Predicting Crystallization Tendency of Polymers Using Multifidelity Information Fusion and Machine Learning

2020 ◽  
Vol 124 (28) ◽  
pp. 6046-6054 ◽  
Author(s):  
Shruti Venkatram ◽  
Rohit Batra ◽  
Lihua Chen ◽  
Chiho Kim ◽  
Madeline Shelton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Fusion ◽  
Crystallization Tendency

scholarly journals Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xin Wang ◽  
Dafang Zhang ◽  
Xin Su ◽  
Wenjia Li
Keyword(s):  
Machine Learning ◽  
Information Fusion ◽  
Malware Detection ◽  
Parallel Machine ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Android Malware ◽  
Detection Model ◽  
Android Apps ◽  
Android Malware Detection

In recent years, Android malware has continued to grow at an alarming rate. More recent malicious apps’ employing highly sophisticated detection avoidance techniques makes the traditional machine learning based malware detection methods far less effective. More specifically, they cannot cope with various types of Android malware and have limitation in detection by utilizing a single classification algorithm. To address this limitation, we propose a novel approach in this paper that leverages parallel machine learning and information fusion techniques for better Android malware detection, which is named Mlifdect. To implement this approach, we first extract eight types of features from static analysis on Android apps and build two kinds of feature sets after feature selection. Then, a parallel machine learning detection model is developed for speeding up the process of classification. Finally, we investigate the probability analysis based and Dempster-Shafer theory based information fusion approaches which can effectively obtain the detection results. To validate our method, other state-of-the-art detection works are selected for comparison with real-world Android apps. The experimental results demonstrate that Mlifdect is capable of achieving higher detection accuracy as well as a remarkable run-time efficiency compared to the existing malware detection solutions.

scholarly journals Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

Biology ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 198
Author(s):  
Diana V. Urista ◽  
Diego B. Carrué ◽  
Iago Otero ◽  
Sonia Arrasate ◽  
Viviana F. Quevedo-Tumailli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Information Fusion ◽  
High Performance ◽  
Antimalarial Activity ◽  
Classification Model ◽  
Operating Characteristics ◽  
Experimental Conditions ◽  
Nanoparticle Delivery ◽  
Random Forest Models

Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.

scholarly journals Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources

2020 ◽  
Vol 63 ◽  
pp. 256-272 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
P. Ghamisi ◽  
M. Piles ◽  
M. Werner ◽  
L. Cuadra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Fusion ◽  
Earth Observation ◽  
Data Sources ◽  
Comprehensive Review

scholarly journals Step Characterization using Sensor Information Fusion and Machine Learning

2015 ◽  
Vol 3 (5) ◽  
pp. 53 ◽  
Author(s):  
Ricardo Anacleto ◽  
Lino Figueiredo ◽  
Ana Almeida ◽  
Paulo Novais ◽  
António Meireles
Keyword(s):  
Machine Learning ◽  
Information Fusion ◽  
Sensor Information
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