Evolving Connectionist Systems

Kasabov, Nikola

doi:10.1007/978-1-4471-3740-5

Cited by 109 publications

(61 citation statements)

References 230 publications

(187 reference statements)

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“…The empirical evaluation showed an average of 64% accuracy for activity recognition in five randomly generated resident ADL scenarios. RiveraIllingworth et al [95] employed a recurrent neural network (RNN) based on Evolving Connectionist System (ECoS) [59] to recognize activities like sleeping, eating, working with computer, and to detect abnormal behaviours. ECoS operates online, which means that new sensors can be added to the architecture, and new activities can be accommodated at any stage.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Studies presented in [59,112] have demonstrated that CRF commonly gives a better accuracy than other probabilistic models in the context of activity recognition. In the first study [59] CRF and HMM were compared using different activity data representations to show that CRF outperforms HMM.…”

Section: Conditional Random Field (Crf)mentioning

confidence: 99%

“…In the first study [59] CRF and HMM were compared using different activity data representations to show that CRF outperforms HMM. In the second study [111] CRF was compared against NB, HMM, and HSMM and CRF scored again better.…”

Section: Conditional Random Field (Crf)mentioning

confidence: 99%

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A review of smart homes in healthcare

Amiribesheli

Benmansour

Bouchachia

2015

J Ambient Intell Human Comput

156

123

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The technology of Smart Homes (SH), as an instance of ambient assisted living technologies, is designed to assist the homes' residents accomplishing their daily-living activities and thus having a better quality of life while preserving their privacy. A SH system is usually equipped with a collection of inter-related software and hardware components to monitor the living space by capturing the behaviour of the resident and understanding his activities. By doing so the system can inform about risky situations and take actions on behalf of the resident to his satisfaction. The present survey will address technologies and analysis methods and bring examples of the state of the art research studies in order to provide background for the research community. In particular, the survey will expose infrastructure technologies such as sensors and communication platforms along with artificial intelligence techniques used for modeling and recognizing activities. A brief overview of approaches used to develop Human-Computer (HC) interfaces for SH systems is given. The survey also highlights the challenges and research trends in this area.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Conditional Random Field (Crf)mentioning

confidence: 99%

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A review of smart homes in healthcare

Amiribesheli

Benmansour

Bouchachia

2015

J Ambient Intell Human Comput

156

123

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“…Personalised medicine and many other areas of science and technology rely now on efficient PM methods. Classical methods, such as kNN, wkNN, wwkNN and other have a limited success on complex problems [1,2,13].…”

Section: Personalised Modellingmentioning

confidence: 99%

Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke

et al. 2014

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The paper presents a novel method and system for personalised (individualised) modelling of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking neural network reservoir system (eSNNr) is proposed for the purpose. The system consists of: spike-time encoding module of continuous value input information into spike trains; a recurrent 3D SNNr; eSNN as an evolving output classifier. Such system is generated for every new individual, using existing data of similar individuals. Subject to proper training and parameter optimisation, the system is capable of accurate spatio- temporal pattern recognition (STPR) and of early prediction of individual events. The method and the system are generic, applicable to various SSTD and classification and prediction problems. As a case study, the method is applied for early prediction of occurrence of stroke on an individual basis. Preliminary experiments demonstrated a significant improvement in accuracy and time of event prediction when using the proposed method when compared with standard machine learning methods, such as MLR, SVM, MLP. Future development and applications are discussed

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“…eSNNs use Thorpe's model and a population rank coding to encode information. eSNNs combine evolving connectionist system (ECoS) (Kasabov 2007) architecture and SNNs. Compared with SNNs, eSNNs have three advantages.…”

Section: Evolving Spiking Neural Networkmentioning

confidence: 99%

Deep Learning

Awad¹,

Khanna²

2015

Efficient Learning Machines

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Any fool can know. The point is to understand. -Albert EinsteinArtificial neural networks (ANNs) have had a history riddled with highs and lows since their inception. At a nodal level, ANNs started with highly simplified neural models, such as McCulloch-Pitts neurons (McCulloch and Pitts 1943), and then evolved into Rosenblatt's perceptrons (Rosenblatt 1957) and a variety of more complex and sophisticated computational units. From single-and multilayer networks, to self-recurrent Hopfield networks (Tank and Hopfield 1986), to self-organizing maps (also called Kohonen networks) (Kohonen 1982), adaptive resonance theory and time delay neural networks among other recommendations, ANNs have witnessed many structural iterations. These generations carried incremental enhancements that promised to address predecessors' limitations and achieve higher levels of intelligence. Nonetheless, the compounded effect of these "intelligent" networks has not been able to capture the true human intelligence (Guerriere and Detsky 1991; Becker and Hinton 1992). Thus, Deep learning is on the rise in the machine learning community, because the traditional shallow learning architectures have proved unfit for the more challenging tasks of machine learning and strong artificial intelligence (AI). The surge in and wide availability of increased computing power (Misra and Saha 2010), coupled with the creation of efficient training algorithms and advances in neuroscience, have enabled the implementation, hitherto impossible, of deep learning principles. These developments have led to the formation of deep architecture algorithms that look in to cognitive neuroscience to suggest biologically inspired learning solutions. This chapter presents the concepts of spiking neural networks (SNNs) and hierarchical temporal memory (HTM), whose associated techniques are the least mature of the techniques covered in this book. Overview of Hierarchical Temporal MemoryHTM aims at replicating the functional and structural properties of the neocortex. HTM incorporates a number of insights from Hawkins's book On Intelligence (2007), which postulates that the key to intelligence is the ability to predict. Its framework was designed as a biomimetic model of the neocortex that seeks to replicate the brain's structural and algorithmic properties, albeit in a simplified, functionally oriented manner. HTM is therefore organized hierarchically, as depicted generically in Figure 9-1. All levels of hierarchy and their subcomponents perform a common computational algorithm.

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Evolving Connectionist Systems

Cited by 109 publications

References 230 publications

A review of smart homes in healthcare

A review of smart homes in healthcare

Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke

Deep Learning

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