Resolution of opiate illicit drugs signals in the presence of some cutting agents with use of a voltammetric sensor array and machine learning strategies

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

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Machine Learning Strategies When Transitioning between Biological Assays

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.1c00293 ◽

2021 ◽

Author(s):

Staffan Arvidsson McShane ◽

Ernst Ahlberg ◽

Tobias Noeske ◽

Ola Spjuth

Keyword(s):

Machine Learning ◽

Learning Strategies ◽

Biological Assays

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Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies

Journal of Stroke ◽

10.5853/jos.2017.02922 ◽

2018 ◽

Vol 20 (3) ◽

pp. 302-320 ◽

Cited By ~ 48

Author(s):

Elisa Cuadrado-Godia ◽

Pratistha Dwivedi ◽

Sanjiv Sharma ◽

Angel Ois Santiago ◽

Jaume Roquer Gonzalez ◽

...

Keyword(s):

Machine Learning ◽

Learning Strategies ◽

Small Vessel Disease ◽

Cerebral Small Vessel Disease ◽

Small Vessel ◽

Vessel Disease

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Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning

Chemistry Proceedings ◽

10.3390/csac2021-10447 ◽

2021 ◽

Vol 5 (1) ◽

pp. 21

Author(s):

Edgar G. Mendez-Lopez ◽

Jersson X. Leon-Medina ◽

Diego A. Tibaduiza

Keyword(s):

Machine Learning ◽

Dimensionality Reduction ◽

Performance Measures ◽

Sensor Array ◽

Three Dimensional ◽

Electronic Tongue ◽

Sensor Arrays ◽

Classification Performance ◽

Supervised Machine Learning ◽

Electrochemical Tests

Electronic tongue type sensor arrays are made of different materials with the property of capturing signals independently by each sensor. The signals captured when conducting electrochemical tests often have high dimensionality, which increases when performing the data unfolding process. This unfolding process consists of arranging the data coming from different experiments, sensors, and sample times, thus the obtained information is arranged in a two-dimensional matrix. In this work, a description of a tool for the analysis of electronic tongue signals is developed. This tool is developed in Matlab® App Designer, to process and classify the data from different substances analyzed by an electronic tongue type sensor array. The data processing is carried out through the execution of the following stages: (1) data unfolding, (2) normalization, (3) dimensionality reduction, (4) classification through a supervised machine learning model, and finally (5) a cross-validation procedure to calculate a set of classification performance measures. Some important characteristics of this tool are the possibility to tune the parameters of the dimensionality reduction and classifier algorithms, and also plot the two and three-dimensional scatter plot of the features after reduced the dimensionality. This to see the data separability between classes and compatibility in each class. This interface is successfully tested with two electronic tongue sensor array datasets with multi-frequency large amplitude pulse voltammetry (MLAPV) signals. The developed graphical user interface allows comparing different methods in each of the mentioned stages to find the best combination of methods and thus obtain the highest values of classification performance measures.

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P2AR.3 - Virtual Gas Sensor Array by Cyclic Optical Activation: Optimization of Activation Profile by Machine Learning

10.5162/imcs2018/p2ar.3 ◽

2018 ◽

Author(s):

G. Njio ◽

T. Wagner

Keyword(s):

Machine Learning ◽

Gas Sensor ◽

Sensor Array ◽

Gas Sensor Array ◽

Activation Profile ◽

Optical Activation

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Short-Term Traffic Flow Prediction Based on Ensemble Machine Learning Strategies

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) ◽

10.1109/ddcls52934.2021.9455594 ◽

2021 ◽

Author(s):

Ximu Zeng ◽

Yixiong Wang ◽

Xin Deng ◽

Jin Wang

Keyword(s):

Machine Learning ◽

Traffic Flow ◽

Learning Strategies ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Ensemble Machine Learning

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Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis

International Journal of Knowledge Discovery in Bioinformatics ◽

10.4018/ijkdb.2017010103 ◽

2017 ◽

Vol 7 (1) ◽

pp. 30-41 ◽

Cited By ~ 12

Author(s):

Prayag Tiwari ◽

Brojo Kishore Mishra ◽

Sachin Kumar ◽

Vivek Kumar

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Maximum Entropy ◽

Learning Strategies ◽

Supervised Machine Learning ◽

Support Vector ◽

N Gram ◽

F Measure ◽

Blog Posts

Sentiment Analysis intends to get the basic perspective of the content, which may be anything that holds a subjective supposition, for example, an online audit, Comments on Blog posts, film rating and so forth. These surveys and websites might be characterized into various extremity gatherings, for example, negative, positive, and unbiased keeping in mind the end goal to concentrate data from the info dataset. Supervised machine learning strategies group these reviews. In this paper, three distinctive machine learning calculations, for example, Support Vector Machine (SVM), Maximum Entropy (ME) and Naive Bayes (NB), have been considered for the arrangement of human conclusions. The exactness of various strategies is basically inspected keeping in mind the end goal to get to their execution on the premise of parameters, e.g. accuracy, review, f-measure, and precision.

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