scholarly journals Machine Learning-Based Pipeline for High Accuracy Bioparticle Sizing

Micromachines ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1084
Author(s):  
Shaobo Luo ◽  
Yi Zhang ◽  
Kim Truc Nguyen ◽  
Shilun Feng ◽  
Yuzhi Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Accuracy ◽  
Spherical Particles ◽  
Biomedical Sciences ◽  
Accuracy Measurement ◽  
Machine Learning Model ◽  
Wide Range ◽  
Size Of Particles ◽  
Individual Nanoparticles ◽  
Improved Accuracy

High accuracy measurement of size is essential in physical and biomedical sciences. Various sizing techniques have been widely used in sorting colloidal materials, analyzing bioparticles and monitoring the qualities of food and atmosphere. Most imaging-free methods such as light scattering measure the averaged size of particles and have difficulties in determining non-spherical particles. Imaging acquisition using camera is capable of observing individual nanoparticles in real time, but the accuracy is compromised by the image defocusing and instrumental calibration. In this work, a machine learning-based pipeline is developed to facilitate a high accuracy imaging-based particle sizing. The pipeline consists of an image segmentation module for cell identification and a machine learning model for accurate pixel-to-size conversion. The results manifest a significantly improved accuracy, showing great potential for a wide range of applications in environmental sensing, biomedical diagnostical, and material characterization.

scholarly journals Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Marcos Fabietti ◽  
Mufti Mahmud ◽  
Ahmad Lotfi
Keyword(s):  
Machine Learning ◽  
Open Access ◽  
Short Term Memory ◽  
The Body ◽  
Data Sets ◽  
Additional Information ◽  
Machine Learning Model ◽  
Signal Characteristics ◽  
Wide Range ◽  
Memory Network

AbstractAcquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.

scholarly journals Survey on Stress Detection Using Multiple Sensors through Wearable Devices

2021 ◽  
Vol 10 (2) ◽  
pp. 787-790
Keyword(s):  
Machine Learning ◽  
Wearable Devices ◽  
Anxiety And Depression ◽  
Accuracy Score ◽  
Machine Learning Method ◽  
Stress Detection ◽  
Multiple Sensors ◽  
Machine Learning Model ◽  
Wide Range ◽  
Principle Objective

An Individual method of living on with a daily existence it directly influences on your overall health. Since stress is the significant infection of our human body. Like depression, heart attack and mental illness. WHO says “Globally, more than 264 million people of all ages suffer from depression.”[8]. Also the report says that most of the time people are stressed because of their work. 10.7% of People disorder with stress, anxiety and depression [8]. There are different method to discovering stress ex. Smart watches, chest belt, and extraordinary machine. Our principle objective is to figure out pressure progressively utilizing smart watches through their Sensor. There are different kinds of sensor available to find stress such as PPG, GSR, HRV, ECG and temperature. Smart watches contain a wide range of data through various sensor. This kind of gathered information are applied on various machine learning method. Like linear regression, SVM, KNN, decision tree. Technique have distinct, comparing accuracy and chooses best Machine learning model. This paper investigation have different analysis to find and compare accuracy by various sensors data. It is also check whether using one sensor or multiple sensors such as HRV, ECG or GSR and PPG to predict the better accuracy score for stress detection.

scholarly journals Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

2020 ◽  
Vol 117 (19) ◽  
pp. 10492-10499 ◽  
Author(s):  
Zhan Ban ◽  
Peng Yuan ◽  
Fubo Yu ◽  
Ting Peng ◽  
Qixing Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Protein Corona ◽  
Functional Composition ◽  
Functional Protein ◽  
Machine Learning Model ◽  
Wide Range ◽  
Corona Formation ◽  
Cellular Recognition ◽  
Organ Targeting

Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by R2 (less than 0.40). Here, the performance with R2 over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R2 (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.

Predicting the Writer's Gender Based on Electronic Discourse

2020 ◽  
Vol 2 (1) ◽  
pp. 17-31
Author(s):  
Szde Yu
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Qualitative Content Analysis ◽  
High Accuracy ◽  
Training Set ◽  
Electronic Discourse ◽  
Machine Learning Model ◽  
Improve Accuracy ◽  
Gender Based ◽  
Accuracy Rates

The present study compared three methods aimed at predicting the writer's gender based on writing features manifested in electronic discourse. The compared methods included qualitative content analysis, statistical analysis, and machine learning. These methods were further combined to create a mixed methods model. The findings showed that the machine learning model combined with qualitative content analysis produced the best prediction accuracy. Including qualitative content analysis was able to improve accuracy rates even when the training set for machine learning was relatively small. Thus, this study presented a concise model that can be fairly reliable in predicting gender based on electronic discourse with high accuracy rates and such accuracy was consistently found when the model was tested by two separate samples.

