Ankit Agrawal mainly investigates Artificial intelligence, Machine learning, Data mining, Deep learning and Materials informatics. His biological study spans a wide range of topics, including Social media, Microblogging and Identification. The study incorporates disciplines such as Classifier, Variety and Data set in addition to Machine learning.
His work carried out in the field of Classifier brings together such families of science as False positive paradox, Information retrieval and Big data. As a part of the same scientific study, Ankit Agrawal usually deals with the Data mining, concentrating on Feature selection and frequently concerns with Cancer, Risk of mortality and Statistical classification. His work deals with themes such as Artificial neural network, Data-driven, Convolutional neural network and Transfer of learning, which intersect with Deep learning.
His main research concerns Artificial intelligence, Data mining, Machine learning, Algorithm and Cluster analysis. His work on Deep learning as part of general Artificial intelligence study is frequently linked to Materials informatics, bridging the gap between disciplines. His Deep learning research focuses on Big data and how it connects with Data science.
He combines subjects such as Cancer, Lung cancer and Feature selection with his study of Data mining. His research on Machine learning focuses in particular on Naive Bayes classifier. His Cluster analysis study deals with Parallel computing intersecting with DBSCAN.
Ankit Agrawal focuses on Artificial intelligence, Deep learning, Artificial neural network, Machine learning and Data mining. His Artificial intelligence study frequently links to other fields, such as Pattern recognition. Parallel computing and Degree of parallelism is closely connected to Scalability in his research, which is encompassed under the umbrella topic of Deep learning.
His research in Artificial neural network tackles topics such as Residual which are related to areas like Regression. His research on Machine learning often connects related topics like Domain knowledge. The Big data study combines topics in areas such as DBSCAN and Cluster analysis.
His primary areas of investigation include Artificial intelligence, Deep learning, Artificial neural network, Materials informatics and Machine learning. His Artificial intelligence research is multidisciplinary, incorporating elements of Flexibility and Pattern recognition. His Deep learning research incorporates themes from Scalability, Bayesian optimization, Convolutional neural network and Data mining.
His Data mining research is multidisciplinary, incorporating perspectives in Fatigue limit, Ensemble learning, Ensemble forecasting, Supervised learning and Feature selection. His study in Artificial neural network is interdisciplinary in nature, drawing from both Tree, Biological system and Cheminformatics. His Machine learning research is multidisciplinary, relying on both Training set and Survivability.
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Classification of sentiment reviews using n-gram machine learning approach
Abinash Tripathy;Ankit Agrawal;Santanu Kumar Rath.
Expert Systems With Applications (2016)
A general-purpose machine learning framework for predicting properties of inorganic materials
Logan Ward;Ankit Agrawal;Alok Nidhi Choudhary;Christopher M Wolverton.
npj Computational Materials (2016)
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
Ankit Agrawal;Alok Choudhary.
APL Materials (2016)
Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection
Kasthurirangan Gopalakrishnan;Siddhartha K. Khaitan;Alok Choudhary;Ankit Agrawal.
Construction and Building Materials (2017)
Twitter Trending Topic Classification
Kathy Lee;Diana Palsetia;Ramanathan Narayanan;Md. Mostofa Ali Patwary.
international conference on data mining (2011)
Real-time disease surveillance using Twitter data: demonstration on flu and cancer
Kathy Lee;Ankit Agrawal;Alok Choudhary.
knowledge discovery and data mining (2013)
Classiﬁcation of Sentimental Reviews Using Machine Learning Techniques
Abinash Tripathy;Ankit Agrawal;Santanu Kumar Rath.
Procedia Computer Science (2015)
A new scalable parallel DBSCAN algorithm using the disjoint-set data structure
Md. Mostofa Ali Patwary;Diana Palsetia;Ankit Agrawal;Wei-keng Liao.
ieee international conference on high performance computing data and analytics (2012)
Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
Logan Ward;Ruoqian Liu;Amar Krishna;Vinay I. Hegde.
Physical Review B (2017)
A predictive machine learning approach for microstructure optimization and materials design
Ruoqian Liu;Abhishek Kumar;Zhengzhang Chen;Zhengzhang Chen;Ankit Agrawal.
Scientific Reports (2015)
Computers, Materials and Continua
(Impact Factor: 3.86)
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