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Correction to: Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

Archives of Computational Methods in engineering 2021年第7期28卷 5039-5039页

作者： Ismail, Leila Materwala, Huned Tayefi, Maryam Ngo, Phuong Karduck, Achim P. Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Norwegian Centre for E-Health Research Tromsø Norway Faculty of Informatics Furtwangen University Furtwangen Germany

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On the Monte Carlo Representation of Uncertain Spatial Constraints

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Procedia engineering 2012年 41卷 37-46页

作者： Jan Čurn Dan Marinescu Vinny Cahill Distributed Systems Group School of Computer Science and Statistics Trinity College Dublin Ireland Lero -The Irish Software Engineering Research Centre

We present a novel approach to the problem of simultaneous localization and mapping (SLAM), that is not based on any of the three major SLAM paradigms: extended Kalman ﬁlters, particle ﬁlters and graph-based optimizers. In this approach, the uncertain spatial constraints are represented as ordered sets of Monte Carlo samples drawn from the space of coordinate frame transformations. Such a representation enables fusion of two or more spatial constraints even if they are correlated, under certain assumptions. The spatial constraints are organised in a compact data structure which models the full posterior over the robot's pose and landmark locations. The number of Monte Carlo samples necessary to accurately represent the posterior does not grow exponentially with the number of state-space dimensions as in conventional particle ﬁlters; in fact, it is a constant parameter. This data structure provides a constant time access to marginal distributions and a newly observed spatial constraint can be accommodated in time linear to the number of landmarks tracked, regardless of the number of spatial constraints that have been observed previously. We provide an experimental evaluation of the method, and discuss its strengths and weaknesses with respect to the well-established SLAM approaches.

关键词： Sensor Fusion SLAM Monte Carlo Ensemble Kalman Filter

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CBARS: Cluster Based Classification for Activity Recognition systems

CBARS: Cluster Based Classification for Activity Recognition...

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作者： Abdallah, Zahraa Said Gaber, Mohamed Medhat Srinivasan, Bala Krishnaswamy, Shonali Centre for Distributed Systems and Software Engineering Monash University Melbourne Australia School of Computing University of Portsmouth Portsmouth Hampshire PO1 3HE United Kingdom Singapore Faculty of Computer and Information Science Ain Shams University Cairo Egypt

ISBN: (纸本)9783642353253

Activity recognition focuses on inferring current user activities by leveraging sensory data available on today's sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel Cluster Based Classification for Activity Recognition systems, CBARS. The novel approach processes activities as clusters to build a robust classification framework. CBARS integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition dataset have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Pattern recognition

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Dynamic forecasting and adaptation for demand optimization in the Smart Grid

Dynamic forecasting and adaptation for demand optimization i...

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International Workshop on software engineering for the Smart Grid (SE4SG)

作者： Eamonn O'Toole Siobhán Clarke Lero-The Irish Software Engineering Research Centre Distributed Systems Group School of Computer Science and Statistics Trinity College Dublin Ireland

The daily peaks and valleys in energy demand create inefficiencies and expense in the operation of the electricity grid. Valley periods force utilities to curtail renewable energy sources such as wind as their unpredictable nature makes it difficult to maintain line frequency across the network within target bounds. Peak periods require additional generators that remain dormant during other periods. Smoothing this demand cycle is one of the fundamental challenges of the Smart Grid, requiring flexibility and coordination between actors throughout the Grid. This paper describes the Smart Grid as a multi-layered system and proposes a cross-layered dynamic adaptation approach to facilitate this flexibility and coordination. This method uses a hierarchical taxonomy to identify appropriate adaptation actions in response to identified mismatches, supported by a run-time predictive statistical framework to predict mismatches, enabling timely adaptations to be triggered.

关键词： Smart grids Generators Electricity Production Renewable energy resources Optimization Wind energy

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StreamAR: Incremental and Active Learning with Evolving Sensory Data for Activity Recognition

StreamAR: Incremental and Active Learning with Evolving Sens...

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International Conference on Tools with Artificial Intelligence

作者： Zahraa Said Abdallah Mohamed Medhat Gaber Bala Srinivasan Shonali Krishnaswamy Centre for Distributed Systems and Software Engineering Monash University School of Computing University of Portsmouth Institute for Infocomm Research (I2R)

ISBN: (纸本)9781479902279

Activity recognition focuses on inferring current user activities by leveraging sensory data available on today's sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel cluster-based classification for activity recognition systems, termed StreamAR. The system incorporates incremental and active learning for mining user activities in data streams. The novel approach processes activities as clusters to build a robust classification framework. StreamAR integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition datasets have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.