FINDING TEMPORAL STRUCTURE IN TEXT: MACHINE LEARNING OF SYNTACTIC TEMPORAL RELATIONS

2007 ◽  
Vol 01 (04) ◽  
pp. 441-457 ◽  
Author(s):  
STEVEN BETHARD ◽  
JAMES H. MARTIN ◽  
SARA KLINGENSTEIN
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Temporal Structure ◽  
Learning Model ◽  
High Accuracy ◽  
Support Vector ◽  
Automatic Extraction ◽  
Temporal Relations ◽  
Tense And Aspect ◽  
Machine Learning Model

This research proposes and evaluates a linguistically motivated approach to extracting temporal structure from text. Pairs of events in a verb-clause construction were considered, where the first event is a verb and the second event is the head of a clausal argument to that verb. All pairs of events in the TimeBank that participated in verb-clause constructions were selected and annotated with the labels BEFORE, OVERLAP and AFTER. The resulting corpus of 895 event-event temporal relations was then used to train a machine learning model. Using a combination of event-level features like tense and aspect with syntax-level features like the paths through the syntactic tree, support vector machine (SVM) models were trained which could identify new temporal relations with 89.2% accuracy. High accuracy models like these are a first step towards automatic extraction of temporal structure from text.

scholarly journals Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Richard Du ◽  
Efstratios D. Tsougenis ◽  
Joshua W. K. Ho ◽  
Joyce K. Y. Chan ◽  
Keith W. H. Chiu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hong Kong ◽  
High Accuracy ◽  
Rt Pcr ◽  
Blood Tests ◽  
Applied Machine Learning ◽  
Laboratory Markers ◽  
Machine Learning Model ◽  
Temporal Validation ◽  
Low Sensitivity

AbstractTriaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9–95.8%; Sensitivity: 55.5–77.8%; Specificity: 91.5–98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.

scholarly journals Mining Pareto-Optimal Counterfactual Antecedents With A Branch-And-Bound Model-Agnostic Algorithm

2021 ◽  
Author(s):  
Marcos M. Raimundo ◽  
Luis Gustavo Nonato ◽  
Jorge Poco
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Pareto Optimal ◽  
Tree Search ◽  
Original Sample ◽  
Objective Functions ◽  
Machine Learning Model ◽  
Wide Range ◽  
Speed Up ◽  
Machine Learning Models

Abstract Mining counterfactual antecedents became a valuable tool to discover knowledge and explain machine learning models. It consists of generating synthetic samples from an original sample to achieve the desired outcome in a machine learning model thus helping to understand the prediction. An insightful methodology would explore a broader set of counterfactual antecedents to reveal multiple possibilities while operating on any classifier. Thus, we create a tree-based search that requires monotonicity from the objective functions (a.k.a. cost functions); it allows pruning branches that will not improve the objective functions. Since monotonicity is only required for the objective function, this method can be used for any family of classifiers (e.g., linear models, neural networks, decision trees). However, additional classifier properties speed up the tree-search when it foresees branches that will not result in feasible actions. Moreover, the proposed optimization generates a diverse set of Pareto-optimal counterfactual antecedents by relying on multi-objective concepts. The results show an algorithm with working guarantees that enumerates a wide range of counterfactual antecedents. It helps the decision-maker understand the machine learning decision and finds alternatives to achieve the desired outcome. The user can inspect these multiple counterfactual antecedents to find the most suitable one and have a broader understanding of the prediction.

scholarly journals Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning

Antioxidants ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1751
Author(s):  
Taiki Fujimoto ◽  
Hiroaki Gotoh
Keyword(s):  
Machine Learning ◽  
Singlet Oxygen ◽  
Molecular Descriptors ◽  
High Accuracy ◽  
Model Ensemble ◽  
Explanatory Variables ◽  
Oxygen Scavenging ◽  
Machine Learning Model ◽  
Feature Importance ◽  
Chemical Knowledge

A chemically explainable machine learning model was constructed with a small dataset to quantitatively predict the singlet-oxygen-scavenging ability. In this model, ensemble learning based on decision trees resulted in high accuracy. For explanatory variables, molecular descriptors by computational chemistry and Morgan fingerprints were used for achieving high accuracy and simple prediction. The singlet-oxygen-scavenging mechanism was explained by the feature importance obtained from machine learning outputs. The results are consistent with conventional chemical knowledge. The use of machine learning and reduction in the number of measurements for screening high-antioxidant-capacity compounds can considerably improve prediction accuracy and efficiency.

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