Advances in Machine Learning [NI-SERV-2020-27]
Top level expertise in Machine Learning, Artificial Neural Networks, Genetic Programming, Data Preprocessing, Noise Reduction, Feature Engineering, Data Augmentation, Regularization, etc..
SHORT DESCRIPTION
The Data Analytics Lab has the mission of developing top quality know how, aimed at improving the state of the art in Machine Learning. Two main research streams exist in this lab: theoretical/basic research and applications. The first stream is aimed at the study and deep understanding of the functioning and principles of the most known Machine Learning techniques, with the final objective of defining new/improved techniques, able to overcome the limits of the existing ones. The second stream aims at applying novel and existing Machine Learning techniques to challenging real-world problems.
OPPORTUNITY TO BE PROMOTED
Service Provision / Expertise
COMPETITIVE ADVANTAGES
Development of ground breaking technologies, able to overcome the Machine Learning state of the art.
APPLICATIONS
Life Sciences, Biotechnology, Health, Economics, Energy Consumption, Logistics, Engineering, Environmental Protection, etc..
FURTHER DETAILS
A small sample of recent scientific publications developed in the context of the Data Analytics Lab follows:
[1] M. Castelli, F. M. Clemente, A. Popovic, S. Silva, and L. Vanneschi. A Machine Learning Approach to Predict Air Quality in California. Complexity. 2020.
[2] N. M. Rodrigues, S. Silva and L. Vanneschi. A Study of Generalization and Fitness Landscapes for Neuroevolution. IEEE Access. 2020.
[3] I. Azzali, L. Vanneschi, I. Bakurov, S. Silva, M. Ivaldi and M. Giacobini. Towards the Use of Vector Based GP to Predict Physiological Time Series. Applied Soft Computing. 2020
[4] M. Castelli, M. Dobreva, R. Henriques, and L. Vanneschi. Predicting Days on Market to Optimize Real Estate Sales Strategy. Complexity. 2020
[5] J. M. Silva, A. Figueiredo, J. Cunha, J. E. Eiras-Dias, S. Silva, L. Vanneschi and P. Mariano. Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes. Plants. 2020
[6] I. Azzali, L. Vanneschi, A. Mosca, L. Bertolotti and M. Giacobini. Towards the Use of Genetic Programming in the Ecological Modelling of Mosquito Population Dynamics. Genetic Programming and Evolvable Machines. 2020.
[7] A. Raglio, M. Imbriani, C. Imbriani, P. Baiardi, S. Manzoni, M. Gianotti, M. Castelli, L. Vanneschi, F. Vico and L. Manzoni. Machine Learning Techniques to Predict the Effectiveness of Music Therapy: A Randomized Controlled Trial. Computer Methods and Programs in Biomedicine. 2020.
[8] J. M. Moreira, I. Santiago, J. Santinha, N. Figueiredo, K. Marias, M. Figueiredo, L. Vanneschi and N. Papanikolaou. Challenges and Promises of Radiomics for Rectal Cancer. Current Colorectal Cancer Reports. 2020.
[9] D. Besozzi, M. Castelli, P. Cazzaniga, L. Manzoni, M. S. Nobile, S. Ruberto, L. Rundo, S. Spolaor, A. Tangherloni and L. Vanneschi. Computational Intelligence for Life Sciences. Fundamenta Informaticae. 2020.
[10] L. Vanneschi, M. Castelli, K. Scott and L. Trujillo. Alignment-Based Genetic Programming for Real Life Applications. Swarm and Evolutionary Computation. Volume 44, pages 840–851, 2019.
[11] S. Ruberto, L. Vanneschi and M. Castelli. Genetic Programming with Semantic Equivalence Classes. Swarm and Evolutionary Computation. Volume 44, pages 453–469, 2019.
[12] W. La Cava, S. Silva, K. Danai, L. Spector, L. Vanneschi and J. H. Moore. Multidimensional genetic programming for feature extraction in nearest centroid classification. Swarm and Evolutionary Computation. Volume 44, pages 260–272, 2019.
[13] M. Castelli, G. Cattaneo, L. Manzoni, and L. Vanneschi. A Distance Between Populations for n-Points Crossover in Genetic Algorithms. Swarm and Evolutionary Computation. Volume 44, pages 636–645, 2019.
[14] A. Rubio-Largo, L. Vanneschi, M. Castelli and M. A. Vega-Rodríguez. Multiobjective Metaheuristic to Design RNA Sequences. IEEE Transactions on Evolutionary Computation. Volume 23, issue 1, pages 156–169, 2019.
