scholarly journals Comprehensive Study of Lithium Adsorption and Diffusion on Janus Mo/WXY (X, Y = S, Se, Te) Using First-Principles and Machine Learning Approaches

2021 ◽  
Author(s):  
Gracie Chaney ◽  
Akram Ibrahim ◽  
Fatih Ersan ◽  
D. Çakır ◽  
Can Ataca
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Learning Approaches ◽  
And Diffusion ◽  
Comprehensive Study

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

scholarly journals A Comprehensive Study on Demand Forecasting Methods and Algorithms for Retail Industries

2021 ◽  
Vol 23 (06) ◽  
pp. 409-420
Author(s):  
Udbhav Vikas ◽  
◽  
Karthik Sunil ◽  
Rohini S. Hallikar ◽  
Pattem Deeksha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Performance Comparison ◽  
Supply Side ◽  
Learning Approaches ◽  
Time Series Regression ◽  
On Demand ◽  
Forecasting Methods ◽  
Comprehensive Study

Without a doubt, demand forecasting is an essential part of a company’s supply chain. It predicts future demand and specifies the level of supply-side readiness needed to satisfy the demand. It is imperative that if a company’s forecasting isn’t reasonably reliable, the entire supply chain suffers. Over or under forecasted demand would have a debilitating impact on the operation of the supply chain, along with planning and logistics. Having acknowledged the importance of demand forecasting, one must look into the techniques and algorithms commonly employed to predict demand. Data mining, statistical modeling, and machine learning approaches are used to extract insights from existing datasets and are used to anticipate unobserved or unknown occurrences in statistical forecasting. In this paper, the performance comparison of various forecasting techniques, time series, regression, and machine learning approaches are discussed, and the suitability of algorithms for different data patterns is examined.

A generalizable machine learning potential of Ag-Au nanoalloys and its application t surface reconstruction, segregation and diffusion

2021 ◽  
Author(s):  
Yinan Wang ◽  
Linfeng Zhang ◽  
Ben Xu ◽  
Xiaoyang Wang ◽  
Han Wang
Keyword(s):  
Machine Learning ◽  
Surface Properties ◽  
Surface Reconstruction ◽  
First Principles ◽  
Interatomic Potential ◽  
Learning Potential ◽  
Free Surfaces ◽  
Wide Range ◽  
Empirical Force Field ◽  
And Diffusion

Abstract Owing to the excellent catalytic properties of Ag-Au binary nanoalloys, nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, has been under intense investigation. To achieve high accuracy in molecular simulations of Ag-Au nanoalloys, the surface properties must be modeled with first-principles precision. In this work, we constructed a generalizable machine learning interatomic potential for Ag-Au nanoalloys based on deep neural networks trained from a database constructed with first-principles calculations. This potential is highlighted by the accurate prediction of Au (111) surface reconstruction and the segregation of Au toward the Ag-Au nanoalloy surface, where the empirical force field failed in both cases. Moreover, regarding the adsorption and diffusion of adatoms on surfaces, the overall performance of our potential is better than the empirical force fields. We stress that the reported surface properties are blind to the potential modeling in the sense that none of the surface configurations is explicitly included in the training database; therefore, the reported potential is expected to have a strong generalization ability to a wide range of properties and to play a key role in investigating nanostructured Ag-Au evolution, where accurate descriptions of free surfaces are necessary.

scholarly journals A COMPREHENSIVE STUDY ON APPLICATION OF DEEP LEARNING IN BRAIN TUMOR DETECTION

2021 ◽  
Vol 23 (07) ◽  
pp. 977-994
Author(s):  
Josmy Mathew ◽  
◽  
Dr. N. Srinivasan ◽  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumours ◽  
Data Augmentation ◽  
State Of The Art ◽  
Network Models ◽  
Data Sets ◽  
Learning Approaches ◽  
Neural Network Models ◽  
Comprehensive Study

Deep Learning is an area of machine learning which, because of its capability to handle a large quantity of data, has demonstrated amazing achievements in each field, notably in biomedicine. Its potential and abilities were evaluated and utilised with an effective prognosis in the identification of brain tumours with MRI pictures. The diagnosis of MRI images by computer-aided brain tumours includes tumour identification, segmentation and classification. Many types of research have concentrated in recent years on conventional or basic machine learning approaches in the detection of brain tumours. Throughout this overview, we offer a comprehensive assessment of the surveys that have been reported so far and the current approaches for detecting tumours. Our review examines the major processes in deep learning approaches for detecting brain tumours including preprocessing, extraction of features and classification and their performance and limitations. We also explore state-of-the-art neural network models to identify brain tumours through extensive trials with and without data augmentation. This review also discusses existing data sets for brain tumour detection assessments.

Sign in / Sign up

Export Citation Format

Share Document