scholarly journals Application of machine learning techniques to electron microscopic/spectroscopic image data analysis

Microscopy ◽  
2019 ◽  
Vol 69 (2) ◽  
pp. 110-122 ◽  
Author(s):  
Shunsuke Muto ◽  
Motoki Shiga
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
Hyperspectral Image ◽  
Statistical Treatment ◽  
Image Data ◽  
Machine Learning Techniques ◽  
Multivariate Curve Resolution ◽  
Chemical Components ◽  
Electron Microscopic ◽  
Analytical Instruments

Abstract The combination of scanning transmission electron microscopy (STEM) with analytical instruments has become one of the most indispensable analytical tools in materials science. A set of microscopic image/spectral intensities collected from many sampling points in a region of interest, in which multiple physical/chemical components may be spatially and spectrally entangled, could be expected to be a rich source of information about a material. To unfold such an entangled image comprising information and spectral features into its individual pure components would necessitate the use of statistical treatment based on informatics and statistics. These computer-aided schemes or techniques are referred to as multivariate curve resolution, blind source separation or hyperspectral image analysis, depending on their application fields, and are classified as a subset of machine learning. In this review, we introduce non-negative matrix factorization, one of these unfolding techniques, to solve a wide variety of problems associated with the analysis of materials, particularly those related to STEM, electron energy-loss spectroscopy and energy-dispersive X-ray spectroscopy. This review, which commences with the description of the basic concept, the advantages and drawbacks of the technique, presents several additional strategies to overcome existing problems and their extensions to more general tensor decomposition schemes for further flexible applications are described.

Weighting hyperspectral image data for improved multivariate curve resolution results

2008 ◽  
Vol 22 (9) ◽  
pp. 482-490 ◽  
Author(s):  
Howland D. T. Jones ◽  
David M. Haaland ◽  
Michael B. Sinclair ◽  
David K. Melgaard ◽  
Mark H. Van Benthem ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Image Data ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Hyperspectral Image Data

scholarly journals Application of Machine Learning for Prediction of Lung Cancer using Omics Data

2020 ◽  
Vol 9 (6) ◽  
pp. 230-236
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Data Mining ◽  
Survival Analysis ◽  
Early Stage ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Omics Data

Cancer is one of the deadly diseases across many countries. However, cancer can be cured, if detected at an early stage. Researchers are working on healthcare for early detection and prevention of cancer. Medical data has reached its utmost potential by providing researchers with huge data sets collected from all over the globe. In the present scenario, Machine Learning has been widely used in the area of cancer diagnosis and prognosis. Survival analysis may help in the prediction of the early onset of disease, relapse, re-occurrence of diseases and biomarker identification. Applications of machine learning and data mining methods in medical field are currently the most widespread in cancer detection and survival analysis. In this survey, different ways to detect and predict lung cancer using latest Machine learning algorithms combined with data mining has been analyzed. Comparative study of various machine learning techniques and technologies has been done over different types of data such as clinical data, omics data, image data etc.

scholarly journals Efficient Machine Learning Techniques to Diagnose and Predict Alzheimer’s disease

2020 ◽  
Vol 9 (3) ◽  
pp. 3953-3960
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Mechanisms ◽  
Learning Techniques

Recent research in computational engineering have evidenced the design and development numerous intelligent models to analyze medical data and derive inferences related to early diagnosis and prediction of disease severity. In this context, prediction and diagnosis of fatal neurodegenerative diseases that comes under the class of dementia from medical image data is considered as the challenging area of research for many researchers. Recently Alzheimer’s disease is considered as major category of dementia that affects major population. Despite of the development of numerous machine learning models for early diagnosis of Alzheimer’s disease, it is observed that there is a lot more scope of research. Addressing the same, this article presents a systematic literature review of machine learning techniques developed for early diagnosis of Alzheimer’s disease. Furthermore this article includes major categories of machine learning algorithms that include artificial neural networks, Support vector machines and Deep learning based ensemble models that helps the budding researchers to explore the scope of research in predicting Alzheimer’s disease. Implementation results depict the comparative analysis of state of art machine learning mechanisms.

