What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of investigation include Artificial neural network, Algorithm, Artificial intelligence, Multilayer perceptron and Mathematical optimization.
His studies link Adaptive system with Artificial neural network.
His biological study spans a wide range of topics, including Signal reconstruction, Blind equalization and Intersymbol interference.
Francesco Piazza has included themes like Machine learning, Maximum a posteriori estimation and Pattern recognition in his Artificial intelligence study.
His work is dedicated to discovering how Multilayer perceptron, Signal processing are connected with Structure and other disciplines.
The various areas that he examines in his Mathematical optimization study include Quantization, Energy management, Algorithm design, Smart grid and Power of two.
His most cited work include:
- On the complex backpropagation algorithm (255 citations)
- Optimal Home Energy Management Under Dynamic Electrical and Thermal Constraints (194 citations)
- On-line learning algorithms for locally recurrent neural networks (148 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Algorithm, Artificial intelligence, Artificial neural network, Speech recognition and Electronic engineering.
His research in Algorithm intersects with topics in Spline, Mathematical optimization, Blind signal separation and Signal processing.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition.
In most of his Artificial neural network studies, his work intersects topics such as Nonlinear system.
His Speech recognition research incorporates elements of Speech enhancement, Noise, Feature extraction and Decorrelation.
His studies in Electronic engineering integrate themes in fields like Loudspeaker, Frequency domain, Audio signal processing and Equalization.
He most often published in these fields:
- Algorithm (23.55%)
- Artificial intelligence (22.16%)
- Artificial neural network (21.05%)
What were the highlights of his more recent work (between 2012-2021)?
- Artificial intelligence (22.16%)
- Speech recognition (18.28%)
- Artificial neural network (21.05%)
In recent papers he was focusing on the following fields of study:
Francesco Piazza focuses on Artificial intelligence, Speech recognition, Artificial neural network, Context and Pattern recognition.
The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence.
Francesco Piazza combines subjects such as Decorrelation, Echo, Mel-frequency cepstrum and Audio signal with his study of Speech recognition.
Francesco Piazza specializes in Artificial neural network, namely Deep belief network.
His Pattern recognition study combines topics in areas such as Autoencoder and Deep learning.
His AC power study incorporates themes from Algorithm and Mathematical optimization.
Between 2012 and 2021, his most popular works were:
- Optimal Home Energy Management Under Dynamic Electrical and Thermal Constraints (194 citations)
- Action dependent heuristic dynamic programming for home energy resource scheduling (99 citations)
- Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview (71 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Operating system
Francesco Piazza mainly investigates Artificial intelligence, Artificial neural network, Pattern recognition, Context and Speech recognition.
His research on Artificial intelligence frequently connects to adjacent areas such as Data mining.
His Artificial neural network research includes elements of Dynamic programming, Smart grid and Energy management.
His work investigates the relationship between Pattern recognition and topics such as Deep learning that intersect with problems in Class.
His Context research includes themes of Home automation, Algorithm, Focus and Audio signal.
His research investigates the connection between Speech recognition and topics such as Mel-frequency cepstrum that intersect with problems in Classifier.
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