scholarly journals Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5207 ◽  
Author(s):  
Anton Gradišek ◽  
Marion van Midden ◽  
Matija Koterle ◽  
Vid Prezelj ◽  
Drago Strle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
The Other ◽  
Classification Model ◽  
Machine Learning Algorithm ◽  
Classification Models ◽  
Functionalized Surfaces ◽  
Other Substances ◽  
Chemical Selectivity ◽  
Excellent Accuracy

We used a 16-channel e-nose demonstrator based on micro-capacitive sensors with functionalized surfaces to measure the response of 30 different sensors to the vapours from 11 different substances, including the explosives 1,3,5-trinitro-1,3,5-triazinane (RDX), 1-methyl-2,4-dinitrobenzene (DNT) and 2-methyl-1,3,5-trinitrobenzene (TNT). A classification model was developed using the Random Forest machine-learning algorithm and trained the models on a set of signals, where the concentration and flow of a selected single vapour were varied independently. It is demonstrated that our classification models are successful in recognizing the signal pattern of different sets of substances. An excellent accuracy of 96% was achieved for identifying the explosives from among the other substances. These experiments clearly demonstrate that the silane monolayers used in our sensors as receptor layers are particularly well suited to selecting and recognizing TNT and similar types of explosives from among other substances.

Empirical Comparison of Various Discretization Procedures

1998 ◽  
Vol 12 (07) ◽  
pp. 1017-1032 ◽  
Author(s):  
Petr Berka ◽  
Ivan Bruha
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Machine Learning Algorithm ◽  
Empirical Comparison ◽  
Numerical Attributes ◽  
Real World Problems ◽  
Discretization Procedure

The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or finance, involve both symbolic and numerical attributes. Therefore, there is an important issue of ML to discretize (categorize) numerical attributes. There exist quite a few discretization procedures in the ML field. This paper describes two newer algorithms for categorization (discretization) of numerical attributes. The first one is implemented in the KEX (Knowledge EXplorer) as its preprocessing procedure. Its idea is to discretize the numerical attributes in such a way that the resulting categorization corresponds to KEX knowledge acquisition algorithm. Since the categorization for KEX is done "off-line" before using the KEX machine learning algorithm, it can be used as a preprocessing step for other machine learning algorithms, too. The other discretization procedure is implemented in CN4, a large extension of the well-known CN2 machine learning algorithm. The range of numerical attributes is divided into intervals that may form a complex generated by the algorithm as a part of the class description. Experimental results show a comparison of performance of KEX and CN4 on some well-known ML databases. To make the comparison more exhibitory, we also used the discretization procedure of the MLC++ library. Other ML algorithms such as ID3 and C4.5 were run under our experiments, too. Then, the results are compared and discussed.

MAITS

2011 ◽  
pp. 189-208 ◽  
Author(s):  
Dharmendra Sharma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Learning Algorithm ◽  
Computer Forensics ◽  
The Other ◽  
Security Requirements ◽  
Machine Learning Algorithm ◽  
Agent Based ◽  
Authentication And Authorization ◽  
Multi Agent

In this chapter, we propose a multi-agent-based information technology (IT) security approach (MAITS) as a holistic solution to the increasing needs of securing computer systems. Each specialist task for security requirements is modeled as a specialist agent. MAITS has five groups of working agents—administration assistant agents, authentication and authorization agents, system log *monitoring agents, intrusion detection agents, and pre-mortem-based computer forensics agents. An assessment center, which is comprised of yet another special group of agents, plays a key role in coordinating the interaction of the other agents. Each agent has an agent engine of an appropriate machine-learning algorithm. The engine enables the agent with learning, reasoning, and decision-making abilities. Each agent also has an agent interface, through which the agent interacts with other agents and also the environment.

scholarly journals Deep Multiphysics and Particle–Neuron Duality: A Computational Framework Coupling (Discrete) Multiphysics and Deep Learning

2019 ◽  
Vol 9 (24) ◽  
pp. 5369
Author(s):  
Alessio Alexiadis
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Learning Algorithm ◽  
The Other ◽  
Particle Methods ◽  
Machine Learning Algorithm ◽  
Computational Framework ◽  
In Series ◽  
Mathematical Algorithms ◽  
Modelling Techniques

There are two common ways of coupling first-principles modelling and machine learning. In one case, data are transferred from the machine-learning algorithm to the first-principles model; in the other, from the first-principles model to the machine-learning algorithm. In both cases, the coupling is in series: the two components remain distinct, and data generated by one model are subsequently fed into the other. Several modelling problems, however, require in-parallel coupling, where the first-principle model and the machine-learning algorithm work together at the same time rather than one after the other. This study introduces deep multiphysics; a computational framework that couples first-principles modelling and machine learning in parallel rather than in series. Deep multiphysics works with particle-based first-principles modelling techniques. It is shown that the mathematical algorithms behind several particle methods and artificial neural networks are similar to the point that can be unified under the notion of particle–neuron duality. This study explains in detail the particle–neuron duality and how deep multiphysics works both theoretically and in practice. A case study, the design of a microfluidic device for separating cell populations with different levels of stiffness, is discussed to achieve this aim.

