the information of amino acids in protein sequences was calculated with the help of the iFeature package, ML algorithms (SVM79, RF80, and KNN81), and deep learning algorithms (DNN82) were trained on the carefully curated positive and negative samples to develop different classifiers...
Recently, utilization of Machine Learning (ML) has led to astonishing progress in computational protein design, bringing into reach the targeted engineering of proteins for industrial and biomedical applications. However, the design of proteins for emerg
Therefore, machine learning is often utilized by SHM to enhance the capabilities of the monitoring systems. In this paper, an ML aided real-time NDE and a FRP design using principal component analysis (PCA) and ANN are proposed to handle the uncountable variables of carbon–aramid hybrid fibers...
Machine Learning aided Molecular Modelling of Taste to Identify Food Fingerprintsdoi:10.3303/CET23102048COFFEEFOOD consumptionMACHINE learningPROTEIN receptorsHUMAN fingerprintsNature has developed fascinating mechanisms for selecting and monitoring nutrients through refined systems for food intake and up...
The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (... T Nguyen,A Kashani,T Ngo,... - Computer-Aided Civil...
Machine Learning on the Edge: Azure ML and Azure IOT Edge Integration By Ted Way | February 2019 The edge is defined as any compute platform that isn’t the cloud. It could range from Azure Stack (a cluster of machines that serves as mini-Azu...
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial. arXiv 2018 paper bib Andrii Shalaginov, Sergii Banin, Ali Dehghantanha, Katrin Franke Machine Learning for Survival Analysis: A Survey. arXiv 2017 paper bib Ping Wang, Yan Li, Chandan K. Reddy The Creation and Detection ...
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial. arXiv 2018 paper Andrii Shalaginov, Sergii Banin, Ali Dehghantanha, Katrin Franke Machine Learning for Survival Analysis: A Survey. arXiv 2017 paper Ping Wang, Yan Li, Chandan K. Reddy The Creation and Detection of Deepfake...
Here, we use a structure-based, machine learning algorithm to engineer a robust and active PET hydrolase. Our mutant and scaffold combination (FAST-PETase: functional, active, stable and tolerant PETase) contains five mutations compared to wild-type PETase (N233K/R224Q/S121E from prediction ...
We have demonstrated a machine learning-aided approach to the design of mycotoxin detoxifiers (MDTs). We used a dataset of experimental in vitro assessment of the adsorption and efficiency of various MDTs against regulated mycotoxins (DON, T2, ZEN, OTA, FB1, AFB1) to build two random forest...