What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
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 most cited work include:
- A general-purpose machine learning framework for predicting properties of inorganic materials (388 citations)
- Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science (317 citations)
- Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection (254 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (32.11%)
- Data mining (19.72%)
- Machine learning (17.43%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (32.11%)
- Deep learning (9.17%)
- Artificial neural network (5.96%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets (91 citations)
- ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition. (88 citations)
- Microstructural Materials Design Via Deep Adversarial Learning Methodology (71 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
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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