H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 60 Citations 12,339 380 World Ranking 1575 National Ranking 87

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of investigation include Sentiment analysis, Artificial intelligence, Data science, Natural language processing and Natural language. His Sentiment analysis research is multidisciplinary, incorporating elements of Information retrieval, Semantic Web, The Internet, Social media and Affective computing. His research integrates issues of Context and Machine learning in his study of Artificial intelligence.

His studies in Data science integrate themes in fields like Scientometrics, Sentic computing, Multi-agent system and Knowledge base. His Natural language processing study combines topics in areas such as Emotion recognition, Polarity, Inference and Handwriting. His work deals with themes such as Semantics and Ambiguity, which intersect with Natural language.

His most cited work include:

  • A review of affective computing (444 citations)
  • Fusing audio, visual and textual clues for sentiment analysis from multimodal content (275 citations)
  • Applications of Deep Learning and Reinforcement Learning to Biological Data (271 citations)

What are the main themes of his work throughout his whole career to date?

Amir Hussain mostly deals with Artificial intelligence, Sentiment analysis, Machine learning, Speech recognition and Natural language processing. His Artificial intelligence study frequently links to other fields, such as Pattern recognition. Amir Hussain interconnects Semantic Web, Social media, Semantics, Natural language and Data science in the investigation of issues within Sentiment analysis.

His Speech recognition research incorporates elements of Artificial neural network, Speech enhancement and Adaptive filter. The concepts of his Artificial neural network study are interwoven with issues in Control theory and Nonlinear system. His biological study spans a wide range of topics, including Active noise control and Wiener filter.

He most often published in these fields:

  • Artificial intelligence (51.42%)
  • Sentiment analysis (15.75%)
  • Machine learning (13.66%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (51.42%)
  • Deep learning (11.57%)
  • Natural language processing (12.14%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Deep learning, Natural language processing, Convolutional neural network and Pattern recognition are his primary areas of study. His research on Artificial intelligence often connects related areas such as Machine learning. The Deep learning study which covers Pattern recognition that intersects with MNIST database.

His Natural language processing study incorporates themes from Named-entity recognition and Arabic. His Pattern recognition research integrates issues from Feature, Robustness and Electroencephalography. His research investigates the connection with Sentiment analysis and areas like Social media which intersect with concerns in Public health.

Between 2019 and 2021, his most popular works were:

  • A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. (38 citations)
  • Deep Learning in Mining Biological Data (37 citations)
  • A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach (35 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

His scientific interests lie mostly in Artificial intelligence, Deep learning, Machine learning, Artificial neural network and Benchmark. His research in Artificial intelligence intersects with topics in Pattern recognition, Speech recognition and Natural language processing. His Natural language processing research focuses on Sentiment analysis in particular.

The various areas that Amir Hussain examines in his Deep learning study include Background noise, Convolutional neural network, Supervised learning, Perceptron and Multilayer perceptron. His Artificial neural network research is multidisciplinary, incorporating perspectives in Algorithm and Kernel. His studies deal with areas such as Preprocessor and Baseline system as well as Benchmark.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A review of affective computing

Soujanya Poria;Erik Cambria;Rajiv Bajpai;Amir Hussain.
Information Fusion (2017)

456 Citations

Agent-based computing from multi-agent systems to agent-based models: a visual survey

Muaz Niazi;Amir Hussain.
Scientometrics (2011)

396 Citations

Fusing audio, visual and textual clues for sentiment analysis from multimodal content

Soujanya Poria;Erik Cambria;Newton Howard;Guang-Bin Huang.
Neurocomputing (2016)

338 Citations

Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

S. Poria;A. Gelbukh;A. Hussain;N. Howard.
IEEE Intelligent Systems (2013)

336 Citations

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud;Mohammed Shamim Kaiser;Amir Hussain;Stefano Vassanelli.
IEEE Transactions on Neural Networks (2018)

329 Citations

Sentic Computing: Techniques, Tools, and Applications

Erik Cambria;Amir Hussain.
(2012)

319 Citations

SenticNet: A Publicly Available Semantic Resource for Opinion Mining

Erik Cambria;Robert Speer;Catherine Havasi;Amir Hussain.
national conference on artificial intelligence (2010)

293 Citations

Group sparse regularization for deep neural networks

Simone Scardapane;Danilo Comminiello;Amir Hussain;Aurelio Uncini.
Neurocomputing (2017)

288 Citations

Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis

Soujanya Poria;Iti Chaturvedi;Erik Cambria;Amir Hussain.
international conference on data mining (2016)

264 Citations

The hourglass of emotions

Erik Cambria;Andrew Livingstone;Amir Hussain.
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems (2011)

245 Citations

Editorial Boards

Cognitive Computation
(Impact Factor: 4.89)

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Best Scientists Citing Amir Hussain

Erik Cambria

Erik Cambria

Nanyang Technological University

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Soujanya Poria

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Fabrício Benevenuto

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Francisco Herrera

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Adel M. Alimi

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Stefan Wermter

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