关键词： Experiential Learning Activity recognition Incremental Sensation

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Resource-aware ECG analysis on mobile devices 11

Resource-aware ECG analysis on mobile devices

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Proceedings of the 2011 ACM Symposium on Applied Computing

作者： Sinha, Abhijat Tayebi, Hossein Krishnaswamy, Shonali Waluyo, Agustinus Borgy Gaber, Mohamed Medhat Centre for Distributed Systems and Software Engineering Monash University Australia University of Portsmouth United Kingdom

ISBN: (纸本)9781450301138

关键词： Time series analysis

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Role of context-awareness for demand response mechanisms

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Lecture Notes in Computer Science

作者： Haghighi, Pari Delir Krishnaswamy, Shonali Centre for Distributed Systems and Software Engineering Monash University 900 Dandenong Rd Caulfield East VIC 3145 Australia

ISBN: (纸本)9783642234460

Recently due to major changes in the structure of electricity industry and the rising costs of power generation, many countries have realized the potential and benefits of smart metering systems and demand response programs in balancing between the supply and the demand. DR mechanisms are capable of controlling the user energy consumption according to load conditions and providing effective energy management. However, they are typically performed regardless of user's situation and current activities. Factoring in the user's contextual information which is relevant to their current or future energy consumption can significantly increase the effectiveness of DR programs and enable adaptive and personalized execution of DR control actions. In this paper, we review current DR techniques and discuss the state-of-the-art smart energy management approaches that take into account contextual information. An overview of context reasoning and learning techniques for smart homes are presented to demonstrate how knowledge of user activities can be utilized in context-aware DR mechanisms. Our aim is to provide a better understanding of DR programs and highlight the importance of the context-awareness in improving smart energy management. © 2011 Springer-Verlag.

关键词： Energy management

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RA-SAX: Resource-aware symbolic aggregate approximation for mobile ECG analysis

RA-SAX: Resource-aware symbolic aggregate approximation for ...

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2011 12th IEEE International Conference on Mobile Data Management, MDM 2011

作者： Tayebi, Hossein Krishnaswamy, Shonali Waluyo, Augustinus Borgy Sinha, Abhijat Gaber, Mohamed Medhat Centre for Distributed Systems and Software Engineering Monash University Melbourne VIC Australia School of Computing University of Portsmouth Portsmouth United Kingdom

ISBN: (纸本)9780769544366

There is a growing focus on 24/7 cardiac monitoring that leverages state of the art mobile phones and commercial-off-the-shelf (COTS) wearable bio-sensors. While many signal processing techniques for mobile ECG analysis have been developed, these techniques tend to be computationally intensive. In this paper, we propose, develop and evaluate a resource-aware and energy-efficient time series analysis technique for real-time ECG analysis on mobile devices based on the well-known SAX (Symbolic Aggregate Approximation) representation for time series termed RA-SAX. © 2011 IEEE.

关键词： Aggregates

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KB-CB-N classification: Towards unsupervised approach for supervised learning

KB-CB-N classification: Towards unsupervised approach for su...

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IEEE Symposium on Computational Intelligence and Data Mining

作者： Said Abdallah, Zahraa Gaber, Mohamed Medhat Centre for Distributed Systems and Software Engineering Monash University 900 Dandenong Rd Caulfield East VIC 3145 Australia School of Computing University of Portsmouth Hampshire PO1 3HE United Kingdom

ISBN: (纸本)9781424499274

Data classification has attracted considerable research attention in the field of computational statistics and data mining due to its wide range of applications. K Best Cluster Based Neighbour (KB-CB-N) is our novel classification technique based on the integration of three different similarity measures for cluster based classification. The basic principle is to apply unsupervised learning on the instances of each class in the dataset and then use the output as an input for the classification algorithm to find the K best neighbours of clusters from the density, gravity and distance perspectives. Clustering is applied as an initial step within each class to find the inherent in-class grouping in the dataset. Different data clustering techniques use different similarity measures. Each measure has its own strength and weakness. Thus, combining the three measures can benefit from the strength of each one and eliminate encountered problems of using an individual measure. Extensive experimental results using eight real datasets have evidenced that our new technique typically shows improved or equivalent performance over other existing state-of-the-art classification methods. © 2011 IEEE.

关键词： Data mining

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RA-SAX: Resource-Aware Symbolic Aggregate Approximation for Mobile ECG Analysis

RA-SAX: Resource-Aware Symbolic Aggregate Approximation for ...

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International Conference on Mobile Data Management

作者： Hossein Tayebi Shonali Krishnaswamy Augustinus Borgy Waluyo Abhijat Sinha Mohamed Medhat Gaber Centre for Distributed Systems and Software Engineering Monash University Melbourne Australia School of Computing University of Portsmouth Portsmouth United Kingdom

关键词： Electrocardiography Mobile communication Clustering algorithms Classification algorithms Smart phones Accuracy

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