Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data

Wrist-worn wearable devices equipped with heart activity sensors can provide valuable data that can be used for preventative health. However, hearth activity analysis from these devices suffers from noise introduced by motion artifacts. Methods traditionally used to remove outliers based on motion data can yield to discarding clean data, if some movement was present, and accepting noisy data, i.e., subject was still but the sensor was misplaced. This work shows that self-organizing maps (SOMs) can be used to effectively accept or reject sections of heart data collected from unreliable devices, such as wrist-worn devices. In particular, the proposed SOM-based filter can accept a larger amount of measurements (less false negatives) with an higher overall quality with respect to methods solely based on statistical analysis of motion data. We provide an empirical analysis on real-world wearable data, comprising heart and motion data of users. We show how topographic mapping can help identifying and interpreting patterns in the sensor data and help relating them to an assessment of user state. More importantly, our experimental results show the proposed approach is able to retain almost twice the amount of data while keeping samples with an error that is an order of magnitude lower with respect to a filter based on accelerometric data

Self-organizing maps in sequence processing

Models are abstractions of observed real world phenomena or processes. A good model captures the essential properties of the modeled phenomena. In the statistical learning paradigm the processes that generate observations are assumed unknown and too complex for analytical modeling, thus the models are trained from more general templates with measured observations. A substantial part of the processes we seek to model have temporal dependencies between observations thus defining templates that can account for these dependencies improves their ability to capture the properties of such processes. In this work we discuss using the self organizing map with sequentially dependent data. Self-Organizing map (SOM) is perhaps the most popular non supervised neural network model that has found varied applications in the field of data mining for example. The original SOM paradigm, however, considers independent data, where context of a sample does not influence its interpretation. However, throwing away the temporal context of an observation when we know we are dealing with sequential data seems wasteful. Consequently methods for incorporating time into the SOM paradigm have been rather extensively studied. Such models if powerful enough would be very usable when tracking dynamic processes. In this work a Self-Organizing map for temporal sequence processing dubbed Recurrent Self-Organizing Map (RSOM) was proposed and analyzed. The model has been used in time series prediction combined with local linear models. Deeper analysis provides insight into how much and what kind of contextual information the model is able to capture. The other topic covered by the publications in a sense considers an inverse problem. In this topic SOM was used to create sequential dependence and order into initially unordered data by modeling a surface and creating a path over the surface for a surface manipulating robot.reviewe

Dynamics and topographic organization of recursive self-organizing maps

Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographicmaps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the models are not well understood. Here, we rigorously analyze a generalization of the self-organizingmap (SOM) for processing sequential data, recursive SOM (RecSOM) (Voegtlin, 2002), as a nonautonomous dynamical system consisting of a set of fixed input maps. We argue that contractive fixed-input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter β (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed-input maps is guaranteed. Some generalizations of SOM contain a dynamic module responsible for processing temporal contexts as an integral part of the model. We show that Markovian topographic maps of sequential data can be produced using a simple fixed (nonadaptable) dynamic module externally feeding a standard topographic model designed to process static vectorial data of fixed dimensionality (e.g., SOM). However, by allowing trainable feedback connections, one can obtain Markovian maps with superior memory depth and topography preservation. We elaborate on the importance of non-Markovian organizations in topographic maps of sequential data. © 2006 Massachusetts Institute of Technology

The use of Artificial Neural Networks (ANNs) in aquatic ecology

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Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

A Machine Learning Framework to Predict Determinant Factors of Seeds

In this paper, we audit the machine learning apparatuses for foreseeing determinant components of seeds. We depict this issue regarding Big Data, ANN, Hadoop and R. We consider Machine-learning techniques especially suited to forecasts dependent on existing information, yet exact expectations about the far off future are frequently on a very basic level unthinkable. Farming is an industry where recorded and current information flourish. This survey researches the various information sources accessible in the horticultural field and dissects them for utilization in Seed determinant factor Predictions. We recognized certain relevant information and researched techniques for utilizing this information to improve forecast inside the farming action