What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Cesare Alippi mostly deals with Artificial intelligence, Real-time computing, Artificial neural network, Wireless sensor network and Algorithm.
The concepts of his Artificial intelligence study are interwoven with issues in Radio-frequency identification and Machine learning.
His work carried out in the field of Real-time computing brings together such families of science as Algorithm design, Feature extraction, Computer network and Probabilistic logic.
His studies in Artificial neural network integrate themes in fields like Approximation theory, Mathematical optimization and Control theory, Nonlinear system.
The study incorporates disciplines such as Default gateway, Quality of service, Key distribution in wireless sensor networks, Energy and Electrical engineering in addition to Wireless sensor network.
Cesare Alippi interconnects Graph classification, Graph neural networks, Graph and Representation in the investigation of issues within Algorithm.
His most cited work include:
- An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes (363 citations)
- Learning in Nonstationary Environments: A Survey (348 citations)
- Genetic-algorithm programming environments (246 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Artificial neural network, Algorithm, Wireless sensor network and Machine learning.
Cesare Alippi combines subjects such as Data mining and Pattern recognition with his study of Artificial intelligence.
Cesare Alippi has researched Artificial neural network in several fields, including Mathematical optimization and Control theory, Robustness.
His Algorithm study combines topics in areas such as Sensitivity, Recurrent neural network and Graph.
He has included themes like Dynamical systems theory, State and Nonlinear system in his Recurrent neural network study.
His work deals with themes such as Wireless, Key distribution in wireless sensor networks, Real-time computing and Energy management, which intersect with Wireless sensor network.
He most often published in these fields:
- Artificial intelligence (32.89%)
- Artificial neural network (21.05%)
- Algorithm (20.07%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (32.89%)
- Graph (7.24%)
- Theoretical computer science (6.91%)
In recent papers he was focusing on the following fields of study:
Cesare Alippi focuses on Artificial intelligence, Graph, Theoretical computer science, Recurrent neural network and Graph neural networks.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Task and Pattern recognition.
His work carried out in the field of Graph brings together such families of science as Stochastic process and Algorithm.
The concepts of his Algorithm study are interwoven with issues in Polynomial, Filter and Autoregressive model.
His studies deal with areas such as Embedding, Autoencoder and Pooling as well as Theoretical computer science.
His Recurrent neural network research is multidisciplinary, incorporating elements of Dynamical systems theory, State, Smart grid and Nonlinear system.
Between 2018 and 2021, his most popular works were:
- Graph Neural Networks with convolutional ARMA filters (51 citations)
- Spectral Clustering with Graph Neural Networks for Graph Pooling (31 citations)
- Graph Neural Networks in TensorFlow and Keras with Spektral (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Cesare Alippi mainly focuses on Theoretical computer science, Nonlinear system, Graph classification, Graph neural networks and Artificial intelligence.
Cesare Alippi studied Theoretical computer science and Manifold that intersect with Metric, Geodesic and Artificial neural network.
His study focuses on the intersection of Nonlinear system and fields such as Recurrent neural network with connections in the field of Topology, Computation, Space, State and Enhanced Data Rates for GSM Evolution.
His work focuses on many connections between Graph classification and other disciplines, such as Pooling, that overlap with his field of interest in Cluster analysis and Spectral clustering.
His study in Graph neural networks is interdisciplinary in nature, drawing from both Algorithm and Polynomial.
His Artificial intelligence study combines topics in areas such as Machine learning and Differentiable function.
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