Potential Of Deep Learning Research Articles

Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential

High entropy materials (HEMs, e.g. high entropy alloys, high entropy ceramics) have gained increasing interests due to the possibility that they can provide challenge properties unattainable by traditional materials. Though a large number of HEMs have emerged, there is still in lack of theoretical predictions and simulations on HEMs, which is probably caused by the chemical complexity of HEMs. In this work, we demonstrate that the machine learning potentials developed in recent years can overcome the complexity of HEMs, and serve as powerful theoretical tools to simulate HEMs. A deep learning potential (DLP) for high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C is fitted with the prediction error in energy and force being 9.4 meV/atom and 217 meV/Å, respectively. The reliability and generality of the DLP are affirmed, since it can accurately predict lattice parameters and elastic constants of mono-phase carbides TMC (TM = Ti, Zr, Hf, Nb and Ta). Lattice constants (increase from 4.5707 Å to 4.6727 Å), thermal expansion coefficients (increase from 7.85×10-6 K-1 to 10.58×10-6 K-1), phonon thermal conductivities (decrease from 2.02 W·m-1·K-1 to 0.95 W·m-1·K-1), and elastic properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C in temperature ranging from 0 °C to 2400 °C are predicted by molecular dynamics simulations. The predicted room temperature properties agree well with experimental measurements, indicating the high accuracy of the DLP. With introducing of machine learning potentials, many problems that are intractable by traditional methods can be handled now. It is hopeful that deep insight into HEMs can be obtained in the future by such powerful methods.

Analysis on Latest Technologies in Medical Imaging for Early Diagnosis and Treatment

Computer Aided Diagnosis enabled by machine learning has revolutionized the way medical industry operates. Medical Imaging has provided a convenient and hassle free diagnosis methods for medical treatment. Medical Imaging has its roots in all spheres of healthcare. In the recent times, availability of quality digital data in medical field along with convergence of various technological tools resulted in exponential growth in various areas including medical industry. Deep learning has emerged as a subset of machine learning with automated feature extraction abilities ensuring at par or higher accuracy as compared to the medical experts. An accuracy of 99.3% and 100% is achieved in classification of individuals suffering for Alzheimer’s disease with respect to the normal individual and with mild cognitive impairment respectively reinforcing the potential of deep learning tools. With the increasing availability of multi-modal imaging data, the need for churning and extracting the key information becomes the key priority for automation and big data precisely performs the same thereby enabling interpretation based personalized imaging and discovering imaging biomarkers. Finally, these two techniques can only be efficient if there is high end computing power and Graphics Processing Unit (GPU) enabled parallel processing has provided the required platform. However, there still exists challenges like lack of annotated data, variety of modalities, varied sources of modalities, variation in class label and uncertainty in the deep learning black box. To address all these issues, this paper aims at exploring the breadth and depth of the outreach of medical imaging ranging from classification, segmentation, abnormalities detection, motion detection, image reconstruction and pharmacological imaging as an assisting tool including the challenges and the future scope.