What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Amir F. Atiya spends much of his time researching Artificial neural network, Artificial intelligence, Time series, Machine learning and Algorithm.
In his research on the topic of Artificial neural network, Limit and Recurrent neural network is strongly related with Control theory.
His work on Sentiment analysis as part of general Artificial intelligence research is often related to Benchmark, thus linking different fields of science.
His Time series research is multidisciplinary, relying on both Technology forecasting, Probabilistic forecasting, Support vector machine and Consensus forecast.
His study in the field of Prediction interval is also linked to topics like Coverage probability.
His study looks at the intersection of Algorithm and topics like Recurrent neural nets with Error function, Computational complexity theory, Theoretical computer science and System identification.
His most cited work include:
- Bankruptcy prediction for credit risk using neural networks: A survey and new results (509 citations)
- How delays affect neural dynamics and learning (373 citations)
- New results on recurrent network training: unifying the algorithms and accelerating convergence (310 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Artificial neural network, Machine learning, Algorithm and Mathematical optimization.
Amir F. Atiya interconnects Natural language processing, Time series and Pattern recognition in the investigation of issues within Artificial intelligence.
In his study, Recurrent neural network is strongly linked to Control theory, which falls under the umbrella field of Artificial neural network.
His work in Machine learning covers topics such as Regression which are related to areas like Regression analysis.
His work on Computational complexity theory as part of general Algorithm study is frequently linked to Training, bridging the gap between disciplines.
His work carried out in the field of Mathematical optimization brings together such families of science as Applied mathematics and Benchmark.
He most often published in these fields:
- Artificial intelligence (39.29%)
- Artificial neural network (36.90%)
- Machine learning (20.24%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (39.29%)
- Natural language processing (4.76%)
- Sentiment analysis (4.17%)
In recent papers he was focusing on the following fields of study:
Amir F. Atiya mostly deals with Artificial intelligence, Natural language processing, Sentiment analysis, Machine learning and Dynamic pricing.
Amir F. Atiya has included themes like Speech recognition and Pattern recognition in his Artificial intelligence study.
His study in Natural language processing is interdisciplinary in nature, drawing from both Supervised learning and Set.
His work in the fields of Arabic sentiment analysis overlaps with other areas such as Context.
His studies in Machine learning integrate themes in fields like Data acquisition, Kullback–Leibler divergence, Mutual information and Regression.
His work is dedicated to discovering how Econometrics, Artificial neural network are connected with Algorithm and other disciplines.
Between 2013 and 2021, his most popular works were:
- ASTD: Arabic Sentiment Tweets Dataset (140 citations)
- A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting (62 citations)
- A Comprehensive Analysis of Synthetic Minority Oversampling TEchnique (SMOTE) for Handling Class Imbalance (35 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Artificial intelligence, Machine learning, Variance, Econometrics and Benchmark are his primary areas of study.
His Artificial intelligence research incorporates elements of Boundary, Oversampling and Dimension.
His work deals with themes such as Point and Feature extraction, which intersect with Machine learning.
In his papers, Amir F. Atiya integrates diverse fields, such as Variance, Competition, Work, Monte Carlo method, Artificial neural network and Variance decomposition of forecast errors.
His research integrates issues of Series and Time series in his study of Monte Carlo method.
The various areas that Amir F. Atiya examines in his Benchmark study include Layer, Speech recognition, Hidden Markov model and Natural language processing.
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