Machine learning approach for analyzing complex data from atomic force microscopes

Scilight ◽  
2019 ◽  
Vol 2019 (25) ◽  
pp. 250002
Author(s):  
Adam Liebendorfer
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Complex Data ◽  
Atomic Force Microscopes ◽  
Atomic Force ◽  
Machine Learning Approach

scholarly journals Machine learning-derived electrocardiographic algorithm for the detection of cardiac amyloidosis

Heart ◽  
2021 ◽  
pp. heartjnl-2021-319846
Author(s):  
Lore Schrutka ◽  
Philip Anner ◽  
Asan Agibetov ◽  
Benjamin Seirer ◽  
Fabian Dusik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Amyloidosis ◽  
Low Voltage ◽  
Imaging Techniques ◽  
Learning Approach ◽  
Complex Data ◽  
Strong Positive Correlation ◽  
Advanced Imaging ◽  
Surface Ecg ◽  
Machine Learning Approach

BackgroundDiagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited.ObjectiveThe aim was to perform a thorough electrophysiological characterisation of patients with CA and derive an easy-to-use tool for diagnosis.MethodsWe applied electrocardiographic imaging (ECGI) to acquire electroanatomical maps in patients with CA and controls. A machine learning approach was then used to decipher the complex data sets obtained and generate a surface ECG-based diagnostic tool.FindingsAreas of low voltage were localised in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualised on the right ventricle. Potential maps revealed an accelerated and diffuse propagation pattern. We correlated the results from ECGI with 12-lead ECG recordings. Ventricular activation correlated best with R-peak timing in leads V1–V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in the inferior leads II, III and aVF. Respective surface ECG leads showed two characteristic patterns. Ten blinded cardiologists were asked to identify patients with CA by analysing 12-lead ECGs before and after training on the defined ECG patterns. Training led to significant improvements in the detection rate of CA, with an area under the curve of 0.69 before and 0.97 after training.InterpretationUsing a machine learning approach, an ECG-based tool was developed from detailed electroanatomical mapping of patients with CA. The ECG algorithm is simple and has proven helpful to suspect CA without the aid of advanced imaging modalities.

scholarly journals Machine Learning Approach to Mobile Forensics Framework for Cyber Crime Detection in Nigeria

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Ibrahim Goni ◽  
Murtala Mohammad
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning Approach ◽  
Complex Data ◽  
Cyber Crime ◽  
Mobile Forensics ◽  
Machine Learning Approach ◽  
Crime Detection

The mobile Cyber Crime detection is challenged by number of mobile devices (internet of things), large and complex data, the size, the velocity, the nature and the complexity of the data and devices has become so high that data mining techniques are no more efficient since they cannot handle Big Data and internet of things. The aim of this research work was to develop a mobile forensics framework for cybercrime detection using machine learning approach. It started when call was detected and this detection is made by machine learning algorithm furthermore intelligent mass media towers and satellite that was proposed in this work has the ability to classified calls whether is a threat or not and send signal directly to Nigerian communication commission (NCC) forensic lab for necessary action. 

The impact of economic plans on the Chinese education system: a machine learning approach

CADMO ◽  
2018 ◽  
pp. 37-49
Author(s):  
Wenjun Lin ◽  
Xuefu Xu ◽  
Francesco Dell’Anna
Keyword(s):  
Machine Learning ◽  
Education System ◽  
Learning Approach ◽  
Chinese Education ◽  
System A ◽  
Machine Learning Approach ◽  
The Impact

Improving Bandwidth Utilization and Fairness between TCP Flows based on a Machine-learning Approach

2013 ◽  
Vol 133 (6) ◽  
pp. 1259-1268
Author(s):  
Akihiro Shiozu ◽  
Syunji Yazaki ◽  
K^|^ocirc;ki Abe
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Bandwidth Utilization ◽  
Machine Learning Approach

A Machine Learning Approach to Anaphora Resolution in Arabic

2014 ◽  
Vol 9 (12) ◽  
pp. 1956
Author(s):  
Abdullatif Abolohom ◽  
Nazlia Omar
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Anaphora Resolution ◽  
Machine Learning Approach

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Sign in / Sign up

Export Citation Format

Share Document