What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Matteo Matteucci mostly deals with Artificial intelligence, Speech recognition, Pattern recognition, Data mining and Computer vision.
His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning.
His Speech recognition study incorporates themes from Feature, Sleep apnea, Brain–computer interface, Autoregressive model and Obstructive sleep apnea.
His studies in Data mining integrate themes in fields like Landslide, Artificial neural network, Cluster analysis and Test set.
His study explores the link between Recurrent neural network and topics such as Cognitive neuroscience of visual object recognition that cross with problems in Benchmark and Convolutional neural network.
His study in the field of Mobile robot is also linked to topics like Benchmarking.
His most cited work include:
- Artificial neural networks and cluster analysis in landslide susceptibility zonation (181 citations)
- ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks. (165 citations)
- Sleep Staging Based on Signals Acquired Through Bed Sensor (147 citations)
What are the main themes of his work throughout his whole career to date?
Matteo Matteucci mainly investigates Artificial intelligence, Computer vision, Robot, Pattern recognition and Robotics.
His Artificial intelligence research incorporates elements of Machine learning and Speech recognition.
His study in Machine learning focuses on Genetic algorithm in particular.
His work on Pixel as part of general Computer vision research is often related to Process, thus linking different fields of science.
His work carried out in the field of Robot brings together such families of science as Software engineering and Embedded system.
His Pattern recognition study is mostly concerned with Feature extraction, Classifier, Linear discriminant analysis and Feature selection.
He most often published in these fields:
- Artificial intelligence (54.43%)
- Computer vision (22.30%)
- Robot (12.79%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (54.43%)
- Computer vision (22.30%)
- Artificial neural network (8.85%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Artificial neural network, Deep learning and Benchmark.
Matteo Matteucci interconnects Frame, Machine learning, Heuristics and Pattern recognition in the investigation of issues within Artificial intelligence.
In his research on the topic of Pattern recognition, Activity recognition and Joint is strongly related with Skeleton.
His Pixel and Feature extraction study in the realm of Computer vision connects with subjects such as Heading and Line.
His research on Artificial neural network also deals with topics like
- Hybrid system which is related to area like Data mining, Model selection, Softmax function, Hidden Markov model and Discriminant function analysis,
- Mixture model that intertwine with fields like Multinomial distribution.
His study in Deep learning is interdisciplinary in nature, drawing from both Image, Hyperspectral imaging, Mathematical optimization and Multispectral image.
Between 2018 and 2021, his most popular works were:
- Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras (23 citations)
- Deep learning for SAR image despeckling (20 citations)
- Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Computer vision, Deep learning, Transformer and Pattern recognition are his primary areas of study.
His research ties Frame and Artificial intelligence together.
The Computer vision study combines topics in areas such as Completeness and Measure.
His Deep learning study integrates concerns from other disciplines, such as Image and Multispectral image, Remote sensing.
His research investigates the connection between Transformer and topics such as Joint that intersect with issues in Motion.
His biological study spans a wide range of topics, including Cognitive neuroscience of visual object recognition, Encoding and Reference model.
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