A Novel Machine Learning Algorithm Predicts Dementia With Lewy Bodies Versus Parkinson’s Disease Dementia Based on Clinical and Neuropsychological Scores

2021 ◽  
pp. 089198872199355
Author(s):  
Anastasia Bougea ◽  
Efthymia Efthymiopoulou ◽  
Ioanna Spanou ◽  
Panagiotis Zikos
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Neuropsychological Tests ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Classification Model ◽  
Support Vector ◽  
Ensemble Model ◽  
Machine Learning Algorithm

Objective: Our aim was to develop a machine learning algorithm based only on non-invasively clinic collectable predictors, for the accurate diagnosis of these disorders. Methods: This is an ongoing prospective cohort study ( ClinicalTrials.gov identifier NCT number NCT04448340) of 78 PDD and 62 DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. We used predictors such as clinico-demographic characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail making A and B). We investigated logistic regression, K-Nearest Neighbors (K-NNs) Support Vector Machine (SVM), Naïve Bayes classifier, and Ensemble Model for their ability to predict successfully PDD or DLB diagnosis. Results: The K-NN classification model had an accuracy 91.2% of overall cases based on 15 best clinical and cognitive scores achieving 96.42% sensitivity and 81% specificity on discriminating between DLB and PDD. The binomial logistic regression classification model achieved an accuracy of 87.5% based on 15 best features, showing 93.93% sensitivity and 87% specificity. The SVM classification model had an accuracy 84.6% of overall cases based on 15 best features achieving 90.62% sensitivity and 78.58% specificity. A model created on Naïve Bayes classification had 82.05% accuracy, 93.10% sensitivity and 74.41% specificity. Finally, an Ensemble model, synthesized by the individual ones, achieved 89.74% accuracy, 93.75% sensitivity and 85.73% specificity. Conclusion: Machine learning method predicted with high accuracy, sensitivity and specificity PDD or DLB diagnosis based on non-invasively and easily in-the-clinic and neuropsychological tests.

scholarly journals Negation Detection on Mexican Spanish Tweets: The T-MexNeg Corpus

2021 ◽  
Vol 11 (9) ◽  
pp. 3880
Author(s):  
Gemma Bel-Enguix ◽  
Helena Gómez-Adorno ◽  
Alejandro Pimentel ◽  
Sergio-Luis Ojeda-Trueba ◽  
Brian Aguilar-Vizuet
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Learning Algorithm ◽  
Detection Task ◽  
The Other ◽  
Machine Learning Algorithm ◽  
Mexican Spanish ◽  
Negation Detection

In this paper, we introduce the T-MexNeg corpus of Tweets written in Mexican Spanish. It consists of 13,704 Tweets, of which 4895 contain negation structures. We performed an analysis of negation statements embedded in the language employed on social media. This research paper aims to present the annotation guidelines along with a novel resource targeted at the negation detection task. The corpus was manually annotated with labels of negation cue, scope, and, event. We report the analysis of the inter-annotator agreement for all the components of the negation structure. This resource is freely available. Furthermore, we performed various experiments to automatically identify negation using the T-MexNeg corpus and the SFU ReviewSP-NEG for training a machine learning algorithm. By comparing two different methodologies, one based on a dictionary and the other based on the Conditional Random Fields algorithm, we found that the results of negation identification on Twitter are lower when the model is trained on the SFU ReviewSP-NEG Corpus. Therefore, this paper shows the importance of having resources built specifically to deal with social media language.

scholarly journals Marker subset selection and decision support range identification for acute myeloid leukemia classification model development with multiparameter flow cytometry

2019 ◽  
Author(s):  
Jang-Sik Choi ◽  
Nguyen Thanh Nguyen ◽  
Hyung-Gi Byun ◽  
Jaewoo Song ◽  
Tae-Hyun Yoon
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Decision Support ◽  
Learning Algorithm ◽  
Model Development ◽  
Classification Model ◽  
Multiparameter Flow Cytometry ◽  
Machine Learning Algorithm ◽  
Sample Classification ◽  
Panel Design

