Bio
Martina Iammarino is a Researcher at the Department of Computer Science at the University of Bari Aldo Moro, Italy. She obtained the PhD in Information Technology for Engineering in February 2023 and the master’s degree in 2019 at the University of Sannio. Her current research activities focus on software engineering , software and data quality, process and data engineering. Specifically, more recently, her research dealing with artificial intelligence techniques have been validated in the medical domain. In this regard, she has published several articles based on machine learning and deep learning techniques, applied to different domains. She has also been a reviewer for numerous international conferences and journals and a member of the organizing and program committees of international conferences.
She is among the main organizers of the CISE special session “Computational Intelligence in Software Engineering” held within IJCNN in 2024.
Teaching
- Machine Learning Methods in eHealth: Strategies for Analyzing Healthcare Data at the PhD Course in Digital Innovation and eHealth
Publications
2025
- What Time Is It? Finding Which Temporal Features is More Useful for Next Activity Prediction
Lerina Aversano, Martina Iammarino, Antonella Madau, Giuseppe Pirlo, Gianfranco Semeraro
IEEE Open J. Comput. Soc. 6: 261-271
DOI: 10.1109/OJCS.2024.3519815 - Process mining applications in healthcare: a systematic literature review
Lerina Aversano, Martina Iammarino, Antonella Madau, Giuseppe Pirlo, Gianfranco Semeraro
PeerJ Comput. Sci. 11: e2613
DOI: 10.7717/PEERJ-CS.2613
2024
- A systematic review on artificial intelligence approaches for smart health devices
Lerina Aversano, Martina Iammarino, Ilaria Mancino, Debora Montano
PeerJ Comput. Sci. 10: e2232
DOI: 10.7717/PEERJ-CS.2232 - Adopting Delta Maintainability Model for Just in Time Bug Prediction
Lerina Aversano, Martina Iammarino, Antonella Madau, Debora Montano, Chiara Verdone
ICSOFT 2024: 419-426
DOI: 10.5220/0012785100003753 - A Machine Learning Approach for the Detection of Thoracic Disease using Chest X-ray reports
Lerina Aversano, Martina Iammarino, Antonella Madau, Debora Montano, Chiara Verdone
KES 2024: 1130-1139
DOI: 10.1016/J.PROCS.2024.09.535 - Raman Spectroscopy of Cancer Cells: An Explainable Classification Model
Andrea Cusano, Martina Iammarino, Marco Pisco, Sara Spaziani, Chiara Verdone
MetroXRAINE 2024: 849-854
DOI: 10.1109/METROXRAINE62247.2024.10796642
2023
- Evo-GUNet3++: Using evolutionary algorithms to train UNet-based architectures for efficient 3D lung cancer detection
Pasquale Ardimento, Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Chiara Verdone
Appl. Soft Comput. 144: 110465
DOI: 10.1016/J.ASOC.2023.110465 - A data-aware explainable deep learning approach for next activity prediction
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Chiara Verdone
Eng. Appl. Artif. Intell. 126: 106758
DOI: 10.1016/J.ENGAPPAI.2023.106758 - Forecasting technical debt evolution in software systems: an empirical study
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Debora Montano
Frontiers Comput. Sci. 17[3]: 173210
DOI: 10.1007/S11704-022-1541-7 - Machine Learning Applied to Speech Recordings for Parkinson's Disease Recognition
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Antonella Madau, Chiara Verdone
DeLTA 2023: 101-114
DOI: 10.1007/978-3-031-39059-3_7 - An Explainable Approach for Early Parkinson Disease Detection Using Deep Learning
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Antonella Madau, Chiara Verdone
DeLTA 2023: 326-339
DOI: 10.1007/978-3-031-39059-3_22 - An Empirical Study on the Relationship Between the Co-Occurrence of Design Smell and Refactoring Activities
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Debora Montano
ENASE 2023: 742-749
DOI: 10.5220/0012006600003464 - Early Parkinson's Disease Detection from EEG Traces Using Machine Learning Techniques
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Debora Montano, Chiara Verdone
EUSFLAT/AGOP 2023: 607-619
DOI: 10.1007/978-3-031-39965-7_50 - Understanding Compiler Effects on Clone Detection Process
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Debora Montano
ICSOFT 2023: 345-352
DOI: 10.5220/0012080100003538 - Early Diagnosis of Parkinson's Disease Exploting Motor and Non-Motor Symptoms: Results from the PPMI Cohort
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Antonella Madau, Chiara Verdone
KES 2023: 2096-2105
DOI: 10.1016/J.PROCS.2023.10.200 - Raman Spectroscopy of Cells for Cancer Classification Through Machine Learning
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Andrea Cusano, Martina Iammarino, Marco Pisco, Sara Spaziani, Chiara Verdone
