, sensing and networking","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129252054","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