Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform

Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

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Seek and learn: Automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning

Methods in Ecology and Evolution ◽

10.1111/2041-210x.13491 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1639-1651

Author(s):

Pritish Chakravarty ◽

Gabriele Cozzi ◽

Hooman Dejnabadi ◽

Pierre‐Alexandre Léziart ◽

Marta Manser ◽

...

Keyword(s):

Machine Learning ◽

Animal Behaviour ◽

Automated Identification ◽

Acceleration Data

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Automated Identification of Postural Control for Individuals with Parkinsonʼs Disease using a Machine Learning Approach

Medicine & Science in Sports & Exercise ◽

10.1249/01.mss.0000561572.20932.d6 ◽

2019 ◽

Vol 51 (Supplement) ◽

pp. 355

Author(s):

Yumeng Li ◽

Shuqi Zhang ◽

Christina Odeh ◽

Li Guan

Keyword(s):

Machine Learning ◽

Postural Control ◽

Learning Approach ◽

Automated Identification ◽

Machine Learning Approach

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Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Frontiers in Plant Science ◽

10.3389/fpls.2020.611622 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yanjie Li ◽

Mahmoud Al-Sarayreh ◽

Kenji Irie ◽

Deborah Hackell ◽

Graeme Bourdot ◽

...

Keyword(s):

Machine Learning ◽

Hyperspectral Imaging ◽

Support Vector ◽

Classification Models ◽

Imaging Data ◽

Weed Species ◽

Automated Identification ◽

Standard Normal Variate ◽

Californian Thistle ◽

Ranunculus Acris

Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70–100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.

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Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling

10.26434/chemrxiv.12950270.v2 ◽

2020 ◽

Author(s):

Ioan-Bogdan Magdau ◽

Thomas Miller

Keyword(s):

Machine Learning ◽

Molecular Mechanisms ◽

Ion Solvation ◽

Automated Identification ◽

Chemical Systems ◽

Machine Learning Approach ◽

Rapid Diversification ◽

Li Ion ◽

Phase Systems

<div>Automated identification and classification of ion solvation sites in diverse chemical systems will improve the understanding and design of polymer electrolytes for battery applications. We introduce a machine learning approach to classify and characterize ion solvation environments based on feature vectors extracted from all-atom simulations. This approach is demonstrated in poly(3,4-propylenedioxythiophene), which is a promising candidate polymer binder for Li-ion batteries. In the dry polymer, four</div><div>distinct Li+ solvation environments are identified close to the backbone of the polymer. Upon swelling of the polymer with propylene carbonate solvent, the nature of Li+ solvation changes dramatically, featuring a rapid diversification</div><div>of solvation environments. This application of machine learning can be generalized to other polymer condensed-phase systems to elucidate the molecular mechanisms underlying ion solvation.</div>

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Thinking like a naturalist: enhancing computer vision of citizen science images by harnessing contextual data

10.1101/730887 ◽

2019 ◽

Author(s):

J. Christopher D. Terry ◽

Helen E. Roy ◽

Tom A. August

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computer Vision ◽

Contextual Information ◽

Network Models ◽

List Type ◽

British Isles ◽

Automated Identification ◽

Neural Network Models ◽

Contextual Data

AbstractThe accurate identification of species in images submitted by citizen scientists is currently a bottleneck for many data uses. Machine learning tools offer the potential to provide rapid, objective and scalable species identification for the benefit of many aspects of ecological science. Currently, most approaches only make use of image pixel data for classification. However, an experienced naturalist would also use a wide variety of contextual information such as the location and date of recording.Here, we examine the automated identification of ladybird (Coccinellidae) records from the British Isles submitted to the UK Ladybird Survey, a volunteer-led mass participation recording scheme. Each image is associated with metadata; a date, location and recorder ID, which can be cross-referenced with other data sources to determine local weather at the time of recording, habitat types and the experience of the observer. We built multi-input neural network models that synthesise metadata and images to identify records to species level.We show that machine learning models can effectively harness contextual information to improve the interpretation of images. Against an image-only baseline of 48.2%, we observe a 9.1 percentage-point improvement in top-1 accuracy with a multi-input model compared to only a 3.6% increase when using an ensemble of image and metadata models. This suggests that contextual data is being used to interpret an image, beyond just providing a prior expectation. We show that our neural network models appear to be utilising similar pieces of evidence as human naturalists to make identifications.Metadata is a key tool for human naturalists. We show it can also be harnessed by computer vision systems. Contextualisation offers considerable extra information, particularly for challenging species, even within small and relatively homogeneous areas such as the British Isles. Although complex relationships between disparate sources of information can be profitably interpreted by simple neural network architectures, there is likely considerable room for further progress. Contextualising images has the potential to lead to a step change in the accuracy of automated identification tools, with considerable benefits for large scale verification of submitted records.

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CytoCensus, mapping cell identity and division in tissues and organs using machine learning

eLife ◽

10.7554/elife.51085 ◽

2020 ◽

Vol 9 ◽

Author(s):

Martin Hailstone ◽

Dominic Waithe ◽

Tamsin J Samuels ◽

Lu Yang ◽

Ita Costello ◽

...

Keyword(s):

Machine Learning ◽

Automated Analysis ◽

Time Lapse ◽

Supervised Machine Learning ◽

Cell Detection ◽

Automated Identification ◽

User Training ◽

General Utility ◽

Multiple Cell ◽

Mutant Phenotypes

A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D ‘point-and-click’ user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on such datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues.

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Automated Identification of Defect Morphology and Spatial Distribution in Woven Composites

Journal of Composites Science ◽

10.3390/jcs4040178 ◽

2020 ◽

Vol 4 (4) ◽

pp. 178

Author(s):

Anna Madra ◽

Dan-Thuy Van-Pham ◽

Minh-Tri Nguyen ◽

Chanh-Nghiem Nguyen ◽

Piotr Breitkopf ◽

...

Keyword(s):

Machine Learning ◽

Spatial Distribution ◽

Manufacturing Process ◽

Heterogeneous Materials ◽

Thermoplastic Composites ◽

Woven Composites ◽

Automated Identification ◽

Banana Fiber ◽

X Ray ◽

Defect Morphology

The performance of heterogeneous materials, for example, woven composites, does not always reach the predicted theoretical potential. This is caused by defects, such as residual voids introduced during the manufacturing process. A machine learning-based methodology is proposed to determine the morphology and spatial distribution of defects in composites based on X-ray microtomographic scans of the microstructure. A concept of defect "genome" is introduced as an indicator of the overall state of defects in the material, enabling a quick comparison of specimens manufactured under different conditions. The approach is illustrated for thermoplastic composites with unidirectional banana fiber reinforcement.

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