A Review of Recent Advances in Machine Learning Approaches for Cyber Defense

2021 ◽  
Author(s):  
Ricardo Buettner ◽  
Daniel Sauter ◽  
Jonas Klopfer ◽  
Johannes Breitenbach ◽  
Hermann Baumgartl
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Cyber Defense ◽  
Recent Advances

scholarly journals Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 37622-37655
Author(s):  
Protima Khan ◽  
Md. Fazlul Kader ◽  
S. M. Riazul Islam ◽  
Aisha B. Rahman ◽  
Md. Shahriar Kamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Brain Disease ◽  
Learning Approaches ◽  
Recent Advances

scholarly journals EXPLAIN IT TO ME – FACING REMOTE SENSING CHALLENGES IN THE BIO- AND GEOSCIENCES WITH EXPLAINABLE MACHINE LEARNING

2020 ◽  
Vol V-3-2020 ◽  
pp. 817-824
Author(s):  
R. Roscher ◽  
B. Bohn ◽  
M. F. Duarte ◽  
J. Garcke
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Neural Networks ◽  
Simulated Data ◽  
High Accuracy ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Recent Advances ◽  
Post Hoc

Abstract. For some time now, machine learning methods have been indispensable in many application areas. Especially with the recent development of efficient neural networks, these methods are increasingly used in the sciences to obtain scientific outcomes from observational or simulated data. Besides a high accuracy, a desired goal is to learn explainable models. In order to reach this goal and obtain explanation, knowledge from the respective domain is necessary, which can be integrated into the model or applied post-hoc. We discuss explainable machine learning approaches which are used to tackle common challenges in the bio- and geosciences, such as limited amount of labeled data or the provision of reliable and scientific consistent results. We show that recent advances in machine learning to enhance transparency, interpretability, and explainability are helpful in overcoming these challenges.

scholarly journals Recent Advances in Stereochemistry Reveal Classification Shortcomings

2020 ◽  
Author(s):  
Peter J. Canfield ◽  
Linda J. Govenlock ◽  
Jeffrey Reimers ◽  
Maxwell J. Crossley
Keyword(s):  
Machine Learning ◽  
Fundamental Form ◽  
Learning Approaches ◽  
Software Packages ◽  
Recent Advances ◽  
Chemical Design ◽  
Polytope Model

We contend that the Polytope model utilized by IUPAC to specify stereoisomerism for species ML<i><sub>n</sub></i> with <i>n</i> > 3 should be universally applied. Such application recently led to the synthesis of isolable compounds displaying a new fundamental form of isomerism, akamptisomerism, pertinent to ML<sub>2</sub> stereocenters. We review 443807 molecules that could be classified as akamptisomers. Some akamptisomers are described as being “wrong” by existing IUPAC rules, hindering molecular conception. For many classes of medicinal and technology-related molecules, software packages like ChemDraw mostly do not handle akamptisomers correctly, databases such as CAS provide 2D representations inconsistent with those presented in the original publications, and often the akamptisomeric identity of compounds remains unknown. These features hinder both human and machine-learning approaches to chemical design. Further, the existence of previously unrecognized isomeric forms has broad implications for patents and pharmaceutical-registration requirements. Hence, the immediate re-examination of stereochemistry is demanded.

scholarly journals Recent Advances in Stereochemistry Reveal Classification Shortcomings

2020 ◽  
Author(s):  
Peter J. Canfield ◽  
Linda J. Govenlock ◽  
Jeffrey Reimers ◽  
Maxwell J. Crossley
Keyword(s):  
Machine Learning ◽  
Fundamental Form ◽  
Learning Approaches ◽  
Software Packages ◽  
Recent Advances ◽  
Chemical Design ◽  
Polytope Model

We contend that the Polytope model utilized by IUPAC to specify stereoisomerism for species ML<i><sub>n</sub></i> with <i>n</i> > 3 should be universally applied. Such application recently led to the synthesis of isolable compounds displaying a new fundamental form of isomerism, akamptisomerism, pertinent to ML<sub>2</sub> stereocenters. We review 443807 molecules that could be classified as akamptisomers. Some akamptisomers are described as being “wrong” by existing IUPAC rules, hindering molecular conception. For many classes of medicinal and technology-related molecules, software packages like ChemDraw mostly do not handle akamptisomers correctly, databases such as CAS provide 2D representations inconsistent with those presented in the original publications, and often the akamptisomeric identity of compounds remains unknown. These features hinder both human and machine-learning approaches to chemical design. Further, the existence of previously unrecognized isomeric forms has broad implications for patents and pharmaceutical-registration requirements. Hence, the immediate re-examination of stereochemistry is demanded.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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