D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 46 Citations 8,692 279 World Ranking 4401 National Ranking 4

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Artificial neural network, Artificial intelligence, Adaptive neuro fuzzy inference system, Mean squared error and Data mining are his primary areas of study. His studies deal with areas such as Hydrology, Water quality, Water resources and Process as well as Artificial neural network. His research in Water resources intersects with topics in Reservoir simulation and Radial basis function.

In the subject of general Artificial intelligence, his work in Preprocessor is often linked to Prospective research, thereby combining diverse domains of study. His Adaptive neuro fuzzy inference system research is multidisciplinary, incorporating perspectives in Neuro-fuzzy and Sensor fusion. His Mean squared error research integrates issues from Support vector machine, Meteorology, Correlation coefficient and Regression.

His most cited work include:

  • Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications (245 citations)
  • Artificial intelligence based models for stream-flow forecasting: 2000–2015 (191 citations)
  • Artificial intelligence based models for stream-flow forecasting: 2000–2015 (191 citations)

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

Ahmed El-Shafie spends much of his time researching Artificial neural network, Artificial intelligence, Mean squared error, Mathematical optimization and Particle swarm optimization. The study incorporates disciplines such as Water quality, Inflow, Data mining and Support vector machine in addition to Artificial neural network. In Data mining, Ahmed El-Shafie works on issues like Streamflow, which are connected to Water resources.

His study in Machine learning extends to Artificial intelligence with its themes. His research in Mean squared error tackles topics such as Adaptive neuro fuzzy inference system which are related to areas like Neuro-fuzzy. Evolutionary algorithm is closely connected to Genetic algorithm in his research, which is encompassed under the umbrella topic of Particle swarm optimization.

He most often published in these fields:

  • Artificial neural network (38.25%)
  • Artificial intelligence (21.99%)
  • Mean squared error (18.67%)

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

  • Artificial neural network (38.25%)
  • Mean squared error (18.67%)
  • Artificial intelligence (21.99%)

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

Ahmed El-Shafie mainly focuses on Artificial neural network, Mean squared error, Artificial intelligence, Algorithm and Adaptive neuro fuzzy inference system. His Artificial neural network study combines topics in areas such as Particle swarm optimization, Data mining, Regression and Time series. His Mean squared error study incorporates themes from Uncertainty analysis, Correlation coefficient, Coefficient of determination, Water level and Water quality.

His Artificial intelligence research is multidisciplinary, relying on both Tropospheric ozone and Machine learning. His research investigates the connection between Algorithm and topics such as Swarm behaviour that intersect with problems in Evolutionary algorithm and Genetic algorithm. Ahmed El-Shafie has included themes like Membership function, Soft computing, Statistics, Water resources and Multilayer perceptron in his Adaptive neuro fuzzy inference system study.

Between 2019 and 2021, his most popular works were:

  • Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm (42 citations)
  • Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series (25 citations)
  • Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series (25 citations)

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

  • Statistics
  • Artificial intelligence
  • Machine learning

His primary scientific interests are in Artificial neural network, Mean squared error, Artificial intelligence, Particle swarm optimization and Machine learning. His research integrates issues of Discrete wavelet transform and Time series in his study of Artificial neural network. His studies in Mean squared error integrate themes in fields like Uncertainty analysis, Wind speed and Correlation coefficient.

His Machine learning study which covers Regression that intersects with Coefficient of determination, Random forest and Gradient boosting. He focuses mostly in the field of Overfitting, narrowing it down to topics relating to Computational intelligence and, in certain cases, Streamflow. His study in Perceptron is interdisciplinary in nature, drawing from both Drainage basin, Data mining and Gradient descent.

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

Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications

A. Noureldin;T.B. Karamat;M.D. Eberts;A. El-Shafie.
IEEE Transactions on Vehicular Technology (2009)

433 Citations

Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications

A. Noureldin;T.B. Karamat;M.D. Eberts;A. El-Shafie.
IEEE Transactions on Vehicular Technology (2009)

433 Citations

Artificial intelligence based models for stream-flow forecasting: 2000–2015

Zaher Mundher Yaseen;Ahmed El-shafie;Ahmed El-shafie;Othman Jaafar;Haitham Abdulmohsin Afan.
Journal of Hydrology (2015)

352 Citations

Artificial intelligence based models for stream-flow forecasting: 2000–2015

Zaher Mundher Yaseen;Ahmed El-shafie;Ahmed El-shafie;Othman Jaafar;Haitham Abdulmohsin Afan.
Journal of Hydrology (2015)

352 Citations

Reservoir Optimization in Water Resources: a Review

Asmadi Ahmad;Ahmed El-Shafie;Siti Fatin Mohd Razali;Zawawi Samba Mohamad.
Water Resources Management (2014)

236 Citations

Reservoir Optimization in Water Resources: a Review

Asmadi Ahmad;Ahmed El-Shafie;Siti Fatin Mohd Razali;Zawawi Samba Mohamad.
Water Resources Management (2014)

236 Citations

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam

Ahmed El-Shafie;Mahmoud Reda Taha;Aboelmagd Noureldin.
Water Resources Management (2007)

230 Citations

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam

Ahmed El-Shafie;Mahmoud Reda Taha;Aboelmagd Noureldin.
Water Resources Management (2007)

230 Citations

Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq

Zaher Mundher Yaseen;Othman Jaafar;Ravinesh C. Deo;Ozgur Kisi.
Journal of Hydrology (2016)

225 Citations

Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq

Zaher Mundher Yaseen;Othman Jaafar;Ravinesh C. Deo;Ozgur Kisi.
Journal of Hydrology (2016)

225 Citations

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