What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Pattern recognition
His primary areas of study are Artificial intelligence, Pattern recognition, Support vector machine, Data mining and Biometrics.
His studies deal with areas such as Machine learning and Computer vision as well as Artificial intelligence.
Loris Nanni has included themes like Feature, Representation and Selection in his Pattern recognition study.
His Support vector machine research incorporates themes from Contextual image classification, Sequence, Pseudo amino acid composition and Dimensionality reduction.
While the research belongs to areas of Data mining, Loris Nanni spends his time largely on the problem of Classifier, intersecting his research to questions surrounding Word error rate, Bin, Multi-swarm optimization and Metaheuristic.
The study incorporates disciplines such as Feature, Facial recognition system, Hash function, Hidden Markov model and Pattern recognition in addition to Biometrics.
His most cited work include:
- Local binary patterns variants as texture descriptors for medical image analysis (357 citations)
- An on-line signature verification system based on fusion of local and global information (311 citations)
- An improved BioHashing for human authentication (209 citations)
What are the main themes of his work throughout his whole career to date?
Loris Nanni mainly focuses on Artificial intelligence, Pattern recognition, Support vector machine, Machine learning and Classifier.
His Artificial intelligence research is multidisciplinary, relying on both Data mining and Computer vision.
His Pattern recognition study often links to related topics such as Contextual image classification.
His Support vector machine research includes themes of Artificial neural network, Local ternary patterns, Texture Descriptor, Histogram and Deep learning.
His study in the field of Ensemble learning is also linked to topics like Source code.
His Classifier research is multidisciplinary, incorporating elements of Training set and k-nearest neighbors algorithm.
He most often published in these fields:
- Artificial intelligence (88.81%)
- Pattern recognition (70.15%)
- Support vector machine (40.67%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (88.81%)
- Pattern recognition (70.15%)
- Convolutional neural network (11.19%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Convolutional neural network, Deep learning and Support vector machine.
Artificial intelligence is often connected to Machine learning in his work.
His Pattern recognition research includes elements of Contextual image classification and Spectrogram.
His Convolutional neural network study incorporates themes from Ensembles of classifiers, Image segmentation, Activation function and Task.
Loris Nanni interconnects Overfitting, Feature, Underwater, Image processing and Representation in the investigation of issues within Deep learning.
His Support vector machine research incorporates elements of Boosting, Discriminative model, Image and Feature extraction.
Between 2017 and 2021, his most popular works were:
- A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images (43 citations)
- Deep learning and transfer learning features for plankton classification (33 citations)
- Learning morphological operators for skin detection (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Pattern recognition
His primary scientific interests are in Artificial intelligence, Pattern recognition, Deep learning, Convolutional neural network and Support vector machine.
His Artificial intelligence study frequently links to other fields, such as Machine learning.
His biological study spans a wide range of topics, including Image segmentation and Digital image.
His research integrates issues of Pixel, Texture and X ray image in his study of Pattern recognition.
His Deep learning study integrates concerns from other disciplines, such as Overfitting, Underwater, Contextual image classification, Feature extraction and Discriminative model.
His studies examine the connections between Support vector machine and genetics, as well as such issues in Benchmark, with regards to Protein tertiary structure, Sequence and Position-Specific Scoring Matrices.
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.