MetroXRAINE 2023: 688-693
DOI: 10.1109/METROXRAINE58569.2023.10405759 - Forecasting the Developer's Impact in Managing the Technical Debt
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino
PROFES 2023: 35-47
DOI: 10.1007/978-3-031-49269-3_4
2022
- Using deep temporal convolutional networks to just-in-time forecast technical debt principal
Pasquale Ardimento, Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino
J. Syst. Softw. 194: 111481
DOI: 10.1016/J.JSS.2022.111481 - Just-in-time software defect prediction using deep temporal convolutional networks
Pasquale Ardimento, Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino
Neural Comput. Appl. 34[5]: 3981-4001
DOI: 10.1007/S00521-021-06659-3 - Using Machine Learning for Classification of Cancer Cells from Raman Spectroscopy
Lerina Aversano, Mario Luca Bernardi, Vincenzo Calgano, Marta Cimitile, Concetta Esposito, Martina Iammarino, Marco Pisco, Sara Spaziani, Chiara Verdone
DeLTA 2022: 15-24
DOI: 10.5220/0011142600003277 - Using Machine Learning for early prediction of Heart Disease
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Debora Montano, Chiara Verdone
EAIS 2022: 1-8
DOI: 10.1109/EAIS51927.2022.9787720 - A Machine Learning approach for Early Detection of Parkinson's Disease Using acoustic traces
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Debora Montano, Chiara Verdone
EAIS 2022: 1-8
DOI: 10.1109/EAIS51927.2022.9787728 - An Empirical Study to Predict Student Performance Using Information of the Virtual Learning Environment
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Debora Montano, Chiara Verdone
HELMeTO 2022: 536-547
DOI: 10.1007/978-3-031-29800-4_41 - Is There Any Correlation between Refactoring and Design Smell Occurrence
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Debora Montano
ICSOFT 2022: 129-136
DOI: 10.5220/0011139400003266 - Early Detection of Parkinson's Disease using Spiral Test and Echo State Networks
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Chiara Verdone
IJCNN 2022: 1-8
DOI: 10.1109/IJCNN55064.2022.9891917 - An enhanced UNet variant for Effective Lung Cancer Detection
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Chiara Verdone
IJCNN 2022: 1-8
DOI: 10.1109/IJCNN55064.2022.9892757 - Technical Debt Forecasting from Source Code Using Temporal Convolutional Networks
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino
PROFES 2022: 581-591
DOI: 10.1007/978-3-031-21388-5_43
2021
- Temporal convolutional networks for just-in-time design smells prediction using fine-grained software metrics
Pasquale Ardimento, Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino
Neurocomputing 463: 454-471
DOI: 10.1016/J.NEUCOM.2021.08.010 - An empirical study on the co-occurrence between refactoring actions and Self-Admitted Technical Debt removal
Martina Iammarino, Fiorella Zampetti, Lerina Aversano, Massimiliano Di Penta
J. Syst. Softw. 178: 110976
DOI: 10.1016/J.JSS.2021.110976 - Transfer Learning for Just-in-Time Design Smells Prediction using Temporal Convolutional Networks
Pasquale Ardimento, Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino
ICSOFT 2021: 310-317
DOI: 10.5220/0010602203100317 - Technical Debt predictive model through Temporal Convolutional Network
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino
IJCNN 2021: 1-8
DOI: 10.1109/IJCNN52387.2021.9534423 - Thyroid Disease Treatment prediction with machine learning approaches
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Paolo Emidio Macchia, Immacolata Cristina Nettore, Chiara Verdone
KES 2021: 1031-1040
DOI: 10.1016/J.PROCS.2021.08.106
2020
- On the Relationship between Self-Admitted Technical Debt Removals and Technical Debt Measures
Lerina Aversano, Martina Iammarino, Mimmo Carapella, Andrea Del Vecchio, Laura Nardi
Algorithms 13[7]: 168
DOI: 10.3390/A13070168 - An Empirical Study on the Evolution of Design Smells
Lerina Aversano, Umberto Carpenito, Martina Iammarino
Inf. 11[7]: 348
DOI: 10.3390/INFO11070348 - Investigating on the Relationships between Design Smells Removals and Refactorings
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Kateryna Romanyuk
ICSOFT 2020: 212-219
DOI: 10.5220/0009887102120219 - A Topic Modeling Approach To Evaluate The Comments Consistency To Source Code
Martina Iammarino, Lerina Aversano, Mario Luca Bernardi, Marta Cimitile
IJCNN 2020: 1-8
DOI: 10.1109/IJCNN48605.2020.9207651
2019
- Self-Admitted Technical Debt Removal and Refactoring Actions: Co-Occurrence or More
Martina Iammarino, Fiorella Zampetti, Lerina Aversano, Massimiliano Di Penta
ICSME 2019: 186-190
DOI: 10.1109/ICSME.2019.00029