scholarly journals Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis

2020 ◽  
Vol 12 (15) ◽  
pp. 2421
Author(s):  
Kasra Rafiezadeh Shahi ◽  
Mahdi Khodadadzadeh ◽  
Laura Tusa ◽  
Pedram Ghamisi ◽  
Raimon Tolosana-Delgado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Hyperspectral Image ◽  
Imaging Techniques ◽  
Clustering Algorithms ◽  
Machine Learning Techniques ◽  
Mixed Data ◽  
Number Of Clusters ◽  
Hyperspectral Image Analysis ◽  
Learning Techniques

Hyperspectral imaging techniques are becoming one of the most important tools to remotely acquire fine spectral information on different objects. However, hyperspectral images (HSIs) require dedicated processing for most applications. Therefore, several machine learning techniques were proposed in the last decades. Among the proposed machine learning techniques, unsupervised learning techniques have become popular as they do not need any prior knowledge. Specifically, sparse subspace-based clustering algorithms have drawn special attention to cluster the HSI into meaningful groups since such algorithms are able to handle high dimensional and highly mixed data, as is the case in real-world applications. Nonetheless, sparse subspace-based clustering algorithms usually tend to demand high computational power and can be time-consuming. In addition, the number of clusters is usually predefined. In this paper, we propose a new hierarchical sparse subspace-based clustering algorithm (HESSC), which handles the aforementioned problems in a robust and fast manner and estimates the number of clusters automatically. In the experiment, HESSC is applied to three real drill-core samples and one well-known rural benchmark (i.e., Trento) HSI datasets. In order to evaluate the performance of HESSC, the performance of the new proposed algorithm is quantitatively and qualitatively compared to the state-of-the-art sparse subspace-based algorithms. In addition, in order to have a comparison with conventional clustering algorithms, HESSC’s performance is compared with K-means and FCM. The obtained clustering results demonstrate that HESSC performs well when clustering HSIs compared to the other applied clustering algorithms.

scholarly journals "Dividing and Conquering" and "Caching" in Molecular Modeling

2020 ◽  
Author(s):  
Xiaoyong Cao ◽  
Pu Tian
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Molecular Modeling ◽  
Materials Science ◽  
Divide And Conquer ◽  
Machine Learning Techniques ◽  
Collective Variables ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
State Models

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, Most of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. To access interesting behavior of complex molecular systems in a wide range of spatial and temporal scales, the molecular modeling community has invested tremendous efforts on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in Metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but no transferability is available. With introduction of machine learning techniques, many new developments, particularly those based on deep learning, have been implemented to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We recently developed the generalized solvation free energy theory , which suggests a third class of algorithm that facilitate molecular modeling through partially transferable in resolution "caching" of local free energy landscape. Connections and potential interactions among these three algorithmic directions are discussed. This brief review is on both the traditional development and the application of machine learning in molecular modeling from the perspective of "dividing and conquering" and "caching", with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms in this regard.

Rapid Defect Detection and Classification in Images Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Peter Warren ◽  
Hessein Ali ◽  
Hossein Ebrahimi ◽  
Ranajay Ghosh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defects ◽  
Image Data ◽  
Rapid Identification ◽  
Machine Learning Techniques ◽  
Great Success

Abstract Several image processing methods have been implemented over recent years to assist and partially replace on-site technician visual inspection of both manufactured parts and operational equipments. Convolutional neural networks (CNNs) have seen great success in their ability to both identify and classify anomalies within images, in some cases they do this to a higher degree of accuracy than an expert human. Several parts that are manufactured for various aspects of turbomachinery operation must undergo a visual inspection prior to qualification. Machine learning techniques can streamline these visual inspection processes and increase both efficiency and accuracy of defect detection and classification. The adoption of CNNs to manufactured part inspection can also help to improve manufacturing methods by rapidly retrieving data for overall system improvement. In this work a dataset of images with a variety of surface defects and some without defects will be fed through varying CNN set-ups for the rapid identification and classification of the flaws within the images. This work will examine the techniques used to create CNNs and how they can best be applied to part surface image data, and determine the most accurate and efficient techniques that should be implemented. By combining machine learning with non-destructive evaluation methods component health can be rapidly determined and create a more robust system for manufactured parts and operational equipment evaluation.