AbstractIn this study, we developed acute myeloid leukemia (AML) classification model through Wilks’ lambda-based important marker-identification method and stepwise–forward selection approach, and spotted important decision-support range of flow-cytometry parameter using insights provided by machine-learning algorithm. AML flow-cytometry data released from FlowCAP-II challenge in 2011 was used. In FlowCAP-II challenge, several sample classification algorithms were able to effectively classify AML and non-AML. Most algorithms extracted features from high-dimensional flow-cytometry readout comprised of multiple fluorescent parameters for a large number of antibodies. Multiple parameters with forward scatter and side scatter increase computational complexity in the feature-extraction procedure as well as in the model development. Parameter-subset selection can decrease model complexity, improve model performance, and contribute to a panel design specific for target disease. With this motivation, we estimated importance of each parameter via Wilks’ lambda and then identified the best subset of parameters using stepwise–forward selection. In the importance-estimation process, histogram matrix of each parameter was used. As a result, parameters, which are associated with blasts gating and identification of immature myeloid cells, were identified as important descriptors in AML classification, and combination of these markers is more effective than an individual marker. A random-forest, supervised-classification machine-learning algorithm was used for the model development. We highlighted decision-support range of the fluorescent signal for the identified important parameters, which significantly contribute to AML classification, through a mean decrease in Gini supported in random forest. These specific ranges could help with establishing diagnosis criteria and elaborate the AML classification model. Because methodology proposed in this study can not only estimate the importance of each parameter but also identify the best subset and the specific ranges, we expect that it would contribute to in silico modeling using flow- and mass-cytometry readout as well as panel design for sample classification.Author summaryFlow cytometry is a widely used technique to analyze multiple physical characteristics of an individual cell and diagnose and monitor human disease as well as response to therapy. Recent developments in hardware (multiple lasers and fluorescence detectors), fluorochromes, and antibodies have facilitated the comprehensive and in-depth analysis of high numbers of cells on a single cell level and led to the creation of various computational analysis methods for cell type identification, rare cell identification, and sample classification. Flow cytometry typically uses panels with a large number of antibodies, leading to high-dimensional multiparameter flow cytometry readout. It increases computational complexity and makes interpretation difficult. In this study, we identified the best subset of the parameters for AML classification model development. The subset would contribute to panel design specific for the target disease and lead to easy interpretation of the results. In addition, we spotted important decision-support range of flow-cytometry parameter via insights provided by machine-learning algorithm. We expect that profiling information of fluorescence expression over the identified decision-support range would complement existing diagnosis criteria.

scholarly journals Functional Connectivity Combined With a Machine Learning Algorithm Can Classify High-Risk First-Degree Relatives of Patients With Schizophrenia and Identify Correlates of Cognitive Impairments

2020 ◽  
Vol 14 ◽  
Author(s):  
Wenming Liu ◽  
Xiao Zhang ◽  
Yuting Qiao ◽  
Yanhui Cai ◽  
Hong Yin ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Functional Connectivity ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Classification Model ◽  
Imaging Biomarkers ◽  
First Episode ◽  
Machine Learning Algorithm ◽  
First Degree Relatives

Schizophrenia (SCZ) is an inherited disease, with the familial risk being among the most important factors when evaluating an individual’s risk for SCZ. However, robust imaging biomarkers for the disease that can be used for diagnosis and determination of the prognosis are lacking. Here, we explore the potential of functional connectivity (FC) for use as a biomarker for the early detection of high-risk first-degree relatives (FDRs). Thirty-eight first-episode SCZ patients, 38 healthy controls (HCs), and 33 FDRs were scanned using resting-state functional magnetic resonance imaging. The subjects’ brains were parcellated into 200 regions using the Craddock atlas, and the FC between each pair of regions was used as a classification feature. Multivariate pattern analysis using leave-one-out cross-validation achieved a correct classification rate of 88.15% [sensitivity 84.06%, specificity 92.18%, and area under the receiver operating characteristic curve (AUC) 0.93] for differentiating SCZ patients from HCs. FC located within the default mode, frontal-parietal, auditory, and sensorimotor networks contributed mostly to the accurate classification. The FC patterns of each FDR were input into each classification model as test data to obtain a corresponding prediction label (a total of 76 individual classification scores), and the averaged individual classification score was then used as a robust measure to characterize whether each FDR showed an SCZ-type or HC-type FC pattern. A significant negative correlation was found between the average classification scores of the FDRs and their semantic fluency scores. These findings suggest that FC combined with a machine learning algorithm could help to predict whether FDRs are likely to show an SCZ-specific or HC-specific FC pattern.

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