Application of Artificial Intelligence and Machine Learning Techniques in Classifying Extent of Dementia Across Alzheimer's Image Data

2021 ◽  
Vol 6 (2) ◽  
pp. 29-46
Author(s):  
Robin Ghosh ◽  
Anirudh Reddy Cingreddy ◽  
Venkata Melapu ◽  
Sravanthi Joginipelli ◽  
Supratik Kar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Image Data ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Mild Dementia ◽  
Extreme Gradient Boosting

Alzheimer's disease (AD) is one of the most common forms of dementia and the sixth-leading cause of death in older adults. The presented study has illustrated the applications of deep learning (DL) and associated methods, which could have a broader impact on identifying dementia stages and may guide therapy in the future for multiclass image detection. The studied datasets contain around 6,400 magnetic resonance imaging (MRI) images, each segregated into the severity of Alzheimer's classes: mild dementia, very mild dementia, non-dementia, moderate dementia. These four image specifications were used to classify the dementia stages in each patient applying the convolutional neural network (CNN) algorithm. Employing the CNN-based in silico model, the authors successfully classified and predicted the different AD stages and got around 97.19% accuracy. Again, machine learning (ML) techniques like extreme gradient boosting (XGB), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN) offered accuracy of 96.62%, 96.56%, 94.62, and 89.88%, respectively.

Machine Learning for Detecting Scallops in AUV Benthic Images

2015 ◽  
pp. 22-40
Author(s):  
Prasanna Kannappan ◽  
Herbert G. Tanner ◽  
Arthur C. Trembanis ◽  
Justin H. Walker
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Template Matching ◽  
False Positive Rate ◽  
Image Data ◽  
Machine Learning Techniques ◽  
Detection And Counting ◽  
Attractive Option ◽  
Positive Rate ◽  
Histogram Of Gradients

A large volume of image data, in the order of thousands to millions of images, can be generated by robotic marine surveys aimed at assessment of organism populations. Manual processing and annotation of individual images in such large datasets is not an attractive option. It would seem that computer vision and machine learning techniques can be used to automate this process, yet to this date, available automated detection and counting tools for scallops do not work well with noisy low-resolution images and are bound to produce very high false positive rates. In this chapter, we hone a recently developed method for automated scallop detection and counting for the purpose of drastically reducing its false positive rate. In the process, we compare the performance of two customized false positive filtering alternatives, histogram of gradients and weighted correlation template matching.

Designing of smart chair for monitoring of sitting posture using convolutional neural networks

2019 ◽  
Vol 53 (2) ◽  
pp. 142-155 ◽  
Author(s):  
Wonjoon Kim ◽  
Byungki Jin ◽  
Sanghyun Choo ◽  
Chang S. Nam ◽  
Myung Hwan Yun
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Content Type ◽  
Sitting Posture ◽  
Posture Classification

Purpose Sitting in a chair is a typical act of modern people. Prolonged sitting and sitting with improper postures can lead to musculoskeletal disorders. Thus, there is a need for a sitting posture classification monitoring system that can predict a sitting posture. The purpose of this paper is to develop a system for classifying children’s sitting postures for the formation of correct postural habits. Design/methodology/approach For the data analysis, a pressure sensor of film type was installed on the seat of the chair, and image data of the postu.re were collected. A total of 26 children participated in the experiment and collected image data for a total of seven postures. The authors used convolutional neural networks (CNN) algorithm consisting of seven layers. In addition, to compare the accuracy of classification, artificial neural networks (ANN) technique, one of the machine learning techniques, was used. Findings The CNN algorithm was used for the sitting position classification and the average accuracy obtained by tenfold cross validation was 97.5 percent. The authors confirmed that classification accuracy through CNN algorithm is superior to conventional machine learning algorithms such as ANN and DNN. Through this study, we confirmed the applicability of the CNN-based algorithm that can be applied to the smart chair to support the correct posture in children. Originality/value This study successfully performed the posture classification of children using CNN technique, which has not been used in related studies. In addition, by focusing on children, we have expanded the scope of the related research area and expected to contribute to the early postural habits of children.

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